1. No category

Energy Software Tools for Sustainable Machine Design
EC - 7th Framework Programme –Theme ICT
Challenge 6: ICT for Mobility, Environmental Sustainability and Energy Efficiency
ICT-2009.6.3: ICT for Energy Efficiency
c) ICT services and software tools enhanced with energy features
Small or medium scale focused research project (STREP)
Grant agreement no. 247982
Task 3.3: development of a “design for energy efficiency”
approach; how to use the ESTOMAD outcomes in a design
process
Deliverable D3.3: “Design for energy efficiency” approach
Authors: Giacomo Bianchi, Stefano Borgia (CNR-ITIA), Wim Siemens, Walter Verdonk, Dirk
Vanhooydonck (FMTC), Adam Macheta (ECE), Bruno Loyer (LMS.Imagine), Daniele Panarese (Fidia),
Dimitri Coemelck (Picanol), Jan Croes (KUL)
Dissemination Level
PU Public
PP Restricted to other programme participants (including the Commission Services)
RE Restricted to a group specified by the consortium (including the Commission Services)
CO Confidential, only for members of the consortium (including the Commission Services)
doc: Deliverable_D3_3.docx
X
1
Summary: Motivations and Scope .................................................................................................. 4
2
Frameworks and general methodologies for machinery energy efficiency .................................... 7
2.1
Regulation and normative about Machinery energetic efficiency .......................................... 7
2.1.1
EU initiatives .................................................................................................................... 7
2.1.2
General indications from Machine Tools and Related Machinery Product Group Study 8
2.2
Energy efficiency and Life Cycle Analysis (LCA) ....................................................................... 9
2.2.1
Introduction ..................................................................................................................... 9
2.2.2
LCA in general .................................................................................................................. 9
2.2.3
LCA in manufacturing .................................................................................................... 10
2.2.4
From LCA to machine improvements ............................................................................ 12
2.3
Energy efficiency and design theory ..................................................................................... 13
2.4
Technologies for energy efficiency........................................................................................ 17
2.5
Scenarios, policies and impact analysis ................................................................................. 18
2.6
Performance evaluation ........................................................................................................ 20
2.6.1
Energy consumption analysis ........................................................................................ 20
2.6.2
Energy – performance indicators .................................................................................. 20
2.6.3
Exergy ............................................................................................................................ 25
2.7
General SW functionalities for energy analysis..................................................................... 26
2.7.1
Introduction ................................................................................................................... 26
2.7.2
Power and energy variables in AMESim submodels ..................................................... 27
2.7.3
Green Design dedicated Analysis Tools ......................................................................... 28
3 General methodology for machinery design and assessment / evaluation of the energy efficiency
– [FMTC] ................................................................................................................................................ 30
3.1
Experimental benchmarking ................................................................................................. 30
3.2
Concept modelling................................................................................................................. 31
3.3
Design guidelines ................................................................................................................... 31
3.4
Post design assessment ......................................................................................................... 32
4
Methodologies for machinery elements ....................................................................................... 33
4.1
Machine structure ................................................................................................................. 33
4.1.1
4.2
D3.3
Alternative solutions, improvement directions, design guidelines for energy saving .. 33
Cooling unit ........................................................................................................................... 35
4.2.1
Eco / Energy efficiency normative ................................................................................. 36
4.2.2
Alternative solutions, improvement directions............................................................. 36
4.2.3
Improvement directions and energy saving guidelines ................................................ 36
Design for energy efficiency
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Estomad project
4.2.4
4.3
Energetic Measurements .............................................................................................. 39
Hydraulic unit ........................................................................................................................ 40
4.3.1
Introduction ................................................................................................................... 40
4.3.2
Normative ...................................................................................................................... 41
4.3.3
Model and analysis procedures..................................................................................... 42
4.3.4
Improvement directions and energy-saving guidelines ................................................ 47
4.4
Electrical motor ..................................................................................................................... 60
4.4.1
Eco / Energy efficiency Normative ................................................................................ 60
4.4.2
Improvements directions and energy-saving guidelines............................................... 60
4.4.3
Scenario definition and optimal management strategies during operation ................. 62
4.4.4
Energetic Indicators ....................................................................................................... 62
4.4.5
Models and analysis procedures ................................................................................... 63
4.5
Transmission chain ................................................................................................................ 63
4.5.1
Introduction ................................................................................................................... 63
4.5.2
Eco / Energy efficiency normative ................................................................................. 64
4.5.3
Improvement directions and energy-saving guidelines ................................................ 64
4.5.4
Scenario definition and optimal management strategies during operation ................. 66
4.5.5
Energetic Performance Indicators ................................................................................. 66
4.5.6
Models and analysis procedures ................................................................................... 67
4.6
Machine Logic Control ........................................................................................................... 68
4.6.1
Scenario definition and optimal management strategies during operation ................. 68
4.6.2
Auto switch off of not used auxiliary devices ................................................................ 70
4.7
Axis Position Control ............................................................................................................. 74
4.7.1
Scenario definition and optimal management strategies during operation ................. 74
4.7.2
New LAH (Look-Ahead) strategy to optimize energy consumption, accuracy and
machining time during operation.................................................................................................. 80
5
Specific sectorial methodologies ................................................................................................... 93
5.1
Machine tools ........................................................................................................................ 93
5.1.1
Eco / Energy efficiency normative ................................................................................. 94
5.1.2
Machine tool development cycle .................................................................................. 94
5.1.3
Design guidelines / Improvement directions / Best practises....................................... 97
5.1.4
Strategies at the operation level ................................................................................... 98
5.2
Weaving machines ................................................................................................................ 98
5.2.1
D3.3
Weaving machine development cycle........................................................................... 99
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Estomad project
5.2.2
Design guidelines ......................................................................................................... 104
5.2.3
Modelling and measuring guidelines .......................................................................... 105
5.3
Badminton Robot ................................................................................................................ 105
5.3.1
Development cycle ...................................................................................................... 106
5.3.2
Design guidelines ......................................................................................................... 106
5.3.3
Modelling guidelines ................................................................................................... 107
5.3.4
Strategies at the operation level ................................................................................. 108
5.4
Pantograph .......................................................................................................................... 108
5.4.1
Approach and methods from literature. ..................................................................... 108
5.4.2
System development cycle .......................................................................................... 111
5.4.3
Design guidelines for energy efficiency ....................................................................... 114
5.4.4
Operation strategies .................................................................................................... 117
6
Conclusions.................................................................................................................................. 118
7
References ................................................................................................................................... 119
8
Appendix A: usage efficiency maps ............................................................................................. 123
8.1
Concept of efficiency maps ................................................................................................. 123
8.2
Obtaining efficiency maps ................................................................................................... 124
8.2.1
Via measurements ....................................................................................................... 124
8.2.2
Via simulation (e.g. AMESim) ...................................................................................... 124
8.3
D3.3
Use of efficiency maps......................................................................................................... 126
8.3.1
Check whether the system/component is used in an efficient “area” ....................... 126
8.3.2
Different loss components (simulation) ...................................................................... 128
8.3.3
Import efficiency maps for motor component modeling............................................ 129
Design for energy efficiency
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Estomad project
1 Summary: Motivations and Scope
Task 3.3 “Development of a “design for energy efficiency” approach; how to use the ESTOMAD
outcomes in a design process” objective is to propose methodologies, information and procedures to
foster the application of Estomad outcomes in industrial contexts.
Task 3.3 relays on activities performed in the other Estomad Tasks and Work Packages:





Requirements for energetic analysis during machinery design (questionnaire in WP 1 (1) ).
Definition of tools and models for energy consumption estimation in machine elements
(component and subsystem level, WP 2 (2) ).
Definition and application of measures related to energy consumption (WP 5 (3) ).
Co-modeling with component supplier: development of tools (the so-called virtual
components) for providing facilities for a co-design procedure between both parties, in order
to obtain that specific combination of selected components that optimizes the energy
efficiency of the whole machine under design (WP 3, T 3.1 (4) ).
Software functionalities for machinery energy assessment in simulation (WP 4 ).
Task 3.3 will further investigate the industrial development process for the reference applications,
with attention in their key energetic aspects, in order to propose and formalize a set of analysis
procedures and methodologies to use ESTOMAD outcomes in industrial development of production
machines. Instead of proposing a single, monolithic, methodology, it is assumed that a set of
complementary or alternative solutions is preferable, to be optimally tuned on each specific
application: machine tools (T 5.3), weaving machines (T 5.4), badminton robot and pantograph (T
3.3). It has to be noted that the industrial cases considered in the project allow to cover a
considerably wide spectrum of different industrial sectors, that share similar technical solutions.
Modeling
Models for energy consumption
estimation in machine elements
(From WP2)
test cases
key machine components, requirements
for energetic evaluation (From WP1)
simulation SW
VirtualComp
co-modeling with
component supplier
(From T3.1)
Design Approach
for Energy
Efficiency T3.3
Software functionalities
for machinery energy
assessment in simulation
(From WP4)
Applications
T5.3, 5.4: Machine tools, weaving machines
T3.3: badminton robot, pantograph
Figure 1: Relationship between Task 3.3 and other project activities
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Estomad project
It must be noted that in general, energetic efficiency depends on how a machine is made (“design”)
and how it is used (“management”). The two aspects cannot be fully separated and a designer must
take into account how the machine will be used. So different scenarios are foreseen by Task 3.3:
1) “eco-optimal design” of a new machine
2) eco-optimization of the use pattern of existing machines (work cycle modification,
management strategies)
3) energetic characterization of existing machines (to be applied in Task 3.2)
Each scenario asks for a deep understanding, developed in project (WP2 and WP5), of key energy
dissipation mechanisms acting in the machine.
In general, task T3.3 must evaluate the following main aspects:
1) how to define energy-related performance objectives
2) what design choices impact on machine energy consumption
3) when and how to perform numerical energetic evaluations during the design process
(concept design, detailed design)
4) when and how to perform experimental energy evaluations during the product development
process (partial prototype, full prototype, in production)
5) how to improve modelling capabilities using experimental data (using tools developed in
Task T3.2)
Task 3.3 scope has been defined along the following path of reasoning:
1) analyze the reference industrial applications
1. identify key machine elements from energy point of view (ref. WP1)
2. define and analyze how the machine use pattern can be described, to estimate an
energetic efficiency and the corresponding machine yield
2) analyze the key (for energy) machine elements
1. description of basic design alternatives (i.e. different typologies or appropriate sizing)
2. proposal of corresponding design guidelines
3. possible modeling approaches (or models in the library) and simulation studies
3) extract and collect general rules for energy assessment
1. from Estomad experience
2. from scientific literature
3. from Directives and standards
4. related SW functionalities
5. Best Practices in numerical analysis
So Deliverable D33 presents the corresponding results, organized vice-versa, from general
methodologies to common machine elements and then specific applications:

D3.3
Level 1: machinery. Energetic analysis methodologies for generic machinery:
o general directives, definition of aspects of energy efficiency, energetic indicators,
payback time, etc. (Ref. Ch. 2, Paragraphs 2.1, 2.3, 2.4, 2.5, 2.6, CNR-ITIA
contribution)
o LCA reference framework (Ref. Ch. 2, Paragraph 2.2, FMTC contribution)
o general software functionality for energy evaluation (Ref. Ch. 2 Paragraph 2.7,
LMS.Imagine contribution)
Design for energy efficiency
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Estomad project
o


general methodological approach of design for energy efficiency defined and
proposed by ESTOMAD Partners (Ref. Ch. 3, FMTC contribution)
Level 2: element (i.e. components or sub-systems). Design guidelines and analysis procedures
for main machine elements (Ref. Ch. 4):
o machine structure (Ref. Paragraph 4.1, CNR-ITIA contribution)
o cooling unit (Ref. Paragraph 4.2, CNR-ITIA contribution)
o hydraulic unit (Ref. Paragraph 4.3, ECE contribution)
o electrical motor (Ref. Paragraph 4.4, FMTC contribution)
o transmission chain (Ref. Paragraph 4.5, KUL contribution)
o control (Ref. Paragraph 4.6, Fidia + CNR-ITIA contribution – 4.7, Paragraph Fidia +
FMTC contribution)
Level 3: application. Design guidelines, analysis procedures, standards and optimal
management strategies for specific applications (Ref. Ch. 5):
o machine tool (Ref. Paragraph 5.1, CNR-ITIA + Jobs contribution)
o weaving machine (Ref. Paragraph 5.2, Picanol contribution)
o badminton robot (Ref. Paragraph 5.3, FMTC contribution)
o pantograph (Ref. Paragraph 5.4, ECE contribution)
General methodologies
…
element
approaches
element
approaches
element
approaches
machine tool
application
weaving
machine
application
badminton
robot
Figure 2: Classification structure for analysis methodologies
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Estomad project
2 Frameworks and general methodologies for machinery energy
efficiency
Definition of energy efficiency, glossary, references, normative, performance indicators, general
guidelines, etc.
2.1 Regulation and normative about Machinery energetic efficiency
2.1.1
EU initiatives
Reducing energy consumption and eliminating energy wastage are among the main goals of the
European Union (EU). EU support for improving energy efficiency will prove decisive for
competitiveness, security of supply and for meeting the commitments on climate change made
under the Kyoto Protocol (5). The Energy Efficiency Action Plan ask for a 20% reduction in energy
usage by 2020, compared to projections. There is significant potential for reducing consumption,
especially in energy-intensive sectors such as construction, manufacturing, energy conversion and
transport.
In July 2005, the EU Council and Parliament adopted the Eco-design Directive (2005/32/EC) (6),
focused on Energy-Using Products (EUPs), which use, generate, transfer or measure energy
(electricity, gas, fossil fuel), such as boilers, computers, televisions, transformers, industrial fans,
industrial furnaces, machinery, machine tools, etc. (all non-transportation products that use energy).
On March 2008, an amending Directive to the 2005 Directive was adopted. The Directive does not
directly introduce binding requirements for particular products (or groups of products). It sets out a
framework of conditions and criteria for setting requirements which address environmentally
relevant product characteristics. To be included products must have a significant environmental
impact and volume of trade in the internal market and a clear potential for cost effective
improvement.
On October 2009, the recast of the Eco-design Directive 2005/32/EC was adopted, extending the
focus on Energy Related Products (ErP) which do not use energy but have an impact on energy and
can therefore contribute to saving energy, such as windows, insulation material, shower heads, taps
etc. The ErP Directive (2009/125/EC) (7) provides the framework for setting minimum performance
requirements for environmental aspects of energy-using and energy-related products that are placed
onto the market. Although products are assessed with a lifecycle perspective, energy consumption
during use is generally the main environmental impact of the product. The implementing measures
seek to target both manufacturers and consumers, by promoting better product design that will
result in improved environmental performance, lower energy consumption, and ultimately lower
costs. Thus, the individual measures either directly or indirectly affect all EU citizens as well as
virtually all EU industry or retail sectors through the design, manufacture, sale and use of products
covered by the requirements.
The Directive sets the framework for adopting EU-wide measures to improve the design of energy
using products. The European Commission adopts mandatory requirements (implementing
measures) on a product-specific basis following meetings with the Consultation Forum. The
Commission takes the comments of the Forum into account whilst making the final decision. The
D3.3
Design for energy efficiency
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Estomad project
Consultation Forum is composed of interested stakeholders (representatives of EU member states,
importers, retailers, trade unions, SMEs, environmental organisations, consumer organisations and
others). These implementing measures are defined on the basis of an individual evaluation study
(preparatory study), which investigates the improvement potential of the environmental
performance of each product group covered by the legislation.
Regarding machining tools and related machinery, the European Commission will adopt specific and
tailored eco-design measures (mandatory requirements). These measures are defined on the basis of
a preparatory study, that will allow to identify and recommend ways to improve the environmental
performance of machine tools throughout their lifetime at their design phase. While designing their
products, manufacturers will have to respect predefined measures, which aim to reduce the
environmental impact of products throughout the whole product lifecycle (such as production, use
and disposal). The Fraunhofer Institute for Reliability and Micro-integration IZM and FraunhoferInstitut für Produktionsanlagen und Konstruktionstechnik (IPK) is conducting this EC Product Group
Study related to the Eco-design of Machine Tools (8). The study will explore whether eco-design
requirements (mandatory requirements) should apply to machine tools and if so, which
requirements are appropriate. The Commission has requested that the consultant works closely with
CECIMO. Stakeholders can follow the progress made in this study on the website set up for this
purpose and can participate in the consultation process. More information can be found on the Eco
Machine Tools.
2.1.2
General indications from Machine Tools and Related Machinery Product Group
Study
This product group study aims to identify and recommend ways to improve the environmental
performance of machine tools and related machinery throughout their lifetime at their design phase
based on the European Commission Methodology for Eco-design of Energy-using Products (MEEuP).
The information provided by the study will be used to prepare for subsequent phases, including
undertaking an impact assessment on policy options and to prepare a paper for the consultation of
the forum. Those phases are to be carried out by the European Commission.
It has to be noted that the results of this study could provide indication about energy efficiency
requirements for a wide range of machinery. A machine tools is defined as a stationary or
transportable, but not portable by hand or mobile assembly, dependent on energy input (such as
electricity from the grid or stand-alone /back-up power sources, hydraulic or pneumatic power
supply, but not solely manually operated) when in operation, consisting of linked parts or
components, at least one of which moves, and which are joined together for a specific application,
which is the geometric shaping of work pieces made of arbitrary materials using appropriate tools
and forming, cutting, physico-chemical processing or joining technologies, resulting in a product of
defined reproducible geometry, and intended for professional use (8).
The condition “product of defined reproducible geometry” explicitly excludes all machinery
processing animal or human material (medical and food processing equipment), textiles
manufacturing and processing, and shredding equipment.
“Related machinery” is machinery for professional use, which contains components and modules of
other machinery, which are similar to those used in machine tools.
The following types of machinery are considered as example in the study (9):
D3.3
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Estomad project








machine tools for separating / cutting and forming of metals, including those using a laser
beam, ultrasonic waves, plasma arc, magnetic pulse, electrolytic etching, etc.
machine tools for turning, drilling, milling, shaping, planing, boring, grinding, etc.
stamping or pressing machine tools
punch presses, hydraulic presses, hydraulic brakes, drop hammers, forging machines, etc.
draw-benches, thread rollers or machines for working wires
machine tools for working wood, cork, bone, hard rubber, hard plastics, cold glass etc.,
including those using a laser beam, ultrasonic waves, plasma arc, magnetic pulse, etc.
stationary machines for nailing, stapling, gluing or otherwise assembling wood, cork, bone,
hard rubber or plastics, etc.
stationary rotary or rotary percussion drills, filing machines, riveters, sheet metal cutters, etc.
The study also identifies some technological solutions and good design practice for energy efficiency
improvement at the level of machinery component (10) and proposes possible policy options that
drive energy efficiency choice adoption in machinery design and use (11).
2.2 Energy efficiency and Life Cycle Analysis (LCA)
2.2.1
Introduction
In general, Life Cycle Analysis or Life Cycle Assessment (LCA) represents a reference framework for
energetic and environmental assessment of products (i.e., from raw material extraction through
materials processing, manufacture, distribution, use, repair and maintenance, and disposal or
recycling) (12). The approaches can also be applied to machinery, production systems and production
plants. Although LCA approaches do not focus on (eco-)design of operations of a single machine, and
as such are out of scope of the ESTOMAD project, the LCA framework provides an adequate general
view of the environmental issues involved in the development of tools and methodologies for ecodesign of machinery. Therefore, at a general level, it is useful to give a brief introduction of existing
LCA approaches and how they can be linked to the ESTOMAD activities.
2.2.2
LCA in general
A thorough LCA includes the following steps: goal definition, scope definition, inventory, and impact
assessment (13) (14) (15).
Various quantitative LCA tools have been developed to provide an indication of how well a product
or process is performing, also at the environmental-level (16). These softwares can be devided In two
groups:

dedicated software packages intended for practitioners (e.g. Eco-indicator 991, ReCiPe2,
SimaPro3, etc.)
1
http://www.pre-sustainability.com/content/eco-indicator-99
http://www.pre-sustainability.com/content/recipe
3
http://www.pre-sustainability.com/content/simapro-lca-software
2
D3.3
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Estomad project

tools with the LCA in the background intended for people who want LCA-based results
without having to actually develop the LCA data and impact measures. Such tools are
typically integrated in Product Lifecycle Management (PLM) systems, e.g. Solidworks CAD
software of Dassault Systèmes4 or PTC’s Windchill Product Analytics5, etc.
Given the complexity of a full blow LCA of a product, tools are still under development to assist
designers in performing an LCA quickly. In the European project LCA2GO6 an SME-compatible
Product Carbon Footprint (PCF) methodology is for example being developed that intends bridging
the language gap between the environmental terminology used in LCAs and the engineering
language of product developers.
2.2.3
LCA in manufacturing
The identification of metrics in the goal definition is very important in realizing the LCA. When
applied to manufacturing applications, these metrics include: energy and other resource usage (as
suggested by (17)), processing times, material removal rate, and mass or volume of material
removed. The scope of the analysis may vary as well ranging from the enterprise or supply chain
down to the individual machine tool.
Several studies using the LCA approach related to machines tools appeared in scientific literature
(e.g. ref to (18)). Niggeschmidt, et al. (19) show that the most relevant cost during the machine tool
life is linked to the use phase and present an interesting framework enabling the integration of green
manufacturing principles into Life Cycle Performance (LCP) evaluation. It consists of three main steps:
a) design and integrate appropriately targeted process monitoring and measurement system(s)
that can obtain the relevant data identified to accurately gauge the full life cycle.
b) characterize the manufacturing system and the role of each component in the system by
utilizing the data obtained in step a).
c) optimize the development of the component or process of interest using the models and
characterizations developed in b).
On the other hand, in the CO2PE! Initiative (20), an LCA-oriented approach is used for systematic
inventory analysis of the environmental footprint of discrete part manufacturing processes with
energy consumption/CO2 emission as first priority. The results of these analyses are used to feed a
Life-Cycle Inventory (LCI) database. The data in this database can then later on be used by LCA
practitioners and eco-designers to evaluate the environmental impact of the manufacturing
processes of new products. As a general production process consists of a sequence of unit processes,
the different unit processes and their inter-relationships have to be described to define where each
intended unit process starts, which operations take place and where the unit process ends (see
Figure 3).
4
http://www.solidworks.com/
http://www.ptc.com/product/windchill/product-analytics
6
http://www.lca2go.eu/index.en.html
5
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Estomad project
Figure 3: System boundaries of a unit process, following (18)
The studies are limited to the operating phase only (use stage), disregarding the rest of the life cycle
of the machine tool itself. Also infrastructure, transport, consumables, exceptional events (e.g.
accidents), material handling systems, centralized production stations for consumables such as
electricity, compressed air, etc. are disregarded, limiting the scope of the LCA. When analyzing the
process, the characteristics of the reference workpiece are of utmost importance and have to be
carefully detailed (e.g. in terms of machine speed, removal volume). However, as the complete
parameter space for a single process or machine will already be very vast, averages will have to be
made. Typical use scenarios of the machine are listed and energy and resource consumption are
related to the different machine usages and machine units or subsystems.
During a first screening of the machine, an inventory and first analysis of the different unit processes
on the machine is made using energy and mass calculations (21). These processes are then analyzed
in detail in a second step (in-depth approach) which includes a time, power, consumables and
emission study and provides more accurate and complete LCI data (see Figure 4). To obtain accurate
results power and emissions should be measured for each consuming unit separately. It is indicated
by Kellens et al. (20) that this data could also be used to help identify the potential for improvements
of the involved manufacturing unit processes, next to just quantifying its consumption and emissions.
Figure 4: Overview of the process inventory phase for the in-depth approach, following
(20)
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2.2.4
From LCA to machine improvements
The CO2PE! method has been applied to amongst others a drilling machine and a laser cutting
process (18). Next to providing energy consumption and emission data for these processes, process
improvement opportunities could be identified. For the laser cutting application it was for example
shown that the CO2 laser source and the chiller unit for the machine under test were the largest
energy consumers during productive time. During non-productive time, 12% of the yearly energy
consumption was required to keep the chiller and other components active. By working on the
control logic only, the consumption of the machine could be considerably reduced (22). However, the
process of identifying such potential process improvements is typically limited to (i) dividing the
measured signals in different sections over time, (ii) allocating these different sections to different
use scenarios, (iii) comparing the energy/consumables used in different sections/scenarios and finally
(iv) trying to understand why an identified major consumption occurs under certain scenarios. This
understanding is typically driven by common sense reasoning. From a signal processing point of view
no advanced techniques are used. Nevertheless, as practice shows, even these simple approaches
allow to identify major opportunities for reduction of energy consumption in certain cases. To
analyze the potential of using the CO2PE! approach for reducing the energy consumption of other
types of machines, the approach was applied to the badminton robot system. This was done in
cooperation with the CIB department of the Katholieke Universiteit Leuven. The system under test
was the linear axis of FMTC’s badminton robot, both with the time-optimal and energy-optimal
controller implementation (see (23) for details). The process considered was a badminton play of
500s.
The results of the analysis can be summarized as follows (Table 1):
stand-by level [W]
Peak [W]
Total energy [kJ]
Energy-efficient
160
1585
111
Time-optimal
157
2100
260
Table 1: Badminton robot test results


the stand-by level, which is determined by the idle currents of all motors and power drives is
considerable and consistent at the level of approximately 160W,
there is a very significant energy saving potential by implementing the energy-optimal
controller, compared to the time-optimal controller.
However, based on the measurements and analysis conducted, no indications could be given on how
the energy consumption of the time-optimal implementation could be reduced (assuming the
energy-efficient implementation not to be available). This shows the limitations of these LCA inspired
approaches for identifying ways to reduce the energy consumption of existing machines and the
opportunities and possible added value of more in-depth analyses approaches. Within ESTOMAD a
number of more detailed data analyses approaches have been developed (24). More importantly, it
has been shown how model-based analysis approaches not only allow better understanding what are
the origins of energy losses in machinery, but moreover allow evaluating the effect of machine
modifications on the energy consumption of the machine itself. This way the effect of a machine
modification can be assessed in detail before it is implemented in reality.
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As already indicated above, starting from the LCA results, is it possible to begin thinking on improving an
existing product by implementing appropriate eco design measures, measures effecting the
environmental impact of the product at different stages of its service life. While in the CO2PE! and
ESTOMAD framework, these measures are related to energy consumption reduction during the use phase
only, in a general LCA setting, they can be much more diverse (e.g. related to material use, distribution,
repair and maintenance, and disposal or recycling). Different measures have to be taken for products
with the main environmental impact at the use stage (use intensive), compared to products with the main
impact during manufacture (manufacture intensive). To assist product designers to take appropriate
measures for their products, various checklists have been developed, e.g. the Ecodesign Pilot developed
by the University of Vienna (25). This tool has amongst others been applied to industrial systems such as a
Digital Voice Recorder and an Injection Moulding Machine (26) (27).
High level advices resulted from these analyses; e.g. for the Digital Voice Recorder under test the
following was concluded with respect to reducing energy consumption at use stage (see (28) for details):





