Multi-objective optimization of slotted stator switched reluctance motor for electric vehicle application

Abstract

The popularity of electric vehicles (EVs) and hybrid electric vehicles (HEVs) is projected to increase as environmental consciousness rises. They have emerged as a potential substitute for traditional electrical machines like the switched reluctance motors (SRMs). SRMs are renowned for their low cost of production and maintenance, built-in fault tolerance, and simple design. Additionally, because the machine's rotor construction does not require copper coils or permanent magnets, production costs are significantly reduced. However, it has disadvantages, including as high non-linearity, high torque ripple, and acoustic noise production. In this research, a method for designing slotted stator teeth switched reluctance motors (SST-SRM) in EVs using a genetic algorithm (GA) optimization design with multiple targets is provided. In order to achieve the best possible balance between peak torque (Tp), average torque (Tavg), and efficiency, the developed optimization function is chosen. The stator/rotor pole arc angle and slot width/depth are chosen as the optimized variables. When compared to traditional SRM, the optimization results of proposed SST-SRM demonstrate improvements in peak torque (24.40%, 36.98%, 42.73%, and 42.45%), average torque (7.40%, 29.94%, 33.00%, and 33.62%), and efficiency (0.27%, 0.52%, 0.97%, and 1.03%).