Multi Objective Optimization of Permanent Magnet Synchronous Motor Based on Taguchi Method and PSO Algorithm

Yinquan Yu, Pan Zhao, Yong Hao, Dequan Zeng, Yiming Hu, Bo Zhang and Hui Yang
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Yinquan Yu: School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
Pan Zhao: School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
Yong Hao: School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
Dequan Zeng: School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
Yiming Hu: School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
Bo Zhang: School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
Hui Yang: School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China

Energies, 2022, vol. 16, issue 1, 1-11

Abstract: To solve the optimization issues of interior permanent magnet synchronous motors (IPMSMs) and ensure a large output torque while minimizing torque ripple and core loss, the multi-objective optimization strategy should be employed. In this study, we took an 8-pole, 48-slot IPMSM as a specimen. First, the width and thickness of the permanent magnet (PM) and the rotor bridge structures were pre-selected as optimization parameters, while torque ripple and core loss were taken as optimization targets. Then, the Taguchi method to perform orthogonal experiments was employed to select the multi-parameter combinations that make the experimental results stable and with little fluctuation. To ensure the optimal results, the function equations were obtained by multivariate nonlinear fitting, while the parameters were optimized by particle swarm optimization (PSO). Finally, the optimal results were verified by the Finite Element Method (FEM). The results show that our proposed hybrid method can provide an optimal design strategy with better performance such as smaller torque ripple and core loss while maintaining a larger output torque.

Keywords: IPMSM; Taguchi method; PSO; multi-objective optimization (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: 2022
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