Use energy efficient components: energy used for operating could be reduced to 25 %
Change of lightning system to LEDs: energy reduction of 20% for lightning display
The stand-by consumption could be minimized via USB cable charging
Use of rechargeable batteries: energy consumption and waste generation during use phase
Decreases to a high extent and improves the environmental profile significantly
These advices are very similar in nature to those reported by the CO2PE! approach, confirming the need
for more in-depth analysis and design approaches as targeted in the ESTOMAD project (see next session
of the deliverable).
2.3 Energy efficiency and design theory
The introduction of aspects of energy assessment into the process for the design of an industrial
machinery could extract useful elements from the field of the design theory.
Theories and models of technical products and product development process have been in the focus
of scientific work for roughly 40-50 years. Today, they are increasingly relevant also in industrial
application (including machinery) because they are vital elements of current strategies such as
concurrent / simultaneous engineering. Design for “X” (with “X” = strength, manufacturing,
assembly, service, recycling, cost, etc. ) is an important strategy especially in engineering design
practice: the introduction of energy requirements into product specification can lead to define
approaches of Design for energy efficiency.
Until now very few structured approaches for machinery design that take into account the energy
impact of the resources have been proposed in the field of design theory and methodologies, and
they have application limited to specific machine typology. Established design methodologies have
not yet considered energy efficiency as a central requirement of technical systems. As an inherent
part of the methodology, it is necessary to take energy efficiency as a central requirement into
account, like the static, the dynamic or the thermal behavior of a machinery.
A systematic approach for the development of energy efficiency machine tools, proposed by
Neugebauer et al. (29), provides general concepts that can be applied to other type of industrial
machinery and could be taken as reference in the possible definition of methodologies for the energy
efficiency design of specific machines. This approach is based on the property-driven design
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methodology by Weber (30), that defines the design process as a process which has to reach given
properties (i.e. requirements) by defining characteristics to ensure this properties (e.g. geometries or
materials).
Figure 5: Property-driven development by Weber (30)
Energy efficiency is taken as central property in the design process and represents a new
requirement / property to be defined in the phase of design problem / task definition. In
contradiction with established methodologies, the approach (Figure 6) includes an initial analysis of
existing technical systems and the individuation and classification of their prior and relevant energy
consumers (sub-systems and processes). The identified major consumers are afterwards
systematically addressed to reduce their energy consumption: several options and solutions – the
system characteristics – are determined and considered, starting with the complete elimination of
the energy usage and ending with the option of recovering energy. A following step of analysis
consists of the verification of the system design, predicting and evaluating the system behavior using
several tools (e.g. FEM analysis, simulation models, experimental analyses, etc.). The design process
provides one or more solutions if the accounted properties are met by the defined characteristics.
D3.3
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Figure 6: Methodology for energy efficiency (29)
Neugebauer et al. presents a schema of a possible general methodology for energy efficiency system
design and its interconnection to the several established design (Figure 7).
Figure 7: General design methodology for energy-efficient systems (29)
A detailed knowledge about the energy flows of the subsystems and a feasible classification of the
subsystems on the base of energetic / functional aspects are necessary for the design of energyefficient machinery.
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Zien presents an axiomatic approach for the definition of energy efficiency measures for the design
and operation of machinery (31). Axiomatic design is a systematic tool that structures and clusters
measures within a design process through mapping of functional requirements and design
parameters. A functional requirement (FR) can be defined as a set of functional needs of a system
(e.g. product or process). A related design parameter (DP) represents a response which fulfills the FR,
hence leading to a structured design process (32). The relationship between FR and DP is defined in a
vector which displays the decomposition of the FR with unique and preferably uncoupled DP. In
accordance with the axiomatic design theory, the decomposition demands to define an initial FR
which states the objective and scope for the design process. At the main decomposition level, the
related DP is rather extensive and will lead with ongoing decomposition to more specific and detailed
solutions that fulfill the requirements. Hence, the axiomatic design methodology enables to link
energy saving potentials for machine tool usage with optimal improvement measures. Moreover, by
ordering the results in a systematic top-down structure and integrating path dependency (reading
from left to right) the decomposition vector provides guidance for the implementation of measures
to minimize the energy demand of a machining cycle.
Considering the specific case of a machine tool, the energy efficient machinery decomposition
focuses solely on energy-related objectives and thus the contribution of FRs on minimizing the
energy demand. The resulting FR and DP of the initial decomposition are displayed in Figure 8. Based
on the initial FR 1, the subsequent level resolves the three FRs to reduce, reuse and recover energy
with the DP to analyze specific saving potentials to fulfill the requirements.
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Figure 8: Energy efficient machine tool decomposition (31)
2.4 Technologies for energy efficiency
The modular system architecture of machinery taken into account in Estomad project allows to
identify and propose eco-design technical solution at the level of element, subsystem and
component of a machine. Many of the components are manufactured by the suppliers and then
implemented / assembled by machinery manufacturers. Moreover, the possible innovations in
machinery strictly depends on the evolution at the component level, because the efficiency of the
components has a relevant impact on the energetic behaviour of the machinery (Figure 9). Also in
the markets of the single machinery components there is an increasing attention towards solution
for energy efficiency. Currently, several energy-efficient technologies are available in the market and
can be considered as best available technologies (BAT) (10).
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Figure 9: a map to support a performance/consumption analysis for a machine tool (CNRITIA)
Most BAT component solutions are compatible with each other and aid to realise energy efficient
machine tools. However, the energy savings to be realised largely depend on the combination of
measures and the savings potential cannot be just aggregated when more than one option is
implemented. The measures have a strong influence on productivity and partly also functionality of
the machinery so that eco-design solutions have to consider conditions of application. Since
significant environmental impacts of machinery are indebted to the consumption of energy during
the use phase. Subsequently, technical options for energy efficiency aim at reducing energy losses
during operating and stand-by modes of a machinery.
The main difficulty during machinery design phase is not to estimate consumption but to understand
if alternative design or management strategies are preferable. To support this phase, collection of
Best Available Technologies will be evaluated, as done in the preparatory studies performed for the
Energy Using Products Directive (10), while no rigid and automated process for design optimization is
foreseen. In deliverable D1.2, the main machinery elements that are relevant from the energetic
point of view have been individuated. For some of these elements (in particular structure, cooling
unit, hydraulic unit and electrical motor), existing technical solutions for energy efficiency and
possible improvement directions will be introduced and exposed in the next chapter – Chapter 3 –
focusing on methodologies at the level of element (component and subsystem).
2.5 Scenarios, policies and impact analysis
In the decisional process of machinery design, choices of energy efficiency are often in trade-off with
other aspects e.g. regarding cost, productivity, service level and normative constraints. Energy
efficiency is only one of the criteria that can be used for evaluate and define the building /
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purchasing of a machinery, the selection of components, the adoption of a technology. The weight of
the energetic aspects in the design choices can depends on the industrial sector and on the scenario
in which company operates. In addition, it is probable that industrial sectors will be subject to
regulation imposing some constraints and indications about machinery energy efficiency
requirements.
Taking as instance the machine tool sector, EC Product Group Study related to the Eco-design of
Machine Tools (11) proposes the mandatory usage of some checklists of good-design-practice for
energy efficiency, making it obligatory to assess feasibility of improvement options. The final
judgement, whether an option is suitable for a given application, would remain with the machinery
developer. Besides such a checklist approach (and closely related with it) power management and
information / declaration requirements can be defined. Whereas power management addresses the
aspect of reducing power consumption in non-productive times without hampering productivity,
standardised information / declaration requirements create transparency and comparability
regarding environmental performance and life cycle costs. In particular, the latter is assumed to have
an influence on purchase decisions.
Such measures could be introduced either through an eco-design implementing measure or through
one or several Voluntary Agreements. No such Voluntary Agreement is in place currently, and some
standards are lacking, which therefore potentially impedes the unambiguous implementation of a
potential Voluntary Agreement.
Three possible policy options (see also Figure 10) are considered in order to yield a change in the
market during the next years and can be assessed against a Business-as-usual (BAU) scenario
regarding their savings potential:



D3.3
LLCC (Least Life Cycle Costs). Implementation of good design practices, accompanied by
power management requirements and declaration obligations, leading to machinery
improvements which correspond to the point of Least Life Cycle Costs (investment and
operative costs during the machine life cycle time).
LCC-BEP (Life Cycle Costs – Break-even Point). This can be considered an “optimistic
scenario”, where good-design-practice yields higher savings. In this case the user choices an
improvement machine solution if this does not imply an increase of the costs respect to the
current / base solution costs. Fiscal incentives furthermore can be assumed to pay off for
part of the additional machinery costs for implementing even more improvement options
than in the LLCC scenario.
10% VA. A Voluntary Agreement is implemented hypothetically, setting a target that all
machine tools sold for example in 2014 and thereafter should be, on average, 10% less
energy-consuming than in 2010. PCF (Product Carbon Footprint) label. For light-stationary
machine tools an effective PCF label scenario is calculated. A combination of both a
Voluntary Agreement with 10% reduction target, and an effective (regardless of whether
mandatory or voluntary) PCF label for small units, yields a total saving of 73 PJ in 2025, or 9%
compared to BAU.
Design for energy efficiency
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Figure 10: Possible design criteria
These policy options represent possible criteria for the design of industrial machinery, including in
the assessments the aspects related to energy efficiency and could be subject to later regulation on
the part of the policy-maker.
2.6 Performance evaluation
2.6.1
Energy consumption analysis
The basic steps of the energetic assessment are:





Definition of machinery requirements / specifications
definition of a use scenario / standard work cycle
definition of energy related performance indicators (see next paragraph)
evaluation, by simulation and/or experimental measures, of such indicators, evaluating
where, in the machine, and when, in the working cycle, energy is dissipated (association
machine function – energy consumption, association machine state – basic energy
consumption level)
evaluate, given the required non-energetic performances (e.g. productivity, cost,..) if better
solutions could be proposed (impact of operative costs)
The single machinery has to be taken into account as single individual entity with its all potential
improvement alternatives. The energy fluxes between its components and devices have to be
defined.
2.6.2
Energy – performance indicators
In order to properly realize an optimal eco-design process, is necessary to define what are the
characteristic that the machine has to satisfy. While the real design process will consider, as usually,
a complex mix of different and competing performance parameters (like production speed, accuracy,
cost, reliability,...), Task T3.3 focus on additional performance indicators that are introduced to take
into account machine energy consumption in the use phase.
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It has to be noted that defining such performance factors is a completely different task in respect to
the energetic analyses developed in WP2 and Task T3.1: in these later cases, the objective was
modeling the relationship between operational conditions and dissipated power (e.g., for an electric
motor, dissipated power as a function of output torque and speed). Task T3.3, instead, has to focus
on the energy dissipated while the machine is executing the requested production: it is necessary
here to refer to a specific use scenario, computing how the instantaneous dissipated power forecast
by WP2 becomes a total dissipated energy.
“Green” technologies are often understood as those capable of meeting product design
requirements while minimizing environmental impact. Minimizing impacts, however, is a necessary
but not sufficient condition for a sustainability strategy. The design of an energy-efficient machinery
has to consider the following aspect:



selection and application of appropriate metrics for measuring manufacturing sustainability,
completion of comprehensive, transparent, and repeatable life-cycle assessment
adjustment/optimization of the system to minimize environmental impacts and cost based
on the chosen metrics and the LCA.
In particular, metric selection and development is very critical as it enables decision making on all
aspects regarding not only design and configuration of a machine, but also the definition of how to
use the machine (processes, working strategies, choice of the resource, etc.).
Reich-Weiser et al. have classified metrics for design and manufacturing decision making can be
classified as either “cost” or “sustainability” indicators. Here, these categories are further broken
down into four distinct metric types (summarized in Table 2 (17)).
Metric Type
Units
Metric Formulation
Intensity
𝑉𝑎𝑙𝑢𝑒
𝑈𝑛𝑖𝑡
𝐼𝑚𝑝𝑎𝑐𝑡𝑖𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡
𝐹𝑢𝑛𝑐𝑡𝑖𝑜𝑛𝑎𝑙 𝑈𝑛𝑖𝑡
Goodness of Investment
𝑆𝑎𝑣𝑖𝑛𝑔𝑠
𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡
𝐼𝑚𝑝𝑎𝑐𝑡𝐵𝐴𝑈 − 𝐿𝐶𝑖𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡
𝐿𝐶𝑖𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡
Sustainability – Availability
Factor
𝑈𝑠𝑒𝑑
𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒
…
Sustainability – Time remaining
Time
…
Notation:
Impact → monetary or environmental cost; LC → total life cycle; BAU → business as usual;
Investment → replacement for BAU
Table 2: Metrics for Energy (17)
The first two metric types are analogous to familiar cost metrics. First are the intensity metrics, which
indicate the cost per functional unit. Second are return on investment metrics that indicate the per
cent savings of a particular investment relative to the input required for the investment. The third
and fourth metric types are based on sustainability concerns relative to resource availability. Use of
resources that are considered “renewable” can be characterized by an availability factor, which
indicates consumption relative to replenishment rates. The availability is the “amount of resource
D3.3
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use” relative to the “total resource availability”. This is comparable to machine tool availability
metrics used in measuring the efficiency of manufacturing systems.
Following an analysis of the state of art about scientific contributions and the experience of the
involved industries, indicators that mainly support the choices of machine design regards the
goodness of investment and the efficiency / energy requirements.
Considering energy efficiency of machinery, some indicators and key parameters that allow the
evaluation of the energy requirement of machinery and components have been defined by
Neugebauer (33). These parameters are shown in Table 3.
Key Parameter
Formula
Remark
specific power requirement P*
𝑃∗ =
𝑃𝑜𝑤𝑒𝑟𝑖𝑛
𝑂𝑢𝑡𝑝𝑢𝑡
defined for working point or average of a
cycle
specific energy requirement W*
𝑊∗ =
𝐸𝑛𝑒𝑟𝑔𝑦𝑖𝑛
𝑂𝑢𝑡𝑝𝑢𝑡
defined for cycle
energy effectiveness
using energy for the right things
electrical efficiency 
𝜂=
𝐸𝑛𝑒𝑟𝑔𝑦𝑖𝑛
𝐸𝑛𝑒𝑟𝑔𝑦𝑜𝑢𝑡
defined for working point (power) or cycle
(energy)
energy efficiency 
𝜀=
𝑂𝑢𝑡𝑝𝑢𝑡
𝐸𝑛𝑒𝑟𝑔𝑦𝑖𝑛
using less energy to provide the same
level of useful work; defined for cycle
energy productivity E
𝑂𝑢𝑡𝑝𝑢𝑡𝑝
𝐸=
𝐸𝑛𝑒𝑟𝑔𝑦𝑖𝑛
output = production output (part,
functional element, money, etc.); defined
for a cycle
energy efficiency index EEI
𝜀𝑟𝑒𝑓𝑒𝑟𝑒𝑛𝑐𝑒
𝐸𝐸𝐼 =
𝜀𝑎𝑐𝑡𝑢𝑎𝑙
in percentage compared with a defined
goal; set has to be defined, includes the
potential
Table 3: Key parameters for machine energy requirements evaluation
Following an analysis of scientific contributions and the experience of the involved industries, two
basic classes of energy related indicators are considered:


Indicators for investment evaluation of a machine adopting energy-efficient solutions
Indexes of energetic efficiency
The first class includes indicators as Net Present Value / Net Present Cost and Payback Period.
Net Present Value (Equation 1) is defined as the sum of the present values of the individual cash
flows of the same entity. Each cash inflow/outflow is discounted back to its present value. Then they
are summed. The eventual residual value of the investment is subtracted. NPV can be used as an
indicator of how much value an energy-efficient solutions adds to the firm.
𝑈𝐿
𝑁𝑃𝑉 = 𝐼0 + 1 − 𝑇𝑎𝑥 ∙
𝑡=1
D3.3
𝐶𝑇𝑜𝑡𝑜𝑝 − 𝑅𝑜𝑝
−
1+𝑘 𝑡
𝑈𝐿
𝑡=1
𝐷𝐸𝑃𝑙𝑖𝑛 ∙ 𝑇𝑎𝑥 𝑅𝑉 ∙ 1 − 𝑇𝑎𝑥
−
1+𝑘 𝑡
1 + 𝑘 𝑈𝐿
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Estomad project
Equation 1
where:


𝑁𝑃𝐶 is the net present cost
𝐶𝑇𝑜𝑡𝑜𝑝 are the total operative costs

𝑅𝑜𝑝 are the profits





𝐴𝑀𝑀𝑙𝑖𝑛 are the linear depreciations
𝑘 is the actualization factor
𝑅𝑉 is the investment residual factor
𝑈𝐿 is the useful life
𝑇𝑎𝑥 is the taxation
If the different alternative solutions to be evaluated provide the same output – and so the same cash
inflows –, only the cost can be taken into account with Net Present Cost indicator (Equation 2). This
represents the sum of all the costs: capital investment, non-fuel operation and maintenance costs,
replacement costs, energy costs (fuel cost plus any associated costs), any other costs such as legal
fees, etc. If a number of options are being considered then the option with the lowest Net Present
Cost will be the most favourable financial option. Future costs are discounted.
𝑈𝐿
𝑁𝑃𝐶 = I0 + 1 − 𝑇𝑎𝑥 ∙
𝑡=1
𝐶𝑇𝑜𝑡𝑜𝑝
−
1+𝑘 𝑡
𝑈𝐿
𝑡=1
𝐷𝐸𝑃𝑙𝑖𝑛 ∙ 𝑇𝑎𝑥 𝑅𝑉 ∙ 1 − 𝑇𝑎𝑥
−
1+𝑘 𝑡
1 + 𝑘 𝑈𝐿
Equation 2
where:


𝑁𝑃𝐶 is the net present cost
𝐶𝑇𝑜𝑡𝑜𝑝 is the total operative costs





𝐴𝑀𝑀𝑙𝑖𝑛 are the linear depreciations
𝑘 is the actualization factor
𝑅𝑉 is the investment residual factor
𝑈𝐿 is the useful life
𝑇𝑎𝑥 is the taxation
The Payback Period (Equation 3) refers to the period of time required for the return on an
investment to "repay" the sum of the original investment.
𝑈𝐿
𝑃𝐵𝑃 = 𝑡 ∖
𝑡=1
𝐶𝐹
1+𝑘
𝑡
=0
Equation 3
where:




D3.3
𝑃𝐵𝑃 is the payback period
𝑡 is the years
𝐶𝐹 are cash flows
𝑘 actualization factor
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Estomad project

𝑈𝐿 useful life
These indicators are more important when the main driving factor for the introduction of energyefficient solutions is to offer customers a machine with a reduced Total Cost of Ownership.
Crude oil
Import price-index
Hot metal, crude steel, rolled steel and ferro-alloy
Producer-price-index
Price development [%] comparative to the year 2000
Price development [%] comparative to the year 2000
300,0
300,0
Price development
Price development
250,0
250,0
Trend
Trend
200,0
200,0
150,0
150,0
100,0
100,0
50,0
50,0
Source:
Destatis
Figure 11: growth trend of energy
costs
0,0
(Destatis 2010)
Jan
00
Jan
01
Jan
02
Jan
03
Jan
04
Jan
05
Jan
06
Jan
07
Jan
08
Jan
09
Destatis
Figure 12: indicative energy share Source:
of the
Total
0,0
Cost Jan
of ownership
for a production machine
Jan Jan Jan Jan Jan Jan Jan Jan Jan
00
01
02(PTW
03
04
05
06
07
08
09
Darmstadt)
While payback period is a fundamental industrial indicator, of immediate commercial applicability, it
has to be taken into account that a significant part of costs related to energy consumption is not seen
by the machine user as a direct cost in the energy bill (then reflected into total cost of ownership): it
weights indirectly on the whole society, in terms of erosion of the available fossil fuels and
environmental pollution related to their transformation into electric energy. That’s why worldwide
and in Europe particularly, regulation initiatives are under development, to prescribe, for several
industrial sectors, maximum levels of energy dissipation. One of the first sectors undergoing this
procedure has been the automotive sector (by the Euro 0,1,.. regulation): after having prescribed a
maximum environmental impact for a vehicle in its use phase, automotive makers are planning (see
the Figure 13, for the Fiat Group) a progressive reduction also for the energy spent in its
manufacturing phase. This trend will be reflected in a request for production processes and
machinery with reduced energy consumption. In this scenario, an energy per part indicator becomes
more significant.
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Figure 13: progressive reduction of energy per manufactured vehicle planned by the Fiat
Group in its 2009 Sustainability Report
2.6.3
Exergy
The concept of Exergy is defined by Szargut et al. (34) as “the amount of work obtainable when some
matter is brought to a state of thermodynamic equilibrium with the common components of the
natural surroundings by means of reversible processes, involving interaction only with the above
mentioned components of nature”. Unlike energy, exergy is not conserved; thus, exergy can be
destructed in a system and so it is connected to entropic production of the process and the reference
temperature that maybe equal to environmental temperature.
The exergy analysis in power generation and process industry systems is common to define the
quality heat exchanger or the quality in term of ability to produce mechanical and consequently
electrical work.
The exergy analysis in a discrete manufacturing system aims to detect and evaluate thermodynamic
imperfection and indicate possibilities for improvement so the exergy efficiency is an important
parameter in achieving these objectives.
While typical energy analysis manages to illustrate different power flows inside the system, leading
to a better focus on proposing energy efficiency improvement measures, the exergy analysis
delineates prospective improvement potential not only from energy point of view, but also from
material and consumables utilization as it is possible to see from figure…
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Figure 14: Example of exergetic view for a machine tool
It also pinpoints improvement potentials which are related to systems variables, such as nesting
efficiency and material recycling or as proposed by Dotchev et al. [19], the degraded powder material
can be reused for a trial work.
Furthermore, the Exergy efficiency serves as a convenient resource efficiency metric which combines
the energy and material utilization.
2.7 General SW functionalities for energy analysis
2.7.1
Introduction
Analysing and increasing the energy efficiency of complete production machines is a difficult task.
It is mandatory to dispose of simulation tools capable of addressing the various phases of the
design: component, subsystem and system levels in consistent manner. In order to assess both the
performance of the design and its green efficiency, a multi-domain system simulation platform that
can be adapted to the “design for efficiency” approach developed in the present deliverable needed
to be selected. The “1D” model complexity level and the bond graph formalism of AMESim appeared
to be a good basis for this purpose. Nevertheless, deep modifications in both the physical libraries
and the software platform (mainly the Graphic User Interface) were necessary to dispose of energy
analysis capabilities:
1. An update of the library components was required to compute all the related power and
energy variables to be used in power or energy balances;
2. An update of the Graphic User Interface was required to help users analysing the energy
flows in their systems.
The idea was to satisfy the needs of machine manufacturers and component/subsystem suppliers in
each of these situations:
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Estomad project

Analysis and improvement of an existing design’s energy efficiency;

Complete redesign of a component/subsystem/machine including green efficiency
performance objectives or constraints.
2.7.2
Power and energy variables in AMESim submodels
Power and energy variables were implemented in about 800 submodels from the physical and
application-oriented libraries (useful for production machines) that were targeted by the ESTOMAD
project. These variables are classified into Resistive (R), Inertial (I) and Capacitive (C) categories, in
accordance with the bond graph formalism. Moreover, they are also tagged according to their energy
form (electrical, hydraulic, mechanical …) and to their main physical cause (friction, damping …), thus
making the physical analysis of a system easier, which can be considered as a good pre-requisite to
perform green design (indeed, energy losses are located precisely but also understood by engineers
on a physical point of view thus allowing improvement directions to be imagined). In addition, it is
worth to mention that these variables can now be used to define relevant green efficiency
performance indicators. These indicators can be integrated into an optimization procedure to handle
the trade-off between pure performance and green efficiency, which is another important condition
to perform green design.
Figure 15: New energy variables in submodels
The phenomenological and scalable approach that has been implemented makes it possible for users
to interpret the energy flows physically and in terms of lost and stored quantities. For validation on
the implemented libraries, LMS Imagine also developed specific components – the power sensors –
to help users checking their power or energy balances starting from the component level, working
up to subsystem level and finally to system level.
Figure 16: Use of two mechanical power sensors on a component (damper)
D3.3
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Even AMESet, the software environment for AMESim user submodel creation was updated to
support natively the newly introduced power and energy variables. Users will therefore be able to
create their own energy analysis capable libraries of components.
Implementation of power sensors, as well as the introduction of power and energy variables in
AMESim submodels, is described in Deliverable 4.1.
2.7.3
Green Design dedicated Analysis Tools
In addition to the previously mentioned developments, and to ensure a high level of usability of the
updated libraries, specific analysis tools, dedicated to green design, were created and introduced in
the AMESim Graphic User Interface (GUI). By taking advantage of the previously mentioned tags, all
types of power, energy or activity variables present in the system are listed, sorted and can be
summed, allowing users to identify the main losses and to assess the energy efficiency of selected
components or subsystems.
Figure 17: Energy Management window
These sorting capabilities are very useful when creating a Sankey chart or when analysing the
power/energy interactions between different components/subsystems. Another advantage of this
tool is that it prevents the user from having to look for the power/energy variables in each submodel
on the sketch (selecting variables in the dialog highlights the corresponding components). Animated
bar plots were also created to visualize the evolutions of these variables during the entire simulation
(see Figure 18). Lastly, it is possible to compare various result sets simultaneously.
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Figure 18: Animated bar chart for a mass, spring, damper system
Figure 19: Example of Power flowchart
Implementation of this GUI analysis tools for Green Design is described in Deliverable 4.2.
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3 General methodology for machinery design and assessment /
evaluation of the energy efficiency – [FMTC]
This section describes a methodology for assessment/evaluation of the energy efficiency and a
design approach from an energetic point of view. It is based on the lessons learned during the
project. The general methodology is schematically represented in Figure 20.
Starting from a new idea or existing system
Developments within ESTOMAD
Benchmarking
(testing for target setting)
Or refinement testing
(testing for improvements & model validation)
Test based energy
analysis techniques
(FMTC, ITIA)
Advanced testing
procedures (Picanol)
Concept
(Component selection / sizing)
Use of multi-losses
models (Kuleuven,
ITIA)
Detailed design
(control strategy or physical designs)
Link from losses models
to detailed design
models/tools (FMTC)
Figure 20 Schematic representation of the general ESTOMAD methodology
for the design of machinery with energetic analysis / assessment
3.1 Experimental benchmarking
Before starting a new design and making design choices, in general it is good practice to define a
benchmarking situation with respect to which potential improvements will be evaluated. Since a first
prototype of the machine is available, this prototype can be used as benchmark. In other cases a
previous version of the machinery can be used or even the machine of a competitor.
The next step is to understand and characterize the current situation, i.e. how energy efficient is the
actual machine, how much energy is not crucial for the process. The best approach is to actually
measure the energy consumption. The measurement plan should specify all the sensors to be used
(e.g. power meters, current probes, encoders, etc.) and contain all relevant operating conditions,
including not only low and heavy duty operations, but also start-up, homing and standby.
Analysis of the measurements should reveal and visualize the energy flows in the machine and the
localization of the losses:

