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Efficient Design Methodology of an All-Electric Vehicle Powertrain using Multi-Objective Genetic Optimization Algorithm
Technical Paper
2013-01-1758
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
This paper presents a methodology to design the powertrain of an electrical vehicle (EV) in an optimal way. The electric vehicle optimal design is carried out using multiobjective genetic optimization algorithm. The developed methodology is based on the coupling of a genetic algorithm with powertrain component models. It allows determining the drive train components specifications for imposed vehicle performances, taking into account the dynamic model of the vehicle and all the components interactions. In this way, the components can be sized taking into account the whole system behavior in an optimal global design. The developed methodology is performed on the European driving cycle (NEDC) to estimate energy consumption gains but also powertrain mass reduction in comparison with a classical step-by-step methodology. This optimal procedure is notably important to increase electric vehicle range or reduce battery size and thus electric vehicle cost.
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Authors
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Citation
Abdelli, A., Le Berr, F., and Benlamine, R., "Efficient Design Methodology of an All-Electric Vehicle Powertrain using Multi-Objective Genetic Optimization Algorithm," SAE Technical Paper 2013-01-1758, 2013, https://doi.org/10.4271/2013-01-1758.Also In
References
- CO2 emissions from fuel combusion - Highlights IEA Statistics 2010
- Yang , Y.-P. & Chuang , D.-S. 2007 Optimal Design and Control of a Wheel Motor for Electric Passengers Cars IEEE Transactions on Magnetics 43 1 January 2007 51 61 0018-9464
- Ehsani , M. ; Gao , Y. ; Gay , S. E. & Emadi , A. 2006 Modern Electric, Hybrid Electric and Fuel Cell Vehicles: Fundamentals, Theory, and Design CRC Press 0 8493-3154-4 Florida, USA
- Le Berr , F. , Abdelli , A. , and Benlamine , R. Sensitivity Study on the Design Methodology of an Electric Vehicle SAE Technical Paper 2012-01-0820 2012 10.4271/2012-01-0820
- Le Berr F. , Abdelli A. , Postariu D.-M. and Benlamine R. Design and Optimization of Future Hybrid and Electric Propulsion Systems : An Advanced Tool Integrated in a Complete Workflow to Study Electric Devices Oil Gas Sci. Technol. - Rev. IFP Energies nouvelles 2012
- Chabot F. , Lajoie-Masenc Analytical model of the design of permanent magnet machines SPEEDAM 98, Symposium on Power Electronics, Electrical Drives, Advanced Machines Sorrento, Italy June 1998
- Lee , Seong Taek Development and Analysis of Interior Permanent Magnet Synchronous Motor with Field Excitation Structure PhD diss. University of Tennessee 2009
- Pyrhonen Juha , Jokinen Tapani , Hrabovcova Valeria Design of Rotating Electrical. Machines 2008 John Wiley & Sons, Ltd. 978-0-470-69516-6
- Roshen W. Iron Loss Model for permanent -Magnet synchronous Motors IEEE TRANSACTIONS ON MAGNETICS 43 8 AUGUST 2007
- Staunton R. H. , Ayers C. W. , Marlino L. D. , Chiasson J. N. Evaluation of 2004 Toyota Prius Hybrid Electric Drive System LLC, Oak Ridge National Laboratory Oak Ridge, Tennessee May 2006
- Centre d'Analyse Stratégique Véhicule de demain, Note de Synthèse - Juin 2011 N°277
- Rousseau , A. , Kwon , J. , Sharer , P. , Pagerit , S. et al. Integrating Data, Performing Quality Assurance, and Validating the Vehicle Model for the 2004 Prius Using PSAT SAE Technical Paper 2006-01-0667 2006 10.4271/2006-01-0667
- Senger , R. , Merkle , M. , and Nelson , D. Validation of ADVISOR as a Simulation Tool for a Series Hybrid Electric Vehicle SAE Technical Paper 981133 1998 10.4271/981133
- Dabadie , J. , Menegazzi , P. , Trigui , R. , and Jeanneret , B. A New Tool for Advanced Vehicle Simulations SAE Technical Paper 2005-24-044 2005 10.4271/2005-24-044
- BOCH-Automotive Handbook 6 th Bentley Publishers October 2004 0-8376-1243-8
- Deb K. , Pratap A. , Agarwal S. et Meyarivan T. A Fast and Elitist Multiobjective Genetic Algorithm : NSGA-II IEEE Transactions on Evolutionary Computation 6 2 Avril 2002
- Miller T.J.E. Brushless Permanent-Magnet and Reluctance Motor Drives Clarendon Press Oxford 1989
- Hwang C. , Chang S.M. , Pan C.T. , Chang T.Y. Estimation of parameters of interior permanent magnet synchronous motors J. Magn. Magn. Mater. 2002 600 603
- André M. The ARTEMIS European driving cycles for measuring car pollutant emissions The Science Of The Total Environment 334-335 2004 73 84
- Butler , K. , Stevens , K. , and Ehsani , M. A Versatile Computer Simulation Tool for Design and Analysis of Electric and Hybrid Drive Trains SAE Technical Paper 970199 1997 10.4271/970199
- Abdelli A. et Berr F. Le Etude comparative par simulation des performances des véhicules électrique et thermique dédiés à un mono-usage IFP Energies
- Deb K. , Pratap A. , Agarwal S. et Meyarivan T. A Fast and Elitist Multiobjective Genetic Algorithm : NSGA-II IEEE Transactions on Evolutionary Computation 6 2 Avril 2002
- Abdelli A. Optimisation multicritère d'une chaîne éolienne passive Thèse INP Toulouse Octobre 2007
- Regnier J. Conception de systèmes hétérogènes en Génie Électrique par optimisation évolutionnaire multicritère Thèse INP Toulouse Décembre 2003
- Büche D. , Müller S. et Koumoutsakos P. Self-Adaptation for Multi-objective Evolutionary Algorithms ETH Zurich
- Abdelli , A. and Le Berr , F. Analytical Approach to Model a Saturated Interior Permanent Magnet Synchronous Motor for a Hybrid Electric Vehicle SAE Int. J. Engines 4 1 301 313 2011 10.4271/2011-01-0347
- Sareni B , Regnier J , Roboam X. Recombination and self-adaptation in multiobjective genetic algorithms Lecture notes in computer science 2936 2004 115 126
- Say M. G. Performance and design of AC machines Pitman London 1970