Design optimization of brushless DC motor using Particle Swarm Optimization

This paper depicts an enhanced Particle Swarm Optimization (PSO) algorithm for Brushless DC Motor (BLDC) design. The algorithm is applied to shape optimization of a 120 W BLDC motor using a multiobjective optimization problem. While the objectives are maximization of average torque and minimization of cogging torque in the BLDC motor, magnet length, air gap and stator slot opening are considered as the design variables. The results obtained using the PSO algorithm indicate that it is capable of giving better design solutions when compared and investigated with those obtained from Genetic Algorithm (GA) and Finite Element Analysis (FEA) based simulation. The results also demonstrate that the proposed algorithm is more effective and outperforms GA in terms of solution quality, accuracy, constraint handling and computational time.