D3.3
In space, i.e. in which components the energy is consumed, e.g. friction in the bearings
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
In time, i.e. under which operating conditions losses are higher, e.g. during acceleration or at
high speeds, etc.
One of the main difficulties is that probably it is not possible to measure all energy losses directly.
Some losses like friction are distributed over the system. In those cases the analyses should be
complemented with a (dynamic) simulation model that allows to split the different energy flows and
contributions to the losses.
Based on an evaluation of the energy losses, one can decide on which element the (re)design should
focus. It is good practice to start with the largest losses and the elements that are easy to modify.
3.2 Concept modelling
During the design process simulation models of a machine are indispensable to get a deeper insight
in its behaviour and evaluate design choices. It is a common misconception that a full blown dynamic
simulation that includes all possible details is required for all steps of the design process. This would
lead to an explosion of the modelling effort and cost without a lot of added value. Nevertheless,
every step will benefit from using models with the right level of details.
As discussed in the previous paragraph, models are used in the benchmarking phase for extracting
more information from the measurements. If a detailed model of the machine is available, the
unknown model parameters can be estimated from the measurements in order to obtain a realistic
simulation model. Afterwards, the energy flows could be easily simulated and visualized. If such a
detailed model is not (yet) available, one should start with a simplified model and refine it until it
contains sufficiently detail to cover the major dynamic and energetic behaviour that is found in the
measurements.
An energetic analysis will reveal where the energy is lost and which components in the machine
should be improved in the concept phase. Sometimes, it is sufficient to focus on one component or a
subsystem of the machine. In that case, the simulation model to be developed can be restricted to
these parts instead of the complete machine. From the concept phase on, physics based models are
required as offered by the software tool AMESim (see also section 2.7). These kind of models allow to
quickly change or build new models from library blocks and compare the possible energetic
improvement of different concepts. For more complex block or subsystems detailed modelling can
be avoided by including efficiency maps or look-up tables.
Once the final concept is chosen, the detailed design phase can start from a simplified model with a
limited level of detail. When more analyses and further optimizations are performed, they might
require to incrementally refine and add more details to the model.
3.3 Design guidelines
The requirement for more energy efficiency should be translated into specifications to be fulfilled in
the detailed design. Similar to reducing cost, there are multiple options to work on improving the
energy efficiency of a machine, so the complete mechatronic system architecture has to be
considered. Some options to make a more energy efficient design are:
-
D3.3
Replace by more energy efficient components
If the losses are concentrated in one component, it should be quite easy to replace it, e.g. a
motor from a better efficiency class. However, the cost will probably rise significantly, but
the envisaged improvement might not be achieved.
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-
-
-
-
Use a more energy efficient concept
This requires multiple components to be changed and a new trade-off to be made. E.g. in
case the resistive losses in the motor are dominant, a direct drive motor is not the best
choice and could be replaced by a motor with gear box. Since this gear box will also add new
losses, a simulation tool will help to make a trade-off comparison between the original and
new concept.
Apply more energy efficient operating conditions
Sometimes the losses are not due to ‘bad’ components, but inherent to the typical use of the
machine. E.g. a lot of motions are programmed to be as fast as possible, without considering
if this is really required. This leads to high accelerations, and hence high electrical losses. The
solution can be to apply different motion trajectories with lower accelerations. Motors can
also be used in a more energy efficient working range. One of the major advantages of this
solution is that mostly only a software adaptation in the controller is required, which
significantly reduces the implementation cost for increased energy efficiency.
Reduce moving mass or inertia
The moving mass is directly related to the required power for the motion. But also indirectly
can a high mass increase the friction losses in the guiding or bearings. A low weight design
can be considered at the trade-off of sufficient stiffness in the system.
Consider energy recuperation and storage
When there are a lot of start and stop motions, the braking energy can be stored and used
for boosting the start-up. As a side effect, the nominal power of the installed motor could be
further reduced.
In most of these design options dynamic simulations will be used to compare different concepts and
evaluate the achievable improvement.
3.4 Post design assessment
When the final concept has been realized, power consumption measurements should be done and
benchmarked to the initial situation to check if the specified goals are met. This validation may lead
to additional refinements.
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4 Methodologies for machinery elements
According with the project description of the work, the modular system architecture of machinery is
taken into account for the eco-design measures.
Several machine “elements” (i.e. a component, like a bearing, or sub-system, like a chiller) are used
in several different kinds of machinery. Those elements are analysed in this chapter, for all aspects
that don’t require a precise definition of the application (these issues will be covered in the following
chapter, dedicated to specific applications).
In order to support a practical energy efficiency analysis during machinery design, this chapter
proposes, for each machine element typology, the following information:







alternative solutions offered on the market and evolution directions, for Element energetic
improvements
if existing, normative on Element energetic assessment
how to define the Element use scenario, to evaluate its energetic performance
indications on optimal Element-related energy-aware management strategies, during
machinery operation
energy-saving general design guidelines
definition and calculation of performance indicators about energy efficiency, to be balanced
with production and cost objectives; definition of an objective function is which each
contribution has a certain weight
how to select the most appropriate numerical model and simulation guidelines
4.1 Machine structure
4.1.1
Alternative solutions, improvement directions, design guidelines for energy saving
Energy-aware structure design can bring to three main design choices:
1) decreasing the inertia of moving parts (e.g. by using different materials)
2) increasing structure stiffness
3) modifying the machine architecture in order to minimize motions, in term of accelerations
and/or velocities and/or displacements
The evaluation of such alternatives doesn’t require a specific approach in the Estomad framework.
4.1.1.1 Reduction in mass
In general, most production machines have similar movement generation and allow therefore energy
saving through the reduction of moving masses. In the same time, longer durability of machinery
parts can be achieved due to smaller load. On the contrary, possible disadvantages of this lightweight
constructions can be the flexibility regarding applied forces and the enhanced tendency to oscillate
caused by a lack of damping.
In order to reduce machine masses and realize energy savings, two main strategies can be pursued in
the same time:
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1) Material lightweight design. Replacement of currently implemented materials with
lightweight alternatives.
2) Structural lightweight design. Structure optimization of machine components which allow
material reduction.
3) System lightweight design.
Material lightweight design suggests the uses of new material to overcome the mass / stiffness
restriction of classical material like steel or cast iron. Examples are the usage of titanium materials,
metal foam or reinforced carbon fibre composites (CFC) or even a combination of classical and nonclassical materials (hybrid material, e.g. metal-foam sandwich). For the choice of the material
designers must be consider not only the material characteristics, but also the specific costs and
technical mastery. Therefore, it can be expected that titanium and CFC materials are only applied in
special cases. Otherwise, the more the technological mastery is given, the higher will be the use of
hybrid structures of aluminium, polymer cement, steel and technical ceramics for lightweight
constructions.
The so-called lightweight factor can be taken into account. This parameter can be calculated as the
ratio of elastic modulus to density for material or the ratio of stiffness to mass for structures (taking
the geometry into account).
Lightweight design measures are implemented to achieve the highest possible lightweight factor for
machine components through appropriate material selection and design.
Heavy components produce weight forced and also, in the case of moving parts, inertial forces. The
following effects in relation to energy can be achieved by reducing the mass:




D3.3
Reduction in reactive energy. In case of accelerating components, inertial forces have an
effect on the necessary engine torques / forces. Acceleration energies in machinery moving
parts are reactive energies, which is applied by drivers prone to losses (electrical losses). A
possible approach can consists of the reduction of the mass given identical cinematic
performance. This allows the proportion of the load-dependent losses to be reduced in drive
system. Due to the lower acceleration forced applied, it may be possible to use small drivers
where appropriate.
Reduction in friction losses. Weight forces influence the amount of friction losses in bearings
and guides by affecting the normal forces in the friction point. By reducing this friction loss,
the energy efficiency of machines can be directly improved. Where necessary, a reduction in
weight can also have the additional effect of making the dimension smaller.
Increase of moving part acceleration. A further approach is based on constant drive forces at
the masses travelling at a reduced acceleration, resulting in a higher potential acceleration.
This does not result in a direct reduction in drive. However, the processing time and thus the
percentage of base load losses per produced unit may be reduced, obtaining an indirect
increase of energy efficiency.
Increase in the guide bandwidth. Normally, an increase in the lightweight factor leads to an
increase in the mechanical natural frequency. This in turn can cause an increase in the guide
bandwidth and an higher flow factor of the controlled feed moving part if the contemplated
mechanical natural frequency is responsible for the bandwidth limits. This may result in an
increase in the machine productivity and thus a reduction in percentage of base load per
produced unit.
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Another possibility to reduce machine tool weight of moving parts and to minimize energy
consumption and costs is the structural lightweight design. The structural optimization tries to build
an ideal structure based on the work load stress. An example of how lightweight structures and
therefore lightweight structural design can be achieved is the topological optimization CAD-tool. The
material is oriented after the tensile loads therefore. A typical mass reduction after a topological
optimization is around 20%. Traditionally, the design engineer carries out these structure
optimizations with the help of his practical knowledge.
The system lightweight design deals with new structural concept and functional integration to reduce
moving masses. One basic idea is to increase or to substitute passive static stiffness by mechatronics,
for example with a PI-controller. Therefore an infinite static stiffness can be reach with minimal
material effort. For example, considering a machine tool, a possible solution consists of the
integration of piezoactoric elements for torsion compensation. The mechanical structure can be
much finer compared to the classical structures because it must only sustain the tensile load, while
the deformation is compensated by the active adaptronic elements (e.g. embedded piezoceramics).
Hence an high mass reduction can be obtained.
Mass reduction, i.e. mass optimization, is – in principle – possible for all machines. Besides the
potentially additional design efforts there are no additional costs of implementing this option. On the
contrary, material savings are directly related to cost savings.
4.1.1.2 Increase in stiffness
An increase of the stiffness can be obtained with the following approaches.


Increase in the process stability. The aim of the approach is to increase the stiffness with a
constant mass. In selected production machines and processes, this can be used to achieve
an increased process stability which in turns leads to a reduction in processing time due to
higher potential time span volumes. This translate into a percentage reduction of base load
loss per produced unit, and thus the result is an indirect increase of the energy efficiency. It
has to be consider that a mass reduction can also decrease the process stability through
lower mass moments of inertia of oscillating capable structures.
Increase in the guide bandwidth. Analogous to the reduction in mass, the natural frequency
in relation to the control process can also be increase by increasing the stiffness.
4.1.1.3 Modification of the machine architecture
Energy-aware structure design can bring to modifying the machine architecture in order to minimize
motions, in term of accelerations and/or velocities and/or displacements.
4.2 Cooling unit
The chiller is a refrigerating machine that removes heat from a liquid through a vapor compression or
absorption cycle. Chillers represents a basic component of most of the industrial machinery and can
be used in a wide range of application: plastics manufacturing, injection molding, printing, laser
rubber, metal cutting process. A chiller unit can be used for generic cooling (e.g. removing heat from
a motor or spindle) or for controlling the temperature in the machinery / component structure: the
use define the precision level that has to be provided in refrigerating.
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4.2.1







Eco / Energy efficiency normative
http://www.ecofreezercom.org/index_1.php preparatory study – Refrigerating appliance:
liquid – cooled
EN 14511:2011 – Air conditioners, liquid chilling packages and heat pumps with electrically
driven compressors: terms and definitions, the test conditions, methods and requirements
for the rating and performance of industrial process chillers
EN 15218:2008 – Air conditioners and liquid chilling packages with evaporatively cooled
condenser and with electrical driven compressors
AHRI 550/590 – 2003 – Standard for performance rating of water chilling packages using the
vapour compression cycle
ANSI/AHRI 560-2000 – Absorption water chilling and water heating packages
CAN/CSA-C743-09 – Performance Standard for Rating Packaged Water Chillers
AS/NZS 4776:2008 – Liquid-chilling using the vapour compression cycle
No mandatory requirements are identified at EU level/MS.
4.2.2
Alternative solutions, improvement directions
Different types of chiller are currently available and used in industrial context and are characterized
by different control mode. Control modes have a direct impact on the cooling performance and their
utilization depends on the application.
The most common typology of chiller are characterized by constant capacity and two position control
of the compressor (also known as on-off control). Chiller activation is commanded on the base of
temperature set point imposed by the user. When application temperature exceeds the set point,
chiller goes from stand by state to up state and cools the application. This solution is very common
because of its simplicity.
A chiller with the hot gas by pass capacity control scheme permits modulation of system capacity to a
very low percentage but, instead of modulating the cooling power according to the heat load
diminution, this method envisages that a fraction of the hot-gas refrigerant (i.e. compressed high
pressure and high temperature refrigerant) is injected back into a specific point of the line. The
injection point is decided according to the specific control scheme. The main drawback is that the
COP is reduced. This implies that better performance of cooling are paid with a loss of efficiency.
Another typology of chiller is equipped with modulating compressor (variable rpm).
4.2.3
Improvement directions and energy saving guidelines
Some suggestions about how the efficiency of a chiller / refrigerating system can be maximized are
formalized, underlining the impact of each refrigeration system component on the efficiency.
4.2.3.1 Refrigerant
In general, very few substances have properties appropriate for a refrigerant and, of these, few have
stood the test of time and continue to be used as refrigerants. There is no ideal refrigerant.

D3.3
Selection of a refrigerant is a compromise between many factors including ease of
manufacture, cost, toxicity, flammability, environmental impact, corrosiveness and
thermodynamic properties as well as energy efficiency. A key characteristic is the
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Estomad project

pressure/temperature relationship. In general, for energy efficiency it is desirable for the
refrigerant critical point (temperature above which the refrigerant cannot condense) to be
high compared with the heat extraction and rejection temperatures.
Good transport and heat transfer properties are also important for energy efficiency as they
reduce running costs and allow smaller temperature differences to be employed in
evaporators and condensers and hence smaller overall temperature lifts. In general,
refrigerants of low molecular weight and low viscosity will have the best properties.
4.2.3.2 Compressor
The component that consumes the largest portion of energy in a refrigeration system is the
compressor. Compressors will lose efficiency if the temperature lift is higher than necessary and they
will also lose efficiency if droplets of refrigerant liquid are present in the suction vapor or if the
suction vapor becomes too hot. Compressor maintenance, where possible, and the preservation of
lubricant quality are important to retain energy efficiency. For some compressor types (particularly
screw and centrifugal), their part-load energy efficiency performance is poor compared with the one
at full load, so sustained part-loaded operation should be avoided. Variable speed drive technology
and improved control systems can minimize the energy penalty but increase capital costs.
4.2.3.3 Condenser
To keep refrigerant heat rejection temperatures as low as possible, condenser heat transfer rates
should be maximized and the cooling medium temperature minimized. Evaporative condensers are
often the most efficient because they reject heat to the wet-bulb temperature of the ambient air.
Water-cooled condensers combined with cooling towers also approach ambient wet-bulb
temperature but there is an additional temperature difference to drive heat from the refrigerant into
the water, so refrigerant heat rejection temperature is generally higher. Water use can be excessive
if a cooling tower is not used. Air-cooled condensers are usually the least efficient method as they
reject heat to the air dry-bulb temperature, which is generally significantly higher than wet-bulb or
water temperature. However, for small systems they are commonly used because they are cheap,
simple and require little maintenance. It is important to keep all types of condenser clean and free
from fouling. Condensers rejecting heat to atmosphere must be allowed plenty of fresh air and
protected against any tendency for the air to re-circulate back to the condenser inlet. Systems that
operate with refrigerant suction pressures less than atmospheric (e.g. low temperature ammonia or
air-conditioning with HCFC-123) should use purgers to remove non-condensable from the
refrigerant.
4.2.3.4 Expansion devices
Many expansion devices require significant pressure difference to allow proper operation. Therefore
condensing pressure is often maintained at artificially high levels, even at low ambient temperatures.
The biggest culprit in this respect is the conventional thermostatic expansion valve which is often
selected because of its very low cost. One solution is to use electronically controlled expansion
valves.
4.2.3.5 Evaporator
As for condensers, evaporators should be designed to operate at minimum economic temperature
difference so that the refrigerant heat extraction temperature can be as high as possible for a given
substance temperature. Increasing heat extraction temperature also reduces the size of the
compressor required.
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As well as evaporator size, aspects such as refrigerant distribution, circuiting and velocity, use of
enhanced surfaces, air speeds (for air coolers) can all significantly affect energy efficiency. Air coolers
that operate at temperatures below freezing must be defrosted regularly to restore performance.
Electric defrost is simple but is least efficient and therefore only suitable for small systems. Electric
defrost has to be paid for at least twice, to put the electric heat into the cooler and to take it out
again. Water defrost, hot gas defrost, and defrost by the circulation of warm fluid through the cooler,
are all potentially more efficient. However, whatever the system, it is important to optimize the
frequency and duration of defrost to avoid unnecessary defrosting.
4.2.3.6 Interconnecting piping
Efficiency can be reduced if interconnecting piping is of the wrong size or is arranged in ways that
cause unnecessary pressure drop or inhibit oil return (e.g. excessive bends and fitting).
4.2.3.7 Control option
A refrigeration system with well-designed components will not operate efficiently unless the
components are correctly matched and controlled. Energy efficiency has not always been the prime
consideration when selecting effective controls. If possible, the following control options should be
avoided to maximize energy efficiency:






slide valve unloading of over-sized screw compressors
hot gas bypass of compressors
throttling valves between evaporators and compressors
evaporator control by starving refrigerant supply
too frequent defrosts
condenser head pressure controls except when necessary
4.2.3.8 Application
The energy consumption of an industrial chiller strictly depends on the context where chiller is used
and on the application that has to be cooled.
The refrigeration of a machinery or parts of machinery is taken as example. A chiller absorbs heat
form a coolant fluid that has to cool one or more machine elements in order to avoid high
temperature or to maintain a predefine temperature gradient in element structure. The
characteristics of the coolant circuit can have a direct impact on work and energy behavior of the
chiller. An over-dimensioning of the coolant pump should be avoid: in this condition, the pump
produce and transfer heat flow to the coolant that can determine an increase of temperature greater
than the temperature increase linked to the application cooling. The inefficiency of the pump can
implies the action of the chiller for removing the heat produced by the pump itself and not for
cooling the application. A wrong dimensioning of the pipe lines could determine the presence
additional hydraulic resistances and prevalence that lead an over-dimensioning of the pump and
inefficiency in the refrigeration. Therefore, the correct design of the all cooling system has to be
performed in order to achieve objectives of energy efficiency and energy consumption reduction in
the use of industrial chiller.
It can be noted that the pumps are particularly important elements of industrial machinery and
motor-driven equipment in many industrial plants. The basic components in a pump system are
pumps, drive motors, piping networks, valves, and system controls. Pumps are used extensively to
pressurize, transport, and circulate water, condensate, process liquids, and wastewater. Perhaps as
D3.3
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Estomad project
much as 20% of the energy consumed by pumping systems could be saved through changes to
pumping equipment and/or pump control systems.
Figure 21: Per cent of Pump Energy Use
Since the initial capital cost of a pump is typically only a small fraction of its total life cycle costs,
maintenance costs and energy costs represent the most significant fraction of a pump’s total life
cycle costs. In some cases, energy costs can account for up to 90% of the total cost of owning a
pump. Thus, the decision to make a capital investment in pumping equipment should be made based
on projected energy and maintenance costs rather than on initial capital costs alone.
4.2.4
Energetic Measurements
4.2.4.1 Indicators
The efficiency or performance of refrigeration system indicators are normally the relation between
the energy input and cooling capacity (output) or unit of internal storage volume. The units
commonly used are:



Coefficient Of Performance (COP): defined as the cooling capacity divided by the energy
input of the compressor, where higher numbers indicate more efficient equipment. This
number does not have a unit of measurement.
Energy Efficiency Ratio (EER): this value is defined as the ratio of net cooling capacity (Btu/h)
to the total electricity input watt hour (Wh). Higher values indicate more efficient
equipment.
Exergy concept (see paragraph 2.6.3)
4.2.4.2 Considerations
An evaluation of chiller process quality must be deduce using the following equation:
𝜂𝑒𝑥 =
𝑇𝑎
𝑄𝑐
−1 ∗
𝑇𝑐
𝑊
Where:

D3.3
𝜂𝑒𝑥
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Estomad project




𝑇𝑎 is the environmental temperature [K]
𝑇𝑐 is the temperature of lower source [K]
𝑄𝑐 ̇ is the subtracted thermal power [W]
𝑊 is the introduced electrical power [W]
It is possible to see that 𝜂𝑒𝑥 is equal to an second principle performance, because ideal compressor
specific work (or power absorption) is compared to real compressor work.
In other word, it is possible to suppose that 𝐸𝑋 𝑄𝑕 = 𝐸𝑋 𝑄𝑎 = 0 because heat temperature source it
is equal to environmental temperature.
𝑇
So 𝐸𝑋 𝑄𝑐 = 𝑄𝑐 1 − 𝑇𝑎 < 0 because 𝑇𝑐 < 𝑇𝑎
𝑐
It is possible to define
𝐸𝑋 𝑄𝑐 = 𝑄𝑐 1 −
𝑇𝑎
<0
𝑇𝑐
because according to practice the heat power is positive when it flows from heater temperature to
lower temperature source.
So
𝑊=
𝑇𝑎
𝑇𝑐
− 1 𝑄𝑐 + 𝐸𝑋𝑑𝑖𝑠
where 𝐸𝑋𝑑𝑖𝑠 is the dissipation.
In order to refer to
𝜂𝑒𝑥 =
𝑇𝑎
𝑄𝑐
−1 ∗
𝑇𝑐
𝑊
It is possible to write is as
𝜂𝑒𝑥 =
𝑇𝑎
− 1 𝜀𝑓
𝑇𝑐
So for a typical EER of 3.5 and 𝑇𝑎 = 20 [°C], 𝑇𝑐 = -10[°C] typical for chiller, the exergetic efficiency is
40%.
4.3 Hydraulic unit
4.3.1
Introduction
Hydrostatic energy in drive and control hydrostatic systems (Figure 22), since its production in
hydraulic supply unit (supply station) till the transformation into mechanical energy, is influenced by
different factors generating losses, related to the way of operation.
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Figure 22: General flowchart representing the energy flow in hydrostatic system
As a result of power balance analysis of typical system (Figure 23), it can be stated that power losses
are present in specific parts of such system: hydraulic unit (ΔNp), pressure, flow and direction control
elements (ΔNuster) and load generating elements such as hydraulic actuators and motors (ΔNodb).
What is more, the overall losses are associated with power loss in the hydraulic lines connecting
previously mentioned components (ΔNlin).
Nwyj
Nwej
ΔNp
ΔN lin
ΔN uster ΔN lin
ΔN odb
Figure 23: Hydrostatic system Power balance: Nwej – input Power, Nwyj– output Power, ΔNp
– power loss in pump system, ΔNlin – power loss in connecting lines, ΔNuster– power loss in
control elements, ΔNodb – power loss in load generating elements
4.3.2






D3.3
Normative
PN-73/M-73020 – Hydraulic and pneumatic drive and control systems. Hydraulic and
pneumatic elements and systems. General classification and symbols.
PN-91/M-73001 (1992 r.) (IDT ISO 5598 –1985) - Hydraulic and pneumatic drive and control
systems. Terminology.
PN-ISO 1219-1, PN-ISO 1219-2 - Hydraulic and pneumatic drive and control systems.
Graphical symbols and system diagrams.
PN-ISO 4413:2005 – Hydraulic drive and control systems. General rules regarding to hydraulic
systems.
PN-ISO 2944:2005 – Hydraulic and pneumatic drive and control systems. Nominal pressures.
PN-C-96057-01:1991 – Petroleum products. Hydraulic oils for hydrostatic hydraulic systems.
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


