基于混合遗传算法的永磁同步变桨电机转矩性能优化与设计

孙敬伟; 陈庆伟; 郭杰; 张召环; 周凯兵; 吴桐

doi:10.19912/j.0254-0096.tynxb.2025-0655

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基于混合遗传算法的永磁同步变桨电机转矩性能优化与设计

更新时间：2026-10-15

- 基于混合遗传算法的永磁同步变桨电机转矩性能优化与设计
- 基于混合遗传算法的永磁同步变桨电机转矩性能优化与设计
- 太阳能学报 2026年47卷第9期页码：680-689
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- DOI：10.19912/j.0254-0096.tynxb.2025-0655
  中图分类号： TK83
- 网络首发：2026-10-10，
  
  纸质出版：2026
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孙敬伟, 陈庆伟, 郭杰, 等. 基于混合遗传算法的永磁同步变桨电机转矩性能优化与设计[J]. 太阳能学报, 2026,47(9):680-689.

孙敬伟, 陈庆伟, 郭杰, et al. 基于混合遗传算法的永磁同步变桨电机转矩性能优化与设计[J]. 2026, 47(9): 680-689.
孙敬伟, 陈庆伟, 郭杰, 等. 基于混合遗传算法的永磁同步变桨电机转矩性能优化与设计[J]. 太阳能学报, 2026,47(9):680-689. DOI： 10.19912/j.0254-0096.tynxb.2025-0655.

孙敬伟, 陈庆伟, 郭杰, et al. 基于混合遗传算法的永磁同步变桨电机转矩性能优化与设计[J]. 2026, 47(9): 680-689. DOI： 10.19912/j.0254-0096.tynxb.2025-0655.

摘要

针对新能源汽车驱动电机对高转矩密度、低转矩波动的需求

围绕内置式永磁同步电机开展设计与优化研究。首先

根据整车动力需求确定电机基本参数

完成初始结构设计

并通过有限元仿真分析磁场与转矩特性

验证方案合理性。针对转矩脉动问题

对比转子修形、开辅助槽及定子槽口优化3种抑制措施;提出混合遗传算法（田口法与遗传算法相结合）对电机转矩性能进行多目标优化

在降低齿槽转矩与转矩波动的同时可提高平均输出转矩

显著提升优化效率与效果。研究结果表明

使用遗传算法后电机平均转矩提升5.4%

齿槽转矩值减少17.27%

转矩脉动降低12%。实验证明该方法可有效抑制转矩脉动

提升电机变桨系统运行稳定性

适用于风力机变桨系统设计。

Abstract

Addressing the demands of high torque density

low torque ripple for traction motors in new energy vehicles

this paper focuses on the design and optimization of interior permanent magnet synchronous motor （IPMSM）. First

the basic parameters of the motor are determined based on the vehicle's power requirements

and an initial structural design is completed. Finite element simulations are then conducted to analyze the magnetic field and torque characteristics

verifying the rationality of the proposed design. To address torque ripple issues

three mitigation measures are compared： rotor shaping

auxiliary slot opening

and stator slot optimization. Subsequently

a hybrid genetic algorithm （combining the Taguchi method with the genetic algorithm） is proposed for the multi-objective optimization of motor torque performance. This approach reduces cogging torque and torque ripple while simultaneously improving the average output torque

significantly enhancing optimization efficiency and effectiveness. The optimization results demonstrate that

with the genetic algorithm

the average torque is increased by 5.4%

the cogging torque is reduced by 17.27%

and the torque ripple is decreased by 12%. Experimental validation confirms that this method effectively suppresses torque ripple and improves the operational stability of the motor's pitch system

making it applicable to the design of pitch systems for wind turbines.

关键词

Keywords

references

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