Powertrain Series Editor: Helmut List Michael Paulweber Klaus Lebert Powertrain Instrumentation and Test Systems Development – Hybridization – Electrification Powertrain Series editor Helmut List AVL List GmbH, Graz, Austria Scientific Advisory Board R Bastien C Beidl H Eichlseder H Kohler J Li R Reitz More information about this series at http://www.springer.com/series/7569 Michael Paulweber • Klaus Lebert Powertrain Instrumentation and Test Systems Development – Hybridization – Electrification Michael Paulweber AVL List GmbH Graz Austria Klaus Lebert University of Applied Sciences Kiel Germany ISSN 1613-6349 Powertrain ISBN 978-3-319-32133-2 ISBN 978-3-319-32135-6 DOI 10.1007/978-3-319-32135-6 (eBook) Library of Congress Control Number: 2016943115 Translation from the German language edition: Mess- und Pr€ ufstandstechnik Antriebsstrangentwicklung • Hybridisierung • Elektrifizierung by Michael Paulweber and Klaus Lebert, # Springer Fachmedien Wiesbaden GmbH 2014 All Rights Reserved # Springer International Publishing Switzerland 2016 This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed The use of general descriptive names, registered names, trademarks, service marks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG Switzerland Foreword to the Series Powertrain For decades, the series of volumes entitled “Die Verbrennungskraftmaschine” (“The Internal Combustion Engine”), edited by Hans List, served as an essential reference for engineers in their practical work and for students at universities Given the pace of technology, I decided, in 2002, to develop a new concept for the series and publish it under the title “Powertrain.” The new title conveyed the idea that internal combustion engines should increasingly be seen as components of drive systems From that time on, the intent of the series was given further thought, and it was finally decided this year to continue the series under the same title (“Powertrain”), however, with a new layout and with a newly appointed scientific board As before, the main intent of the series is still to identify and discuss all interactions between the various individual components of an automotive powertrain The new idea is to increasingly promote the English versions alongside the German editions Starting with the fundamentals that include a description of the required background information, the purpose of the series is also to address the new components of future drive systems and the way they impact each other in a system-level analysis In addition to the technical contents, the series also deals with the tools, methods, and processes needed for component development It examines the conditions in different economic areas and discusses the influences these have on the concepts The series of volumes is intended not only for students at universities or advanced technical colleges but also as a reference book for those working in the industry It invites readers wishing to acquire the necessary in-depth knowledge to draw from the authors’ wealth of experience Special thanks go to the members of the Scientific Board for their assistance in the organization of this very wide-ranging topic and in the choice of authors The members of the Scientific Board are: Re´mi Bastien, Vice President, Renault Christian Beidl, Professor, Technical University Darmstadt Helmut Eichlseder, Professor, Technical University Graz v vi Foreword to the Series Powertrain Herbert Kohler, Vice President, Daimler Jun Li, Vice President, FAW Rolf D Reitz, Professor, University of Wisconsin-Madison I would like to take this opportunity to thank all the authors who expressed their willingness to share their knowledge in this series of books and contributed their time and effort I also wish to thank Springer-Verlag AVL List GmbH, Graz, Austria Helmut List Preface In order to master the great challenges society faces today, the automotive industry, too, is required to contribute its part CO2 and emission reduction efforts, advancements toward accident-free mobility, especially also for the aging population, or the need to adapt vehicles to local requirements in a global economy are placing totally new demands on the drive system development process On the one hand, software is becoming more and more dominant; on the other hand, powertrain architecture is no longer the constant it used to be (internal combustion engine—transmission—shafts—wheels) As a result, there is now also a great need for simulation, a technology that has meanwhile become a firmly established part of engineering work at test beds The greatest challenge in the area of instrumentation and test bed engineering is to manage the tremendously increased complexity Failing to so will result in development costs (and therefore testing