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

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