IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i10p2326-d1392808.html
   My bibliography  Save this article

A Practical Hybrid Hysteresis Model for Calculating Iron Core Losses in Soft Magnetic Materials

Author

Listed:
  • Xiaotong Fu

    (Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China
    University of Chinese Academy of Sciences, Beijing 101408, China)

  • Shuai Yan

    (Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China)

  • Zhifu Chen

    (Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China)

  • Xiaoyu Xu

    (Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China)

  • Zhuoxiang Ren

    (Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China
    Laboratoire de Génie Electrique et Electronique de Paris, GeePs, Sorbonne Université, CNRS, 75005 Paris, France)

Abstract

Accurately calculating the losses of ferromagnetic materials is crucial for optimizing the design and ensuring the safe operation of electrical equipment such as motors and power transformers. Commonly used loss calculation models include the Bertotti empirical formula and hysteresis models. In this paper, a new hybrid hysteresis model method is proposed to calculate losses—namely, the combination of the Jiles–Atherton hysteresis model (J–A) and the Fourier hysteresis model. The traditional Jiles–Atherton hysteresis model is mainly suitable for fitting the saturation hysteresis loop, but the fitting error is relatively large for internal minor hysteresis loops. In contrast, the Fourier hysteresis model is suitable for fitting the minor hysteresis loops because the corresponding magnetic induction strength or magnetic field is lower and the waveform distortion is small. Moreover, Fourier series expansion can be expressed with fewer terms, which is convenient for parameter fitting. Through examples, the results show that the hybrid hysteresis model can take advantage of the strengths of each model, not only reducing computational complexity, but also ensuring high fitting accuracy and loss calculation accuracy.

Suggested Citation

  • Xiaotong Fu & Shuai Yan & Zhifu Chen & Xiaoyu Xu & Zhuoxiang Ren, 2024. "A Practical Hybrid Hysteresis Model for Calculating Iron Core Losses in Soft Magnetic Materials," Energies, MDPI, vol. 17(10), pages 1-15, May.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:10:p:2326-:d:1392808
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/10/2326/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/10/2326/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gustav Mörée & Mats Leijon, 2023. "Review of Hysteresis Models for Magnetic Materials," Energies, MDPI, vol. 16(9), pages 1-66, May.
    2. Jaco Schutte & Albert Groenwold, 2005. "A Study of Global Optimization Using Particle Swarms," Journal of Global Optimization, Springer, vol. 31(1), pages 93-108, January.
    Full references (including those not matched with items on IDEAS)

    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. Srđan Divac & Marko Rosić & Stan Zurek & Branko Koprivica & Krzysztof Chwastek & Milan Vesković, 2023. "A Methodology for Calculating the R - L Parameters of a Nonlinear Hysteretic Inductor Model in the Time Domain," Energies, MDPI, vol. 16(13), pages 1-14, July.
    2. Babaei, Sadra & Sepehri, Mohammad Mehdi & Babaei, Edris, 2015. "Multi-objective portfolio optimization considering the dependence structure of asset returns," European Journal of Operational Research, Elsevier, vol. 244(2), pages 525-539.
    3. Wan, Chunqiu & Wang, Jun & Yang, Geng & Gu, Huajie & Zhang, Xing, 2012. "Wind farm micro-siting by Gaussian particle swarm optimization with local search strategy," Renewable Energy, Elsevier, vol. 48(C), pages 276-286.
    4. Thomas Weise & Yuezhong Wu & Raymond Chiong & Ke Tang & Jörg Lässig, 2016. "Global versus local search: the impact of population sizes on evolutionary algorithm performance," Journal of Global Optimization, Springer, vol. 66(3), pages 511-534, November.
    5. Ermin Rahmanović & Martin Petrun, 2024. "Analysis of Higher-Order Bézier Curves for Approximation of the Static Magnetic Properties of NO Electrical Steels," Mathematics, MDPI, vol. 12(3), pages 1-24, January.
    6. Bigdeli, Nooshin, 2015. "Optimal management of hybrid PV/fuel cell/battery power system: A comparison of optimal hybrid approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 377-393.
    7. Ahmad Wedyan & Jacqueline Whalley & Ajit Narayanan, 2017. "Hydrological Cycle Algorithm for Continuous Optimization Problems," Journal of Optimization, Hindawi, vol. 2017, pages 1-25, December.
    8. Gökalp Erbeyoğlu & Ümit Bilge, 2016. "PSO-based and SA-based metaheuristics for bilinear programming problems: an application to the pooling problem," Journal of Heuristics, Springer, vol. 22(2), pages 147-179, April.
    9. Emile Glorieux & Bo Svensson & Fredrik Danielsson & Bengt Lennartson, 2017. "Constructive cooperative coevolution for large-scale global optimisation," Journal of Heuristics, Springer, vol. 23(6), pages 449-469, December.

    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:17:y:2024:i:10:p:2326-:d:1392808. 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.