الفهرس 
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Abstract
Due to its unique characteristics, switching reluctance motor (SRM) has increased interest in research and industry. The simple structure, without windings or permanent magnets on the rotor, makes the motor robust, reliable, and, most importantly, a low manufacturing cost. SRM also provides high starting torque and high efficiency over a wide range of speeds which is strongly desired in electric vehicle (EVs) applications. However, these advantages of SRMs come with some challenges. Torque ripples, low power density, and temperature rise are common problems in SRMs. This work mainly studies the design and optimization of SRM. The design and optimization process aims to fulfill the general requirements of EV applications. In this thesis, a multi-objective optimization of SRM design is achieved to obtain most of the SRM desired characteristics with minimization of the machine’s common drawbacks. The optimization considers the eleven dimension variables, current density, and five characteristics. These characteristics include average torque, efficiency, iron weight, torque ripples, and maximum temperature rise. Different design candidates are produced every generation during the optimization process. The best design candidates are selected after that, depending on the analysis results. The electromagnetic analysis of each design candidate is performed by finite elements analysis (FEA). The performance indices of SRM are calculated based on FEA analysis results using Lua script to achieve accuracy and speed. The multi-objective genetic algorithm technique (MOGA) uses a multi-term objective function to optimize the design. Each term of the objective function represents a particular SRM characteristic. An efficiency map, torque profile, and dynamic motor simulation are provided to verify the optimal design.