Optimal design of interior permanent magnet synchronous motor by using a new surrogate assisted multi-objective optimization

Abstract

In this paper, a new surrogate assisted multi-objective optimization algorithm is presented to optimize an interior permanent magnet synchronous motor (IPMSM) design for fuel cell electric vehicle. The proposed algorithm is a multi-objective algorithm (MOO) that can both maximize the efficiency and torque amplitude and minimize the torque ripple to improve the power transmission efficiency, noise, and vibration considering various design variables. While the conventional MOO algorithms have a problem of requiring too many function calls, the proposed algorithm can make a well-distributed Pareto front set with fewer function calls. The superior performance of the proposed algorithm is verified by comparing with conventional MOO algorithms. Finally, the proposed algorithm is applied to an optimal design process of an IPMSM.