Optimal Design of an Interior Permanent Magnet Synchronous Motor by Using a New Surrogate-Assisted Multi-Objective Optimization

Abstract

To optimize an interior permanent magnet synchronous motor (IPMSM) design for a fuel cell electric vehicle, a new surrogate-assisted multi-objective optimization (MOO) algorithm is proposed in this paper. The proposed algorithm is a multi-objective algorithm (MOO) that can account for three kinds of objectives such as the torque amplitude, torque ripple, and magnet usage simultaneously to improve the power transmission and to reduce the noise, vibration, and cost for various design variables. While the conventional MOO algorithms have a series that requires many function evaluations, especially considering many objectives and design variables, the proposed algorithm can create an accurate and well-distributed Pareto front set with few function evaluations. In comparison with the conventional MOO algorithms, the outstanding performance of the proposed algorithm is verified. Finally, the proposed algorithm is applied to an optimal design process of an IPMSM.