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Automotive Engineering ›› 2025, Vol. 47 ›› Issue (3): 529-540.doi: 10.19562/j.chinasae.qcgc.2025.03.015

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Research on Multi-objective Parameter Hierarchical Optimization Method of High Frequency High-Force Electromagnetic Actuator

Mingming Qiu1,2(),Zengyuan Li1,Yiming Sun1,Ji Li3,Han Zhao1,2   

  1. 1.School of Mechanical Engineering,Hefei University of Technology,Hefei 230009
    2.National and Local Joint Engineering Research Center of Automotive Technology and Equipment,Hefei 230009
    3.Anhui Weiwei Rubber Parts Group Co. ,Ltd. ,Tongcheng 231400
  • Received:2024-08-14 Revised:2024-10-02 Online:2025-03-25 Published:2025-03-21
  • Contact: Mingming Qiu E-mail:hfutqmm@hfut.edu.cn

Abstract:

In order to meet the requirements of large output force value, high working frequency and good linearity of force-displacement of electromagnetic actuator for active mounting, a multi-objective parameter hierarchical optimization method is proposed to solve the problems of different influence of different structural parameters on optimization objectives, difficulty of expression of dynamic electromagnetic force by analytical formula, and difficulty of realization of optimal characteristics at the same time of the output force value, working frequency and force-displacement. In the upper layer, Taguchi algorithm is used to preliminarily optimize parameters, screen sensitive parameters and update the optimization range of high sensitivity parameters. In the lower layer, the backpropagation (BP) neural network prediction model is used to characterize the dynamic electromagnetic force, and the multi-objective genetic algorithm (NSGA-II) is used to search and optimize the dynamic electromagnetic force. Through simulation and experiments, the results show that the parameters of electromagnetic actuator obtained by the optimization method in this paper have better comprehensive performance, which verifies the effectiveness of this method.

Key words: hierarchical optimization, multi-objective optimization design, electromagnetic actuator, Taguchi method, BP neural network