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

Soo-Hwan Park and Myung-Seop Lim ()
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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

Mathematics, 2025, vol. 13, issue 6, 1-16

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.

Keywords: active transfer learning; deep neural network; electric vehicles; interior permanent magnet synchronous motors; pulse-width modulation; traction motor (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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