Energy Consumption Analysis of Different Bev Powertrain Topologies by Design Optimization

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Date: Oct. 2018
Publisher: Springer
Document Type: Report
Length: 302 words

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Abstract :

To access, purchase, authenticate, or subscribe to the full-text of this article, please visit this link: Byline: Bin Wang (1), David Ling-Shun Hung (2,3), Jie Zhong (3), Kwee-Yan Teh (3) Keywords: Battery electric vehicles; Energy consumption; Optimal design; Powertrain topology; Drive cycle analysis Abstract: Flexible layout of electric motors in battery electric vehicles (BEVs) has enabled different powertrain topologies to be used. However, these different powertrain topologies also affect the overall efficiency of energy conversion from the electrochemical form stored in the battery to the mechanical form on the driving wheels for vehicle propulsion. In this study, a methodology combining an energy-based BEV simulation model with the genetic algorithm optimization approach is applied to evaluate the energy efficiency of three different BEV powertrain topologies. The analysis is carried out assuming two different urban driving conditions, as exemplified by the New European Drive Cycle (NEDC) and the Japanese JC08 drive cycle. Each of the three BEV powertrain topologies is then optimized -- in terms of its electric motor size and, where applicable, gear reduction ratio -- for minimum energy consumption. The results show that among the three powertrain topologies, the wheel-hub drive without gear reducers consumes the least energy. The energy consumption of BEVs under the more aggressive JC08 drive cycle is consistently 8 % above that under NEDC for all three powertrain topologies considered. Author Affiliation: (1) 0000 0004 0368 8293, grid.16821.3c, State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China (2) 0000 0004 0368 8293, grid.16821.3c, National Engineering Laboratory for Automotive Electronic Control Technology, Shanghai Jiao Tong University, Shanghai, 200240, China (3) 0000 0004 0368 8293, grid.16821.3c, University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, 200240, China Article History: Registration Date: 06/09/2018 Received Date: 19/06/2017 Accepted Date: 31/03/2018 Online Date: 12/09/2018

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Gale Document Number: GALE|A554054493