costs) skyrocketing even further This book is an attempt to provide an overview of the ways in which these trends are impacting the instrumentation and test systems needed to develop advanced powertrains Due to the breadth of topics covered, the book required the assistance of many experts The authors would like to express their sincere gratitude to all specialists for their valuable contributions Our special thanks go to Mrs Hermine Pirker Without her tireless work and organizational support, this book would never have been completed We also owe a big thank you to Sarah Toăfferl for the linguistic revision of the manuscript and the preparation of the illustrations as well as Anita Hoffmann and Elisabeth Stossier for the translations into English This book is intended for powertrain (component) development engineers, test bed planners, test bed operators and beginners and deals with the increasingly complex test systems for powertrain components and systems It seeks to convey an overview of the vii viii Preface diverse types of test beds for all components of an advanced powertrain Additionally, the book focuses on specific topics such as instrumentation, control, simulation, hardware-inthe-loop, automation or test facility management Graz Kiel September 2014 Michael Paulweber Klaus Lebert Contents Introduction 1.1 Drivers of Automotive Development 1.2 Demands on Instrumentation and Test Systems 1.2.1 Development Methodology in Powertrain Engineering 1.2.2 Impact of Development Methodology 1.2.3 Networked Development Environments 1.3 How the Book Is Organized References 1 4 Types of Test Beds 2.1 Combustion Engine Test Beds 2.1.1 Scope of Application 2.1.2 Setup of a Test Bed for Internal Combustion Engines 2.1.3 Steady-State Engine Test Beds 2.1.4 Non-Steady-State Test Beds 2.1.5 Research Test Beds 2.1.6 Special-Purpose Engine Test Beds 2.2 Component Test Beds 2.2.1 Test Beds for Components of Internal Combustion Engines 2.2.2 Test Beds for Hot Gas Components 2.2.3 Test Beds for Transmission Components 2.2.4 Starter Motor Test Bed 2.2.5 Electric Motor Test Bed 2.2.6 Inverter Test Bed 2.2.7 Battery Test Bed 2.2.8 Fuel Cell Test Bed 2.3 Control Unit Test Beds (HiL) 2.3.1 Introduction 2.3.2 Setup 2.3.3 Control Unit Component Testing 2.3.4 Control Unit Integration Testing 11 11 11 14 15 17 20 23 25 26 31 41 42 44 47 50 51 54 54 55 58 61 ix 5.4 Data and Information Management 403 The functionalities in the calibration data management system are very extensive and start with process support for the involved engineers Each team can work from different locations on one or multiple projects Automatically generated notifications and warnings sent by e-mail facilitate the workflow Clearly defined responsibilities, which are saved in the system, enable global operation, ruling out any redundancies and conflicts between the calibration engineers’ different work packages well in advance One of the most essential features of calibration databases is the ability to merge calibration results, which different people have created with different kinds of calibration systems, to create consistent overall calibration data snapshots These data snapshots are usually created every or weeks, and the system makes them available to all of the involved engineers As very old calibration data snapshots are rarely needed for tests, this ensures a high level of data security and data consistency Thanks to reports and clearly defined versioning processes, calibration databases offer the possibility to trace every work step Project managers, in particular, benefit a great deal from reports because they can check the project progress any time they like and immediately initiate corrective measures As there are several systems on the market that allow modifying, creating and combining calibration data, the ASAM consortium [1] has already standardized a large number of the data formats used in this environment (see also Sect 4.1.2) The most common data formats supported by almost all systems in this environment are listed below: A2L (ASAM MCD-2 MC, see also Sect 4.1.2): The data format A2L is the description file for measuring and calibration quantities in an engine control unit (ECU) Within the A2L file, all characteristic quantities in the ECU are defined and described which external systems are able to access The A2L file additionally stores the address of the individual parameters in the control unit microcontroller’s hex dump HEX/S19: These are the most widespread formats used for ECU memory maps The hex file contains several memory segments The most important ones are: – the operating system – the control software that maps the engine control logic, – and the calibration section containing the characteristic quantities to be calibrated (calibration