4.3.3
PN-C-96057-03:1991 - Petroleum products. Hydraulic oils for hydrostatic hydraulic systems.
L-HH hydraulic oils.
PN-C-96057-04:1991 - Petroleum products. Hydraulic oils for hydrostatic hydraulic systems.
L-HL hydraulic oils.
PN-C-96057-6:1994 - Petroleum products. Hydraulic oils for hydrostatic hydraulic systems. LHV hydraulic oils.
Model and analysis procedures
The modeling and analysis phase of hydraulic systems requires the thorough knowledge about the
losses components present in the considered system. The sources of power loss in particular
elements of hydraulic system are pressure and volumetric losses generated during the operation.
4.3.3.1 Volumetric losses
The volumetric losses are associated with the leakages (leak of hydraulic liquid) in hydrostatic
system. The leakages can be observed mainly in the gaps (in pumps, valves, motors and actuators).
The quantity of these losses depends on the shape of gap (flat, ring) and its location in hydraulic
element. On the system level design, the level of volumetric losses can be obtained from static
characteristics and datasheets provided for components by the manufacturers. In case of
development of new elements (pumps, valves, motors) or modification of existing ones, volumetric
losses determination requires the use of Navier-Stokes and continuity of flow equations. For
hydrostatic pumps and motors (and certain control elements), these losses can be expressed as a
volumetric efficiency coefficient:
𝜂𝑣 =
𝑄𝑤𝑦
𝑄𝑤𝑒
Or
𝜂𝑣 = 1 −
𝛥𝑄
𝑄𝑤𝑒
Where:
𝑄𝑤𝑒 − 𝑕𝑦𝑑𝑟𝑎𝑢𝑙𝑖𝑐 𝑚𝑒𝑑𝑖𝑢𝑚 𝑓𝑙𝑜𝑤 𝑜𝑛 𝑡𝑕𝑒 𝑖𝑛𝑝𝑢𝑡 𝑜𝑓 𝑡𝑕𝑒 𝑒𝑙𝑒𝑚𝑒𝑛𝑡
𝑄𝑤𝑦 − 𝑕𝑦𝑑𝑟𝑎𝑢𝑙𝑖𝑐 𝑚𝑒𝑑𝑖𝑢𝑚 𝑓𝑙𝑜𝑤 𝑜𝑛 𝑡𝑕𝑒 𝑜𝑢𝑡𝑝𝑢𝑡 𝑜𝑓 𝑡𝑕𝑒 𝑒𝑙𝑒𝑚𝑒𝑛𝑡
𝛥𝑄 − 𝑣𝑜𝑙𝑢𝑚𝑒𝑡𝑟𝑖𝑐 𝑙𝑜𝑠𝑠 𝑎𝑠 𝑎 𝑠𝑢𝑚 𝑜𝑓 𝑙𝑜𝑠𝑠𝑒𝑠 𝑖𝑛 𝑒𝑣𝑒𝑟𝑦 𝑔𝑎𝑝 𝑖𝑛 𝑡𝑕𝑒 𝑒𝑙𝑒𝑚𝑒𝑛𝑡
𝛥𝑄 =
𝑄𝑠𝑡𝑟
Where:
𝑄𝑠𝑡𝑟 − 𝑓𝑙𝑜𝑤 𝑙𝑜𝑠𝑠 𝑖𝑛 𝑝𝑎𝑟𝑡𝑖𝑐𝑢𝑙𝑎𝑟 𝑔𝑎𝑝 𝑖𝑛 𝑡𝑕𝑒 𝑒𝑙𝑒𝑚𝑒𝑛𝑡
𝑄𝑠𝑡𝑟 =
𝛥𝑝
𝑋
𝜇
Where:
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𝛥𝑝 − 𝑝𝑟𝑒𝑠𝑠𝑢𝑟𝑒 𝑑𝑟𝑜𝑝 𝑖𝑛 𝑡𝑕𝑒 𝑕𝑦𝑑𝑟𝑎𝑢𝑙𝑖𝑐 𝑒𝑙𝑒𝑚𝑒𝑛𝑡
𝜇 − 𝑑𝑦𝑛𝑎𝑚𝑖𝑐 𝑣𝑖𝑠𝑐𝑜𝑠𝑖𝑡𝑦 𝑜𝑓 𝑕𝑦𝑑𝑟𝑎𝑢𝑙𝑖𝑐 𝑚𝑒𝑑𝑖𝑢𝑚
𝑋 − 𝑐𝑜𝑛𝑠𝑡𝑎𝑛𝑡 𝑜𝑓 𝑝𝑟𝑜𝑝𝑜𝑟𝑡𝑖𝑜𝑛𝑎𝑙𝑖𝑡𝑦
Finally, the volumetric efficiency is determined by the following formula:
𝛥𝑝 𝑛
𝑋
𝜇 𝑖 𝑖
𝜂𝑣 = 1 −
𝑄𝑤𝑒
In case of positive displacement pumps and motors, volumetric losses 𝛥𝑄 can be represented as a
sum of losses resulting from dynamic viscosity of hydraulic liquid and its density.
𝛥𝑄 = 𝛥𝑄µ + 𝛥𝑄𝜌
𝛥𝑄 = 𝐶µ
𝛥𝑝
2𝛥𝑝 3 2
𝑞 + 𝐶𝜌
𝑞
2𝜋𝜇𝑤𝑒
𝜌𝑤𝑒
Where:
𝑞 − 𝑝𝑢𝑚𝑝 𝑚𝑜𝑡𝑜𝑟 𝑑𝑖𝑠𝑝𝑙𝑎𝑐𝑒𝑚𝑒𝑛𝑡
𝜇𝑤𝑒 − 𝑑𝑦𝑛𝑎𝑚𝑖𝑐 𝑣𝑖𝑠𝑐𝑜𝑠𝑖𝑡𝑦 𝑜𝑓 𝑚𝑒𝑑𝑖𝑢𝑚 𝑜𝑛 𝑡𝑕𝑒 𝑖𝑛𝑝𝑢𝑡 𝑜𝑓 𝑝𝑢𝑚𝑝 (𝑚𝑜𝑡𝑜𝑟)
𝜌𝑤𝑒 − 𝑑𝑒𝑛𝑠𝑖𝑡𝑦 𝑜𝑓 𝑚𝑒𝑑𝑖𝑢𝑚 𝑜𝑛 𝑡𝑕𝑒 𝑖𝑛𝑝𝑢𝑡 𝑜𝑓 𝑝𝑢𝑚𝑝 (𝑚𝑜𝑡𝑜𝑟)
𝐶µ − 𝑣𝑜𝑙𝑢𝑚𝑒𝑡𝑟𝑖𝑐 𝑙𝑜𝑠𝑠 𝑐𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡 𝑟𝑒𝑠𝑢𝑙𝑡𝑖𝑛𝑔 𝑓𝑟𝑜𝑚 𝑙𝑖𝑞𝑢𝑖𝑑 𝑣𝑖𝑠𝑐𝑜𝑠𝑖𝑡𝑦
𝐶𝜌 − 𝑣𝑜𝑙𝑢𝑚𝑒𝑡𝑟𝑖𝑐 𝑙𝑜𝑠𝑠 𝑐𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡 𝑟𝑒𝑠𝑢𝑙𝑡𝑖𝑛𝑔 𝑓𝑟𝑜𝑚 𝑙𝑖𝑞𝑢𝑖𝑑 𝑑𝑒𝑛𝑠𝑖𝑡𝑦
As a result, the efficiency is expressed as:
𝛥𝑝
2𝛥𝑝 3 2
𝐶µ 2𝜋𝜇 𝑞 + 𝐶𝜌 𝜌
𝑞
𝛥𝑄
𝑤𝑒
𝑤𝑒
𝜂𝑣 = 1 −
=1−
𝑄𝑡
𝑛𝑞
Where:
𝑄𝑡 − 𝑡𝑕𝑒𝑜𝑟𝑒𝑡𝑖𝑐𝑎𝑙 𝑝𝑢𝑚𝑝 𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 𝑓𝑙𝑜𝑤
𝑛 − 𝑟𝑜𝑡𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝑠𝑝𝑒𝑒𝑑 𝑜𝑛 𝑝𝑢𝑚𝑝 𝑠𝑕𝑎𝑓𝑡
𝑞 − 𝑑𝑖𝑠𝑝𝑙𝑎𝑐𝑒𝑚𝑒𝑛𝑡 𝑜𝑓 𝑝𝑢𝑚𝑝
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As a conclusion, the volumetric efficiency of hydraulic component rises along with the flow increase
on the input to the element and with increase of hydraulic liquid viscosity. On the other hand, the
pressure increase leads to volumetric efficiency drop. The dimensions and the number of flow gaps
impacts significantly volumetric efficiency value, therefore the optimization of existing elements and
development of new solutions focuses on the reduction of clearance fit between mating parts.
4.3.3.2 Pressure losses
Pressure losses in hydrostatic systems can be divided into linear losses and local losses. Linear
pressure losses are strictly associated with the length of hydraulic hoses connecting the elements of
the system. Local losses are the result of local obstacles such as changes of cross-section area of
hydraulic line or change of flow direction. Both mentioned types of losses, occurring during the flow
of hydraulic liquid depend on kind of flow (turbulent or laminar). The quantity describing the type of
flow is the Reynolds number 𝑅𝑒 (inertia to viscosity ratio). Determining the actual value of 𝑅𝑒 and
regarding it to its critical value (𝑅𝑒𝑘𝑟 ) is the criterion for defining the actual flow type in the system.
In case of Re value lower than critical, for certain flow conditions, flow in the system is laminar.
4.3.3.2.1 Linear losses modeling
Linear pressure losses during the flow in hydraulic line can be determined with use of Darcy’s
equation as follows:
𝛥𝑝 = 𝑝1 − 𝑝2 = 𝜆
𝑙𝜌 2
𝑣
𝑑2
Where:
𝜆 − 𝑓𝑙𝑜𝑤 𝑙𝑖𝑛𝑒𝑎𝑟 𝑟𝑒𝑠𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑐𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡
𝑑 − 𝑖𝑛𝑡𝑒𝑟𝑛𝑎𝑙 𝑑𝑖𝑎𝑚𝑒𝑡𝑒𝑟 𝑜𝑓 𝑙𝑖𝑛𝑒
𝑙 − 𝑙𝑒𝑛𝑔𝑡𝑕 𝑜𝑓 𝑙𝑖𝑛𝑒
𝜌 − 𝑙𝑖𝑞𝑢𝑖𝑑 𝑑𝑒𝑛𝑠𝑖𝑡𝑦
𝑣 − 𝑙𝑖𝑞𝑢𝑖𝑑 𝑣𝑒𝑙𝑜𝑐𝑖𝑡𝑦
The type of flow in the hose is determined by the value of flow resistance coefficient 𝜆, which is
calculated or obtained from characteristic describing the 𝜆 value as a function of the Re number.
𝑅𝑒 =
𝑢𝑑
𝑣
Where:
𝑣 − 𝑘𝑖𝑛𝑒𝑚𝑎𝑡𝑖𝑐 𝑣𝑖𝑠𝑐𝑜𝑠𝑖𝑡𝑦 𝑜𝑓 𝑙𝑖𝑞𝑢𝑖𝑑
𝑑 − 𝑙𝑖𝑛𝑒 𝑑𝑖𝑎𝑚𝑒𝑡𝑒𝑟
𝑢 − 𝑣𝑒𝑙𝑜𝑐𝑖𝑡𝑦 𝑜𝑓 𝑙𝑖𝑞𝑢𝑖𝑑
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In case of laminar flow (𝑅𝑒 < 𝑅𝑒𝑘𝑟 ), the value of 𝜆 coefficient is equal to:
𝜆=
96
𝑅𝑒
In case of turbulent flow (𝑅𝑒 > 𝑅𝑒𝑐𝑟 ), the value of 𝜆 coefficient is determined by Blasius equation:
𝜆 = 0.3164𝑅𝑒 −0.25
4.3.3.2.2 Local losses modeling
Local pressure losses are determined with use of the following equation:
𝜌
𝛥𝑝 = 𝜉 𝑣 2
2
Where:
𝜉 − 𝑟𝑒𝑠𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑐𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡 𝑜𝑓 𝑙𝑜𝑐𝑎𝑙 𝑜𝑏𝑠𝑡𝑎𝑐𝑙𝑒
𝜌 − 𝑑𝑒𝑛𝑠𝑖𝑡𝑦 𝑜𝑓 𝑕𝑦𝑑𝑟𝑎𝑢𝑙𝑖𝑐 𝑙𝑖𝑞𝑢𝑖𝑑
𝑣 − 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑣𝑒𝑙𝑜𝑐𝑖𝑡𝑦 𝑜𝑓 𝑙𝑖𝑞𝑢𝑖𝑑 𝑏𝑒𝑕𝑖𝑛𝑑 𝑡𝑕𝑒 𝑜𝑏𝑠𝑡𝑎𝑐𝑙𝑒
The value of local obstacle resistance coefficient 𝜉 is obtained from some characteristics determined
experimentally. Frequently, the loss value calculated in this way is the rough approximation of actual
loss value generated by the hydraulic component. Therefore, the characteristics of pressure drop as
function of flow for specific elements provided by the manufacturers can be crucial for proper loss
analysis.
As a conclusion, taking into account linear and local pressure losses in the energetic analysis of
hydraulic system, the overall pressure loss can be estimated with the equation:
𝛥𝑝 = (𝜆
𝑙
+
𝑑
𝜉)
𝑣2
𝜌
2
4.3.3.3 Modeling and simulation approach for optimized design
For successful design of hydraulic system, it is required to use simulation study which can provide
data upon the performance of the system for defined parameters of components and operational
conditions.
The design of hydraulic system for specific application requires state-of-the art methodology that
involves number of aspects and utilize various modeling simulation tools.
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Because of complexity and variety of hydraulic systems designed individually for specific application,
it is difficult to define clear and consistent methodology for approaching of various issues
encountered in industry. The methodology needs to cover number of aspects: initial approach, data
requirements, modeling of pumping system, CFD calculations, model validation.
Some tools that can be used for analysis of hydraulic system are: hydraulic modeling tools, CFD tools,
process simulators. Although each application is unique, there is a try to define certain common
scheme/methods that can aid design process of the hydraulic pump/system with use of 1D
simulation software. This approach should specify which factors have to be taken into account when
analysing performance and efficiency.
The approach of ESTOMAD project is to integrate energetic aspects and put stress on efficiency of
the system in design process. Hydraulic systems designers consider this aspect and the factor of
energy savings is becoming of higher priority issue in recent years.
The development of general model that can be used for energy efficiency analysis on design level of
hydraulic system (hydraulic supply unit) will help designers to provide energy optimized hydraulic
system. Number of aspects have to be considered. In general, following elements needs to be part of
methodology for analysis of the hydraulic system on design level:
Equation of state (EOS)
EOS is a thermodynamic equation describing the state of matter under a given set of physical
conditions. It is an equation which provides a mathematical relationship between two or more state
functions associated with the matter, such as its temperature, pressure, volume, or internal energy.
Fluid properties
Here, fluid composition is considered: density, viscosity, bulk modules, vapour pressure, heat,
thermal conductivity, diffusion coefficient. Some parameters can be calculated with use of EOS
(equation of state) solver.
The minimum requirements for liquid model are: viscosity, bulk modules, vapour pressure.
Boundary conditions
To have a consistent picture of actual operational conditions, following is required: pressure, flow,
pressure, temperature and speed.
For modeling of specific pumping system following characteristics and elements are needed:










D3.3
boundary conditions
geometry and volume of tank/reservoir
pump’s performance and efficiency characteristics
ambient temperature
recycle loop for the pump and bypass valve for pipeline
fluid properties
EOS definition
Piping layout, geometry and elevation
Heat transfer coefficient for pipeline, coatings
vales – part of the system for start-up, shutdown of the pumping station, process control and
isolation of specific pipeline branches or sections
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Validation of model
The validation of performed simulation is critical element and it is recommended to carry out
experiments on designed system for model verification. The simulation comprises number of
algorithms that are created to predict behaviour of the system in specific conditions. The accuracy of
the simulation is usually high, however experiments play important role and needs to be part of the
methodology for hydraulic system design/development.
4.3.4
Improvement directions and energy-saving guidelines
The methodology of selection and optimization of hydraulic unit for specific application requires
number of aspects that should be taken into account. With use of broad engineering knowledge and
experience, a concept for required solution with taking into account number of possibilities of
optimization can be worked out. However for better understanding of behavior of the system in
operational conditions, simulation is required.
4.3.4.1 Analytical analysis based on engineering experience
The diagram below shows the elements that needs to be considered when specifying solution for
hydraulic application.
Boundary conditions
Analysis of potential cycles of work and identification of most
critical conditions
Analysis of most suitable pump
Analysis of efficient motor
Analysis of control strategy for considered application
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Consideration of additional components
Figure 24: The flowchart presents the optimization procedure for
hydraulic unit
4.3.4.1.1 Analysis of potential cycles of work and identify most critical
The optimization of any existing solution requires the knowledge about the device and character of
work. Considering hydraulic systems, one of the option is to create a cycles of work chart . In
optimization process it is essential not only to get to know the parameters of devices supplied
(operating pressure, flow) by the unit, but also load in function of time for each device. The cycles of
work diagram provides information about the load of the supply unit in relation to time. In this way,
it is possible to find out how much of the provided hydraulic pressure is used within the operation
time period. As a result, it is possible to precisely plan the usage of energy.
Figure 25: The example of cycles of work for single components of a system
4.3.4.1.2 Analysis of most suitable pump
The choice of pump for an application depends on the demanded parameters of hydraulic liquid. The
essential parameters such as pressure and flow should be taken into account at first. The optimum
solution for medium pressure application (up to 200 bar) and relatively high flow is a gear pump. It is
a reliable device with very simple mechanical structure. However, the pump is built as fixed
displacement unit, therefore the displacement cannot be changed due to mechanical structure
limitations. Therefore, a gear pump is suitable for constant flow applications. However, considering
the speed of the drive (electric motor) it is possible to control the flow by using the inverter but
provided that the speed ranges between maximum and minimum values defined by the
manufacturer.
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A vane pump is similar in performance to a gear pump but the crucial difference is the ability to
change the displacement. This solution complicates the structure but gives an ability to control
power use. However, the pressure produced by this pump may be insufficient in certain applications.
A piston pumps have good pressure (>200 bar) and flow performance. They are efficient in high and
low viscosity applications. Many of them offers a variable displacement option. However the
mechanical structure is far more complicated compared to other types.
4.3.4.1.3 Analysis of efficient motor
Considering only the drive, it is possible to reduce the energy consumption using energy-efficient
motor. The aim is to reduce the amount of electric energy used with maintaining the same
mechanical energy on motor’s (pump’s) shaft. The energy-saving effect is achieved by optimizing
internal structure of motor. Minimizing the losses in bearings is possible by improving the lubrication
and proper selection for dimensions and clearances. Also the losses of electricity are decreased by
changing the proportions of materials and geometry of elements. As a result the diameter of cooling
fan is reduced due to lower operating temperatures. The stator and rotor of such motor are
redesigned to ensure better air flow.
The rated speed of energy efficient motor is slightly different than the standard one. It is caused by
lower overall losses comparing to the standard motor. Of course, in particular application the speed
could be set by a controller according to actual pressure and flow demand.
4.3.4.1.4 Analysis of control strategy for considered application
One of the option to decrease the amount of wasted energy is to intelligently modulate the speed of
electric motor. However, in most hydraulic applications, the motor can’t be stopped for a period of
time. The aim is to keep the value of shaft’s speed at the proper level, depending on actual demand
for hydraulic pressure. The idea can be realized using for example PID controller implemented on
PLC. It constantly reads the pressure feedback from the sensor (transducer) and helps to keep
external values, like pressure, in certain limits. [1]
4.3.4.1.5 Consideration of additional components
Another solution combines hydraulic and electric modifications. The main idea is to store an amount
of spare hydraulic energy in the circuit. It could be used to maintain the pressure while the machine
needs it and the motor is in idle mode. This possibility gives a hydraulic accumulator. The device is
able to store hydraulic liquid under pressure using an external, compressive source (gas, spring, etc.,
see Figure 26). In this way, the electric motor could be switched off by electronic control system (as
mentioned above). For this short period of time when the motor is accelerating to the optimum
speed the energy stored in accumulator could cover the demand. Apart from economic aspects,
including an hydraulic accumulator in the system reduce the external impact of the inertia and the
mechanical forces coming from the supplied devices. The losses due to the leakages are also
compensated. [2]
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Figure 26: Hydraulic accumulator
4.3.4.2 Energy efficiency analysis of basic hydraulic drive and control system
Every hydraulic system, besides losses in particular elements of hydraulic line (hoses, angle joints,
connectors), generates power loss depending on its specific structure and components used. Such
loss is commonly called structural loss of the system. Structural loss level is usually greater than loss
generated in the lines, therefore design phase of the system should focus on minimizing it.
The examples below present the existing hydraulic system used for speed control of hydraulic
actuator. The modifications of basic system presented in the following chapter lead to energy
consumption decrease, while maintaining main functionalities.
4.3.4.2.1 Basic system for speed control of hydraulic actuator – modification steps
Figure 27 presents the described system, as a base for the further energetic modifications aimed at
the power loss reduction while maintaining initially assumed functions of the system.
D PM
D PM
D PL
D PL
Fr, Vr
Qr, Pr
Qp, Pp
Zd
P
Pz
Zr
Zb
DQ
Figure 27: Diagram of hydrostatic system for speed control of actuator with full structural
loss (ΔPL – linear pressure loss, ΔPM – local pressure loss, ΔQ – volumetric loss)
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The main function of system (setting actuator’s speed) is realized by throttle valve Zd. Linear (𝛥𝑃𝐿)
and local (𝛥𝑃𝑀) pressure losses values have been used for estimation of so-called energy transfer
hydraulic loss. Volumetric losses have been labeled in the figure as 𝛥𝑄. Power balance of presented
system includes all of the power losses types generated between generator (positive displacement
pump) and hydraulic power receiver (actuator).
The actual power on the output of pump is expressed as follows:
𝑁𝑝 = 𝑃𝑝 𝑄𝑝
The structural power loss in this particular system with throttle valve fitted in the line between pump
and actuator is strictly associated with operation of overflow valve (Zb) and throttle valve (Zd). The
loss can be divided into structural volumetric loss 𝛥𝑁𝑍𝑏 generated by overflow valve and structural
hydraulic loss 𝛥𝑁𝑕 resulting from throttle valve operation and losses in the hydraulic line.
𝛥𝑁𝑍𝑏 = 𝛥𝑄𝑃𝑝 = (𝑄𝑝 − 𝑄𝑟 )𝑃𝑝
and
𝛥𝑁𝑕 = 𝛥𝑄𝑟 (𝑃𝑝 − 𝑃𝑟 ) = 𝑄𝑟
𝛥 𝑃𝐿 +
𝛥 𝑃𝑀
The effective power 𝑃𝑒𝑓𝑓 on the input of hydraulic receiver (actuator) is equal to:
𝑁𝑟 = 𝑄𝑟 𝑃𝑟
The power balance without taking into account losses in pump and actuator is represented with the
following expression:
𝑁𝑝 = 𝛥𝑁𝑍𝑏 + 𝛥𝑁𝑕 + 𝑁𝑟
A graphical presentation of power balance of analyzed system is presented in the figure below.
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P
Pp
ΔNh
Pr
ΔNZb
Nr
Q
Qr
Qp
Figure 28: Power balance of hydraulic system – hatched areas represent system power
losses
Based on the balance presented in Figure 28. it can be stated that realization of such system with use
of throttle valve located in series with the pump and actuator, generates great amount of losses and
therefore decreases the overall efficiency. The optimization process should be therefore focused on
minimizing or eliminating the existing power losses. The example of energetic modification towards
eliminating volumetric structural loss in the presented system is the replacement of fixed
displacement pump with variable displacement one equipped with pressure controller (Figure 29).
D PM
Sh
D PM
D PL
D PL
Fr, Vr
Qr, Pr
Qp, Pp
Zd
P
Pz
M
f
Zr
Zb
Figure 29: Diagram of hydrostatic system for speed control of actuator after eliminating
volumetric structural loss (ΔPL – linear pressure loss, ΔPM – local pressure loss)
Despite elimination of volumetric structural loss, hydraulic structural loss 𝛥𝑁𝑕 is still present in the
system and impacts significantly the overall efficiency. The level of this loss can be expressed with
the following equation:
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𝛥𝑁𝑕 = 𝑄𝑟 (𝑃𝑝 − 𝑃𝑟 )
The power balance of modified system without taking into account power losses in pump and
actuator is equal to:
𝑁𝑝 = 𝛥𝑁𝑕 + 𝑁𝑟
P
Pp
ΔNh
Pr
Nr
Qr=Qp
Qp (ε=1)
Figure 30: Power balance of modified system - hatched area represents system power
losses
Further modification presented in Figure 31 aimed in structural hydraulic loss 𝛥𝑁𝑕 minimizing. In
order to do so, three-way valve Zro has been fitted into the system. The valve works with pressure
controller of variable displacement pump and maintain a constant, minimal difference of pressure in
throttle valve (Zd).
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D PM
D PM
Sh
Zd
D PL
D PL
Fr, Vr
Qp, Pp
Qr, Pr
Pz
p
DQ
Zb
Zr
Zro
Figure 31: Diagram of hydrostatic system for speed control of actuator after eliminating
volumetric structural loss and reducing hydraulic structural loss (ΔPL – linear pressure loss,
ΔPM – local pressure loss)
The final hydraulic system generates slight hydraulic structural loss and completely eliminates
volumetric loss. The power balance of such system is presented in Figure 32.
P
ΔNh
Pp
Pr
Nr
Q
Qr=Qp
Qp(ε=1)
Figure 32: Power balance of modified system - hatched area represents system power
losses
In the design solutions of system for actuator’s speed control presented above, the structural power
loss has been generated by throttle valve located in series with pump and actuator. The source of
great amount of loss can be also a supply unit. The unit ensures the proper flow amount for
connected receivers (actuators and motors) according to the cycles of work characteristic. The
analysis of the cycles of work leads to the conclusion that flow demand in the hydraulic system is
rarely constant during the operation. Normally, the hydrostatic energy demand is variable and ranges
from minimum to maximum levels.
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Q [dm3/min]
Qmax=Qr1+Qr2..+...Qri
T [s]
Figure 33: The example of cycles of work characteristic (Q=f(T))
Further chapters contain the analysis of simplified systems of supply units in terms of energetic
behavior.
4.3.4.2.2 Basic supply unit system
The following chapter presents the energetic modification steps of hydraulic supply unit. The
simplified diagram of a single pump supply unit used for supplying devices with maximum flow
demand of 𝑛 receivers has been presented in the Figure 34.
Figure 34: Simplified supply unit diagram – basic system: P – pump, Zb – overflow valve, Fs
– filter
The flow demand can be expressed as follows:
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𝑄𝑟 =
𝑛
𝑖=1
𝑄𝑟𝑖
Where:
𝑄𝑟𝑖 − 𝑓𝑙𝑜𝑤 𝑑𝑒𝑚𝑎𝑛𝑑 𝑜𝑓 𝑖 𝑟𝑒𝑐𝑒𝑖𝑣𝑒𝑟
The selection a pump with maximum flow equal to the sum of flow demands for all receivers
connected to the system will result in high structural loss due to unloading the surplus of liquid by
overflow valve Zb during the operation. The operation cycle includes work cycle (receiving the
hydraulic medium connected devices) and idle cycle (no demand for hydraulic medium). The
structural power loss in this particular system can be determined as follows:
𝛥𝑁 = 𝑄𝑠𝑡𝑟 𝑃𝑧𝑏 + 𝑄𝑝 𝑃𝑧𝑏
Where:
𝑄𝑠𝑡𝑟 − 𝑠𝑢𝑟𝑝𝑙𝑢𝑠 𝑜𝑓 𝑝𝑢𝑚𝑝 𝑓𝑙𝑜𝑤 (𝑓𝑙𝑜𝑤 𝑙𝑜𝑠𝑠)
𝑄𝑝 − 𝑝𝑢𝑚𝑝 𝑓𝑙𝑜𝑤
𝑃𝑧𝑏 − 𝑚𝑎𝑥𝑖𝑚𝑢𝑚 𝑝𝑟𝑒𝑠𝑠𝑢𝑟𝑒 𝑠𝑒𝑡 𝑖𝑛 𝑡𝑕𝑒 𝑜𝑣𝑒𝑟𝑓𝑙𝑜𝑤 𝑣𝑎𝑙𝑣𝑒
Q [dm3/min]
Qp=Qmax=Q1+Q2..+...Qi
Quk
Qsr
Qstr
Qstr cj
T [s]
Figure 35: Cycles of work Q=f(T): Quk – actual flow demand In the system, Qstr – flow loss
In work cycle (surplus of flow), Qstr cj – flow loss In idle cycle (surplus of flow)
The minor improvement of system efficiency can be obtained by periodical unloading of positive
displacement pump. In the solution presented below such functionality has been realized with use of
two-step maximum valve (with relief function) Zod.
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Figure 36: Simplified diagram of supply unit – system equipped with relief valve: P – pump,
Zodc – overflow valve with relief function, Fs – filter, Zz – check valve
The structural power loss of such system is the sum of the losses during the work cycle and idle cycle.
The idle cycle loss will be generated at the pressure slightly higher than atmospheric (𝑃𝑎𝑡𝑚 ).
𝛥𝑁 = 𝑄𝑙𝑜𝑠𝑠 𝑃𝑧𝑏 + 𝑄𝑝 𝑃𝑎𝑡𝑚
Where:
𝑄𝑙𝑜𝑠𝑠 − 𝑠𝑢𝑟𝑝𝑙𝑢𝑠 𝑜𝑓 𝑝𝑢𝑚𝑝 𝑓𝑙𝑜𝑤 (𝑓𝑙𝑜𝑤 𝑙𝑜𝑠𝑠)
𝑄𝑝 − 𝑝𝑢𝑚𝑝 𝑓𝑙𝑜𝑤
𝑃𝑧𝑏 − 𝑚𝑎𝑥𝑖𝑚𝑢𝑚 𝑝𝑟𝑒𝑠𝑠𝑢𝑟𝑒 𝑠𝑒𝑡 𝑖𝑛 𝑡𝑕𝑒 𝑜𝑣𝑒𝑟𝑓𝑙𝑜𝑤 𝑣𝑎𝑙𝑣𝑒 𝑖𝑛 𝑤𝑜𝑟𝑘 𝑐𝑦𝑐𝑙𝑒
𝑃𝑎𝑡𝑚 − 𝑤𝑜𝑟𝑘 𝑝𝑟𝑒𝑠𝑠𝑢𝑟𝑒 𝑖𝑛 𝑡𝑕𝑒 𝑜𝑣𝑒𝑟𝑓𝑙𝑜𝑤 𝑣𝑎𝑙𝑣𝑒 𝑖𝑛 𝑟𝑒𝑙𝑖𝑒𝑓 𝑐𝑦𝑐𝑙𝑒
Q [dm3/min]
Qp=Qmax=Q1+Q2..+...Qi
Quk
Qsr
Qstr
Qstr cj
T [s]
Figure 37: Cycles of work Q=f(T): Quk – actual flow demand In the system, Qstr – flow loss
due to flow surplus, Qstr cj – flow loss in idle cycle
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A significant decrease of structural power loss can be achieved with a solution presented in Figure
38. In presented system the pump for average flow demand has been selected. Peak values of
demand are covered by the hydrostatic energy stored previously in the hydraulic accumulator. What
is more, the pump used in the system is of variable displacement type equipped with pressure
controller. As a result, when maximum pressure is reached in the system the pump can work with
minimum flow (as a result of displacement change) and any energy losses are covered by charged
accumulator. As in previous solution, the system is equipped with maximum valve with relief
function that also minimize idle cycle losses.
Figure 38: Simplified diagram of supply unit – system equipped with accumulator: P –
pump with pressure controller, Ah – accumulator, Zodc – overflow valve with relief
function, Fs – filter, Zz – check valve
In the considered system, the structural power loss has been completely eliminated. The rest of
power losses are mainly associated with local and linear losses in the hydraulic components and
hydraulic line itself. These losses will be minimized if during the design phase of system the
recommendations summarized in the conclusions are met.
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Figure 39: Cycles of work Q=f(T): Quk – actual flow demand covered by pump flow, Qak in
– surplus of energy stored in the accumulator (charging), Qak out – surplus of energy given
back to the system by the accumulator (discharging)
As a conclusion of considerations presented above, design of energy-efficient hydraulic system taking
into account all of assumed functionalities requires the thorough analysis of possible design solutions
in terms of minimizing the power losses of considered system. The knowledge of many construction
and exploitation factors is essential in accurate estimation of power losses resulting from pressure
losses and leakages. In case of complex hydrostatic systems, losses analysis and estimation can be
facilitated with use of specific software for modeling purpose of such systems.
The recommendations for design and exploitation of a functional and energy saving hydraulic
systems, the supply units have been listed below:

