Abstract: To mitigate the electromagnetic vibration noise of wheel hub motors, a structural optimization design method integrating an improved coati optimization algorithm (ICOA) and parameter scanning method (PSM) was proposed, which took an 18-slot 16-pole 14-inch permanent magnet wheel hub motor as an example. A cogging torque database based on PSM was established to analyze the influence mechanism of stator auxiliary slot quantity on cogging torque. An improved coati optimization algorithm incorporating adaptive boundary and elimination mechanisms was developed, and an ICOA-based solver was designed to optimize the auxiliary slots of the wheel hub motor and was compared with the optimization performance with three solvers based on COA, MA and SSA. A coupling simulation model of the wheel hub motor integrating multiple physics fields such as structural field, electromagnetic field and acoustic fields was established, and the noise sound pressure levels before and after optimizing the stator armature structure were compared. Research results demonstrate: the ICOA solver outperforms other solvers in convergence speed and solution accuracy and the optimized cogging torque amplitude decreases by 59.08%. When unloaded, the axial vibration of the motor shaft weakens by 9.916×103 mm/s2, the radial vibration of the shaft weakens by 2.1 919×104 mm/s2, and A-weighted sound pressure level declines by 3.818 dB. When unloaded, the axial vibration of the shaft weakens by 4.845 9×104 mm/s2, the radial vibration of the shaft weakens by 4.422 6×104 mm/s2, and A-weighted sound pressure level declines by 7.648 dB. The 7-fold vibration has been effectively suppressed, and the overall noise level has been reduced from 70 dB to 60 dB, improving the safety and comfort of drivers and passengers.

Key words: vehicle engineering; wheel hub motor; noise optimization; improved coati optimization algorithm; parameter scanning method; multi-field coupling

摘要： 为削弱轮毂电机电磁振动噪声，以18槽16极14吋永磁轮毂电机为例，提出了一种融合改进浣熊优化算法（ICOA）及参数扫描法（PSM）的结构优化设计方法。建立基于PSM的齿槽转矩数据库，解析定子辅助槽数量对齿槽转矩的影响机理；构建基于自适应边界和淘汰机制的改进浣熊优化算法，设计基于ICOA的求解器对轮毂电机辅助槽进行优化，并与基于COA、MA、SSA的3种求解器对比寻优性能；搭建轮毂电机的结构场、电磁场及声场等多物理场耦合仿真模型，对比定子电枢结构优化前后的噪声声压级。研究结果表明：ICOA求解器在收敛速度和结果精度上优于其他求解器；优化后齿槽转矩幅值削弱59.08%；在空载时，电机转轴轴向的振动削弱了9.91 6×103 mm/s2，转轴径向的振动削弱了2.191 9×104 mm/s2，A计权声压级减小了3.818 dB；在负载时，转轴轴向的振动削弱了4.845 9×104 mm/s2，转轴径向的振动削弱了4.422 6×104 mm/s2，A计权声压级减小了7.648 dB；7倍频振动得到有效抑制，噪声总体水平从70 dB级削弱到60 dB级，提高了驾乘人员的安全性和舒适性。

关键词: 车辆工程；轮毂电机；噪声优化；改进浣熊优化算法；参数扫描法；多场耦合