data) DCM: This is one of the most common exchange formats for calibration data Its contents are used to distribute changes in the calibration parameters from the test bed or from the vehicle Essentially, this format is a human-readable text format CDF (ASAM CDF): This is one of the most common exchange formats for calibration data Its contents are used to distribute changes in the calibration parameters from the test bed or from the vehicle This file format is an exchange format based on XML With the help of these formats it is possible to build a calibration database and trace every change made by the involved calibration engineers Without this standardization, it 404 Software Perspective: The Test Facility would hardly be possible to ensure that all calibration parameters are correctly stored and that they behave identically in real-world vehicle operation A suitable interface to other calibration programs, the eCDM interface enables operation across calibration applications The eCDM interface offers ways to exchange description files or calibration parameters with other calibration programs and is already available from a number of manufacturers 5.4.3 Model Management As shown in the previous sections, the significance of test-bed simulation in powertrain development is continuously on the rise Accordingly, reliable simulation models are crucial for efficient and dependable test execution These consist of two parts: simulation model structure description, and parameter values for each of the parts of the simulation structure (simulation model parameters) Being split into two parts makes it possible to manage multiple variants of a unit-undertest component with the same simulation structure This structure is very similar to that described in the previous section: The parameter values in this section correspond to the description file in A2L format mentioned in the previous section; the simulation model structure description in this section corresponds to the HEX/S19 file in the other section Likewise, the workflow needed to manage simulation data requires a similar approach to that for calibration states in control unit software, as described in the previous section We therefore distinguish between the actual model and the parameters This facilitates comparability and the full traceability of changes The model itself can be stored as an attachment The model parameters are typically subject to many changes (when creating different variants of one and the same component described by the simulation model), meaning that lower data volumes are generated if the parameters are managed separately from the simulation model files For test facility operation, constant access to information about the reliability and relevance of the simulation models and parameters is extremely important State attributes in the database for each of the model components can provide this capability Meaningful states are: In progress Completed but not yet checked Model checked but not yet calibrated Model calibrated but not yet checked Model calibrated and checked 5.4 Data and Information Management 405 The individual model creation steps are stored in a workflow To begin with, experts at one of the component development departments develop a model (state “In progress”) Once the model is completed (state “Completed but not yet checked”) it has to be checked and released through a quality assurance system (state “Model checked but not yet calibrated”) The model is now capable of describing many variations of a component, provided that the relevant parameters have been defined Defining simulation model parameters for a specific component instance usually requires measurements on a component test bed Often, time-consuming identification procedures are needed Once the simulation parameters are found, they are stored in the model database under the status “Model calibrated but not yet checked” A final quality assurance step, in which the simulation results are compared with measuring results from corresponding component test beds, leads to the status “Model calibrated and checked” Only after ensuring that simulation results and measured values from corresponding component test beds are consistent can a model be used for XiL or HiL test-bed testing Simulation models or simulation parameters from different simulation domains are kept in a central database which we call a model management database This is where simulation engineers can store their changes or download a new model version A structure customizable by the user with appropriate access rights and defined responsibilities ensures good traceability in the model management database 5.4.4 Name Management in the Test Facility All measured, calculated or simulated quantities are normally