IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i12p2374-d241519.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/12/2374/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/12/12/2374/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tian, Yang & Zhang, Yahui & Li, Hongmin & Gao, Jinwu & Swen, Austin & Wen, Guilin, 2023. "Optimal sizing and energy management of a novel dual-motor powertrain for electric vehicles," Energy, Elsevier, vol. 275(C).
    2. Cherif Guerroudj & Yannis L. Karnavas & Jean-Frederic Charpentier & Ioannis D. Chasiotis & Lemnouer Bekhouche & Rachid Saou & Mohammed El-Hadi Zaïm, 2021. "Design Optimization of Outer Rotor Toothed Doubly Salient Permanent Magnet Generator Using Symbiotic Organisms Search Algorithm," Energies, MDPI, vol. 14(8), pages 1-25, April.
    3. Lei, Fei & Bai, Yingchun & Zhu, Wenhao & Liu, Jinhong, 2019. "A novel approach for electric powertrain optimization considering vehicle power performance, energy consumption and ride comfort," Energy, Elsevier, vol. 167(C), pages 1040-1050.
    4. Wentao Gao & Yufeng Zhang & Guanghui Du & Tao Pu & Niumei Li, 2022. "Comprehensive Comparison of a High-Speed Permanent Magnet Synchronous Motor Considering Rotor Length–Diameter Ratio," Energies, MDPI, vol. 15(14), pages 1-21, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:12:p:2374-:d:241519. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.