Multi-objective Parameter Optimization of Permanent Magnet Synchronous Motor based on Improved Gray Wolf Algorithm

Abstract

In today's critical fields such as industrial automation and electric vehicles, permanent magnet synchronous motors (PM synchronous motors) are favored for their excellent high efficiency, high power density, and outstanding control characteristics. However, with the continuous improvement of motor performance standards in industrial production, how to further improve the operating efficiency of PM synchronous motors and effectively suppress torque pulsation has become the core issue of motor performance optimization. In order to improve the operating efficiency of permanent magnet synchronous motors and suppress torque pulsations, this study proposes an improved gray wolf algorithm, which finely adjusts the convergence factor by exploiting the dynamic output of the chaotic mapping while updating the alpha wolf position in the gray wolf algorithm. In addition, the updating mechanism of X₂ is also improved. Finally, a comparative analysis between the initial design scheme and the optimized scheme was carried out by means of finite element analysis. The finite element analysis results show that the motor efficiency before optimization is 88.52% and the torque pulsation is 8.13%, while the motor efficiency after multi-objective optimization is 89.39% and the torque pulsation after multi-objective optimization is 4%. Obviously, the Amoy Gold Gray Wolf algorithm is superior to the Gray Wolf algorithm in the multi-objective optimization design of the motor, which can quickly achieve the optimal parameter configuration of the permanent magnet synchronous motor.