Surrogate Assisted Contour Algorithm for Optimal Design of Interior Permanent Magnet Synchronous Motor for Electric Vehicles

Abstract

In this paper, to solve the multimodal optimization problem of electric motors, the surrogate assisted contour algorithm (SACA) is proposed. The surrogate models of SACA are generated and updated with fewer number of function calls by using the added samples of the proposed algorithm. By utilizing the contour region, SACA efficiently adds samples to generate surrogate model. The outstanding performance of the proposed algorithm is verified by comparison with niching genetic algorithm at two mathematical test functions. Finally, the SACA is applied to the optimal design of the interior permanent magnet synchronous motor for electric vehicles.