Cogging Torque Reduction of Permanent Magnet Motor Based on Particle Swarm Optimization Algorithm

Abstract The permanent magnet motor will inevitably produce cogging torque because of its inherent structure, which will cause vibration and noise and affect its application in certain high-precision applications. Firstly, considering the actual cogging structure of the stator, a general analytical model of cogging torque of the permanent magnet motor is obtained by the energy-based approaches. On the basis of the further derivation of cogging torque analytical expressions with different segmented pole structures, a cogging torque combination reduction method is presented based on the particle swarm optimization algorithm, by comprehensively optimizing three different combinations of the opening width of stator slot, the calculating pole arc coefficient and the number of PM pole segments, the method can reduce the “peak-to-peak value” of cogging torque. The finite element analysis (FEA) results prove the validity of the proposed method.