SSO-DRSS Axial Flux sensing in Switching Permanent Magnet Motor for cogging torque mitigation

Abstract

This paper addresses the imperative requirements of high efficiency, power density, and speed in Axial Flux-Switching Permanent Magnet Motors (AxFSPM) for electric vehicles. Recognizing the limitations of conventional motor designs, especially their weight and size, hindering their universal applicability, this study proposes a novel approach. A Double Rotor Single Stator (DRSS) configuration is introduced, and its parameters are systematically optimized using the Shark Smell Optimization (SSO) algorithm. The SSO algorithm refines critical motor parameters, including axial length, turns per phase, and stator and rotor dimensions, resulting in a more compact AxFSPM motor with enhanced torque. Geometrical constraints are similarly optimized with SSO, and magnet skewing angles are employed for cogging torque reduction. The optimization process, implemented in MATLAB and ANSYS Maxwell, yields an average torque below 1.4 N m and cogging torque below 0.8 N m, demonstrating the effectiveness of the proposed approach. Comparative analyses against various motor models underscore the superiority of the SSO-optimized DRSS motor in terms of efficiency and compactness.