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<p>To meet the current design requirements for low torque pulsation and low cogging torque in drive motors for new energy electric vehicles, an automotive hairpin winding double-layer magnet interior permanent magnet synchronous motor (IPMSM) has been designed. Firstly, based on the sensitivity analysis of the motor’s optimized parameters, an improved optimal iterative genetic algorithm (OIGA) is employed for multi-objective optimization. Secondly, Halbach magnetization is applied to the double-layer magnets, and the layer with the superior optimization effect is chosen. Parametric optimization search is then conducted to determine the optimal magnetization angle and length. Next, single-pole two-pair auxiliary slots are incorporated into the rotor, and optimization is carried out via parametric scanning to select the optimal slotting angle and depth. Finally, a Maxwell-Simplorer-Simulink co-simulation model is established. An improved super-helix control rate and sliding mode speed perturbation observer are utilized for control analysis. The results indicate that after several rounds of optimization, the motor’s average torque has increased 2.54%, while cogging torque and torque ripple are significantly reduced by 96.15% and 33.54%. Moreover, the adopted control strategy significantly enhances the system’s robustness and anti-interference capability.</p>

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Design and Control of Automotive Hairpin Winding Double V-Shaped Interior Permanent Magnet Synchronous Motor Based on OIGA

  • 期刊论文

  • 发表日期：

    2026年3月16日

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  • Yuhang Zhang,
  • Zhonggen Wang,
  • Han Lin,
  • Tianlong Deng

摘要

To meet the current design requirements for low torque pulsation and low cogging torque in drive motors for new energy electric vehicles, an automotive hairpin winding double-layer magnet interior permanent magnet synchronous motor (IPMSM) has been designed. Firstly, based on the sensitivity analysis of the motor’s optimized parameters, an improved optimal iterative genetic algorithm (OIGA) is employed for multi-objective optimization. Secondly, Halbach magnetization is applied to the double-layer magnets, and the layer with the superior optimization effect is chosen. Parametric optimization search is then conducted to determine the optimal magnetization angle and length. Next, single-pole two-pair auxiliary slots are incorporated into the rotor, and optimization is carried out via parametric scanning to select the optimal slotting angle and depth. Finally, a Maxwell-Simplorer-Simulink co-simulation model is established. An improved super-helix control rate and sliding mode speed perturbation observer are utilized for control analysis. The results indicate that after several rounds of optimization, the motor’s average torque has increased 2.54%, while cogging torque and torque ripple are significantly reduced by 96.15% and 33.54%. Moreover, the adopted control strategy significantly enhances the system’s robustness and anti-interference capability.