Electric Propulsion Systems Design Supported by Multi-Objective Optimization Strategies

Electric drive systems consisting of battery, inverter, electric motor and gearbox are applied in hybridor purely electric vehicles. The layout process of such propulsion systems is performed on system level under consideration of various component properties and their interfering characteristics. In addition, different boundary conditions are taken under account, e. g. performance, efficiency, packaging, costs. In this way, the development process of the power train involves a broad range of influencing parameters and periphery conditions and thus represents a multi-dimensional optimization problem. Stateof-the-art development processes of mechatronic systems are usually executed according to the V-model, which represents a fundamental basis for handling the complex interactions of the different disciplines involved. In addition, stage-gate processes and spiral models are applied to deal with the high level of complexity during conception, design and testing. Involving a large number of technical and economic factors, these sequential, recursive processes may lead to suboptimal solutions since the system design processes do not sufficiently consider the complex relations between the different, partially conflicting domains. In this context, the present publication introduces an integrated multi-objective optimization strategy for the effective conception of electric propulsion systems, which involves a holistic consideration of all components and requirements in a multi-objective manner. The system design synthesis is based on component-specific Pareto-optimal designs to handle performance, efficiency, package and costs for given system requirements. The results are displayed as Pareto-fronts of electric power train system designs variants, from which decision makers are able to choose the best suitable solution. In this way, the presented system design approach for the development of electrically driven axles enables a multi-objective optimization considering efficiency, performance, costs and package. It is capable to reduce development time and to improve overall system quality at the same time.

1. Hirz M., Dietrich W., Gfrerrer A., Lang J. (2013) Integrated computer-aided design in automotive development: development processes, geometric fundamentals, methods of CAD, knowledge-based engineering data management. Springer https://doi.org/10.1007/978-3-642-11940-8

2. Stadler S., Hirz M., Thum K., Rossbacher P. (2013) Conceptual full-vehicle development supported by integrated computer-aided design methods. Computer-aided design, 10 (1), 159-172. http://doi.org/10.3722/cadaps.2013.159-172

3. Volkswagen Newsroom: Drive Train configurations of the Golf. Available at: https://www.volkswagen-newsroom.com/en (accessed 26 September 2019).

4. Volkswagen Newsroom: Modular electric drive matrix (MEB). Available at: https://www.volkswagen-newsroom.com/en/modular-electric-drive-matrix-meb-3677 (accessed 26 September 2019).

5. MAGNA etelligentdrive, mаgna.cоm/electrification. Available at: http://electrification.magna.com/wp-content/uploads/2017/11/A_MPT_eDrive_Brochure_EN_221117.pdf (accessed 26 September 2019).

6. Development Method for Mechatronic Systems, VDI Guideline 2206. The Association of German Engineers, Germany, 2003.

7. Ernst M. (2016) KPI-related analysis methods to optimise mechatronic product development processes. Doctoral thesis. Institute of Automotive Engineering, University of Technology Graz.

8. Sell R., Tamre M. (2005) Integration of V-model and SysML for advanced mechatronics system design. Int. Workshop on Research & Education in Mechatronics, At ANNECY. France. 276-280.

9. Janschek K. (2012) Mechatronic Systems Design, Methods, Models, Concepts. Springer Publisher. http://doi.org/10.1007/978-3-642-17531-2

10. Balzert H. (1998) Lehrbuch der Softwaretechnik. Software-Management, Software-Qualitätssicherung, Unterneh-mensmodellierung. Spektrum Akademischer Verlag, Berlin.

11. Hofstetter M., Hirz M., Ackerl M. (2016) System design optimization of eEV-axledrives with package restrictions. FISITA 2016 World Automotive Congress Korea. Available at: https://www.researchgate.net/publication/308971790

12. Mathoy A. (2011) Drivetrain architectures and their impact on the choice of the electrical machine. IEEE 14th European Conference on Power Electronics and Applications. Available at: https://ieeexplore.ieee.org/document/6020671

13. Rahman K., Jurkovic S., Hawkins S., Tarnowsky S., Savagian P. (2014) Propulsion System Design of a Battery Electric Vehicle. IEEE Electrification Magazine, 2 (2), 14-24. http://doi.org/10.1109/MELE.2014.2316977

14. Siemens integrates EV motor and inverter in single housing; common cooling and SKiN. Green Car Congress. Available at: http://www.greencarcongress.com/2014/10/20141017-siemens.html (accessed 28 June 2016).

15. Eghtessad M. (2014) Optimale Antriebsstrangkonfigurationen für Elektrofahrzeuge. PhD thesis. TU Braunschweig (in German).

16. Schulte-Cörne C. (2015) Multikriterielle integrierte System-optimierung von hybriden Plug-In-Antriebssystemen. PhD thesis, RWTH Aachen (in German).

17. Meier T. (203) Multikriterielle Optimierung hybrider Antriebsstränge mittels statistischer Versuchsplanung. PhD thesis, TU Darmstadt (in German).

18. Hofstetter M., Lechleitner D., Hirz M., Gintzel M., Schmidhofer A. (208) Multi-objective gearbox design optimization for xEV-axle drives under consideration of package restrictions. Forschung im Ingenieurwesen, 82 (4), 361-370. http://doi.org/10.1007/s10010-018-0278-9

19. Hofstetter M., Hirz M., Gintzel M., Schmidhofer A. (2018) Multi-Objective System Design Synthesis for Electric Powertrain Development. IEEE Transportation Electrification Conference and Expo, http://doi.org/10.1109/ITEC.2018.8450113

20. ANSYS Inc. ANSYS RMxprt. Available at: http://www.ansys.com/Products/Electronics/ANSYS-RMxprt. (accessed 3 April 2016).

21. STAR-CCM+ Siemens PLM Software Solutions. Available at: https://www.plm.automation.siemens.com/global/de/products/simcenter/STAR-CCM.html (accessed 20 September 2019).

22. ANSYS Inc. ANSYS Maxwell. Available at: https://www.ansys.com/products/electronics/ansys-maxwell (accessed 20 September 2019).

23. Hirz M., Harrich A., Rossbacher P. (2011) Advanced computer aided design methods for integrated virtual product development processes. Computer-Aided Design and Applications, 8 (6), 901-913. http://doi.org/10.3722/cadaps.2011.901-913

24. Schleiffer J.-E., Lange A. (December 2015) Optimization of Parallel Hybrid Electric Vehicle (HEV) Fleets. CTI Journal.

25. Roberts J., Kochenderfer R. (2014) Mathematical Optimization, Pareto Optimality. Lecture Script at Stanford University.