Optimal design of a BLDC motor using African vulture optimization algorithm

Abstract

BLDC motors are extensively favoured in robots, electric vehicles, and many industrial applications due to their superior torque, efficiency, and speed control features as compared to traditional motors. In this paper, collective intelligence-based optimization approaches are used to improve the cost-effectiveness, volume, and total loss of a BLDC motor. The geometric dimensions of the motor are considered the optimization parameters. The study employs several optimization algorithms, such as the Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO), Cuckoo Search Algorithm (COA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the African Vulture Optimization algorithm (AVOA), used for the optimal BLDC motor design. The investigation can provide data on the convergence rates and optimal design parameters of a BLDC motor through optimization approaches. The standard deviation has also been calculated for each optimization technique to assess the stability and consistency of finding the optimum solution. The paper also includes a sensitivity analysis of the AVOA regulating parameter to obtain the best solutions. In comparison to GA, PSO, COA, GWO, and WOA, the AVOA technique achieves faster convergence. Additionally, the ANOVA test is employed as a statistical test to validate the efficacy of the AVOA. According to the results, AVOA outperforms all other optimization algorithms for the design of the BLDC motor.

Keywords

• Brush-less DC motors
• Whale optimization algorithm
• Cuckoo search algorithm
• Particle swarm optimization algorithm