volume | PIER Journals

Abstract

This paper presents an efficient Taguchi-preconditioned genetic algorithm (TPGA) strategy for the design optimization of a 630-kW permanent magnet vernier motor (PMVM). In the TPGA, firstly, the Taguchi method is combined with comparative finite element analyses (FEA) to judge the influence factors of six typical structural parameters on the torque output. Secondly, four influential parameters are taken from the six typical ones and decided as the variables in the global optimization processes coupling genetic algorithm (GA) and FEA. As two variables with small influence factors are set to constants in the computationally costly optimization processes, the calculation burden can thus be effectively reduced. Thirdly, with the four influential optimization variables, FEA-assisted GA is used to maximize the output torque of the PMVM. During the global optimization processes, a preliminarily optimized structural configuration obtained from the Taguchi analyses is used as the initial values of the variables. Finally, the working performances of the machine with the optimal parameters are obtained through FEM calculations. The optimization effectiveness is validated by comparing the output torque of the GA-optimized machine with that of the initial and the Taguchi-preliminary optimized ones.

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Dr. Gaojia Zhu

Dr. Shijie Liu

Dr. Longnv Li

Dr. Yishuang Zhao

Dr. Shengyang Hu

Dr. Yiran Yun