Optimal Design of Switched Reluctance Motor Using Genetic Algorithm

Abstract

Switched reluctance motor (SRM) is gaining more interest in both research and industry. Its simple structure without windings or permanent magnets on the rotor makes the motor robust and reliable with reduced manufacturing cost. The SRM also provides high starting torque and high efficiency over a wide range of speeds, which is strongly desired for electric vehicles’ applications. However, these advantages of switched reluctance motors come with some challenges. Torque ripples, low power density, and temperature rise are common questions about SRM. This paper utilizes multi-objective optimization of SRM design to get most of the SRM desired characteristics with minimization of the machine’s common drawbacks. The optimization process has considered twelve variables and five objective functions. These functions include average torque, efficiency, iron weight, torque-ripples, and maximum temperature rise. The electromagnetic analysis of each candidate is performed by the finite elements method (FEA). The performance indices of SRM are calculated based on FEA analysis results via calculations that compensate for accuracy and computation time. The multi-objective genetic algorithm technique (MOGA) combines the objective functions into a single objective function. Verifying the optimal design comprises generating the efficiency map, torque profile, and dynamic simulation of the motor. This paper mainly focuses on the design and optimization of SRM to fulfill the general requirements of electric vehicle applications