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Computationally Efficient Design of 16-Poles and 24-Slots IPMSM for EV Traction Considering PWM-Induced Iron Loss Using Active Transfer Learning

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  • Soo-Hwan Park

    (Department of Mechanical, Robotics, and Energy Engineering, Dongguk University, Seoul 04620, Republic of Korea)

  • Myung-Seop Lim

    (Department of Automotive Engineering, Hanyang University, Seoul 04763, Republic of Korea)

Abstract

The efficiency of the traction motor is highly concerned with the PWM-induced iron loss, so the PWM-induced iron loss should be considered in designing the traction motor. However, analyzing the PWM-induced iron loss requires a high computational cost because the inverter-motor model should be included in the calculation process. In surrogate-based design optimization, collecting a large amount of data is essential. However, for PWM-induced iron loss, extremely small time steps are required to accurately capture high-frequency components, resulting in a significantly high computational cost for data acquisition and making the optimization process inefficient. From this point of view, we propose a computationally efficient design process for the traction motor considering the PWM-induced iron loss. By using the proposed method, it is possible to train the accurate surrogate model for predicting the PWM-induced iron loss with a small amount of PWM-induced iron loss using active transfer learning. After training the surrogate model, multi-objective optimization was conducted for designing a high efficiency 14.5 kW traction motor for personal mobility. In order to verify the design result, an optimized traction motor was fabricated, and experiments were conducted. As a result, the performance of the trained surrogate model was verified by measuring the no-load back electromotive force, PWM current, and main drive efficiency.

Suggested Citation

  • Soo-Hwan Park & Myung-Seop Lim, 2025. "Computationally Efficient Design of 16-Poles and 24-Slots IPMSM for EV Traction Considering PWM-Induced Iron Loss Using Active Transfer Learning," Mathematics, MDPI, vol. 13(6), pages 1-16, March.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:6:p:915-:d:1608864
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    References listed on IDEAS

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    1. Fischer, M. & Weber, M. & Schwartz, P.V., 2009. "Corrigendum to "Batteries: Higher energy density than gasoline?" [Energy policy 37 (2009) 2639-2641]," Energy Policy, Elsevier, vol. 37(9), pages 3709-3709, September.
    2. Fischer, Michael & Werber, Mathew & Schwartz, Peter V., 2009. "Batteries: Higher energy density than gasoline?," Energy Policy, Elsevier, vol. 37(7), pages 2639-2641, July.
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