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Battery Pack Grouping and Capacity Improvement for Electric Vehicles Based on a Genetic Algorithm

Author

Listed:
  • Zheng Chen

    (Faculty of Transportation Engineering, Kunming University of Science of Technology, Kunming 650500, China)

  • Ningyuan Guo

    (Faculty of Transportation Engineering, Kunming University of Science of Technology, Kunming 650500, China)

  • Xiaoyu Li

    (Faculty of Transportation Engineering, Kunming University of Science of Technology, Kunming 650500, China)

  • Jiangwei Shen

    (Faculty of Transportation Engineering, Kunming University of Science of Technology, Kunming 650500, China)

  • Renxin Xiao

    (Faculty of Transportation Engineering, Kunming University of Science of Technology, Kunming 650500, China)

  • Siqi Li

    (Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China)

Abstract

This paper proposes an optimal grouping method for battery packs of electric vehicles (EVs). Based on modeling the vehicle powertrain, analyzing the battery degradation performance and setting up the driving cycle of an EV, a genetic algorithm (GA) is applied to optimize the battery grouping topology with the objective of minimizing the total cost of ownership (TCO). The battery capacity and the serial and parallel amounts of the pack can thus be determined considering the influence of battery degradation. The results show that the optimized pack grouping can be solved by GA within around 9 min. Compared with the results of maximum discharge efficiency within a fixed lifetime, the proposed method can not only achieve a higher discharge efficiency, but also reduce the TCO by 2.29%. To enlarge the applications of the proposed method, the sensitivity to driving conditions is also analyzed to further prove the feasibility of the proposed method.

Suggested Citation

  • Zheng Chen & Ningyuan Guo & Xiaoyu Li & Jiangwei Shen & Renxin Xiao & Siqi Li, 2017. "Battery Pack Grouping and Capacity Improvement for Electric Vehicles Based on a Genetic Algorithm," Energies, MDPI, vol. 10(4), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:4:p:439-:d:94642
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    References listed on IDEAS

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    Cited by:

    1. Benitto Albert Rayan & Umashankar Subramaniam & S. Balamurugan, 2023. "Wireless Power Transfer in Electric Vehicles: A Review on Compensation Topologies, Coil Structures, and Safety Aspects," Energies, MDPI, vol. 16(7), pages 1-46, March.
    2. Horatiu Pop & Alin Grama & Alexandra Fodor & Corneliu Rusu, 2023. "Infrastructure Development for Electric Vehicle Charging Stations in Cluj-Napoca Municipality—A Case Study," Energies, MDPI, vol. 16(8), pages 1-14, April.

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