Reducing linear pressure loss by reduction of the length of hydraulic cables (the linear loss of
pressure increases with length of cable),

Minimizing the local pressure loss by reducing the number of arcs, bends, connectors,
branches and changes of cross section area of power supply cables and direction of flow,

Ensuring high efficiency of hydraulic system (cables) by the exploitation with sufficiently high
operational pressure,

Choose of hydraulic liquid with optimal (due to losses) value of kinematic viscosity– high level
of kinematic viscosity results in reduction of volumetric losses (volumetric efficiency
increases), while hydraulic efficiency decrease (the resistance of movement increases),

Selection of hydraulic cables with value of diameter appropriate to lines with high and low
supply pressure (enlargement of the return cable diameter leads to reduced movement
resistance, and thus the possible loss of power which is transformed to heat is minimized),

Construction of hydraulic supply unit with components made with use of the cartridges
technique (in contrast to the plate mounting elements). This leads to better integration of
the hydraulic system, thereby can help to reduce pressure losses in the system,
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
Thermal stabilization of the hydraulic medium based mainly on convective heat dissipation
through the walls of tank and system components. When it is necessary to use additional
thermal stabilization (e.g. inclusion of the cooler), the chosen cooler will have less rated
power. This will improve energy balance between output power of hydraulic supply unit and
power supplied to the station (raising the overall efficiency of power station),

Minimizing losses at idle run of pumps.
4.4 Electrical motor
[FMTC contribution]
The importance of electric motor driven systems in industrial applications and especially their share
in electricity consumption is well documented and accepted. In Europe, for example, electric motors
in industry consumed around 690 TWh in the year 2000, or approximately 65% of the total electricity
consumption by industry for that year. Moreover, predictions show that the consumption by electric
motors will only increase. Therefore, given the demand for greener machines, it is worth taking also a
closer look at electric motors while speaking about design for energy efficiency.
Current design guidelines for motor selection focus primarily on performance: the mechanical
specifications of the application are determined (nominal and peak torque, nominal and peak speed,
nominal and peak power, safety factors, etc.) as well as the conditions (outdoor vs. indoor, available
space in the machine, low voltage vs. high voltage, etc.). Based on these specifications, a motor can
be selected in cooperation with the motor supplier(s). Energy consumption used to be only a
secondary concern, but due to increased awareness and new standards, motor efficiency is more and
more taken into account during machine design.
4.4.1





4.4.2
Eco / Energy efficiency Normative
The IEC standard 60034-30 defines the efficiency classes for single-speed, three-phase, cageinduction motors. Three classes are specified, being Standard Efficiency (IE1), High Efficiency
(IE2) and "Premium efficiency" (IE3). The standard also foresees a IE4 level (“Super premium
efficiency”) for future motors.
The IEC standard 60034-2-1 provides standard methods for determining losses and efficiency
for these motors from tests.
A draft has been made for IEC standard 60034-2-3 on specific test methods for determining
losses and efficiency of converter-fed AC motors
Regulation No 640/2009 that implements Directive 2005/32/EC of the European Parliament
and of the Council with regard to eco-design requirements for electric motors specifies from
which date the minimum efficiency classes for motors have to be IE2 and IE3, or when they
have to be equipped with a variable speed drive
http://www.eco-motors-drives.eu/Eco/Home.html EU preparatory study
Improvements directions and energy-saving guidelines
Currently, the majority of electrical motors that are being used in industry are single-speed, threephase, cage-induction motors, without variable speed drive. For that reason, the standards and
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directives that were discussed in the previous section focus mainly on this type of motors. There is
however room for improvement with respect to energy efficiency:





Motor dimensioning: Sometimes, motors are overdimensioned for the application where they
are used. Motors that have been overdimensioned tend to be used in an operating point where
their efficiency is lower, since highest energy is typically achieved when the load is between 70%
and 100% of nominal load.
Variable speed drives (or frequency converters): These allow to vary the speed of electrical
motors in an electrical rather than a mechanical way. In the absence of variable speed drives,
motors run continuously at nominal speed, and mechanical means are used to vary and control
the output of the driven machine. When electric motors run at nominal speed, regardless of
production demands, a lot of unnecessary electrical power is used. When the speed is slightly
reduced by means of variable speed drives, for example, for fan or pump applications, the power
consumption is also significantly reduced. For that reason, the usage of variable speed drives was
also included in the aforementioned regulation of the European commission.
Motor design: Motor efficiency itself can be improved by the manufacturers through
optimization of the design. This would lead to motors in the IE2 and IE3 (and IE4) efficiency
classes, rather than the IE1 class. Design optimization can be achieved , amongst other things, by:
o thicker copper wires (less copper losses),
o copper die-cast rotor cages instead of aluminium ones (less copper losses),
o higher grade laminated steel cores (less iron losses),
o thinner air gaps,
o better bearings (less friction losses),
o etc.
Different motor technology: Though induction motors are still the most widely spread motor type
in industry, the usage of other motor types that are intrinsically more efficient is increasing as
well. Some examples of these types are: permanent magnet synchronous motors (PMSM),
brushless dc motors (BLDC) and switched reluctance motors (SRM). The main reason why these
motors are conceptually more efficient is the fact that the induction motor has current flowing in
both the stator and the rotor (the cage with the induced current), whereas those other motor
types have only current flowing in the stator and therefore produce less copper losses.
Regenerative variable speed drives: These specific variable speed drives are capable of feeding
electrical energy that is generated during motor braking/deceleration back to the power supply.
Standard motor drive systems dissipate braking energy as heat in braking resistors. If, however,
the specific application for which the motor is used produces a lot of braking energy, these
regenerative drives can be useful.
Some of these directions can also be found in the reports of the EC Product Group Study related to
the Ecodesign of Energy-related Products (ErP) Directive 2009/125/EC (recast of the former EuP
Directive 2005/32/EC) for ENTR Lot 5 Machine Tools and related machinery, more specifically in task
5 (www.ecomachinetools.eu) (10).
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4.4.3
Scenario definition and optimal management strategies during operation
In certain sectors, specific standard scenarios have been defined for which the energetic
performance of a machine can be evaluated. This is for example the case for cars, for which specific
driving profiles have been defined. There are however no standard scenarios defined for every
machine in general.
Nevertheless, while using an electrical motor in your application, some management strategies can
be taken to reduce unnecessary losses:




Avoid standby losses: Once a motor is switched on, it starts consuming (a small amount of)
energy, even when it is not producing mechanical power. Therefore, if the machine has longer
moments when it is in standby mode, it is better to switch of the motor.
Use an efficiency map of the motor: An efficiency map of the motor shows in which areas of the
torque-speed range the motor is efficient or not. The projection of the operating point (torquespeed set point) of the application or the sequence of operating points (torque-speed trajectory)
on the map, allows to inspect and verify visually whether the application uses the motor in an
area of high efficiency or not. If the data behind the map are available, numerical inspection can
be done as well and a prediction of the losses can be made. If the motor is not being used in an
efficient area, the torque-speed set point or trajectory could be adapted, provided that the
application allows to do so. Another option is to add a gearbox to move the operating point(s) to
a more efficient location (however, in this case, the efficiency of the gearbox has to be taken into
account as well). More information on efficiency maps can be found in deliverable D2.1 and in
appendix A.
Limit accelerations: When one of the functions of the motor is to accelerate an inertia, it has to
be noted that higher accelerations lead to higher torques and therefore to higher currents.
Higher currents on their turn produce higher copper losses. Therefore, it is worth considering to
reduce accelerations, as long as the application allows to do so.
Model the driveline: The ESTOMAD SW tools can be used to build a model of your driveline (as
described in D2.3). With this model, the losses for different scenarios (trajectories) can be
simulated, and a trade-off can be made between efficiency and dynamic performance of the
scenario (e.g. the acceleration as mentioned before).
4.4.4


Energetic Indicators
Efficiency: The most commonly used energetic indicator for an electrical motor is its efficiency,
being the ratio of useful mechanical power and consumed electrical power. When the motor
operates as a generator, that ratio is inversed: the efficiency is then defined as the ratio of
produced electrical power and consumed mechanical power.
Efficiency map: In datasheets of motors, usually one efficiency value is found (and/or one
efficiency class), but this is the efficiency for one operating point (usually nominal operation).
This does not indicate what the efficiency of the motor would be for other operating points. Due
to the introduction of variable speed drives, motors are increasingly used in other operating
points as well. Efficiency maps (as discussed in the previous section) are therefore increasingly
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
employed. More information on efficiency maps can be found in deliverable 2.1 and in appendix
A.
Payback period: More efficient solutions often require a higher investment than standard
solutions: e.g. a permanent magnet synchronous motor is typically more expensive than an
induction motor in the same power range; an IE3 motor is typically more expensive than an IE1
motor in the same power range; adding a variable speed drive is an extra cost; etc. However, the
reduced energy cost, due to the efficiency increase, will pay back the investment after a certain
amount of time. This time depends on the usage of the motor and on the electricity cost.
Regarding this indicator, see also section 2.6.2.
4.4.5
Models and analysis procedures
Equation based analytical model (D2.3):




Deliverable D2.3 describes how an analytical equation based model of an electrical motor
can be constructed, based on electrical motor theory.
A part of the parameters for the model can be derived from the datasheets of the motor
(and gearbox if included).
Some parameters (especially friction parameters) are often not available and need to be
determined by measurement. The friction parameters can for example be identified on a test
bench by measuring the produced motor torque (for the same torque set point) while a load
motor varies the speed. Some other relevant parameters that are available in the datasheets
might also have to be recalibrated, e.g. the electrical resistance determines the copper losses
and might have to be measured again when for example long wires (connectors) have been
used. This is discussed in more detail in D2.3 and D5.2.
With this model, a good prediction of the power consumption during operation could be
made (verified on the real setup).
Reduced model with efficiency maps:

If an efficiency map of the motor system is available (either through measurement or
through simulation), this can also be used for energetic modelling of the motor. More
information on efficiency maps can be found in deliverable 2.1 and in appendix A.
4.5 Transmission chain
4.5.1
Introduction
The main purpose of the transmission chain is to create a relationship of force and velocity between
two points in a driveline. In the example of a wind turbine, the small rotational velocity at the input
shaft, introduced by the wind load, is transferred to a higher rational velocity at the generator side.
The most often used transmission elements are:

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




Belts
Chains
Cams drives
Mechanisms
Screw transmissions
Each transmission element consists out of multiple parts where the kinematic properties are linked
by a certain contact relationship. This particular relationship has a strong influence on the element
efficiency as it is related to the friction between both elements. Each transmission element has a
number of joints. A joint is defined as a component that constrains the movement some directions
while maintaining a free motion in the other degrees of freedom. Also in this case, contact between
two parts is initiated and friction occurs. Therefore the joint types in the transmission chain have a
significant influence on the energy efficiency. Bearings are most often used as joints. The most often
used bearings are:







Journal bearings
Hydrostatic bearings
Hydrodynamic bearings
Roller bearings
Aerostatic bearings
Magnetic bearings
Flexure bearings
In the current design methodologies, the choice of the bearing is mainly dependent on the price,
accuracy and working range rather than energy efficiency. Taking energy efficiency into the equation
requires more information about the system since it is in direct conflict with other criteria.
It should also be noted that the removal of the transmission chain has to be considered and to go for
a direct drive. This is beneficial if the efficiency of the direct drive is higher than the product of the
actuator efficiency and the transmission chain efficiency.
4.5.2

4.5.3
Eco / Energy efficiency normative
FZG test procedure DIN 51 354
Improvement directions and energy-saving guidelines
The main challenge in the design of an energy efficient drive lines in the fact that it is highly tailored
to a specific application. It is not impossible to state that one typical transmission chain is superior to
another one. The energy efficiency of a transmission should be evaluated at system level where it is
possible to assess how the element affects the global behavior and what the impact is on the overall
energy consumption. In the next paragraphs, a short overview of the different transmission chains is
given:
Gear transmissions are used when high power transmission and high precision are necessary. Due to
its stiff construction, the torque density7 is very high compared to belts and chains. The stiff
connection shifts the dynamic behavior to higher frequencies and allows for a robust kinematic
7
torque density: ratio of torque capability to volume expressed in units of torque per volume
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relationship between input and output. The efficiency for straight gears can be up to 99% but it
decreases for helical gears and worm gears. The gear contact has to be lubricated to ensure the high
efficiencies.
Belts are suitable for medium to long center distances compared to gears which are only suitable for
short center distances. The ratio drive is not exact due to the slip between the belt and the rollers.
The advantage of a belted drive is that it acts as a shock absorber and does not require any
lubrication. The efficiency is generally lower than for gears.
Chains provide an intermediate solution between belt and gears. They provide a positive drive as
there is no slippage and inherently also transfer shock loading. Very high torques may be transmitted
by chains, beyond the capacity of belt drives and can manage longer center distances than gears.
Chains generally require lubrication and a heavy duty chain drive may require a sealed housing
incorporating bath or jet lubrication, which leads to an increased cost. One of their weaknesses is
that the chains tend to ‘whip’ if the center distances are too long.
Cam drives are different compared to belts, gears and chains as they can obtain a complex kinematic
relationship between the input and output. Cam drives consist of a cam that generates the motion of
the follower by means of sliding/rolling contact between both parts. The stroke of the follower is
generally small and the efficiency is highly dependent on the construction of the cam.
Mechanisms provide the same function as cam drives as they can obtain the same kinematic
relationships based on a number of linkages connected to each other by joints. The strokes of the
output profile can be significantly larger and the efficiency depends on the inertial properties of the
linkages and the friction in the joints.
Screw mechanisms are used to transfer a rotation motion in a linear motion by means of an
interaction between the screw and the nut. Generally, the mechanical arrangement is such that the
power screw rotates and the nut translates (i.e. moves linearly) along the screw, although in
applications such as the screw jack the nut rotates and the screw moves linearly to raise the jack.
Screw mechanisms provide an alternative to the rack and pinion which is a geared transmission.
The purpose of this overview is not to give a clear idea of the benefits and drawbacks of each type of
transmission but rather to impress that multiple options are available for the same type of
application and it is only by means of a detailed (virtual) analysis that is possible to quantify how a
the transmission influences that global energy consumption. This can be illustrated by a simple
example; If distance between the centers of the input and output shaft are too far for a single gear
transmission and the transferable power is high, several options can be explored:




A two (or three) staged gearbox that has one (or two) intermediate gears
A chain drive
A toothed belt
A four bar mechanism
Each concept should be evaluated based on performance, robustness, lifetime and energy efficiency
before the most suitable option can be chosen. The type of joints has to be taken into account in the
analysis as it has a significant influence on each of the design criteria. A three-stage gearbox can lead
to significant inertial loads resulting in high friction losses in the bearings while the toothed belt
diminishes the shock loads and hence decreases the bearing friction. An additional flexible coupling
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in the gearbox can be implemented to serve as a shock absorber and again making the module more
competitive.
The general rule for an energy efficient drive train is to optimize the working area of each dissipating
component. For the bearings, the friction losses scale with the velocity and the radial/axial load but
in a different manner for each bearing type. By redistributing the bearings loads, the working area of
each particular bearing can be altered as such that the global energy consumption is decreasing. The
redistribution of the bearing loads can occurs by changing the relative position of the bearing,
altering the transmission ratio etc. Changes in the machine structure can also lead to a more optimal
working area for the bearings. When active bearings are used, it is important to examine whether the
energy required to actuate the bearings is smaller than the gained energy efficiency in the main
drive.
For the transmission elements, the lubrication regime has a significant effect on the efficiency.
Therefore a series of experiments with different oil flow rates can provide more insight in this
particular area. The drivetrain always has to be designed concurrent with the choice of the actuator
as they continuously interact with each other.
4.5.4
Scenario definition and optimal management strategies during operation
In most of the applications, a transmission chain is a passive construction and no optimal
management strategies are currently used during operation. Adding intelligence to the transmission
chain can lead to a vast increase in efficiency. As mentioned earlier, the lubrication regime has a
significant influence on the energy consumption of the elements in transmission chain. The function
of lubrication is twofold; the main purpose is to obtain a smooth contact between two parts and to
decrease the friction, while the second purpose is to drain the heat generation by the energy
dissipation in the system. If the lubrication flow rate is too high, the friction will increase due to the
extra resistance of the fluid, while a low flow rate can cause starvation effects and would not convey
the heat. If the regime would be actively controlled, an optimum can be reached. During the project,
the importance of this issue has been discovered but no detailed models are currently available to
understand the physical nature of these effects.
A second aspect regarding optimal management strategies is the motion path planning techniques.
As the velocity and acceleration profile are directly related to the losses in the system, the profile
parameters can be optimized to reduce the energy consumption to a minimum.
A third but nevertheless important aspect is the actuation of the actively controlled bearings like the
magnetic, hydrostatic, hydrodynamic and aerostatic bearings. This aspect was not investigated
during the project and will not be discussed any further.
4.5.5
Energetic Performance Indicators
Transmission chains are represented in the automotive, production manufacturers, precision
engineering and various other sectors. As the area is so vast, no general energetic/performance
indicators exist as far as the authors are aware of. In general, the quality of the drive train is
established by the following factors:


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



Precision (discrepancy between desired and real trajectory);
Lifetime;
Reliability;
Energy efficiency.
The weights of the first three criteria depend on the particular application. For a wind turbine, a high
torque density is required while the velocity range and precision are less important. In an offshore
turbine, the last three factors receive a higher weighting coefficient. The last three items are strongly
related to the total cost of ownership (TCO). The TCO includes the purchase cost and operation
(maintenance, energy...) cost. This criterion is taken into account for any application.
4.5.6
Models and analysis procedures
In the virtual analysis of the energetic behavior of a drive train, the framework described in
deliverable 2.2 can be used. In D2.2, a system architecture is proposed to model elements of
different nature into one environment. To assess the effect of the drive train in the total system, all
the components and their interaction has to be taken into account. In addition to the general
architecture requirements, two additional criteria are defined:


The description of the losses in the system needs to have a reasonable level of accuracy;
An accurate description of the dynamic behavior is crucial as the dynamic and kinematic
quantities are the input to the loss models.
In most models, friction is often modeled with simple relations like Coulomb or viscous friction. For
such a representation it is difficult if not impossible to obtain the proper parameters. If there is little
confidence in the choice of the parameters, the evaluation of the energy consumption has no value.
Therefore, three different methods are available:
A. Do an experimental campaign to obtain the relationship between the parameters that
influence the dissipation and formalize it as a function or a multidimensional map
B. Acquire the dissipation characteristics/formulations of the specific component from the
supplier and implement them in a model
C. Model the component in detail from a phenomenological point of view to assess the
dissipative behavior
Each of these particular methods has been put into practice during transmission chain modeling in
the ESToMaD project. Method A is used to describe the losses of a custom made bearing in D5.2. A
similar exercise for gears is also described in D5.2. This deemed necessary since no information from
the supplier was available. Method B is used for the other bearings. The bearing model was provided
by manufacturer SKF and is further detailed in D2.1. Modeling the component in detail from a
phenomenological point of view is the most time consuming task as it requires a lot of insight from
the user. In a phenomenological model, several parameters are uncertain and an experimental
campaign is often unavoidable. This approach will be illustrated in D5.4 on a cam follower module.
The choice of the method depends on the available information and the desired accuracy.
The manufacturer already needs to have a lot of insight in his system to determine which loss
components contribute to the global loss behavior and which are negligible. To obtain more
information regarding the dissipative behavior, a step by step approach has to be adopted. At first a
rudimentary model has to be used that can be systematically updated. If the addition of new
component does not contribute to the energetic and dynamic behavior, it can be removed again.
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This procedure has to be repeated until the manufacturer is satisfied with the accuracy of the model.
A detailed modeling exercise of a mechanical drivetrain is made in D2.3 to illustrate this approach.
Parallel to the modeling of the dissipative elements in the system, the inertial and capacitive
elements need to be added since those element influence the dynamic behavior of the system which
in turn alters the friction.
As models often lack some of the necessary data experimental procedures are needed to estimate
the uncertain parameters. Such a campaign is described in D3.2.
4.6 Machine Logic Control
4.6.1
Scenario definition and optimal management strategies during operation
Referring to the definition of energetically optimal machine tool management strategies, two main
approaches will be considered: management at logic level (switching on and off peripheral and
auxiliary devices) and at continuous control (definition of optimal motion strategies). A richer set of
options will be possible for the machine tools applications, given the capabilities of the Numerical
Control.
Considering logic control, it has to be mentioned that (see deliverable D1.2), auxiliary equipment,
while producing a limited percentage of the peak adsorbed power, is responsible for a significant
portion of the total consumed energy, especially in relationship with the unavoidable unproductive
times (also related to part change on a machine, micro failures, machine tuning, etc.). The following
figure reports, for example, the total consumption of a large production plant: in yellow it is
highlighted the energy consumption related to the stand-by state.
Figure 40: an example total energy consumption of a production plant in one month
(Energy Efficiency; Siemens AG 2010)
Also at ISO level [ISO TC 39 WG (14955): Environmental evaluation of machine tools (metal cutting
and metal forming), at which CNR-ITIA is participating], it is foreseen the definition of additional
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energy-aware stand-by state. Particular considerations will be made on the possibility to automate
some machine state switching, to support or substitute the machine operator in the selection of the
optimal state.
power
consumption
processing
warm up
ready
stand by 1
stand by 2
off
time
production
yield
ecoefficiency
time
time
warm up
time
Figure 41: energy consumption and energy efficiency in different machine states.( source:
G. Bianchi at ISO/TC 39/WG 12)
The basic goal is to minimize energy consumption in non-productive phases, while considering also
the time required to go back, from them, to normal processing. In some cases, especially when
thermal stability is involved, the start-up time can be significant. It is therefore proposed to consider
also the corresponding timing in the definition of the various stand-by levels.
INITIAL STATE
OFF
OFF
FINAL STATE
STAND BY
periph OFF
STAND BY
periph ON
READY
STAND BY STAND BY
READY WARM UP PROCESS.
periph OFF periph ON
1s
90s
1s
1s
…
40s
…
20s
WARM UP
3s
PROCESS.
3s
1s
1s
10s
10s
5s
5s
2s
2s
300s
Figure 42: definition of switching times between different machine states ( source: Comau
spa and G. Bianchi at ISO/TC 39/WG 12)
Let’s consider now machine motion optimization (by speed control of the central motor or by a
Numerical Controller). As a general rule, energy consumption depends in a non-linear way on
production rate (see Figure 43): a low production a fixed stand-by consumption prevails, while at
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high production rates energy consumption growth more than proportionally (consider for example
that friction losses increase wit speed and Joule losses are proportional to the current squared).
There is therefore an energetically optimal level of production rate: referring to the plot on the right
of the following figure, if the machine is characterized by a maximum achievable productivity
indicated by the blue line “2”, when the production plan doesn’t saturate completely the machine,
it’s possible to define an “eco-mode” processing strategy, that minimize the overall energy
consumption while delivering the required production. It has to be noted, nevertheless, that the
position of the optimum is related to the relevance of the stand-by consumption: if the latter is
comparably large, the local optimum can happen at unachievable production rates, as in case “1” in
the figure. In such cases the usual machine management strategy that maximize productivity is
already optimal also from the energetic point of view.
Energy
C
 eff
1 max perf
B
A
 eff
B
A
Hourly Prod
2 max perf
C
Hourly Prod
Figure 43: energy consumption and energy efficiency as a function of production rate
4.6.2
Auto switch off of not used auxiliary devices
Discussions of the efficient use of energy have become more frequent in many sectors of industry.
Machine tools are equipped with numerous motors and auxiliary components whose energy
consumption can vary strongly during machining.
Several different ideas have been tried for increasing energy efficiency in manufacturing with
machine tools. Fidia has focused on optimal management strategies during operation.
As mentioned in the previous section, the optimization of machine tool strategies can be done in two
main ways: management at logic level (PLC command of peripheral and auxiliary devices) and
management at motion control level (CNC definition of paths to be executed during machining).
Regarding the first aspect, this paragraph considers that potential savings can be achieved looking at
the base load of machine tools that require energy consumption even in non-productive phases. The
base load is determined substantially by the auxiliary components of a machine.
Besides the use of energy-efficient motors in the auxiliary components, many possibilities for
reducing the base load can be found in proper energy management. With energy management,
consuming components are specifically switched off by the machine control in non-productive
phases.
On a FIDIA numerical controller the logic management is normally implemented inside the CNC by
means of a PLC program written in the proprietary AUCOL or standard IEC language. Often the
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machine tool builder needs to develop its own additional logic depending on the specific
configuration of the machine structure.
CNC
MACHINE
PLC Interface
Machine
Logic (Aucol or IEC1131)
Additional logic
Figure 44: Schematic machine structure
The customer can use the CNC in two working modes: MANUAL or AUTOMATIC. When the CNC is in
AUTOMATIC mode, CNC controls the machine tool. Instead when the CNC is in MANUAL mode, the
machine tool is controlled by the operator or by an external logic. You can switch from MANUAL to
AUTOMATIC if you want to run a CNC program, and you can also switch from AUTOMATIC to
MANUAL if you want to stop a CNC program (for example in emergency situations).
Manual and automatic modes differ in consumptions from machine to machine depending on the
HW configuration they have. In this specific case (model K199) depending on the hardware installed
they are very similar.
In automatic mode with active position loop, brakes are open and axes are in loop position (often
some of them against gravity). This implies servodrives have a higher consumption for maintaining
the axis position and chiller has a higher consumption for cooling the cabinet. Moreover several 24V
devices and relays are active only in automatic mode.
Fidia has added to the current PLC SW that we find on each sold machine, special functions
developed to optimize energy management, so that consuming components are specifically switched
off by the machine control in non-productive phases.
Fidia new PLC SW developments are confidential and cannot be published in an explicit way,
therefore it follows just a simple example for clarification: the implementation of a M function which
implement the NIGHT/DAY use of the machine. A NIGHT/DAY selector has been added and the
machine tool is switched off only if the selector is in the NIGHT position. In this case the CNC acts as
follows:

