Using Multi-objective Optimization and Finite Element Method to Reduce Cogging Torque in a Brushless DC Motor

Abstract

A significant issue in the design of the Brushless Direct Current (BLDC) motors is the cogging torque reduction that leads to negative effects on the BLDC motor's performance such as vibration and noise. However, most methods proposed to reduce cogging torque influence the output torque. Hence, this research aims to reduce cogging torque without having a significant effect on the output torque. For achieving the desired aim which is minimizing the cogging torque concerning the value of output torque, multi-objective optimization is a reliable approach. In this paper, some well-known multi-objective optimization including Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Simulated Annealing (MOSA), and Multi-Objective Red Deer Algorithm (MORDA), are employed to obtain the optimal design of a BLDC motor. In all used optimization algorithms, Simulation results are satisfying and display a significant reduction in the cogging torque, as well as the output torque increases.