为满足电动汽车对续航与动力的需求,驱动电机向高功率密度发展,却因内部发热量剧增引发绝缘老化、永磁体退磁等问题,威胁电机安全与寿命。文章分析驱动电机现有风冷、液冷、油冷技术的应用局限,探究定子、转子等关键部件结构与散热路径的关联,提出基于多物理场耦合的结构-热管理协同优化策略,结合遗传算法、粒子群算法构建多目标优化模型,并通过ANSYS有限元仿真与试验验证方案可行性。结果表明,该策略可有效平衡电机轻量化、低温升与高可靠性,为下一代高性能电动汽车驱动系统开发提供参考。

To meet the demands of electric vehicles for range and power, drive motors are developing towards higher power density. However, due to the sharp increase in internal heat generation, problems such as insulation aging and demagnetization of permanent magnets occur, threatening the safety and lifespan of the motors. This article analyzes the application limitations of the existing air cooling、liquid cooling and oil cooling technologies for drive motors, explores the correlation between the structures of key components such as stators and rotors and the heat dissipation paths, proposes a structure-thermal management collaborative optimization strategy based on multi-physical field coupling, constructs a multi-objective optimization model by combining genetic algorithms and particle swarm optimization algorithms, and verifies the feasibility of the scheme through ANSYS finite element simulation and experiments. The results show that this strategy can effectively balance motor lightweighting, low temperature rise and high reliability, providing a reference for the development of the next generation of high-performance electric vehicle drive systems.