A practical approach to cogging torque reduction in a Permanent Magnet Synchronous Motor using Non-dominated Sorting Genetic Algorithm

In this paper, Non-dominated Sorting Genetic Algorithm (NSGA) is used to reduce cogging torque in Permanent Magnet Synchronous Motor (PMSM). NSGA is a Multiple Objective Optimization (MOO) algorithm. Three parameters that are related to magnets of machine i.e. pole embrace, magnet thickness and pole offset are used as optimization variables in the algorithm. The goal of algorithm is to minimize the peak value of cogging torque while the average air gap flux density remains unchanged. Also the algorithm tries to minimize the area of the magnets. In each iteration of GA, Finite Element Method (FEM) is used to calculate the cogging torque and to obtain the air gap flux density in this study. The results show that the cogging torque is reduced by more than 10 times using proposed method.