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Automotive Engineering ›› 2025, Vol. 47 ›› Issue (10): 1942-1952.doi: 10.19562/j.chinasae.qcgc.2025.10.010

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Research on Optimization and Suppression of Torque Ripple of Vehicle Motor Based on Small Sample WPRBF-MEVO

Long Chen1,3(),Feihong Li1,Xiaobin Chen1,Chuanhao Lu1,2,Xiaonan Zhao1,Qiaobin Liu4   

  1. 1.School of Mechanical Engineering,Taiyuan University of Technology,Taiyuan 030024
    2.School of Aeronautics and Astronautics,Taiyuan University of Technology,Taiyuan 030606
    3.China Coal Technology Engineering Group Taiyuan Research Institute Co. ,Ltd. ,Taiyuan 100048
    4.School of Mechanical and Automotive Engineering,South China University of Technology,Guangzhou 510641
  • Received:2025-03-07 Revised:2025-05-06 Online:2025-10-25 Published:2025-10-20
  • Contact: Long Chen E-mail:chenlong@tyut.edu.cn

Abstract:

The torque ripple of the drive motor directly affects the NVH performance of the entire vehicle. To address the optimization and suppression challenge of this issue, a collaborative optimization framework integrating the Weighted Average and Polynomial Augmented Radial Basis Function (WPRBF) surrogate model and the Multi- Objective Energy Valley Optimizer (MEVO) algorithm is proposed. Firstly, a parametric finite element model of the motor is established and verified through bench tests. Secondly, Latin hypercube sampling is employed for experimental design to obtain samples, and the WPRBF method is proposed to construct a high-precision surrogate model. Finally, the MEVO algorithm is used for multi-objective optimization design, and the entropy weight-fuzzy set theory comprehensive decision-making mechanism is introduced to obtain the Pareto front optimal solution. The results show that: (1) Under the same modeling accuracy, the WPRBF model requires approximately 40% fewer samples than the traditional KRG surrogate model; (2) After optimization, the mean value of the motor torque output increases by 6.83%, with the torque ripple coefficient decreasing by 20.00%, and the peak value of cogging torque reduced by 23.80%. This verifies the effectiveness of the method proposed in this paper.

Key words: electric drive system, multi-objective optimization, surrogate model, FEA