Cogging torque minimization in brushless DC motor using PSO and GA based optimization

This paper proposes an optimal design of brushless permanent magnet DC motor (BLDC) based on the capability of Particle Swarm-based Optimization (PSO) Algorithm and Genetic Algorithm (GA) in finding the optimal solution. The procedure makes use of slot opening, magnet length and air gap length as design variables. Cogging torque is treated as the performance indices. The proposed algorithms are more effective in terms of solution quality, accuracy, constraint handling and computational time. The simulation results investigated with finite element analysis (FEA) indicate the use of PSO and GA based optimization tools for giving better design solutions.