addressable by their names, which are also used in parameterizations, display screens or evaluations To enable reuse of these parameterizations or evaluations in international organizations at different test beds or in different tests, standardized names for quantities are required These names are often referred to as “normnames” The total of these names is called a namespace To operate test facilities efficiently, several namespaces are needed Namespace for equipment on the test bed Namespace for the unit under test Namespace for standardized tests Global namespace for an entire organization During runtime, a measurement, simulation or calculation quantity gets assigned one name from each of the namespaces This process is also referred to as name mapping One and the same test, which uses the names from the test facility’s namespace for standardized tests in its test scripts, can be executed in various different test bed configurations An example is a control unit function test which is executable on an MiL, SiL, HiL and powertrain test bed and requires the engine speed as measuring 406 Software Perspective: The Test Facility quantity In German-speaking countries, this quantity in the namespace “unit under test on the test bed” is assigned the designation “Drehzahl” from the namespace of the German organization On test beds in the U.K the name “Engine Revolutions” was introduced To run a test in a global test facility, both national names are mapped to the designation “Engine Speed” in the namespace “Test” during the test preparation phase Separation of the “Test” namespace from the (local) test facility namespaces also allows easy reuse of unit-under-test setups on different test beds In many existing test bed automation systems, namespaces are not separated adequately Instead, one namespace is used for multiple types of parameter units such as test runs and test bed configurations This leads to a loss of flexibility and extra work when changes are needed because name changes in test runs often have to be carried out synchronously in multiple test bed description parameter files too The lower complexity during commissioning due to the lower number of names to be defined leads to higher maintenance requirements during test facility operation The optimum can only be found in specific applications, for example it depends on whether or not test descriptions are to be executed on differing test bed configurations The quantities typically have other additional attributes which are also managed in a central database together with the names Examples of such attributes are: SI unit Quantity value range Alternative display names that can be selected in the test bed visualization system Additional display units (are often taken from a database that conforms to ASAM ODS) Quantity description (e.g by measuring site and physical quantity) The namespaces are usually managed in a database The ASAM ODS standard is often used for this purpose (see Sect 4.1.2) 5.4.5 Result Data Warehouse Apart from ASAM ODS databases, further de-facto standard formats for storing data from particular application areas have become established over the years Unfortunately, this trend continues, so we have to assume that the number of data formats will not stop increasing any time soon For these specific data formats, appropriate evaluation tools are on the market Test engineers wishing to evaluate different data are therefore often forced to use several different tools with differing operating patterns However, users would obviously like to be able to look for relevant information in all of the stored data using a uniform post-processing concept The solution to this issue is to consistently separate navigation from technical data evaluation By doing so, it becomes possible to offer data navigation tools that operate across evaluation systems and formats Such tools allow searching the data without having 5.5 Data Management in Distributed Test Facilities 407 Data house Data evaluation tools INDICom INCA Canape CAMEO Fox Concerto Matlab Excel DIAdem UNIPlot AVL navigator ASAM ODS data base External data: • INCA recorder • Canape recorder • Index data •… • Transfer process • Data linker • Access methods • Backup/archiving Central data server (AVL Santorin) Fig 5.18 Example of navigation integration to convert them into a standard format first Test-bed measured data are often saved to a database directly in ASAM ODS format Other test results remain in their original format and are linked to the test bed data in the database During import of the data (whether in ASAM ODS or a special file format), the data warehouse system extracts meta-data from the measured data files and creates searchable attributes in the database together with links to the bulk of the