it sets a bit for one second, then returns it to zero; the leading or trailing edge of this bit can
be used to activate an external device (timer) that will power off the machine after a predetermined time period;

at the same time, a shutdown application is launched which starts the procedure for
correctly powering off the PC of the controller.
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At present on almost all machine tools at the end of a machining work, the machine remains in
automatic mode with position loop on (see consumption under letter e). In absence of the user (e.g.
during the night) the machine consumes energy.
Fidia has improved so that if the user activates the night logic, a PLC function after finishing the
machining process, sets a command to close the user interface, to shut-down the operative system
and the machine. Therefore in this condition the machine consumption is zero.
The above mentioned PLC logic has been tested on a FIDIA K199 machine.
Figure 45: FIDIA K199 test bench
Measurement equipment used: Merlin Gerin PM750 power meter installed directly in the electrical
cabinet.
The following consumptions have been reported in different use conditions:
a) machine with AUXILIARY SERVICES OFF: 3 kW
(thermal conditioning of the machine is always active also with auxiliary services off, consumptions
are high to maintain the thermal stability of the machine - see chiller issues)
b) machine with AUXILIARY SERVICES ON: 3 kW
c) machine in MANUAL MODE: 3 kW
d) machine in AUTOMATIC MODE with blocked axes: 3 kW
e) machine in AUTOMATIC MODE with axes in closed position loop: 4 kW
Further measurements:
f) spindle rotating in air at 1500 rpm, axes in closed position loop: 8 kW
g) spindle rotating in air at 24000 rpm, axes in closed position loop: 14 kW
Usually when Fidia machines are not in an automatic machining cycle (i.e. waiting for new
instructions) consumption recorded is under e).
One simple implemented function now brings the machine in condition under d) at the end of the
executed part-program.
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Further PLC developments allow the partial/total switch off of the machine using the NIGHT function
and the selector DAY/NIGHT on the CNC.
Different cases can occur:
1) Parameter NIGHT=ON selector=DAY
The user has decided to activate the NIGHT logic but not switching off the machine.
Once the machining process has been executed, the machine stops and the chip removal
system/conveyor lasts for 15 minutes according to the builder specifications.
The machine goes in manual mode, axes and spindle are disabled but other PLC driven auxiliaries are
still active.
Energy used in this case is under c).
2) Parameter NIGHT=OFF selector=DAY
The user has decided to not activate the NIGHT logic and not switching off the machine.
Once the machining process has been executed, the machine stops and the chip removal
system/conveyor lasts for 15 minutes according to the builder specifications.
Machine stays in automatic mode and all circuits are active.
Energy used in this case is under e).
3) Parameter NIGHT=ON selector=NIGHT
The user has decided to activate the NIGHT logic and switching off the machine.
Once the machining process has been executed, the machine stops and the user interface and the
operative system are commanded to shut down. Machine goes in manual mode and it starts a timer
that switches off the machine acting on the main electrical switch.
Energy used in this case is null.
4) Parameter NIGHT=OFF selector=NIGHT
The user has decided to not activate the NIGHT logic even if the selector is in NIGHT mode.
PLC interprets this not coherent condition and it ignores that the selector is in a wrong position. The
machine is not switched off.
Energy used in this case is under e).
Results of the NIGHT functionality are very interesting as the energy consumption is reduced of 100%
in non-productive phases (see condition 3).
Taking idea from the DAY/NIGHT management of the machine, the same concept can be applied to
LUNCH and COFFEE breaks. The first break lasts from 30 to 60 minutes and the second from 10 to 20
minutes. To reduce consumptions to 0 kW in non-productive phases is of fundamental importance
for the energy efficiency.
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Even if this paragraph is strictly related to the machine tool world, the concepts exposed are part of
the larger concept of avoiding stand-by in domestic/non domestic appliances.
Auto switch off of not useful equipment can be generalized to other fields of interest.
4.7 Axis Position Control
4.7.1
Scenario definition and optimal management strategies during operation
A very convenient way to influence energy consumption in machinery with position controlled axes is
to optimize the controller tuning and trajectory generation in order to get, for a defined application
scenario, the best compromise between energy consumption reduction and cycle time increase (35).
First, explorative studies have been performed by FMTC on the badminton robot, working on the
controller of the linear axis, moved by a direct linear motor, of which the electrical circuit is depicted
in the following figure.
Figure 46: Schematic overview of the energy flows and losses in the full robot. More
details about the full robot can be found deliverables D5.2 and D3.2.
The analyses are based on the AMESim simulation model derived for this motor axis and
experimentally tuned, that reproduces the following energies information:







E_grid: total energy consumption from the grid
E_cu: accumulated copper losses (including eddy currents)
E_fric: accumulated friction losses (including the remaining iron losses)
E_cap: energy stored in the capacitor of the DC-bus in the drive
E_inertia: kinetic energy stored in the mass of the forcer (follows velocity profile)
E_rect: accumulated energy loss in the rectifier
E_inv: accumulated energy loss in the inverter
As experiments have shown that energy is mainly spent in copper and friction losses, energy
consumption can be reduced by taking measures in the design and operation of the robot that work
on both aspects:
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
Reduce the friction losses. This can be done by replacing the guideway with one that
produces less friction, provided that such a guideway exists.

Reduce the copper losses. The copper losses are proportional to the square of the current.
The current on its turn is proportional to the force. A large part of the force is used to
accelerate the forcer. Therefore, a reduction of the acceleration could provide a reduction of
the copper losses. This is an aspect that is explored in the next section, since the acceleration
is determined by the trajectory generator parameters of the PTOS algorithm, being the
maximum velocity vmax and maximum acceleration amax (see (36) for details on this
algorithm).
Moreover, also some secondary effects occur: 1) since a part of the force is also used to overcome
friction, a reduction of the friction will on its turn also reduce the copper losses, and 2) a reduction of
the maximal acceleration will reduce the velocity and therefore also the friction losses.
Sensitivity analysis of the PTOS controller parameters
The goal of this analysis was to investigate the impact of varying certain parameters of the PTOS
algorithm on the system’s efficiency and dynamics (i.e. the total time of the trajectory of the forcer).
The current parameter setting of the PTOS is done in such a way that the power and force of the
linear motor are maximally used and the robot is moving in a time-optimal manner. As a result, there
is often some spare time between the arrival of the linear motor at its end position and the next
robot action (hitting the shuttle). One could exploit this time to move the robot slower and with
lower accelerations, this way reducing its energy consumption, without losing performance.
A first ad-hoc and manual analysis was performed to investigate whether that spare time can be used
to improve the energy efficiency, by varying the vmax and amax parameters of the PTOS.
Figure 47: simulated copper and friction
losses [J] during a movement of the linear
axis, for different values of amax
Figure 48: simulated response time for a
2m stroke as a function of the max.
acceleration
Figure 48 shows the simulated time for a complete translation stroke of 2 m as a function of amax.
As can be expected, the time increases with a decreasing amax. Between the extreme values
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chosen for amax, the response time varies by as high as 0.12 seconds or an increase of 14 %.
However, 30 m/s2 is a high acceleration and the energy consumption is reduced by 349 J or more
than 32 % on the same range (cf. Figure 47). This means that a limited reduction of performance
can result in a significant reduction of energy consumption and that it is worthwhile taking into
account the expected arrival time of the shuttle in the trajectory control of the linear motor.
As has been shown in Virtual WP deliverable (WP-MS2) and (36), these simulation results are
confirmed by measurements.
It would be interesting if one could modify the time-optimal controller to an energy-optimal
controller on the badminton robot, without changing the controller hardware, but just by changing
the software setting of the controller. If this would be possible, such approach would allow reducing
the energy consumption of motion systems without the need for expensive investments or
complicated mechanical machine modifications. One such approach could consist in calculating
energy-optimal control signals to the motor using Model-based Predictive Control (MPC) approaches,
as explored in e.g. (37) (38). However, this would however require significant changes to the control
software. A more straightforward approach would consist of selecting the trajectory parameters in
the PTOS controller (vmax and amax) for each motion in such way that the robot reaches the desired
end-point (the expected interception point) just in time (the expected interception moment). This
way the time-optimal robot controller can be transformed in and energy-efficient just-in-time robot
controller. To obtain the energy optimal vmax and amax parameters as function of the desired travel
distance and travel time, multi-objective optimisation algorithms can be used (39).
Genetic algorithms (GA) are a popular and practical way to solve multi-objective optimisation
problems (40), as they allow to compute an approximation of the entire Pareto front in a single
algorithm run. This can be done in simulation, using the AMESim model of the system. The idea here
is to determine a 3-D Pareto-front, where the energy consumption and motion duration of the robot
are minimized and the travelled distance is maximised. This problem is solved using a multi-objective
GA. Each individual of a generation of the GA is parameterized by a chromosome containing a
different triplet (amax, vmax, ydes), with ydes being the distance that needs to be travelled. Using
numerical simulation, for a given desired distance ydes of the robot’s linear motion and for a given
maximum acceleration amax and maximum velocity vmax in the PTOS algorithm, the values of the time
(T) and energy (E) needed to reach ydes are calculated:
amax , vmax , ydes → (T, E)
The cost function used to minimize the values in the sense of Pareto has following form:
1
amax , vmax , ydes → (T, E,
)
ydes
The fitness of each individual is determined based on the multi-objective cost triplet (E, T, 1/ydes).
Remark that the value 1/ydes, which is represented as a part of each chromosome, is also included as
a component of the cost triplet returned by the cost function. In this way we obtain a 3-D Pareto
front corresponding to many different distances ydes and reachability times T in a single genetic
algorithm run. An alternative would be to repeat many times the full GA procedure for many fixed
ydes and for each of them obtain a corresponding 2-D (T,E) Pareto front. The slices of these 2dimensional Pareto fronts can then be arranged in a single 3-D Pareto front.
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The resulting front with 1610 is shown in Figure 49 (see (23) for more details on the GA procedure
followed).
Figure 49: 3- dimensional Pareto front for minimal T, maximal ydes and minimal E
The optimal values of the parameters amax and vmax can not be directly derived from the Pareto front
in Figure 49. However, for each point (E, T, ydes) on the Pareto front, the corresponding acceleration
and velocity values from the chromosome triplet (amax, vmax, ydes) are also known. This permits us to
construct the following functional mappings
(T, ydes) → amax
(T, ydes) 7→ vmax
which can be used to determine the PTOS settings in an energy-optimal way. Although it is expected
that locally linear relations exist between the motion duration and the distance to be travelled on the
one hand and the parameters amax and vmax on the other, the functional mappings are non-smooth.
Therefore a locally linear analytical expression is searched to approximate the functional mappings
(see (23) for details). Two look-up tables are then build consisting of an evaluation of the local linear
approximations on the grid points. They are shown on Figure 50 and Figure 51 together with the
points used to calculate them.
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2
amax [m/s ]
Figure 50: Maximum acceleration look-up-table (T, ydes) → amax
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vmax [m/s]
Figure 51: Maximum velocity look-up-table (T, ydes) → vmax
These look-up-tables are then introduced in the initial control scheme (see Figure 52) as shown in
Figure 53.
Figure 52: Initial control scheme
Figure 53: Introduction of the look-up-tables in the initial control scheme
The proposed scheme was implemented on the real badminton robot hardware and tested. It is
difficult to compare the performance and energy-efficiency of the robot during a real badminton
game because the same motions are never repeated exactly. Therefore, a standard test motion of
approximately 10 minutes was recorded and repeated twice on the robot, once using the timeoptimal PTOS controller and once using the energy optimal modification. A total of 84 shuttle hits are
performed during the play. For this sequence a reduction of 44% of the consumed energy could be
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realised! As the interception position and timing of the robot with the shuttle is estimated, and there
can be errors present on this estimate, it is also important to check the performance of the energyefficient controller compared to the time-optimal controller. Analysis showed that the time-optimal
PTOS control is able to intercept 98% shuttlecocks with error less than 5 cm, while the modified
energy-efficient version is able to intercept close to 94% shuttlecocks with less than 5 cm error. This
clearly demonstrates that the energy reduction can be obtained at the cost of only a small decrease
of performance.
Furthermore test human players reported they preferred to play against the energy optimal robot as
it exhibits a more natural behaviour. In addition to that humans did not get the feeling that the robot
has a deteriorated performance.
This implementation clearly shows the potential of energy-performance optimised motion
controllers for point-to-point systems and presents a straightforward approach to derive the optimal
controller parameters. Moreover, for the given implementation only very minor modifications to the
controller software were needed; the initial controller architecture could be reused and only a minor
add-ons were needed. As a results, no complicated or expensive modifications of the system were
required.
4.7.2
New LAH (Look-Ahead) strategy to optimize energy consumption, accuracy and
machining time during operation
As already mentioned, the optimization of machine tool strategies can be done in two main ways:
management at logic level (PLC command of peripheral and auxiliary devices) and management at
motion control level (CNC definition of paths to be executed during machining).
In the previous paragraph we have focused on the energetically efficient management of the
machine tool at logic level. In this paragraph the attention is given to the motion control level.
4.7.2.1 Why LAH (Look-Ahead)
Look-ahead control is a typical feature of predictive control or dynamic programming. Generally
speaking it is commonly used in order to focus management attention on what is supposed to
happen at some time in the future, and to encourage actions in the present that cause that desired
future.
It is applied in several different fields from automotive to energy, from production planning to
machine tools, for instance: look-ahead control for heavy trucks to minimize trip time and fuel
consumption, look-ahead control for electrical active power dispatching system, etc.
In the machine tool sector, the look-ahead control appeared with the high-speed precision
machining.
In modern CNC systems, component/product design is highly automated using computer-aided
design (CAD) and computer-aided manufacturing (CAM) programs. The programs produce a
computer file that is interpreted to extract the commands needed to operate a particular machine
via a postprocessor, and then the file is loaded into the CNC machines for production (part-program).
The look-ahead (LAH) is that part of the SW CNC that observes the execution of the profile of the
program in advance and determines the appropriate speed for confronting curves and angles. If
there were no Look-ahead, the program would, in fact, be executed at the same speed (the one
programmed).
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Even if this execution would be rather advantageous in terms of time, the result would be disastrous
on the machine and subsequently on the workpiece. Higher centripetal accelerations (and
proportionally linear on the individual axes) would be required of the system on curves for high feed
values and the radius of small curvatures than it would be capable of executing.
There would be very violent exchanges in speed between the axes on angles (or edges). These
phenomena would therefore be harmful for the kinematic mechanisms and destructive for the
workpiece (in the sense that they would be executed with unacceptable errors).
It can be easily understood that the trajectory generation based on the look-ahead affects the energy
consumption of the machine. Therefore a convenient way to influence energy consumption in
machine tools with position controlled axes is to optimize the controller tuning and trajectory
generation in order to get the best trade-off among energy consumption, accuracy and machining
time requested. Explorative measurements have been performed by Fidia, with the purpose of
evaluating as the CNC, running different LAH strategies, can drive the machine using more or less
energy. This influences the total energy consumption during a machining process.
Taking into account that in automotive and aeronautic fields, machining processes of complex parts
can take several days, it is evident how this aspect is relevant for the machine user from an energetic
point of view.
4.7.2.2 Fundamentals of different LAH strategies
The part-program is a continuous succession of blocks or segments. Each segment forms an angle
with respect to the next one. This is measured by the look-ahead V1 which calculates a speed in
function of the minimum and maximum angles defined in specified CNC parameters. The speed will
be zero for angles greater than the maximum angle; while there will not be any variations for angles
less than the minimum angle. A proportional speed will be calculated for intermediate angles. This
logic works in most cases, nevertheless when the criteria that the CAM uses to develop the points
generates very short segments and pronounced angles, the look-ahead will be the cause of
significant slowdowns. At times, these slowdowns can be damaging due to the consequences that
arise from them as previously mentioned (braking, deceleration, axis compression, error on the
workpiece at the point of the pass that will probably not be repeated on the next pass and
consequently that much more evident).
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Figure 54: Look-Ahead strategy
In CNCs using look-ahead V3 the trajectory is pre-elaborated according to a spline base and a new
trajectory, based upon part program points, is generated passing through the points geometrically,
with nor angles nor interruptions according to the nature of the spline itself.
The experience on a machine tool axis (servodrive + motor + mechanical transmission of motion) has
shown that energy is mainly spent in copper losses (motor) and friction losses (guideways,etc.),
therefore energy consumption can be reduced by taking measures in the operation and design of the
machine tool that work on both aspects.
On the operational side, since the acceleration is determined by the trajectory generator parameters
of the look-ahead algorithm, having smoothed acceleration profiles (less abrupt changes) can
increase energy savings.
In fact the copper losses are proportional to the square of the current. The current on its turn is
proportional to the force. A large part of the force is used to accelerate the forcer. Therefore, a
reduction of the acceleration could provide a reduction of the copper losses.
The look-ahead V5 uses new generation complex algorithms to improve global performance. The
fundamental objective of such kind of new algorithms is the reduction of abrupt changes of
acceleration in executing a trajectory during machining.
The physical concept of acceleration is rather well known and easy to understand. Within the context
of the Fidia CNC, acceleration is a value set by a parameter and which influences vector feed
variations, i.e., on the vector feed of the path developed by the tool. This means that an acceleration
given in the calculation of the trajectory would not necessarily mean an equal acceleration in the axis
in question since it will be distributed on all the axes involved in the interpolation. In general, it can
be stated that the accelerations set in the CNC parameters are the maximum that can be applied to
the axes.
In reality, there is another acceleration that is applied to the axes (and not necessarily to the vector
feed), i.e. the one resulting from the action of the look-ahead in which the constant feed applied on a
circle or on an angle corresponds to a gradual feed change between the interpolated axes. The effect
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of accelerations on the axes is the compression and therefore the distortion of their trajectory that is
also visible on the workpiece itself.
Changes in acceleration are practically continuous when executing a part-program since every
change in radius, angle and direction of the pass due to the look-ahead corresponds to a change in
feed and consequently to acceleration applied to each axis which is interested by the interpolation.
When the set acceleration is able to let work the system with current values lower than the nominal
current values (drive and motor), no problem is detected.
The acceleration depends on
- tool path speed
- tool path strategy
- how long the motor-drive will be able to tolerate the thermal effects
As the content of LAH strategies is strictly confidential, we cannot explain algorithms in further
details. Each of these strategies takes into account a different set of parameters that are the main
features of the implemented algorithm (see figure 53).
Figures below show 3 different screenshots taken from the CNC, in which the user can visualize and
modify parameters to run differently the different LAH options.
The large number of parameters of LAH V3 and V5 screenshots shows that these last use quite more
complex and advanced algorithms than LAH1.
(a)
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(b)
(c)
Figure 55 a,b,c: CNC parameterization of different Look-ahead versions (LAH1,LAH3,LAH5)
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Figure 56: CNC parameterization of Look-ahead version 1
4.7.2.3 Tests and actions on machine tool application
The main scope in ESTOMAD project was to optimize LAH algorithms (implemented in the new LAH
V5) to take into account of the total energy consumption in the global performance of a CNC during a
machining process.
The validation tests have been carried on using the OSCILLOSCOPE software tool during the
execution of a part-program.
The OSCILLOSCOPE is a SW tool of the FIDIA CNC that allows seeing the trends of the selected signals
sampled by the CNC. The oscilloscope can display binary and analogic data such as parameters or
memory locations.
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Figure 57: Fidia OSCILLOSCOPE SW tool
A special part-program used by Fidia for tuning and acceptance of the machine before the shipment
to the final customer, known as Mercedes test, has been executed in air.
This profile contains a fairly good number of typical conditions on which to base the test of the Lookahead.
Figure 58: Visualization of Mercedes Test trajectory errors
4.7.2.4 Experimental set-up
The experimental set-up aims at measuring from the machine useful signals to demonstrate which
LAH shows the best trade-off between the trajectory errors, the execution time and the energy
consumption.
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Figure 59: Physical machine under testing
LAH versions under testing during the experimental campaign are listed below:

LAH1 (implemented in the SW emulator)

LAH3 (currently in use on advanced FIDIA CNCs)

LAH5 (currently under development improving global performance)
The benchmark will be the Mercedes test with different accelerations during execution:

0.5 m/s2

1 m/s2

2 m/s2
Concerning the measurement approach, the diagram in Figure 60 proposes possible acquisition
points and signals to be measured for energetic analysis. Data coming from this external equipment,
if installed, can further validate and enrich information coming from tools already existing on the
machine tool itself.
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Figure 60: Architecture of the measurement set-up
The OSCILLOSCOPE is a very powerful SW tool that can display any parameter available in the CNC list
in base time. Data from sensors installed on the machine (linear encoders, temperature sensors, etc.)
are directly acquired using the internal OSCILLOSCOPE at a sampling frequency of 500 Hz (2 ms).
The recording session can be saved with the desired name in order to be reused in future or to be
post processed. After defining the work session, we can start recording the signal of interest.
After stopping recording, we can see again what we recorded sliding along the samples. We can also
change the time base and if we want to analyse the data we must zoom to read the values of the
tracks using the functionalities offered from the SW.
The oscilloscope is equipped with a function called Write File that also generates TXT file with very
compact structure. It starts with a header including all basic information and after this data is
inserted one track after the other, with only a separator between one time frame and the next.
Figure 61: Example of FIDIA oscilloscope output file
Using external SW tools, data resulting from the TXT file can be visualized in a graphic way so that
direct evaluations can be done.
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Figure 62: Example of post-processing of recorded oscilloscope file
The analysis of recorded current signals and the indication of the power absorbed during the
execution of a test path will provide adequate input to show the improvements achieved in the last
LAH version still under development.
The basic idea is to use new developed LAH algorithms to create a SW tool, coherent to the
framework of Estomad models and tools, to support energetic optimization in industrial machinery
and design with special attention to the machine tool field.
4.7.2.5 Results on machine tool application
First results show that executing the same part-program using three different LAH strategies the LAH
V5 has achieved the objective of decreasing both the energy used and the time required to execute it
(see Figure 63 below).
An improvement of energy efficiency of 4-5% can be observed using LAH5 instead of LAH3. A
reduction of execution time of 15-20% has been reached with LAH5.
LAH version
LAH5
LAH3
LAH1
Time (n° of samples*)
557 813
716 652
679 461
1,76e+12
1,84e+12
1,78e+12
*Sampling frequency 2 ms
Energy ( A2s)
Figure 63: Comparison of results
Figures below (Figure 64, Figure 65 and Figure 66) show the post-processing of the acquired data
from CNC OSCILLOSCOPE.
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Taking into consideration the motion along 3 axes of the machine tool, an equivalent parameter E
(proportional to the current absorbed) has been introduced to combine the energy of all axes and to
provide a comparable value among different tests.
Figure 64: Post-processing of recorded oscilloscope file (LAH5)
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Figure 65: Post-processing of recorded oscilloscope file (LAH3)
Figure 66: Post-processing of recorded oscilloscope file (LAH1)
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The new look-ahead strategy (LAH V5) developed by Fidia is part of a wider CNC SW improvement
research. It encompasses the dynamic and the speed control of axes; moreover it has specifically
been implemented for 5 continuous axes against 3+2 of LAH V3.
Taking into account all new features that LAH V5 presents, we can conclude that it is always the best
solution to machine a workpiece in terms of accuracy, energy and execution time. Similar to all new
software developments, at present, the main drawback is the stability of the algorithms.
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5 Specific sectorial methodologies
5.1 Machine tools
Considering industrial sector and producing machinery, machine tools are high energy consuming
products and am increasing attention is focused on concept of design of energy efficiency machines,
optimization of existing machine from the energy point of view and improvement of strategies at the
operation level for minimizing the energy consumption during the use phase.
The European Union, in the scenario of the Worldwide emission reduction policy, has been facing the
energetic issue through specific directives, dealing also with machine tools and production systems:
the European Eco-design Directive for energy-using Products EuP 2005/32/EC (6), the European
Directive for Energy End-Use Efficiency and Energy Services 2006/32/EC (41) and the European Ecodesign Directive for energy-related Products ErP 2009/125/EC (7). According with the EuP Directive,
CECIMO has started a self-regulation initiative to develop a new energy efficiency normative: the aim
is to increase the ecological performance of machine tools without forcing limitations in system
innovation and development. In the meantime, the Commission mandated a preparatory study (7) to
identify and recommend ways to improve the environmental performance of machine tools
throughout their lifetime at their design phase. A possible future scenario could consist on the
application of a new regulation imposing constraints and requirements about energy efficiency of
new machine tools.
Currently, the design of a machine is basically defined on the meeting of objective requirements and
performance (production output, dynamic and kinematic properties, etc.) at the minimum cost.
The idea at the base of this contribution is to propose energy-efficiency as a new additional and
central property in the design process of a machine tool supported by specific methodologies. A
systematic machine design approach has to take into account both the machine tool performance
and the energy efficiency (main challenge involved in ESTOMAD Project).
This section starts from a description of the actual methodologies and practises for the design of a
machine tool and then takes into account the following aspects concerning the energy efficiency:








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Introduction of aspects of energy efficiency and energy assessment into the current design
cycle of a machine tool
Propose of approaches supporting “eco-optimal” development of new machine tools and
characterization / modification of existing machine tools
Identification of reference industrial / production scenarios in which energy efficiency of a
machine and its components can be evaluated
Use / Execution of energy analyses (simulation models, analytic analyses, measurements,
ESTOMAD outcomes)
Indicators that drive design choices
Best available technologies at the element (component or subsystem) level
Improvement directions and Best Practises
Energy optimization at the operation level of existing machine (operation strategies)
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5.1.1






5.1.2
Eco / Energy efficiency normative
NF E 01-005: eco –design framework for mechanical products
draft TC39 14955: guidelines for Integrating environmental aspects into design and
development for machine tools. Test method for power consumption of machine tool
NF E 01-005: eco –design framework for mechanical products
(JSA) Standard n.: TS B 0024 – 2010 – Parts 1, 2,3 and 4: Machine tools -- Test methods for
electric power consumption
SRI Cecimo – Self-regulation initiative
The International Organization for Standardization have been focusing on environmental
evaluation of machine tool defining the norm ISO/TC 39/WG 12. For further information see
D32.
Machine tool development cycle
5.1.2.1 Machine Typical design
The usual development cycle of a machining centre is taken into account (Figure 67).
Figure 67: Usual development cycle of a machining centre
Some specifications of the machine, as some performance and the morphology of the machine (e.g.
structure configuration, number of axes, dynamic parameters, footprints, working area, geometrical
accuracy according to ISO 230-2, ISO 10791-2/3) are input data for machine design and are provided
by commercial office on the base of contacts with clients, application sector market analyses or
competitor solutions.
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A first step consists of pre-study, in which a draft schema of the machine tool is defined: choices
about machine dimensioning and motorization have been made on the base of machine mass and
costs. An objective cost is present and represents one of the aspects that determine the feasibility of
the machine under development. If costs are too high, then machine specifications are modified and
/ or adjusted and, in extreme cases, the project is aborted.
If the feasibility of the pre-study is verified, a team of the technical office works on a second phase
for the detail design of the machine. This second step includes:




Calculations for correct dimensioning of structures and machine elements
Generation of the bill of materials and sub-components / skeleton of the machine
Realization of machine technical drawings (CAD) for the building and the assembly of the
machine
Verification steps of the coherence and conformity of what defined in pre-study phase and
possible revision of the previously defined specifications.
Then the prototype of the machine tool is built and tested in order to verify the meeting of the
required performance. After possible modification / reconfiguration actions (if they are necessary),
the machine tools is ready.
5.1.2.2 Propose for the use of methodologies and approach for energy efficiency
Into the design phase of a machine tool it is possible to include aspects of energy efficiency and
energy analysis. Some indications provided by ESTOMAD methodology can be used and applied in
the present contest.
A constraint linked to a specific imposed level of energy consumption could be added to the machine
requirements and it could be related to the machining process that the user has to executes. It must
be noted that some current design choices are already coherent with the object of energy efficiency
(e.g. the need / objective of decreasing frictions also goes in the direction of improving the
efficiency).
If no reference values of consumption are available for the application, a possible application must
be considered as benchmarking situation with respect to which potential improvements will be
evaluated. If a first prototype of the machine is available, this prototype can be used as benchmark.
In other cases a previous version of the machinery can be used or even the machine of a competitor.
On the other side, some researchers (33) suggest to start from the analysis of similar existing
machine tools in order to find energy efficiency solutions for new machine to be designed and
developed:




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analysis of the energy flow inside the existing machine
individuation of the major energy consumer elements (sub-systems or components)
individuation of solutions for the improvement of the energy efficiency of the selected
components
implementation of improvement actions / choices at the element level into the development
of a new machine
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A fundamental step for making the energy efficiency analyses during machine tool design phase is to
formalize the scenarios in which the machine tool will be used. The operative scenario can provide
indications about the possible utilization of the machine and of the installed power.
A possible solution for defining / emulating a working scenario is to create part program representing
a usual production of the client. Usually the machine tools are grouped in families; the machines of a
same family have similar morphologic characteristics and the same application sectors. So for each
sector, one or more part programs / work cycles can be created. For instance, Jobs Spa has defined
specific test cycles (e.g. the Mercedes cycle, ref. Deliverable D53) for energy evaluation of machine
tools that will be used in certain market sectors. Given the test part programs, ESTOMAD tools can
be used in order to estimate energy consumption, evaluate power losses and provide decisional
support to the designer (The applications of Estomad methodologies on the reference industrial test
case “machine tool” are presented in D53). In particular, the following tools can be considered:



NC part program emulator created by Fidia (ref deliverable)
Simulation models of machine tools and their components (ref. D53, D31 and D23)
AMESim energetic software functionalities
This action can be also used for efficiency improvements of existing machine, obtaining indication
about what components could be replaced by more efficient ones.
It must be noted that some models require a preliminary experimental-based characterization phase
(for the estimation of unknown parameters): so the use of the model is based on a previous
characterization of the model on another version of the same machine and the creation of a model
library. If tuning phase has not been previously executed, models can’t be used during the design of
the currents machine, but experimental activity can be conducted on this machine (once it is built) in
order to characterized the relative energetic model, that can support


the machine builder during the future design of the same / similar type of machine
the client, in order to make energetic optimization during the use phase, allowing to
individuate possible eco-efficiency optimal strategies
For a general and indicative prediction of the energy consumption of a machine tool during the
design phase, it is possible to consider power nominal data of a machine and their components
(catalogue data). From statistics about the use of the machine tools previously sold, it is possible to
estimated how long a machine has been in a particular state (e.g. use state, power on state , standby state, emergency state, etc.) given a certain time horizon and to assume what average
percentages of the component nominal power is used. So general indications about energy
consumption, that can provide decisional support to the designer, can be obtained using catalogue
data under specific hypotheses.
Experimental measurement sessions can be conducted once the prototype is available.
Measurements can be useful to have warnings about unexpected consumption of some components,
that could be modified or substituted in order to improve the efficiency of the machine. Measures
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can be executed also during the testing of the developed machine tool (before the delivered), also
with the aim of simulation model characterization. Regarding measurements, see D32.
5.1.3
-
-
-
Design guidelines / Improvement directions / Best practises
Replace of some components by more energy efficient components
Usually the main contribution to global machine tool energy consumption is associated to
some components, e.g. cooling units, motorization, etc. It can be useful to replace
components with more efficiency ones. The choice / evaluation of Best Available
Technologies adoption for each machine element should be considered in relation to the
scenario in which machine has to be used. E.g. considering the cooling units, the use of chiller
inverter technology can be taken into account (ref. chapter 4.2)
Reduce moving mass or inertia
The moving mass is directly related to the required power for the motion. But also indirectly
can a high mass increase the friction losses in the guiding or bearings. A low weight design
can be considered at the trade-off of sufficient stiffness in the system (ref. chapter 4.1 of the
present document).
Consider energy recovering
The energy recovering means the secondary usage of energy, which would otherwise be lost.
The general mode of electrical servo drives to reefed energy into the link is an example of
this second energy use.
Some examples to achieve energy efficiency for the subsystem of a machine tool are reported in
Figure 68.
Figure 68: Energy efficiency improvement actions (29)
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5.1.4
Strategies at the operation level
It must be noted that in general, energetic efficiency depends on how a machine is made (“design”)
and how it is used (“management”). The two aspects cannot be fully separated and a designer must
take into account how the machine will be used. So strategies at the operation level have been
considered. The main ones are reported as follows:
-
-
-
-
-
Optimization of the tool path
Minimization of the rapid trajectories in order to reduce the energy consumption in nocutting phases.
Best working / cutting strategies
Optimization of the cutting process in terms of working strategies and cutting parameters
trying to minimizing the energy needed for material removal and use of new Look-Ahead /
motion control strategies (see section 4.6). Because the power adsorbed by auxiliary systems
is often significant, the optimal value of feed rate is close to the one that minimises the
production time. For this reason, all the strategies that aim at decreasing the processing time
are also energy efficient (42).
Lubrication strategies
The use of cutting fluid decreases the friction coefficient and thus the cutting pressure,
however this has a negative impact on the other dimensions of the process sustainability.
Studies report that by using dry cutting it is possible to reduce the energy consumption of
the machining process from 4% to 16% (43).
Use of tool coating
Coating of tools is used to decrease their wear or to increase the material removal rate of the
cutting.
Strategies for auto-switch of the not used auxiliary systems
Because of the high consume of the auxiliary systems during their stand by state, the use of
system control with automatic switch-off of not used auxiliary systems can increase the
energy efficiency of the machine. Ref. to section 4.6.2.
5.2 Weaving machines
The system that has been considered throughout the project is the main driveline of a rapier weaving
machine. The current version of the weaving machine is used as a benchmark for new measures that
can be taken towards energy efficiency.
A weaving machine has mainly three operating conditions:
1. Nominal behaviour: In this situation the machine is producing fabric at a certain constant
speed. In the case of the Optimax rapier machine this speed is 700 rpm.
2. Start-up behaviour: A weaving machine needs to be at nominal speed in the first machine
cycle in order to avoid starting marks in the fabric. Therefore a high start-up torque/power is
requested from the switched reluctance motor during each start-up of the machine.
3. Stand-by behaviour: When a weaving loom is stopped energy is recovered from the motor
and the machine goes in stand-by mode.
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Because a weaving loom stops approximately 2 times per hour for a few seconds the energy
consumption during start-up and stand-by behaviour is very small compared to nominal behaviour.
Moreover customers compare the energy consumption of weaving looms of different manufactures
during the nominal behaviour of the machines.
Therefore this section describes a possible approach for reducing the energy consumption in the
current design of the rapier machine at nominal behaviour.
5.2.1
Weaving machine development cycle
In Picanol each development on a weaving machine is defined as a project. These developments can
be either major changes or minor changes. Moreover these changes sometimes have nothing to do
with energy efficiency eg. changing parts for better mounting purposes.
These projects are managed by a project follow-up system in the form of excel tables. In what follows
the working principle of this follow-up system will be explained and how energy efficiency could be
integrated.
The first page of the project status report shows some administrative details about the project as
number, name, responsible, duration and a brief description. It has to be mentioned that a project
can be triggered by several means:
1.
2.
3.
4.
5.
Cost reduction
Complaints
Customer demands
Research
…
This shows that not every project is aimed at increasing the energy efficiency but nevertheless
efficiency should be taken into account in each project.
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Figure 69: Project status report
The second page of the project status report shows the different phases in the project. These phases
are choosen ad hoc and by the responsible of the project. Possible phases in a project concerning
energy efficiency could be:





Making an energy model of the considered system in the project
Energy measurements on a prototype
FEM analysis of the new system
Cost analysis of the new system
…
All these phases have a due date and especially in a research project these phases can change during
the project depending on the results in a former fase.
The third page gives an overview of the costs that will be/are made during the project. These costs
are split up in three parts:
1. Use of human resources during the project eg. FEM engineer, cost engineer, designer,…
2. Costs of prototype parts
3. Investments to be made eg. molds
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Figure 70: PSR delivery
The fourth page contains a cost estimation of the different products developed in the project. In this
way the cost of the product is monitored/updated throughout the whole project.
The fifth page contains some indicators that have to be kept in mind for that project and during the
project. Examples are:
1.
2.
3.
4.
IP
Scrap cost
Time loss during assembly
…
Picanol is planning to add an indicator towards energy to make sure that the project has no impact
on energy if the project is not energy related. If the project however is energy related the aimed
energy reduction will be set at the beginning of the project.
It has to be mentioned that most of the times the last phases of the project are tests of the
considered system/product at customer site. In parallel with the workflow in the project more
administrative workflows are running to determine suppliers, checking safety regulations,... It is
beyond the scope of this text to describe these administrative workflows in detail.
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Figure 71: PSR – Planning and Cost
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Figure 72: PSR – Product Cost
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Figure 73: PSR quality
5.2.2
Design guidelines
As explained in D5.4 the considered system is the main driveline of a rapier weaving machine.
Concerning the design of a mechanical drive train Picanol learned the following lessons towards
energy efficiency:
1. It is possible to replace individual components by more efficient ones eg. a ball bearing
consumes less energy than a roller bearing. Sometimes bearing manufactures offer a more
efficient bearing by adapting the internal geometry of the standard bearing.
2. Changing the kinematic and dynamic conditions of a bearing/gear can reduce the energy
consumed in that component e.g. changing the cam profile and diameter of the roller
reduces the speed of the rollers and hence the energy consumption. A more detailed
explanation of this can be found in D5.4.
3. Changes in the oil flow rate of the lubrication system of the machine changes the energy
consumption although this is not a parameter in the energetic model. A more sophisticated
model that takes into account this phenomenon could help to understand it better. A more
detailed explanation of this can be found in D5.4.
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4. Adding damping to some parts in the system reduces the forces in some components and
probably also the losses. This a possible way to deal with the negative effect of flexible
behaviour. How this can be implemented in practice is not so clear.
5. Reducing inertias in the different mechanisms can also have a positive influence on energy
efficiency but is obvious that durability also has to be taken into account.
A remark that has to be made is that if Picanol wants to improve its current (mechanical) design the
guidelines developed during Estomad are very useful.
If Picanol however wants to implement a complete new concept (e.g. pneumatic drive line) these
guidelines should be again determined as they were determined for the mechanical design meaning
an extra learning phase will be necessary compared to the previous case.
5.2.3
Modelling and measuring guidelines
Concerning the weaving machine following guidelines/rules of thumb were learned to do an energy
assessment and making an energetic model of a coupled mechanical drive system in general:
1. An (quick) experimental analysis can help a lot to determine the most dominant losses. In the
case of the weaving machine this is done by doing power measurements on different
configurations. These different configurations are formed by starting from a complete
machine and incrementally taking away different parts. More details can be found in D3.2.
2. It is not so easy to find a good balance between the level of detail of the energetic model and
the desired accuracy of the results.
3. To make an energetic model of the system a co-simulation was needed as Amesim was not
able to determine kinematic and dynamic properties of 3D mechanisms. These kinematic and
dynamic properties are needed to determine the losses in components e.g. bearings.
4. Model updating of the energetic model is possible in two steps. Firstly the kinematic and
dynamics of the model can be validated through kinematic and dynamic measurements.
Secondly the energetic behaviour can be validated through power and temperature
measurements.
It can be stated that measurements are as important as modelling in assessing the energy behaviour
of a mechanical system.
5.3 Badminton Robot
The FMTC badminton robot is a machine that is not intended to be commercially sold and produced
in large series nor many variant will ever be available. Nevertheless, this robot has a typical structure
and characteristics. It belongs to a class of machines with point to point motions where only the
accuracy in the points is of crucial importance for the process, but not the path between two
subsequent points. This class of machines is more frequently found in industry. The guidelines and
conclusions therefore hold for all these machines.
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5.3.1
Development cycle
At the start of the design of a completely new type of machine, the initial steps mainly focus on
generating ideas for a functional machine architecture that achieves the performance objectives. In
the case of the badminton robot it was chosen to use off-the-shelf components like a linear actuator
for realizing the large displacement on the field and rotary actuators for the hitting mechanism.
The first dimensioning and component selection aims at a design that can withstand maximal
stresses or forces. The dimensioning is based on basic calculations that take into account the moving
mass, the worst case acceleration and some safety margins to account for losses. This could easily
lead to oversizing, larger masses and increased energy consumption. Therefore, it would be
beneficial to build a (rather simple) simulation model for the badminton robot that could consist of a
moving mass, a linear actuator with a certain efficiency and some friction and do an energetic
analysis already in this very early phase.
When a prototype or previous version is available, as it was the case for the badminton robot, a
benchmarking experiment can be set up to assess the energy consumption in order to support
redesign decisions. Measuring the total energy consumption during playing with the badminton
robot reveals that a lot of energy is consumed when accelerating the moving mass.
During the concept selection and detailed analysis, the design will undergo several iterations to
guarantee that the construction is resistant to all maximal forces and that all regulations and norms
are satisfied. Beside this, the energetic analysis should also be repeated to assess the effect of design
changes. As components get selected, the simulation model can easily be extended or upgraded with
new blocks, e.g. the variable speed drive, power converter, linear actuator model from which
parameter values could be extracted from data sheets. This allows for a more reliable evaluation if
the energetic requirements are met.
5.3.2
Design guidelines
For machinery like the badminton robot with a mass that is moving fast from point to point, some
guidelines for a high energy efficient design can be derived from the conclusions of the ESTOMAD
analysis.
A first guideline is very straightforward: reduce the moving mass (see also section 4.1). A lower mass
will have a direct influence on the required actuator power and energy consumption. However, the
mass is mostly being fixed early in the mechanical design process and results from strength
calculations e.g. using finite element analysis. Here a trade-off has to be made.
Closely related with reducing the mass is working on the motion profile by lowering the acceleration
and velocity (see also section 4.7). It is generally shown that implementing an energy optimal
trajectory generator (in the case of the badminton robot a just-in-time solution was chosen) instead
of a time optimal version can result in a significant reduction in energy consumption for a small
relaxation of the time constraints. These adaptations can be implemented in the controller software
at a low cost.
It is advisable to at least analyse the effect of commonly accepted solutions for improving energy
efficiency. For instance, braking energy can be recovered and stored in capacitors. Although it was
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not the miracle solution for the badminton robot because other losses were dominating, the
conclusion might be different for other applications. Moreover, it is a solution that is easily added to
an existing system at a limited development cost.
The previously mentioned examples show that it is not always clear what will be the energetic impact
of a design modification, even if the idea is very simple. Therefore, it is good practice to follow the
developed ESTOMAD design approach and build (sufficiently detailed) simulation models to analyse
the energy consumption, incl. the internal energy flows, and to perform sensitivity analyses that
evaluate the expected improvements before implementing.
This ESTOMAD approach was successfully demonstrated on the badminton robot for two potential
design improvements. First for the more efficient just-in-time controller which proved easy and
cheap to implement, and secondly for the hydrostatic bearing where the energy consumption could
be drastically reduced.
In the second improvement the designer still has to take the difficult decision if it is economically
viable. The conclusions of the sensitivity analysis and expected gain in energy consumption can be
used to trade off against the additional implementation cost (if any).
5.3.3
Modelling guidelines
Applying the ESTOMAD methodology for performing an energetic analysis relies on having a
simulation model. The questions arises in how much detail the badminton robot should be modelled.
The answer depends on the design phase and the available information.
In the benchmarking phase the aim is to process the energy measurements on the badminton robot
in order to reveal how the energy flows and where the energy losses are located. Since only the total
energy consumption can be measured and not the individual loss components, they should be
derived from the simulation model. However, mostly no sufficiently reliable model is available. In
order to get a first quantitative view on the energy flows, a linear regression approach can be applied
on the measurement data. From an energy point of view, the following terms could be considered:






Constant: there is always a certain power consumption even if the machine is not moving
Acceleration * velocity: this term is related to the power required for the (useful) motion of
the machine
(Velocity)2 : the square of the velocity is related to the velocity dependent viscous friction
|velocity| : absolute value of the velocity is related to Coulomb friction
(acceleration) 2 : the square of the acceleration reflects the electrical losses in a resistor, e.g.
in the motor or motor drive
...
Although this may look as a rather simple model, it will be very informative on where power is
consumed. This information can be used to decide which part of the machine is most relevant and on
which elements the more detailed analysis should focus. The energy measurements on the
badminton robot proved that in a first stage it was sufficient to focus on the linear axis. The model
pointed out that the main contributors to the losses were related to accelerating the mass and
viscous friction.
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Further in the development cycle where it comes to concept selection and detailed design, there is a
need for more detailed simulation models. The simple model can help to find realistic initial
estimates of some model parameter values. Within the ESTOMAD project AMESim has been used for
doing the energetic analysis supporting the design. All required modelling blocks were found in the
extensive AMESim libraries; the corresponding parameter values were taken from the datasheets.
Before proceeding to the concept analysis, it is always a good idea to first do a model validation to
check if all phenomena are taken into account and if the model behaves as expected. This is essential
to build some confidence in the simulation model. The model of the badminton robot explicitly
contained the friction losses. Copper losses were covered by the stator winding resistor of the
actuator model. The iron losses were not explicitly modelled, but could be considered as part of the
copper losses. The model validation will typically look at the system response for a realistic input, e.g.
the step response to a change in set point. Since we are interested in using the simulation model for
calculating energy consumption, the energetic behaviour should also be checked, e.g. by comparing
the power or energy consumption at the grid for real system and simulation.
Once a reliable model is available, a sensitivity analysis can be performed to evaluate the (energetic)
effect of a design change. The controller algorithm can be changed to a more efficient one, or the
bearing model can be replaced by another library block in the AMESim physical modelling
environment. If the component block is not (yet) in the library, it should be developed in cooperation
with the component supplier.
5.3.4
Strategies at the operation level
Within the ESTOMAD project the analysis was focused on the motion of the robot to reach the
desired position to hit the shuttle. There are also other operating conditions for the badminton
robot: waiting when not playing, and waiting after hitting until the player hits back. In the current
strategy the robot remains at the same position. During this period, only a constant amount of power
is consumed by the drives, sensors, etc. If a reduction of these losses would be envisaged, a
start/stop feature that switches of power in these situations could be evaluated with the simulation
model. Also the impact of strategy changes on the energy consumption can easily be assessed.
5.4 Pantograph
5.4.1
Approach and methods from literature.
The pantograph mechanism is one of the most important system in railway, affecting the proper
operation of railway transportation system. Since the design/structure of pantograph is responsible
for the general reliability of tram and the safety of use, the design must follow the general
requirements and rules included in the available literature and standards. Therefore the literature
applicable to the design process of pantograph covers the following aspects:


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




Vibration resistance
Dynamic behaviour
Contact with the overhead line (exerted force)
Maximum current load
Rising and lowering time
The main standard that should be considered in the design process is PN-K-92004 concerning the
general requirements for tram pantographs and methods of testing. Mentioned document considers
the issues listed above and presents the methodology for testing the system to check if the
requirements have been met.
Taking into account the requirements for maximum value of overall dimensions as well as spacing of
the mounting points is the essential aspect for application of the pantograph structure in most of
railway vehicles (trams) in the targeted region/country. The maximum width of kinematics cannot
exceed 3460 mm while the spacing of mounting points must be equal to 520 mm according to the
norm.
The durability of structure is ensured by following the rules included in standards concerning
welding, cutting, surface preparation (corrosion protection). The components (bearings, joints etc.)
should withstand the environmental conditions such as exposure to moisture for long period of time.
A tram pantographs must withstand the external (environmental) vibrations with the frequency and
magnitude specified in the related standard (PN-E-06120:1969) and defined as environmental
conditions.
The required dynamic behaviour of mechanism is determined by characteristic presented in the
considered standard and refers to dynamics of the system within the up and down motion (work
cycle). What is more the dynamics is strictly associated with the force acting in the slider and
overhead line contact point. With static force in range from 7N to 70N exerted on the overhead line
the current collection must be continuous (without drop of the static force to zero) at the maximum
speed specified for the vehicle and traction.
The pantograph structure must also withstand a temperature rise as a result of current flow through
the structure. The maximum temperature gain for the particular elements of mechanism is strictly
specified in the standard. During the operation with the rated current, the temperature for the
elements should not exceed such values.
The rising and lowering time of the kinematics must be within certain limits determined by the
standard.
The standard considered in this document is related with the norms listed below:




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PN-E-06120:1969 – Rail vehicles – Direct current electric devices – General requirements and
tests.
PN-E-90160:1988 – Electrical lines – Design of copper and aluminium wires.
PN-K-92002:1997 – Public transportation system – Tram and trolleybus traction.
PN-K-92005:1997 – Rolling stock – Slider overlay for pantographs – Main dimensions.
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The standards associated with pantograph systems do not specify the maximum energy consumption
of the system as well as the desired energy efficiency. The standards also do not specify the direct
requirements for the type of drive system. It doesn’t take into account energetic performance of
drive systems, but focuses mostly on reliability of the solution, compliance with norm requirements.
However, during testing procedure of the pantograph system which requires high number of cycles
to be done before putting each single pantograph for commission energy aspect can be reasonable
considered. During long duration tests, the drive system should prove its robustness in mechanical
and thermal aspect. High losses in the electrical/mechanical components of the drive system can lead
to overheat and failure, which was experienced by pantograph designers during development of
various drive mechanisms. Moreover, tested pantographs consume remarkable amount of energy
when submitting to long duration tests. Therefore there is another aspect here and an opportunity to
apply energy efficient solutions of drive appears.
Since all of the requirements are taken into account in the design phase, the designer can follow one
of the paths associated with the selection of specific type of kinematic structure. Pantograph
mechanisms can be divided into two main categories:


