IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i21p8254-d963790.html
   My bibliography  Save this article

Optimal Design of Asymmetric Rotor Pole for Interior Permanent Magnet Synchronous Motor Using Topology Optimization

Author

Listed:
  • Huihuan Wu

    (Department of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong, China)

  • Shuangxia Niu

    (Department of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong, China)

  • Weinong Fu

    (Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

Abstract

As asymmetric interior permanent magnet synchronous motor (AIPMSM) has excellent performance but complicated topological structure, a novel high-resolution encoding and edge smoothing method is proposed for topology optimization of the asymmetric rotor of interior permanent magnet synchronous motor (IPMSM) in this study. This method aims to solve complex electromagnetic design problems with time-dependent performance through a multi-objective genetic algorithm (MOGA) integrated with a high-resolution encoding and edge smoothing method. The complex structure is represented by a high-resolution image-like matrix and then vectorized by the edge smoothing method. Therefore, the commonly used discrete binary encoded variables related to the finite element (FE) model are replaced with a vectorized topological structure and other control variables. In this sense, high-resolution matrix and edge smoothing methods are used for the first time to represent the rotor topology of AIPMSMs. Compared with the traditional topology optimization method, the proposed method has the advantage of expressing more complex and vectorized topological structures; meanwhile, the obtained performance is accurate and trustworthy using conventional FE simulation. Numerical results show that a stable convergence is achieved with the avoidance of checkerboards and material overlapping. It is shown that the proposed method can find solutions with better performances, in comparison with the reference model.

Suggested Citation

  • Huihuan Wu & Shuangxia Niu & Weinong Fu, 2022. "Optimal Design of Asymmetric Rotor Pole for Interior Permanent Magnet Synchronous Motor Using Topology Optimization," Energies, MDPI, vol. 15(21), pages 1-15, November.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:8254-:d:963790
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/21/8254/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/21/8254/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
    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. Wenxiang Zhao & Liang Xu & Bo Wang, 2023. "Multi-Factor Coupling Analysis and Optimization Method for High-Quality Electrical Machine Systems," Energies, MDPI, vol. 16(7), pages 1-3, March.