measured data in their native format Figure 5.18 shows an example of such a concept which is called a data warehouse When searching for results, the extracted meta information is used for navigation This allows quick search and navigation procedures Special data access drivers convert the bulk data from their native format to the data warehouse exchange format Evaluation tools can read this exchange format and therefore have access to all data formats New data formats are easily integrated into this framework and are then immediately available for navigation to all users A further advantage of this method is that the navigation system is standardized across all kinds of application areas All users use the same system to find their data and always work with the same software tool 5.5 Data Management in Distributed Test Facilities Globally positioned OEMs operate test beds at different locations, which they merge to shared virtual test facilities Development tasks at one location can so be tested on test beds at another location to obtain a global test-bed utilization optimum Keep in mind, though, that it is still necessary to leave the responsibilities with regard to setup, 408 Software Perspective: The Test Facility SANTORIN Enterprise HOST Systems (Level 2) Database (ASAM) Test preparation local data validation SANTORIN HOST Systems (Level 1) Database (ASAM) Database (ASAM) Test bed system (AVL or third party) Fig 5.19 Measured data management levels for test facilities modification and maintenance with the relevant location itself in order to ensure efficient and effective test bed work One of the key factors in a virtual test facility distributed across different locations is a measured data management system which provides access to a central evaluation database for all systems and users as described in the previous chapter One approach that has proved successful in practice is a three-level evaluation structure: Level 1: Local test bed level Level 2: Local server level (HOST Systems Level 1) Level 3: Global server level (HOST Systems Level 2)—data warehouse Level and are implemented for every location to provide each of them with a secure central evaluation platform Level is typically provided globally and, in most cases, represents a central measuring data server for all development locations (Fig 5.19) The measured data on the lower level are replicated to the next level During this process, the measured data might also be filtered or restructured After measurement completion, the Level test bed systems send their data to the local HOST system (Level 1) On this local level, the data are typically filtered and processed After their release, they are then forwarded to Level 3, i.e HOST System Level With the data being available across systems and locations, development engineers draw their data from HOST System Level References 409 References ASAM e.V., ASAM Standards, 2013 [Online] http://www.asam.net/nc/home/asam-standards html Accessed 28 Apr 2014 AVL List GmbH, AVL Testfactory Management Suite™—TFMS Test Facility Management, 2014 [Online] https://www.avl.com/avl-testfactory-management-suite-tfms Accessed May 2014 J Gantz, D Reinsel, Extracting value from chaos IDC iView Study, 2011 Index A Absolute pressure measurement, 162 Absolute time, 289 Accelerated-aging approach, 84 Acceleration, 163 resistance, 321 Accelerator pedal actuation, 69 Acquisition time, 289 Adjustment, 253 Air conditioning, 100 Air flow measurement, 178 Air resistance, 320 Air-mass flow, 178 Altitude simulation, 151 AMA cycle, 84 Anti-aliasing, 292 Apparent flow, 176 Application, 63 Arbitration mechanism, 259 Area ratio, 224 ASAM AE standard, 281 ASAM CAT standard, 281 ASAM COMMON standard, 286 ASAM MCD, 61 ASAM ODS, 356, 400 ASAM-HiL, 63 ASAM-XiL, 63 Association for Standardization of Automation and Measuring Systems (ASAM), 60, 279 Asynchronous machine, 142 Automation layer, Automotive open system architecture (AUTOSAR), 62 Availability, 291 B Base plate, 115 Base plate type, 117 Battery emulator, 77, 145 Battery test bed, 50 Break in frequency, 295 Breakout box, 55 Bus communication, 58 Bus system, 257 C Calibration, 4, 16, 55, 63–65, 69, 165–168, 178, 181, 220, 251, 253, 389, 394, 396, 397, 401 of control units, 55 database, 402 model-based, 63 process, 401 standards, 251 status, 251 tools, 15 Calibration data, management, 387, 397, 401 CANLOAD, 107 CANOpen, 265 Centering system, 77 # Springer International Publishing Switzerland 2016 M Paulweber, K Lebert, Powertrain Instrumentation and Test Systems, Powertrain, DOI 10.1007/978-3-319-32135-6 411 412 Challenge, societal, Charge air conditioning, 156 Charge motion, 220 Charging hybrid, 33 mechanical, 32 Chassis dynamometer, 75 Chemiluminescence detector (CLD), 194 Classification, 