Multi-Objective Optimization Analysis of Electromagnetic Performance of Permanent Magnet Synchronous Motors Based on the PSO Algorithm

Yufei Cen, Haoyu Shen, Xiaoyuan Wang, Yongming Wu and Jingjuan Du ()
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Yufei Cen: School of Construction Machinery, Chang’an University, Xi’an 710064, China
Haoyu Shen: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Xiaoyuan Wang: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Yongming Wu: Leadrive Technology (Liuzhou) Co., Ltd., Liuzhou 545000, China
Jingjuan Du: School of Control and Mechanical Engineering, Tianjin Chengjian University, Tianjin 300384, China

Energies, 2024, vol. 17, issue 18, 1-13

Abstract: In order to optimize the electromagnetic performance of a permanent magnet synchronous motor (PMSM) during operation, this paper takes the size of the stator slot structure of the motor as the optimization variable and the peak cogging torque and no-load back electromotive force (EMF) amplitude of the motor as the optimization objectives. A multi-objective optimization method based on the particle swarm optimization (PSO) algorithm is adopted to obtain a structural parameter combination that minimizes the peak cogging torque and no-load back EMF amplitude while meeting the reasonable range requirements of magnetic flux density amplitude. The optimized motor structure design prototype is experimentally verified. The results show that through multi-objective optimization based on the PSO algorithm, the electromagnetic performance of the motor has been improved, with a reduction of 36.33% in peak cogging torque and 2.65% in peak no-load back EMF, indicating a reasonable magnetic flux density amplitude. The experimental results of the optimized prototype show that the difference between the theoretical simulation values and the experimental values is within a reasonable range, which verifies the effectiveness of the multi-objective optimization method.

Keywords: permanent magnet synchronous motor; particle swarm optimization algorithm; multi-objective optimization; electromagnetic performance (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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