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Towards Digital Twin Modeling and Applications for Permanent Magnet Synchronous Motors

Author

Listed:
  • Grace Firsta Lukman

    (Department of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Republic of Korea)

  • Cheewoo Lee

    (Department of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Republic of Korea)

Abstract

This paper explores the potential of Digital Twin (DT) technology for Permanent Magnet Synchronous Motors (PMSMs) and establishes a foundation for its modeling and applications. While DTs have been widely applied in complex systems and simulation software, their use in electric motors, especially PMSMs, remains limited. This study examines physics-based, data-driven, and hybrid modeling approaches and evaluates their feasibility for real-time simulation, fault detection, and predictive maintenance. It also identifies key challenges such as computational demands, data integration, and the lack of standardized frameworks. By assessing current developments and outlining future directions, this work provides insights into how DTs can be implemented for PMSMs and drive advancements in industrial applications.

Suggested Citation

  • Grace Firsta Lukman & Cheewoo Lee, 2025. "Towards Digital Twin Modeling and Applications for Permanent Magnet Synchronous Motors," Energies, MDPI, vol. 18(4), pages 1-24, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:4:p:956-:d:1592967
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    References listed on IDEAS

    as
    1. Song Guo & Xiangdong Su & Hang Zhao, 2024. "Optimal Design of an Interior Permanent Magnet Synchronous Motor for Electric Vehicle Applications Using a Machine Learning-Based Surrogate Model," Energies, MDPI, vol. 17(16), pages 1-19, August.
    2. Lin Liu & Youguang Guo & Wenliang Yin & Gang Lei & Jianguo Zhu, 2022. "Design and Optimization Technologies of Permanent Magnet Machines and Drive Systems Based on Digital Twin Model," Energies, MDPI, vol. 15(17), pages 1-26, August.
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