368 Classification matrix, 368 Climate test bed, 23 Clock master, 289 Clock master tick (CMT), 289 Closed loop, 58 control system, 297 Clutch actuator, 69 CNG See Compressed natural gas (CNG) Controller Area Network (CAN), 70, 259 message, 261 Co-simulation, 339 CO2 tracer technique, 103 Combustion diagnostics, 15 Combustion engine observer, 312 Commercial vehicle, 109 Component test, 41 Component test bed, 25 Compressed natural gas (CNG), 34 Compressor circuit, 37 Compressor map, 40 Condensation nucleus counter, 211 Conditioning, 33 equipment, 69 system, 37, 149 Conformity of production (COP), 96, 186 Connection shaft, 116 Consistency, Constant volume sampler (CVS), 102, 108, 188 Continuously variable transmission (CVT), 41 Control on chassis dyno test bed, 317 concept, 307 modal, 315 Control systems on internal combustion engine test bed, 308 Control unit calibration, 15, 346 Control unit component testing, 58 Control unit test beds, 54, 55 Controlled system, 297 Controller, 296 Index Coolant conditioning, 151 Coolant pressure, 152 COP See Conformity of production (COP) Coriolis sensor, 174 Coupling capacitive, 255 galvanic, 254 inductive, 255 mechanism, 254 of real-time systems, 340 Cradle mounting, 165 Current, 160 CVS See Constant volume sampler (CVS) CVT See Continuously variable transmission (CVT) Cycle optimization, 353 D Data acquisition, 286 angle-synchronized, 288 event-driven, 288 time-synchronized, 287 Data age, 290 Data analysis, 359 Data browser, 355 Data comparison, 361 Data consistency, 291 Data efficiency, 259 Data integration across test facility, Data management, 64, 401 Data mining, 357 Data preprocessing, 291 Data processing across test facility, Data recorder, 306 Data recording, 304 Data storage across test facility, Data synchronization, 363 Data transmission protocol, 258 Data visualization, 358 Data volume, large, 360 Dc shunt-wound machine, 141 Decoupling system, 117 Density of air, 32 Density measurement, 176 Design of experiments (DoE), 89, 346 Development environment, networked, 7–8 Development methodology, Development task, 41 Index Diagnostic coverage (DC), 373 Diagnostic function, 59 Diagnostics, 56 Differential pressure measurement, 162 Diffusion charging sensor, 220 Dilution tunnel, 208 DIN EN ISO 9000, 395 Direct current (DC) machine, 139 Disturbance feedforward control, 316 DoE See Design of experiments (DoE) Doppler global velocimetry (DGV), 235 Driver, virtual, 331 Driving cycle, 101 Driving robot, 144 Driving simulator, 144 Dual-mass oscillator, 308, 310 Dynamic, 130 Dynamometer, 13 active, 134 hydraulic, 133, 143 passive, 130 E Early damage detection, 69 Eddy current dynamometer, 131, 142 Electric-motor test bed, 44 Electrochemical impedance spectroscopy (EIS), 244 Electromagnetic compatibility (EMC), 77, 253 Emergency stop, 377 Emission certification, 80, 324 test bed, 12 Emission legislation, 96, 186 Emission limits, 98 Emission measurement equipment, 15 Emission test bed, 96 Emission testing, 101, 109 EN ISO 12100, 371 EN ISO 13849-1, 373 End of line, 12 Endurance testing, 40, 77 Energy management, 327 Engine mounting system, 116, 120 Engine pallet, 122 Engine simulation, 94 Engine test bed, 124 Engine torque estimator, 312 EtherCat, 265 413 Ethernet, 271 Ethernet Powerlink, 267 Evaporative emission, 106 Event evaluation, 366 Exhaust back pressure valve, 149 Exhaust emission analyzing system, 103, 201 Exhaust gas dilution system, 203 Exhaust gas opacity, 209 Exhaust gas power, 35 Exhaust gas vacuum system, 150 Exhaust turbocharging, 33 F Federal Test Procedure (FTP), 78, 79 Fieldbus system, 257 Filter, digital, 295 Filtering, 295 FireWire, 272 Fixed set-point control, 298 Flame ionization detector (FID), 192 FlexRay, 268 Flow coefficient, 224, 237 Flow field, 222 Flow number, 225 Flow processes, transient, 239 Flow sensor, 177 Flow test bed, 12, 22, 221 FMI standard, 280 Follow-up control, 298, 316 Force, 161 Force control, 317 Formula, 294 Formula engine, 365 Foundation pit, 117 Four-wheel test bed, 74 Fourier transform infrared spectrometer (FTIR), 103, 195 Frequency converter, 136 Frontloading, FTP-75 (Federal Test Procedure), 79 Fuel balance, 174 Fuel cell electrical equivalent circuit, 246 system, 243 test bed, 51 Fuel consumption measurement, 171 Fuel measurement uncertainty, 175 FUELLOAD, 107 414 Full-flow dilution, 108, 188, 189, 206, 210 Function check, 304 Function test(ing), 38, 331 Functional mock-up interface (FMI), 338, 340 G Gain error, 250 Gear-shifting system, 69 General purpose interface bus (GPIB), 273 Gradient resistance, 321 H Hall sensor, 160 Hardware layer, Hardware-in-the-loop (HiL), 48, 54, 63 High-altitude test bed, 25 High-dynamic, 130 HiL See Hardware-in-the-loop (HiL) Hooke’s law, 161 Hot-gas component, 31 Hot-wire measurement, 178 Hybrid