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

Author

Listed:
  • Yinquan Yu

    (School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
    Key Laboratory of Conveyance and Equipment of Ministry of Education, East China Jiaotong University, Nanchang 330013, China
    Institute of Precision Machining and Intelligent Equipment Manufacturing, East China Jiaotong University, Nanchang 330013, China)

  • Pan Zhao

    (School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
    Key Laboratory of Conveyance and Equipment of Ministry of Education, East China Jiaotong University, Nanchang 330013, China
    Institute of Precision Machining and Intelligent Equipment Manufacturing, East China Jiaotong University, Nanchang 330013, China)

  • Yong Hao

    (School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
    Key Laboratory of Conveyance and Equipment of Ministry of Education, East China Jiaotong University, Nanchang 330013, China
    Institute of Precision Machining and Intelligent Equipment Manufacturing, East China Jiaotong University, Nanchang 330013, China)

  • Dequan Zeng

    (School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
    Key Laboratory of Conveyance and Equipment of Ministry of Education, East China Jiaotong University, Nanchang 330013, China
    Institute of Precision Machining and Intelligent Equipment Manufacturing, East China Jiaotong University, Nanchang 330013, China)

  • Yiming Hu

    (School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
    Key Laboratory of Conveyance and Equipment of Ministry of Education, East China Jiaotong University, Nanchang 330013, China
    Institute of Precision Machining and Intelligent Equipment Manufacturing, East China Jiaotong University, Nanchang 330013, China)

  • Bo Zhang

    (School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
    Key Laboratory of Conveyance and Equipment of Ministry of Education, East China Jiaotong University, Nanchang 330013, China
    Institute of Precision Machining and Intelligent Equipment Manufacturing, East China Jiaotong University, Nanchang 330013, China)

  • Hui Yang

    (School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China)

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.

Suggested Citation

  • Yinquan Yu & Pan Zhao & Yong Hao & Dequan Zeng & Yiming Hu & Bo Zhang & Hui Yang, 2022. "Multi Objective Optimization of Permanent Magnet Synchronous Motor Based on Taguchi Method and PSO Algorithm," Energies, MDPI, vol. 16(1), pages 1-11, December.
    • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:267-:d:1015922
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    References listed on IDEAS

    as
    1. Zia Ullah & Bilal Ahmad Lodhi & Jin Hur, 2020. "Detection and Identification of Demagnetization and Bearing Faults in PMSM Using Transfer Learning-Based VGG," Energies, MDPI, vol. 13(15), pages 1-17, July.
    2. Lien-Kai Chang & Shun-Hong Wang & Mi-Ching Tsai, 2020. "Demagnetization Fault Diagnosis of a PMSM Using Auto-Encoder and K-Means Clustering," Energies, MDPI, vol. 13(17), pages 1-12, August.
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