Optimal Design of a Brushless DC Motor Aiming at Decreasing Cogging Torque

One of the important issues in designing high-performance brushless direct current (BLDC) motors is reducing the cogging torque since it results in mechanical vibration, audible noises, and torque ripples, which adversely impact the performance of the motor, which is awkward high-accuracy applications. This paper proposes an optimum design for BLDC motors aimed at reducing the cogging torque based on the capability of metaheuristics algorithms in finding the optimal solution. For this purpose, a simplified cogging torque equation is used as the objective function whose design variables include air gap length, magnet height, slot height, slot opening, and motor axial length. These are the five most influential parameters of cogging torque. On the other hand, we employ not only the old metaheuristics algorithms like the Genetic Algorithm (GA) and Simulated Annealing (SA) but also more recent algorithms such as Keshtel Algorithm (KA) along with the hybrid ones to benefit from their strength. The simulation is performed in the Matlab package. First, five selected optimization algorithms are applied and the results are investigated. The results of all the algorithms show a significant reduction in the cogging torque. Eventually, the proposed algorithms are compared to one another in terms of their value of cogging torque. The results show the superiority of the KASA algorithm in comparison with the others.