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Multi-Physics Multi-Objective Optimal Design of Bearingless Switched Reluctance Motor Based on Finite-Element Method

Author

Listed:
  • Jingwei Zhang

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Honghua Wang

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Sa Zhu

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Tianhang Lu

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

Abstract

The bearingless switched reluctance motor (BSRM) integrates the switched reluctance motor (SRM) with the magnetic bearings, which avoids mechanical bearings-loss and makes it promising in high-speed applications. In this paper, a comprehensive framework for the multi-physics multi-objective optimal design of BSRMs based on finite-element method (FEM) is proposed. At first, the 2-D electromagnetic model of a fabricated initial design prototype is built and solved by the open-source FEM software, Elmer. The iron loss model in Elmer based on the Fourier series is modified by a transient iron loss model with less computation time. Besides, a simplified lumped-parameter (LP) thermal model of the BSRM is applied to estimate the temperature rise of BSRM in the steady state. Then, the comprehensive framework for the multi-physics multi-objective optimal design of BSRMs based on FEM is proposed. The objectives, constraints, and decision variables for optimization are determined. The multi-objective genetic particle swarm optimizer is utilized to obtain the Pareto front of optimization. The electromagnetic performance of the final optimal design is compared with the initial design. Comparison results show that the average electromagnetic torque and the efficiency are significantly enhanced.

Suggested Citation

  • Jingwei Zhang & Honghua Wang & Sa Zhu & Tianhang Lu, 2019. "Multi-Physics Multi-Objective Optimal Design of Bearingless Switched Reluctance Motor Based on Finite-Element Method," Energies, MDPI, vol. 12(12), pages 1-18, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:12:p:2374-:d:241519
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    References listed on IDEAS

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    1. Yunyun Chen & Yu Ding & Jiahong Zhuang & Xiaoyong Zhu, 2018. "Multi-Objective Optimization Design and Multi-Physics Analysis a Double-Stator Permanent-Magnet Doubly Salient Machine," Energies, MDPI, vol. 11(8), pages 1-15, August.
    2. Lei, Fei & Gu, Ke & Du, Bin & Xie, Xiaoping, 2017. "Comprehensive global optimization of an implicit constrained multi-physics system for electric vehicles with in-wheel motors," Energy, Elsevier, vol. 139(C), pages 523-534.
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    Cited by:

    1. Chiweta Emmanuel Abunike & Ogbonnaya Inya Okoro & Sumeet S. Aphale, 2022. "Intelligent Optimization of Switched Reluctance Motor Using Genetic Aggregation Response Surface and Multi-Objective Genetic Algorithm for Improved Performance," Energies, MDPI, vol. 15(16), pages 1-23, August.
    2. Grace Firsta Lukman & Xuan Son Nguyen & Jin-Woo Ahn, 2020. "Design of a Low Torque Ripple Three-Phase SRM for Automotive Shift-by-Wire Actuator," Energies, MDPI, vol. 13(9), pages 1-13, May.

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