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Accuracy of Core Losses Estimation in PMSM: A Comparison of Empirical and Numerical Approximation Models

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  • Michael Nye

    (Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA
    Center of Automotive Research, The Ohio State University, Columbus, OH 43212, USA
    Center of High Performance Power Electronics, The Ohio State University, Columbus, OH 43210, USA)

  • Matilde D’Arpino

    (Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA
    Center of Automotive Research, The Ohio State University, Columbus, OH 43212, USA
    Center of High Performance Power Electronics, The Ohio State University, Columbus, OH 43210, USA
    Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH 43210, USA)

  • Luigi Pio Di Noia

    (Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy)

Abstract

The estimation of core loss in permanent magnet synchronous machines (PMSMs) is a fundamental step for the optimization of the performance of PMSM drives. However, there is a lack of literature which fully demonstrates the goodness of some of the empirical approximations that are commonly used in industrial and automotive sectors. This work investigates how different approximations for the core loss estimation of PMSMs can lead to considerable error across the entire machine operating domain. An interior PMSM (IPMSM) is modeled in finite element analysis (FEA) and used to calibrate the coefficients of the Bertotti equation. Approximations of the Bertotti equation are then made, which are calculated from a simple algebraic expression of measurable states, and these are used to estimate machine core loss in the whole direct-quadrature ( d − q ) domain of operation. The estimated core loss obtained with the approximations are finally compared to FEA core loss results. The approximations are shown to have considerable variability in their accuracy compared to FEA results. The results of this work can be used as guidance during the development of estimation algorithms for PMSM losses and the development of control strategies.

Suggested Citation

  • Michael Nye & Matilde D’Arpino & Luigi Pio Di Noia, 2025. "Accuracy of Core Losses Estimation in PMSM: A Comparison of Empirical and Numerical Approximation Models," Energies, MDPI, vol. 18(17), pages 1-19, August.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:17:p:4494-:d:1731245
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    References listed on IDEAS

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    1. Youguang Guo & Yunfei Yu & Haiyan Lu & Gang Lei & Jianguo Zhu, 2024. "Enhancing Performance of Permanent Magnet Motor Drives through Equivalent Circuit Models Considering Core Loss," Energies, MDPI, vol. 17(8), pages 1-17, April.
    2. Oğuz Mısır & Mehmet Akar, 2022. "Efficiency and Core Loss Map Estimation with Machine Learning Based Multivariate Polynomial Regression Model," Mathematics, MDPI, vol. 10(19), pages 1-18, October.
    3. Alexandros Sergakis & Marios Salinas & Nikolaos Gkiolekas & Konstantinos N. Gyftakis, 2025. "A Review of Condition Monitoring of Permanent Magnet Synchronous Machines: Techniques, Challenges and Future Directions," Energies, MDPI, vol. 18(5), pages 1-35, February.
    4. Lian Hou & Youguang Guo & Xin Ba & Gang Lei & Jianguo Zhu, 2024. "Efficiency Improvement of Permanent Magnet Synchronous Motors Using Model Predictive Control Considering Core Loss," Energies, MDPI, vol. 17(4), pages 1-18, February.
    5. Vasileios I. Vlachou & Georgios K. Sakkas & Fotios P. Xintaropoulos & Maria Sofia C. Pechlivanidou & Themistoklis D. Kefalas & Marina A. Tsili & Antonios G. Kladas, 2024. "Overview on Permanent Magnet Motor Trends and Developments," Energies, MDPI, vol. 17(2), pages 1-48, January.
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