Symmetrical
Asymmetrical
Both solutions have been schematically presented in Figure 74 and Figure 75 with a description of
main elements of the structures.
Figure 74. Symmetrical design of pantograph kinematics. (a-support frame, b-lower arm, cupper arm, d-slider, e-compensation mechanism, f-supporting insulator, g-insulator
support, h-lower arm shaft, i-bypass line, j-vehicle roof)
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Figure 75. Asymmetrical design of pantograph kinematics. (a-support frame, b-lower arm,
c-upper arm, d-slider, e-slider support, f-upper arm rod, g-lower arm rod, h-lower arm
shaft, i-supporting insulator, j- insulator support, k-vehicle roof, l-bypass line)
Both designs are compatible with existing overhead lines and can be used for bi-directional
movement. Most of modern rail vehicles use the asymmetrical pantograph type due to its similar
driving properties to symmetrical design with a significant mass reduction (elimination of almost half
of the mechanical structure). However the symmetrical structure is characterized by higher
transverse rigidity compared to asymmetrical.
The pantographs may have either a single or a double arm. Double-arm pantographs are usually
heavier due to doubled kinematic structure and as a result require more power to be moved to the
operational position. However they are more rigid and durable compared to the single arm design.
As previously mentioned, the specific type of drive system (responsible for up and down motion of
the slider) is not strictly regulated by the available standards. Therefore the choice of the concrete
solution is rather a result of individual specification of application. Drive systems of railway
pantographs are propelled with electrical or pneumatic energy. However the design of tram
pantograph requires the use of electric power due to lack of pneumatic system in modern trams.
Pneumatic drive is frequently used in train pantograph systems. As a conclusion, drive system of
tram pantograph should transform electrical power to the mechanical in any desired way in
compliance with regulations concerning the aspects already presented in this chapter.
5.4.2
System development cycle
At the initial phase of the system development cycle, some main assumptions have to be made
according to the standard references, patents and individual customer requirements. Standards
concerning the requirements for traction systems determine the main operational and geometrical
parameters which cannot be exceeded in order to introduce the system into the market and make it
applicable for all vehicle types. All of essential requirements connected with the proper operation of
pantograph must be fulfilled as detailed in previous section of this document.
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The evaluation of the complete system answers the question concerning the functionality,
performance, safety issues, etc. Such assessment can be an introduction to the further development
of system.
During the development process the aspects concerning kinematics, drive system and electrical
issues can be considered.
The development of pantograph system involves virtual prototyping: CAD, FEM, Multibody analysis.
The prototyping with respect to energetic aspect refers to drive mechanism of the system and can
be supported with 1D simulation tools. Here the ESTOMAD approach can be used for
power/energetic analysis of the drive which can be considered as separate subsystem of the
pantograph. This aspect has been developed during the ESTOMAD project and incorporated in
development process of drive mechanism of tram pantograph designed by ECEngineering. As a
result, the energetic simulations have been done for the first design of pantograph drive system and
helped to decide what components are crucial from the energetic point of view.
The modelling activity associated with virtual analysis of possible modifications helps to decide what
components must be considered in the new prototype. In such model based approach a new
AMESim model has been created based on the results of analysis of the previous design. In this phase
the previous concept is a good benchmark and can support the redesign decisions. The detailed
description of using numerical models created in AMESim and the simulation environment itself is
presented in D4.1 and D4.2. The choice of components can be also supported with general
engineering experience, manufacturers catalogues, etc.
As a result, the most suitable (energy efficient) components are to be used in the system which
leads to significantly better energetic performance. The modifications are proposed based on the
experience gained with previous solutions, standards and individual customers demands but in a big
way also based on results of energetic analysis in simulated environment. Additional measurement
campaign can be a verification of system energetic behaviour and can be used in a validation process
of numerical model. The development cycle of the pantograph system is presented in this section as
a flowchart (Figure 76), based on the experience and knowledge acquired during the development of
ECEngineering pantograph mechanism. Presented flowchart shows a path from initial design
assumptions to the fully functional and reliable solution ready to be introduced into the market.
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Figure 76. Development cycle of pantograph system.
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5.4.3 Design guidelines for energy efficiency
The development cycle can be observed in the modification process of pantograph system in frame
of the ESTOMAD project. The initial prototype of pantograph has been built in accordance with the
standards for rail vehicles. Introduced system is an asymmetrical single-arm pantograph, proper for
high speed railway vehicles. The guidelines for optimal and energy-conscious design can be
formulated based on the “what if” analysis of fully correlated and validated numerical model
representing the system. Such analysis consider the influence of individual modifications introduced
to the structure on the energetic effect, mainly on the efficiency of the complete system.
This particular analysis focused on the influence of three characteristic parameters for components
included in drive system of new pantograph prototype. Therefore three analysis of numerical model
have been performed:



Analysis of dc motor speed
Analysis of ball screw pitch diameter
Analysis of gear reduction ratio in spur gear reducer
First analysis considered electric motor speed influence on the electric power consumption. In the
figure below several characteristics have been compared. Every single run represents electric power
consumption as a function of time for each value of speed ranging from 2700 rpm to 2900 rpm.
Figure 77. Influence of motor speed on the electric power consumption.
In case of the second analysis the electric power characteristics have been prepared for different
pitch diameters of ball screw. The parameter ranged from 0.00408m to 0.00608m. The results are
presented in Figure 79.
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Figure 78. Influence of ball screw pitch diameter on the electric power consumption.
The last analysis considered differentiation of reduction ratio and the influence of such modification
on the power consumption. The characteristics represent consumed electrical power in function of
time for gear ratio from 7.5 to 12.5 (Figure 79).
Figure 79. Influence of reduction ratio on the electric power consumption.
Analysis of obtained characteristics leads to the conclusion that modification of individual
components can result in significant change in power consumption. While the speed of electric
motor has been increased the overall power consumption has grown. Greater value of ball screw
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pitch diameter also contributed to the higher consumption level. In case of gear ratio analysis, higher
value of reduction ratio caused the significant drop of electric power consumption.
Based on the presented modeling activity the exemplary energy consumption map has been
prepared. Such map is a function of motor speed and gear ratio as shown in Figure 80. The map can
be used for searching the optimum working point for the system maintaining the expected
parameters on the output of mechanism.
Figure 80. Energy consumption map as a function of motor speed and reduction ratio.
“What if” analysis performed with use of correlated and validated numerical model of pantograph
presented the relationship between parameters characterizing the components of drive mechanism
and the overall energetic effect (electric power consumption). The results provide the guidelines for
improvement of the pantograph in efficiency context. Such information can be also used for further
modifications of system according to the customer demands and different standards requirements
(application of pantograph for other type of vehicle). Taking into account all of presented
characteristics for every component the designer can choose the most optimal way to achieve the
assumed performance on the output of the mechanism while maintaining highest efficiency of the
system.
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5.4.4
Operation strategies
The operational conditions are strictly regulated and cannot be modified in any way. The mechanism
should assure raising/lowering time and height in certain range, which is to be accomplished by
optimal drive system. The way how to arrange drive system is rather open and not restricted by any
standard. So, here the designer needs to decide and follow best possible direction that will provide
reliable solution. Reliable means here:





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Compact dimensions
Lightweight
Energy saving
Resistant to the operational and environmental conditions
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6 Conclusions
Deliverable D3.3 proposes and formalizes methodologies, information and procedures to foster the
application of ESTOMAD outcomes in industrial contexts and to consider energy efficiency into the
design of production machinery.
Since energetic efficiency depends on how a machine is made (“design”) and how it is used
(“management”) and these two aspects cannot be fully separated, a designer must take into account
how the machine will be used. So different scenarios are analyzed, both related to
the eco optimal design of a new machine and related to the eco - optimization / energetic characterization of
existing machines.
In general, the following main aspects has been evaluated:




how to define energy-related performance objectives
what design choices impact on machine energy consumption
when and how to perform numerical energetic evaluations during the design process
(concept design, detailed design)
when and how to perform experimental energy evaluations during the product development
process (partial prototype, full prototype, in production)
Starting from an investigation of the industrial development process for the reference applications
(machine tools, weaving machines, badminton robot and pantograph), with attention to their key
energetic aspects, methodologies to use ESTOMAD outcomes in industrial development of
production machines have been proposed. Instead of proposing a single, monolithic, methodology, it
is assumed that a set of complementary or alternative solutions is preferable, to be optimally tuned
and selected on each specific application. The industrial cases considered in the project allow to
cover a considerably wide spectrum of different industrial sectors, that share similar technical
solutions. The definition of the operative production scenarios that have to be taken as reference
scenarios for energy analysis represent a critical step, which could be addressed in further activities.
Considering the machinery as a modular entity (in coherence with the MEEUP methodology (44)), the
main machine elements from the energetic point of view (structure, cooling unit, hydraulic unit,
motors, transmissions and control) have been taken into account, proposing the corresponding
design guidelines, basic design alternatives and possible model-based simulation approaches that can
be adopted.
General rules for energy assessment have been extracted and formalized in terms of normative,
existing approaches taken form literary, general performance indicator, reference framework for
energy efficiency design, Best Practices and software functionalities.
Deliverable D3.3 presents the corresponding results that are organized in the opposite order respect
to the adopted path of reasoning and it is coherent with the reports of EC Product Group Study
related to the Eco-design of Energy-related Products – ErP (8) and existing ISO norms. This document
can be seen as a brief manual containing a collection of aspects, methodologies and approaches that
can support energy efficiency design of industrial production machinery.
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8 Appendix A: usage efficiency maps
The purpose of this appendix is to give an overview of how efficiency maps can be used as possible
methodology for sustainable machine design and are used (by FMTC) in the framework of the
ESTOMAD project. Section 8.1 will present a short description of the concept of efficiency maps,
whereas section 8.2 describes how such a map can be obtained. The sections that follow afterwards
will treat possible applications of efficiency maps for sustainable machine design and illustrate these
applications with examples on cases from the ESTOMAD project.
8.1 Concept of efficiency maps
Efficiency maps - also called iso-efficiency contours - are (usually) 2D plots of a system’s or a
component’s efficiency as a function of 2 input/output variables of that system or component. For
hydraulic systems like pumps, these variables are typically pressure and flow. For a rotary electrical
motor, these variables are typically torque and speed (while force and speed are used for a linear
motor). Figure 81 shows an example of an efficiency map for a typical rotary induction motor.
Figure 81: Typical measured efficiency map for 7.5kW induction machine with a variable
speed drive. The blue lines indicate the nominal torque and power of this motor
The motor’s efficiency in each point in the map is defined as the ratio of produced mechanical power
over consumed electrical power, or:
𝜂𝑚𝑜𝑡 =
𝑃𝑚𝑒𝑐 𝑕
𝑇. 𝜔
=
𝑃𝑒𝑙𝑒𝑐
𝑃𝑒𝑙𝑒𝑐
Equation 4
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Figure 81 shows the efficiency map for the motor when it is in motor operation (first and third
quadrant of the speed-torque space), but a similar map can be obtained for generator operation
(second and fourth quadrant of the speed-torque space) as well. In that case:
𝜂𝑔𝑒𝑛 =
𝑃𝑒𝑙𝑒𝑐
𝑃𝑒𝑙𝑒𝑐
=
𝑃𝑚𝑒𝑐 𝑕
𝑇. 𝜔
Equation 5
More details on the concept of efficiency maps for rotary electrical motors can also be found in D2.1
and in (45),(46) and (47).
8.2 Obtaining efficiency maps
Efficiency maps can basically be acquired in 2 ways, being either through measurement or via
simulation.
8.2.1
Via measurements
In this case, input and output power of the system are measured for a dense grid of operating points
(i.e. combinations of torque and speed when the system is a rotary electrical motor). The remainder
of the map is then created by interpolation in that grid.
The measurements are usually performed on a dedicated test bench and the system is operated in
each operating point for a certain amount of time in order to allow a good measurement. In this
sense, acquiring the efficiency map for a rotary electrical motor by measurement is more
straightforward than for a linear motor, since the rotary motor can be operated at a constant speed
for an amount of time that is only bounded by maintenance and (possible) overheating, whereas for
a linear motor, it is also bounded by the physical stroke length.
The maps are measured in stationary conditions, but the differences with the efficiency in dynamic
conditions are expected to be small. More details on the measurement procedure for rotary
electrical motors can be found in (46).
8.2.2
Via simulation (e.g. AMESim)
Efficiency maps can also be determined through simulation, if a good (motor) model is available. The
procedure is comparable to the one via measurement: simulate the input and output power for a
dense grid of operating points (stationary) and create the remainder of the map by interpolation.
Figure 82 shows an efficiency map that was created through simulation in AMESim for a linear
electrical motor (motor and generator operation).
Remark: the white areas are either areas outside the operating range of the motor or areas where
the efficiency is below -40%. Negative efficiencies occur in generator operation and indicate that
electrical energy is being consumed instead of generated to compensate for the losses.
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Figure 82 : Efficiency map of a linear motor with drive, acquired via simulation, for motor
and generator operation
The procedure that was followed to obtain this efficiency map in AMESim proceeds through the
following steps:

Prepare the model:
o Build a model of the motor with drive.
o Connect power sensors to measure:
 the electrical power flow at the electrical power supply (grid or dc bus)
 the mechanical power flow at the motor axis
If required, extra sensors can be added for specific power flows and losses within the
system (e.g. between drive and motor).
o



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Connect a constant velocity source to the motor axis and a constant force set point
to the drive input (the map is measured for torque control mode). In this step it is
important to respect the causality constraints (which could mean that the model has
to be adapted if necessary).
Define an export setup:
o Inputs are: the force set point and the velocity source.
o Outputs are: the resulting force and speed, and the different power flows and power
losses.
Perform a batch run:
o Start a batch of simulations that sequentially simulates the model for different values
of the defined inputs. Each simulation lasts for 10s (simulation time, not CPU time)
until a stationary situation is reached.
o Log the outputs at the end of each simulation, together with the corresponding
inputs.
Post-process the data in the data files (interpolation) to obtain the efficiency map.
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One of the outcomes of the ESTOMAD project is that functionalities to automate this procedure have
been implemented in the new releases of AMESim.
8.3 Use of efficiency maps
Efficiency maps can be used in several ways as aids for sustainable machine design. This section
discusses some of these ways.
8.3.1
Check whether the system/component is used in an efficient “area”
The efficiency maps can be used to check whether the system/component is used in an efficient
operating “area” for a specific application. The “application” is here defined as a sequence of
operating points.
The check can be done qualitatively and/or quantitatively as we will illustrate for the efficiency map
of the linear motor in Figure 82. This is a map that was obtained by modeling the linear motor of the
FMTC badminton robot. The specific application is in this case a typical motion of the linear motor
during badminton playing. This application is therefore defined by the speed and force during this
motion, which can be determined by measurement on the real robot or by simulation of the model
of the linear motor. Figure 83 plots the speed and force obtained by simulation.
Figure 83: Speed [m/s] (left) and force [N] of the application (through simulation)
The speed and force of the application can be plotted as a 2D curve or trajectory on the efficiency
map, as shown in Figure 84. Visual inspection immediately indicates that the motor is used in a large
operating range, spanning both efficient and inefficient areas.
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Figure 84 : Plot of the application’s force-speed “trajectory” on the efficiency map of the
linear motor
In a situation like in Figure 84, where the motor is not constantly used in an efficient area, nor
constantly in an inefficient area, it is difficult to say whether the total (electrical) energy consumption
will be high or low. This also depends on the relative durations in the efficient and inefficient areas,
and the time component has been eliminated by going from Figure 83 to Figure 84.
Nevertheless, if one is interested in the quantitative energy consumption of the application, this can
be calculated based on the data of the application and the data behind the efficiency map: the
application is defined as a sequence of operating points (force and speed) and for each of these
points, the instantaneous (electrical) power consumption can be calculated using the efficiency map;
by integration (over time), the energy consumption can be found. More details on this procedure can
be found in (47). Figure 85 plots the instantaneous electrical power consumption and the evolution
of the energy consumption during this application, as calculated with the efficiency map.
Figure 85 : Electrical power consumption (left) and energy consumption (right) of the
application (i.e. a linear motion), calculated by means of the efficiency map.
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8.3.2
Different loss components (simulation)
As mentioned earlier, in order to determine the efficiency map of a system/component via
simulation, a model of that system/component is needed that is capable of predicting the energy
flows. This implies that the different loss components or loss phenomena8 are also somehow
modelled and can be extracted from the simulation.
Figure 86 shows the total loss power map of the linear motor for which the efficiency map was
already shown in Figure 82. The concept of a loss power map is the same as for an efficiency map,
except that now the loss power [W] is plotted as a function of force and speed.
Figure 86 : Total loss power [W] map for the linear motor, generated from simulation.
In the employed model of the linear motor, 3 different loss components are modelled:



Inverter losses
Copper losses in the motor
Friction losses (in the linear guideways)
The different loss maps for each loss component are shown in Figure 87. The sum of these 3 maps is
again the map of Figure 86.
8
The term “loss component” is used to describe the different loss phenomena (e.g copper losses) and is not to
be confused with the “physical component” of which the efficiency map has been determined (e.g. a motor).
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Figure 87 : Different loss component maps: inverter losses (upper left), copper losses
(upper right) and friction losses.
From Figure 87 it can be noticed that the friction losses are clearly speed depended, whereas the
copper losses are mainly depending on the torque.
8.3.3
Import efficiency maps for motor component modeling
A third application of efficiency maps is to use them (i.e. their data table) as an alternative (virtual)
motor component in the energetic model of a complete machine. In this sense, they can be used to
replace detailed analytical motor models based on equations from electrical motor theory. Two main
motivations to use these alternative models are:


The equation based analytical models require a lot of parameters, many of which cannot
always be found in the motor datasheets and have to be measured by the model/machine
builder him/herself.
Simulation of a complete machine model that contains an equation based motor component
is expected to take more CPU time than the same machine model with a motor component
based on a data table (i.e. the efficiency map).
We will illustrate this for the hitting mechanism of FMTC’s badminton robot. This mechanism
contains a rotary motor and more specifically a brushless DC motor (BLDC). The hitting mechanism is
schematically displayed in Figure 88 and the corresponding model of the complete system, using an
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analytical model for the BLDC motor component, has been extensively discussed in deliverable D2.3.
Figure 89 shows the implementation of the mechanism’s model in AMESim9.
Figure 88: Schematic overview of the hitting mechanism (model) of the badminton robot.
9
The AMESim libraries contain a BLDC component, based on equations from electrical motor theory, that can
be used here.
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Figure 89 : AMESim model of the hitting mechanism, following the schematic overview of
Figure 88 and using an analytical model for the BLDC motor component.
The idea behind the usage of efficiency maps, is that the analytical model of the motor system
(consisting of drive, motor and gearbox) is replaced by a model that is based on those efficiency
maps.
For the BLDC motor system of this hitting mechanism, two efficiency maps were available:

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A map that was measured on the motor itself, as discussed in section 8.2.1
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
A map that was generated from the analytical model (Figure 89), following the procedure
discussed in section 8.2.2. The parameters that were necessary for this analytical model were
either derived from the datasheets or measured on the motor (this is also discussed in
deliverables 2.3 and 5.2)
The measured efficiency map is displayed in Figure 90, whereas the generated efficiency map is
displayed in Figure 91. Both figures show the first and second quadrant. The torque and speed on the
X and Y-axis are the torque and speed at the gearbox output.
Figure 90 : Measured efficiency map for the BLDC motor of the badminton robot’s hitting
mechanism (motor operation on top, generator operation at the bottom).
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Figure 91 : Generated efficiency map for the BLDC motor of the badminton robot’s hitting
mechanism (motor and generator operation in one figure)
Some remarks have to be added concerning these maps:

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Figure 92 shows the difference between both efficiency maps, for the operating area where
measurement data were available. As can be seen, there is a difference, mainly for lower
velocities, and even more outspoken in generator operation (the white area for low
generator torque indicates a difference that is higher than the scale of the figure). For higher
velocities, the difference is smaller. Probable explanations for the difference are:
1. no iron losses were present in the model
2. in reality, the motor system starts consuming a (small) amount of energy if it is just
switched on, which is not the case in simulation
3. the coulomb friction is not determined for velocity = 0
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Figure 92 : Difference between measured and simulated efficiency (in the white areas, no
measurement data was available, or the absolute difference was higher than 1)
As mentioned higher, the efficiency maps can be used to replace the combined analytical model of
the drive, motor and gearbox. This means that the schematic overview of Figure 88 changes to the
overview of Figure 93.
Figure 93 : Schematic overview of the hitting mechanism model, in case an efficiency map
is used.
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Figure 94 : AMESim’s tabulated motor component
AMESim offers a “tabulated motor” component that can be used for this purpose (Figure 94).
The functionality of this component is as follows:





The component has 3 data tables:
1. a table with power losses as a function of torque and speed10 (this is actually the
efficiency map reworked);
2. a table with the maximum allowed torque as a function of speed;
3. a table with the minimum allowed torque as a function of speed.
The torque set point is sent to port 4.
The output torque at port 3 is calculated by limiting (if necessary) the set point torque to the
min. and max. torque for the given relative speed and by filtering it with a time constant
(changeable parameter). Remark: the given relative speed is the speed difference between
ports 3 and 6 (port 6 is used to connect the motor component to the environment).
The specific power loss is found by interpolation in the data table for the output torque at
port 3 and the given relative speed. This loss is added to the mechanical power (i.e. the
product of torque and relative speed) to obtain the electrical power. The current at ports 1
and 2 (which are each other’s reverse) are then calculated by dividing the electrical power by
the voltage difference (between ports 1 and 2).
Port 5 can be used to take temperature influences into account.
To evaluate the usage of efficiency maps for energetic modeling of systems, three models of the
hitting mechanism:
 model 1: with analytical motor model (cf. Figure 88)
 model 2: with generated efficiency map (cf. Figure 93)
 model 3: with measured efficiency map (cf. Figure 93)
were simulated for the same position trajectory. This position trajectory is generated by feeding the
trajectory generator (Figure 88 and Figure 93) with a sequence of desired end positions and end
times. The trajectory generator (which is the same for the 3 models) generates than a position
trajectory that should be followed. Figure 95 shows the desired trajectory that was used in this
example, which represents a soft hitting action.
10
Extensions are possible to make the losses also function of temperature and voltage.
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Figure 95 : Desired position trajectory (rotational position of gearbox output axis)
The resulting effectively simulated position trajectories for the three models are plotted in Figure 96.
As can be seen there, the differences between the different simulated trajectories and the desired
trajectory are very small.
Figure 96: Desired position vs. simulated position for each of the three models
The velocity is plotted for the three models in Figure 97. A plot of the torque-velocity curve on the
measured efficiency map leads to Figure 98 (cf. section 8.3.1).
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Figure 97: Velocity for each of the three models
Figure 98: Torque speed curve on the measured efficiency map
For all the three models, also the energetic analysis has been performed with this position trajectory.
Figure 99 plots the instantaneous electrical power consumption during the motion, whereas Figure
100 shows the accumulated electrical energy consumption.
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Figure 99: Instantaneous electrical power consumption for each of the three models
Figure 100: Accumulated electrical energy consumption for each of the three models
The difference between the power and energy consumption for models 1 and 2 is small. This is due
to the fact that the efficiency map for model 2 was directly generated from the analytical model in
model 1. The small remaining difference is to be explained by the difference in dynamic behaviour
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between models 1 and 2, leading to a (very) small difference of torque and speed as explained
before. The difference is in any case small enough to conclude that model 2 presents a valid
alternative for model 1 (at least for this mechanism with position feedback loop).
The difference between the power and energy consumption for models 1 and 2 on the one hand and
model 3 on the other hand is larger. This is due to the difference in efficiency map (and loss map). As
could be seen in Figure 92, this difference was higher for lower speeds in general and for lower
torques in generator operation. By comparing Figure 97 and Figure 99, one can see that (1) the
lowest difference in power consumption is indeed located at higher speed, and that (2) the system
spends quite some time in the area where the efficiency difference is high, specifically in the low
torque generator area (check Figure 99). This results in an error of about 40% on the total energy
consumption.
At first sight, this high error seems to be in contradiction with the findings in deliverable D2.3
(section on rotary electro motors). There it was illustrated that for the real hitting trajectory
(different from the trajectory of figure 16 here), there is hardly a difference between the simulated
power/energy consumption and the measured power consumption. The simulation was performed
using the same analytical model as model 1 here while the measurement was done on the same
physical motor as on which the efficiency map used for model 3 was measured. Therefore, an error
of 40% was not expected here. The explanation for this error is – as mentioned earlier – due to the
difference between the simulated and measured efficiency maps. The real hitting trajectory (used in
D2.3) produces higher torques (in both motor and generator operation) than the trajectory that has
been used here (Figure 95) and passes therefore through areas of the efficiency map where the
difference is smaller, leading to a better match for that trajectory between measurement and
simulation
The difference between both efficiency maps can be explained by the fact that the analytical model is
less “rich” in areas of low power, which is mainly due to:


the fact that some parameters of the analytical model have been fine-tuned using the real
hitting trajectory, which does not pass through areas of low power consumption
and the fact that a real motor system starts consuming/loosing (a small amount of) energy
when it is switched on, even when it is not moving; this standby loss can be measured but is
not included in the analytical model.
This example shows that:



It is useful to make a visual inspection of the motor trajectory plotted on the efficiency map
in order to get more insight.
If trajectories are used to tune model parameters, these trajectories should be sufficiently
diverse (“rich”).
Extra (standby) loss components might have to be included in the analytical model.
Summarized: Care has to be taken when determining energy consumption using models; although
the model can be accurate in a broad torque-speed range, significant deviations of the estimated
energy consumption can occur for trajectories that spend a lot of time in an area where the model is
not accurate.
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A last remark concerns the CPU times needed to complete each of the three simulations. These times
are (on the same machine):
 model 1: 76 seconds
 model 2: 56 seconds
 model 3: 41 seconds
This shows that model simulation with the efficiency maps can decrease the required CPU time
considerably (in this case more than 25%).
This kind of analysis can be used to evaluate the efficiency of different potential motor(-drive
combination)s for specific load profiles in a model-based way. By performing the analysis using
different measured efficiency maps (for the different motor combinations), the required energy for
each actuation alternative for that specific application can be calculated. The solution requiring the
minimal energy can then easily be selected.
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