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<p>Interior Permanent Magnet Synchronous Motors (IPMSMs) are a leading technology for electric vehicles (EVs) owing to their high efficiency and power density, yet their performance is limited by torque ripple, drive losses, and sensitivity to parameter variations. This paper presents a robust rotor profile optimization (RPO) framework that integrates a modified adaptive particle swarm optimization (MAPSO) algorithm with finite-element analysis to enhance torque production and minimize torque fluctuations. Unlike conventional optimization approaches, the proposed framework incorporates sensitivity and robustness evaluation, ensuring stable performance under input uncertainties. The optimized rotor design achieves an 8.65% increase in average torque (92.45 → 100.45 Nm), a 69.61% reduction in torque ripple (22.17% → 6.74%), and a slight efficiency improvement (97.08% → 97.12%) while maintaining the same 25&#xa0;kW power rating and 2250&#xa0;rpm base speed. Additionally, the 6% refers to the overall or representative robustness level, confirming robustness against parameter deviations. These results demonstrate that the proposed optimization method enables reliable rotor geometry refinement within the same motor envelope, leading to higher torque density, reduced pulsations, and improved suitability for high-performance EV traction applications.</p>

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Rotor profile optimization for high-performance IPMSMs in electric vehicles

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月11日

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  • Muhammad Usman Sardar,
  • Mannan Hassan,
  • Muhammad Shahid Mastoi,
  • Md Shafiullah,
  • Rao Atif

摘要

Interior Permanent Magnet Synchronous Motors (IPMSMs) are a leading technology for electric vehicles (EVs) owing to their high efficiency and power density, yet their performance is limited by torque ripple, drive losses, and sensitivity to parameter variations. This paper presents a robust rotor profile optimization (RPO) framework that integrates a modified adaptive particle swarm optimization (MAPSO) algorithm with finite-element analysis to enhance torque production and minimize torque fluctuations. Unlike conventional optimization approaches, the proposed framework incorporates sensitivity and robustness evaluation, ensuring stable performance under input uncertainties. The optimized rotor design achieves an 8.65% increase in average torque (92.45 → 100.45 Nm), a 69.61% reduction in torque ripple (22.17% → 6.74%), and a slight efficiency improvement (97.08% → 97.12%) while maintaining the same 25 kW power rating and 2250 rpm base speed. Additionally, the 6% refers to the overall or representative robustness level, confirming robustness against parameter deviations. These results demonstrate that the proposed optimization method enables reliable rotor geometry refinement within the same motor envelope, leading to higher torque density, reduced pulsations, and improved suitability for high-performance EV traction applications.