IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v153y2018icp83-95.html
   My bibliography  Save this article

Cogging torque reduction of permanent magnet synchronous motor using multi-objective optimization

Author

Listed:
  • Ilka, Reza
  • Alinejad-Beromi, Yousef
  • Yaghobi, Hamid

Abstract

Cogging torque is an important issue in design of permanent magnet motors, especially in certain high accuracy applications. Most of the methods utilized for cogging torque reduction lead to motor structure complexity, increasing manufacturing cost and also influencing the output torque. This research tries to find an optimal solution set of the PMSM with the aim of reducing the cogging torque while the output torque is not affected. For this purpose, multi-objective optimization is a proper and reliable approach which can provide the solution set involving conflicting functions simultaneously. Multi-objective optimization determines the logical range of cogging torque reduction with respect to the output torque. In this paper, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), which is a powerful and well-known multi-objective optimization method, is applied to find the optimal design of a surface-mounted Permanent Magnet Synchronous Motor (PMSM). Simulation results show efficacy of the NSGA-II. In the suggested design solutions that are selected from the Pareto-optimal set, cogging torque is reduced considerably while the output torque has experienced a slight decrease with respect to the nominal value. At last, time-stepping Finite-element Analysis (FEA) is used to validate the multi-objective optimization.

Suggested Citation

  • Ilka, Reza & Alinejad-Beromi, Yousef & Yaghobi, Hamid, 2018. "Cogging torque reduction of permanent magnet synchronous motor using multi-objective optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 153(C), pages 83-95.
  • Handle: RePEc:eee:matcom:v:153:y:2018:i:c:p:83-95
    DOI: 10.1016/j.matcom.2018.05.018
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475418301423
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.matcom.2018.05.018?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Arti Aniqa Tabassum & Haeng Muk Cho & Md. Iqbal Mahmud, 2024. "Essential Features and Torque Minimization Techniques for Brushless Direct Current Motor Controllers in Electric Vehicles," Energies, MDPI, vol. 17(18), pages 1-27, September.
    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.
    3. Jean-Michel Grenier & Ramón Pérez & Mathieu Picard & Jérôme Cros, 2021. "Magnetic FEA Direct Optimization of High-Power Density, Halbach Array Permanent Magnet Electric Motors," Energies, MDPI, vol. 14(18), pages 1-19, September.

    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:eee:matcom:v:153:y:2018:i:c:p:83-95. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    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.