Reduce Cogging Torque in a Brushless DC Motor Via Multi-Objective Optimization

Due to the negative effect of the cogging torque on the Brushless Direct Current (BLDC) motors performance such as the noise and vibration, the cogging torque reducing is became an important issue in design of BLDC. On the other hand, the most of methods used to alleviate cogging torque have an effect on the output torque. Therefore, the purpose of this research is to minimized cogging torque without having a significant effect on the output torque. Multi-objective optimization is a reliable method to achieve the desired aim which reduce the cogging torque with a view to the value of output torque. In this work, the Multi Objective Simulated Annealing (MOSA), Multi Objective Particle Swarm optimization (MOPSO) and Multi Objective Red Deer Algorithm (MORDA), which are well-known optimization way, is employed to obtain the best design of a BLDC motor. Simulation results display that in all optimization algorithms used, the cogging torque is significantly reduced in the time the output one is maximized.