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Study on Multi-Objective Optimization of Power System Parameters of Battery Electric Vehicles

Author

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  • Jie Hu

    (School of Mechanical and Automotive, Guangxi University of Science and Technology, Liuzhou 545006, China
    Guangxi Key Laboratory of Automobile Components and Vehicle Technology, Guangxi University of Science and Technology, Liuzhou 545006, China)

  • Wentong Cao

    (School of Mechanical and Automotive, Guangxi University of Science and Technology, Liuzhou 545006, China)

  • Feng Jiang

    (School of Mechanical and Automotive, Guangxi University of Science and Technology, Liuzhou 545006, China
    Guangxi Key Laboratory of Automobile Components and Vehicle Technology, Guangxi University of Science and Technology, Liuzhou 545006, China)

  • Lingling Hu

    (School of Mechanical and Automotive, Guangxi University of Science and Technology, Liuzhou 545006, China)

  • Qian Chen

    (Guangxi Automobile Tractor Research Institute, Liuzhou 545006, China)

  • Weiguang Zheng

    (School of Mechanical and Automotive, Guangxi University of Science and Technology, Liuzhou 545006, China)

  • Junming Zhou

    (School of Mechanical and Automotive, Guangxi University of Science and Technology, Liuzhou 545006, China)

Abstract

The optimization of power parameters is the key to the design of pure electric vehicles. Reasonable matching of the relationship between various parameters can effectively reduce energy consumption and achieve energy sustainability. In this paper, several vehicle performance indexes such as maximum vehicle speed, acceleration time and power consumption per 100 km were used as optimization target vectors, and transmission ratio was used as optimization variable to establish the optimization problem of parameter matching. Then, the feasible domain of the transmission ratio was obtained by taking the lowest performance index of the vehicle as the constraint condition. In the feasible domain, the multi-objective genetic algorithm is used to solve the optimization problem. The Pareto optimal solution set is obtained for fixed ratio transmission and two-gear transmission, which is used as an alternative solution set. The final parameter-matching scheme is determined by comparing the alternative scheme set of different motors comprehensively. The results show that the competition relationship between multiple optimizable indexes can be described effectively by solving the Pareto front. Specifically, the Pareto optimal solution set for the motor A + fixed transmission scheme is 1.33~1.85; the Pareto optimal solution set for the motor A + 2 transmission scheme is [1.72, 0.98]~[2.99, 1.57], and the Pareto optimal solution set for the motor B + 2 transmission scheme is [2.99, 1.40]~[2.99, 1.57]. The motor A + fixed transmission scheme does not require A clutch and does not require designing a shift algorithm. Therefore, after comprehensive consideration, the motor A + fixed transmission ratio transmission scheme is set as the final scheme.

Suggested Citation

  • Jie Hu & Wentong Cao & Feng Jiang & Lingling Hu & Qian Chen & Weiguang Zheng & Junming Zhou, 2023. "Study on Multi-Objective Optimization of Power System Parameters of Battery Electric Vehicles," Sustainability, MDPI, vol. 15(10), pages 1-23, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:8219-:d:1150048
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    References listed on IDEAS

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