    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. Geeraert, Joke & Rocha, Luis E.C. & Vandeviver, Christophe, 2024. "The impact of violent behavior on co-offender selection: Evidence of behavioral homophily," Journal of Criminal Justice, Elsevier, vol. 94(C).
    2. Furqan Dar & Samuel R. Cohen & Diana M. Mitrea & Aaron H. Phillips & Gergely Nagy & Wellington C. Leite & Christopher B. Stanley & Jeong-Mo Choi & Richard W. Kriwacki & Rohit V. Pappu, 2024. "Biomolecular condensates form spatially inhomogeneous network fluids," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    3. López Pérez, Mario & Mansilla Corona, Ricardo, 2022. "Ordinal synchronization and typical states in high-frequency digital markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    4. Jessica M. Vanslambrouck & Sean B. Wilson & Ker Sin Tan & Ella Groenewegen & Rajeev Rudraraju & Jessica Neil & Kynan T. Lawlor & Sophia Mah & Michelle Scurr & Sara E. Howden & Kanta Subbarao & Melissa, 2022. "Enhanced metanephric specification to functional proximal tubule enables toxicity screening and infectious disease modelling in kidney organoids," Nature Communications, Nature, vol. 13(1), pages 1-23, December.
    5. Dennis Bontempi & Leonard Nuernberg & Suraj Pai & Deepa Krishnaswamy & Vamsi Thiriveedhi & Ahmed Hosny & Raymond H. Mak & Keyvan Farahani & Ron Kikinis & Andrey Fedorov & Hugo J. W. L. Aerts, 2024. "End-to-end reproducible AI pipelines in radiology using the cloud," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    6. Pablo García-Risueño, 2025. "Historical Simulation Systematically Underestimates the Expected Shortfall," JRFM, MDPI, vol. 18(1), pages 1-12, January.
    7. Lauren L. Porter & Allen K. Kim & Swechha Rimal & Loren L. Looger & Ananya Majumdar & Brett D. Mensh & Mary R. Starich & Marie-Paule Strub, 2022. "Many dissimilar NusG protein domains switch between α-helix and β-sheet folds," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    8. Ali Rezaei & Virág Kocsis-Jutka & Zeynep I. Gunes & Qing Zeng & Georg Kislinger & Franz Bauernschmitt & Huseyin Berkcan Isilgan & Laura R. Parisi & Tuğberk Kaya & Sören Franzenburg & Jonas Koppenbrink, 2025. "Correction of dysregulated lipid metabolism normalizes gene expression in oligodendrocytes and prolongs lifespan in female poly-GA C9orf72 mice," Nature Communications, Nature, vol. 16(1), pages 1-17, December.
    9. Oren Amsalem & Hidehiko Inagaki & Jianing Yu & Karel Svoboda & Ran Darshan, 2024. "Sub-threshold neuronal activity and the dynamical regime of cerebral cortex," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    10. Matthew Rosenblatt & Link Tejavibulya & Rongtao Jiang & Stephanie Noble & Dustin Scheinost, 2024. "Data leakage inflates prediction performance in connectome-based machine learning models," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    11. Sayedali Shetab Boushehri & Katharina Essig & Nikolaos-Kosmas Chlis & Sylvia Herter & Marina Bacac & Fabian J. Theis & Elke Glasmacher & Carsten Marr & Fabian Schmich, 2023. "Explainable machine learning for profiling the immunological synapse and functional characterization of therapeutic antibodies," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    12. Venkat Ram Reddy Ganuthula & Krishna Kumar Balaraman & Nimish Vohra, 2025. "Hedonic Adaptation in the Age of AI: A Perspective on Diminishing Satisfaction Returns in Technology Adoption," Papers 2503.08074, arXiv.org.
    13. Khaled Akkad & David He, 2023. "A dynamic mode decomposition based deep learning technique for prognostics," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2207-2224, June.
    14. Romain Fournier & Zoi Tsangalidou & David Reich & Pier Francesco Palamara, 2023. "Haplotype-based inference of recent effective population size in modern and ancient DNA samples," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    15. Matt C. J. Denton & Luke D. Smith & Wenhao Xu & Jodeci Pugsley & Amelia Toghill & Daniel R. Kattnig, 2024. "Magnetosensitivity of tightly bound radical pairs in cryptochrome is enabled by the quantum Zeno effect," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    16. Laura Portell & Sergi Morera & Helena Ramalhinho, 2022. "Door-to-Door Transportation Services for Reduced Mobility Population: A Descriptive Analytics of the City of Barcelona," IJERPH, MDPI, vol. 19(8), pages 1-20, April.
    17. Caroline Haimerl & Douglas A. Ruff & Marlene R. Cohen & Cristina Savin & Eero P. Simoncelli, 2023. "Targeted V1 comodulation supports task-adaptive sensory decisions," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    18. Mite Mijalkov & Ludvig Storm & Blanca Zufiria-Gerbolés & Dániel Veréb & Zhilei Xu & Anna Canal-Garcia & Jiawei Sun & Yu-Wei Chang & Hang Zhao & Emiliano Gómez-Ruiz & Massimiliano Passaretti & Sara Gar, 2025. "Computational memory capacity predicts aging and cognitive decline," Nature Communications, Nature, vol. 16(1), pages 1-14, December.
    19. Pullinger, Martin & Zapata-Webborn, Ellen & Kilgour, Jonathan & Elam, Simon & Few, Jessica & Goddard, Nigel & Hanmer, Clare & McKenna, Eoghan & Oreszczyn, Tadj & Webb, Lynda, 2024. "Capturing variation in daily energy demand profiles over time with cluster analysis in British homes (September 2019 – August 2022)," Applied Energy, Elsevier, vol. 360(C).
    20. Chung-Yuan Chang & Yen-Wei Feng & Tejender Singh Rawat & Shih-Wei Chen & Albert Shihchun Lin, 2025. "Optimization of laser annealing parameters based on bayesian reinforcement learning," Journal of Intelligent Manufacturing, Springer, vol. 36(4), pages 2479-2492, April.

    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:15:y:2022:i:21:p:8254-:d:963790. 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.