Optimization and characteristics analysis of high torque density 12/8 switched reluctance motor using metaheuristic gray wolf optimization algorithm

Abstract

In this paper, the optimization and characteristics analysis of a three-phase 12/8 switched reluctance motor (SRM) based on a Grey Wolf Optimizer (GWO) for electric vehicles (EVs) application is presented. This research aims to enhance the output torque density of the proposed SRM. Finite element method (FEM) was used to analyze the characteristics and optimization process of the proposed motor. The proposed metaheuristic GWO combines numerous objective functions and design constraints with different weight factors. Maximum flux density, current density, and motor volume are selected as the optimization constraints, which play a significant role in the optimization process. GWO performs optimization for each iteration and sends it to FEM software to analyze the performance before starting another iteration until the optimized value is found. Simulations are employed to understand the characteristics of the proposed motor. Finally, the optimized prototype motor is manufactured and performance is verified by experiment. It is shown that the torque can be increased by 120% for the same outer volume, by using the proposed method.