Multiobjective Optimization of a Dual Stator Brushless Hybrid Excitation Motor Based on Response Surface Model and NSGA 2
Xu Wang, Ying Fan, Xingchi Lu, Qiushuo Chen, Christopher H. T. Lee
Abstract
This article proposes a multiobjective optimization design framework for a double stator brushless hybrid excitation motor (DSBHEM) to provide high torque, wide flux regulation ability and low torque ripple. The design variables are divided into sensitive level and insensitive level by sensitivity analysis. The response surface model (RSM) and nondominated sorting genetic algorithm 2 (NSGA2) are organically combined to generate a set of Pareto solutions for the sensitive level variables, from which the optimal values of the sensitive level variables are obtained. In addition, the optimal values of insensitive variables are obtained through single parameter scanning optimization, and a set of final optimal design variables are obtained. The electromagnetic performance of the initial design and the optimal design are compared with finite element analysis. Finally, a prototype is manufactured to verify the proposed concepts.