powertrain test bed, 75 Hybrid vehicle, Hydraulic dynamometer, 132, 143 Hydrostatic dynamometer, 141 Hysteresis, 250 I IEC 62061, 371 IEEE1394, 273 Ignition timing collection, 182 Ignition timing measurement, 181 Ignition voltage, 183 iLink, 272 Impedance spectroscopy, 242 In-line design, 77 Incremental encoder, 170 Indicating data, 360 Indicating measurement technology, 241 Indicating system, 183 Industrial Ethernet, 265 Intake air conditioning, 149 Integration platform, 336 Interface standard, 280 Internal combustion engine component, 26 Internal combustion engine test bed, 11 Interpolation error, 252 Index Inverter, 48 Inverter test bed, 47 ISO 26262, 49 ISO TS 16949, 397 Isolated base plate, 117 K Kalman filter, 312 Key performance indicators (KPI), 387, 389 L Lambda probe, 183–184 Lambert law, 217 Laser diode spectroscopy (LDS), 198 Laser Doppler anemometry, 230–232 Laser induced incandescence (LII), 219 Latency period, 290 Layout, 369–370 Light scattering sensor, 218 Limit monitoring, 295–296 LIN bus, 268 Linearity error, 250 Linearization, 292 Load cell, 161, 165 Load system, mechanical, 129–143 Loads, electric, 55 Local models, 351 Low-pass filter, 292 M Machinery directive 2006/42/EC, 391 Main control, 316 Maneuver, Maneuver-based testing, 20, 69 Map visualization, 360 Master/slave principle, 264 Mean pressure calculation, 32 Measured-data evaluation, 355–371 Measurement data acquisition, 286–293 data selection, 355–358 dynamic, 352 transient, 352 uncertainty, 251–252 Measuring chain, 249–250 Measuring equipment management, 394 Measuring error, 250 Index Measuring site, 250 Megatrend, global, Micro-electromechanical system (MEMS) technology, 163 Modal analysis, 190 Modal criteria, 291 Model creation step, 405 Model management, 404–405 Model manager, 338 Modeling, 89, 350 empirical, 56 environment, 338 physical, 56 Models global, 351 local, 351 MOST bus, 268–269 Motor-in-the-middle design, 77 Motor-in-the-roller design, 77 Multi-configuration, 66 Multi-domain approach, 329 Multibody system, 329 Multivariable control, 315 N Name management, 405–406 NEDC See New European Driving Cycle (NEDC) Negative temperature coefficient (NTC) thermistor, 158 Network protocol, 257 Network time protocol (NTP), 291 Networking, Networks, artificial neural networks, 350 New European Driving Cycle (NEDC), 18, 78, 79 Noise, vibration and harshness (NVH),77, 85–86 Non-dispersive infrared sensor (NDIR), 191–192 Non Road Transient Cycle (NRTC), 110 NTC thermistor See Negative temperature coefficient (NTC) thermistor NVH See Noise, vibration and harshness (NVH) Nyquist diagram, 245 415 O OBD See On-board diagnosis (OBD) ODX, 60 Offline optimization, 348 Oil conditioning, 152–156 Oil consumption measurement, 179–181 Oil-circuit component, 27–29 On-board diagnosis (OBD), 61, 97 Online optimization, 347 Online processing, 291 Opacimeter, 213–217 Opacity, 217 Open loop, 58 Operating mode, 377 Operating times acquisition, 389 Optimization, 89, 353–354 OSI model, 257 P Paddle speed, 227 Paddle wheel, 225–226 Pallet system, 116, 121–122 Paramagnetic detector (PMD), 195 Partial-flow dilution, 107, 210 Particle imaging velocimetry (PIV), 232–235 Particulate emissions, 107 Particulate measurement, 209–220 Particulate measuring device, 103 Particulate sampler, 207–209 PCI, 269 PEMFC See Polymer-electrolyte-membrane fuel cell (PEMFC) Performance level (PL), 371 Performance test, 331 Photoacoustic soot measurement, 218–219 Photoelectric aerosol sensor, 219–220 PID controller, 299–301 time-discrete, 302–303 Plausibility check, 304 PLU measuring principle, 174 Point mass model, 319–321 Polarization curve, 244 Pollutant component, 187 Polymer-electrolyte-membrane fuel cell (PEMFC), 51 416 Positive temperature coefficient (PTC) thermistor, 158 Post-mortem recording, 307 Power effective, 31 recovery, 254 Powerlink, 266 Powertrain automotive, architecture, test bed, 65–69 test bed controllers, 313–314 Pre-drying facility, 150 Pressure, 162 Pressure transmitter, installation, 250 Prime mover, 70–75 Probe switching unit, 202 Processes in test facility, 385–386 PROFIBUS, 262–264 PROFINET, 266 Prototype, virtual, 329 Prototype vehicle, 96 Pt100, 158 Pt1000, 158 PTC thermistor See Positive temperature coefficient (PTC) thermistor PWM, 55 PXI, 273 R Racing, 91–93 Racing test bed, 90–95 Real driving emissions (RDE), 19, 64, 188 Real-time capability, 319 Real-time classification, 69 Recording, continuous, 306–307 Relative (gauge) pressure measurement, 162 Relative time, 289 Remote control, 393 Remote monitoring, 391, 393 Research test bed, 20–22 Residual bus simulation, 49, 58, 70, 340–341 Result data management, 398–401 Result data warehouse, 406–407 Ring buffer, 307 Risk analysis, 371–372 Risk assessment, 371–372 Risk graph, 373 RL SHED, 107 Index Road