Abstract
Design of an electric machine such as the axial flux permanent magnet synchronous motor (AFPMSM) requires a 3-D finite-element method (FEM) analysis. The AFPMSM with a 3-D FEM model involves too much time and effort to analyze. To deal with this problem, we apply a surrogate assisted multi-objective optimization (SAMOO) algorithm that can realize an accurate and well-distributed Pareto front set with a few number of function calls, and considers various design variables in the motor design process. The superior performance of the SAMOO is verified by comparing it with conventional multi-objective optimization algorithms in a test function. Finally, the optimal design result of the AFPMSM for the electric bicycle is obtained by using the SAMOO algorithm.
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