load simulation, 317, 324–326 Rolling resistance, 81, 320 Rotary piston gas meter, 179 Rotation coefficient, 225 RS232, 269–270 RS422, 270 RS485, 270 S Safety, 371–380 covers, 125–126 functional, 330 hardware, 375–376 switching device, 376 test, 330 Safety integrity level (SIL), 371 Safety-relevant system, 373–374 Sample conditioning, 200 Sample gas conditioning, 200–203 Sampling bag, 188–189 Sampling theorem, 292 Scaling, 292 Screening test, 349 Sealed Housing for Evaporative Determination (SHED) chamber, 106 Sensor piezoelectric, 161 piezoresistive, 161 volumetric, 174 Server level, 408 Shaft connection, 123–216 Shaft dimensioning, 126–127 Shaft system, 124–125 Shaft torque control, 312 SHED chamber See Sealed Housing for Evaporative Determination (SHED) chamber Shunt resistance, 160 SI system, 292 Sicherheitsfunktion, 374–375 Signal generator, 293 Signal processing, 293–304 Signal quality, 304 Simulation, 319 platform, 338 Single-cylinder engine test bed, 12, 20–22 Slip curve, 323 Slip simulation, 322–324 Index Smoke meter, 213 Software architecture, 277 Solid oxide fuel cells (SOFC), 52 Soot emission, 211 Soot measurement, 218–219 Special-purpose engine test bed, 23–25 Speed measurement, 169–171 Standard Road Cycle (SRC), 84 Starter motor test bed, 42–44 Steady-state, 130 measurement, 306 Stiff shaft, 311 Strain, 160 Strain gauge, 161 Swirl, 220 Swirl number, 225 reduced, 226 Synchronization, 291–292 Synchronous machine, 43–44 with permanent-magnet excitation, 137–138 System of units, 292 T Tandem dynamometer, 142 Task scheduling, 386 Temperature, 160 Temperature measurement, 156 Test automation, 62, 342 maneuver-and event-based, 332 track-based, 332 Test bed automation, 15 base, 115 dynamic, 17 high-dynamic, 17 mechanics, 114 non-steady-state, 17 operating hours, 390 state control, 345 steady-state, 15 types, 11 virtual, 63 Test cell service mode, 379 Test creation, 343 Test cycle, 188 Test design, 88, 348 Test drive, virtual, 69, 326 417 Test equipment configuration, 396 management, 387, 394 Test facility, 387 logbook, 387 management, 387 processes, 385 scheduling, 388 status monitoring, 386 status overview, 393 Test management, 345 Test mode, 377 Test order management (TOM), 387 Test procedure, 342 Test strategy, 331 THC measurement See Total hydrocarbon emissions (THC) measurement Thermistor, 158 Thermocouple, 159 Thermometer, 159 Throttle actuator, 143 Tilt test bed, 23 Time base shifting, 362 Time-triggered CAN (TTCAN), 262 Tire simulation, 323 Token passing, 264 Torque, 165 flange, 137, 164 meter, 226 Torsional vibration analysis, 126, 321 Total harmonic distortion analysis (THDA), 247 Total hydrocarbon emissions (THC) measurement, 107 Tracer method, 181 Tractive force control, 318 Traffic-to-follow model, 330 Transient, 130 test bed, 17 Transmission component, 41 Transmission control protocol (TCP), 272 Trend, global, Tumble, 220, 228 Tumble coefficient, 228, 229 Tumble number, 229, 232 reduced, 229 Tuning rule, 301 Turbocharger test bed, 33, 34 Two-step lambda probe, 184 418 U Ultrasonic flow measurement, 179 Unit-under-test management, 387 Unit-under-test pallets, 122 Universal serial bus (USB), 272 US Heavy Duty Transient Cycle (USHDTC), 108 User datagram protocol (UDP), 272 Utilization optimization, 387 V V model, V2I See Vehicle-to-infrastructure (V2I) V2V See Vehicle-to-vehicle (V2V) Vehicle development process, 26 fastening, 77 model, 319–326 test bed, 75–90 virtual, 308 Vehicle integration virtual, 336–340 Vehicle-to-infrastructure (V2I), 62 Vehicle-to-vehicle (V2V), 62 Velocity control, 317 Virtual prototype, 329 Virtual test drive, 326–336 Virtual instrument software architecture (VISA), 273 Index Voltage, electric, 159 VXI, 273 W Water-circuit component, 29–31 WHSC See World Heavy Duty Stationary Cycle (WHSC) WHTC See World Harmonized Transient Cycle (WHTC) Wideband lambda sensor, 184 WLTP See World Harmonized Light Vehicles Test Procedure (WLTP) Work environment for evaluations, 370 Workflow management, 387 World Harmonized Light Vehicles Test Procedure (WLTP), 79 World Harmonized Transient Cycle (WHTC), 108 World Heavy Duty Stationary Cycle (WHSC), 108 X X in the loop, XiL, Z Zero error, 250 ... bed – Flow test bed (see also Sect 3.3.15) • Development: – Performance test bed – Function test bed – Endurance test bed – Calibration test bed – Emission certification test bed • Production: –. .. proportions vs type of test bed 1.2 Demands on Instrumentation and Test Systems xCU Test Office E-Motor testbed Battery testbed Engine testbed Powertrain testbed Vehicle CD testbed Road test Virtual Real... Switzerland 2016 M Paulweber, K Lebert, Powertrain Instrumentation and Test Systems, Powertrain, DOI 10.1007/978-3-319-32135-6_2 11 12 Types of Test Beds • Research: – Single-cylinder engine test