Design optimization comparison of BLPM traction motor using bees and Genetic Algorithms

Electric machines designs for traction applications are concerned with high power density, high efficiency and low manufacturing cost. In order to achieve better combination of parameters that satisfy the machine electromagnetics, mechanical, and thermal designs limitations; an optimization algorithm is very necessary. Therefore; this work focuses on comparing two optimization algorithms that are utilized to search for optimal solution to design Brushless Permanent Magnet (BLPM) Motor. The target machine design has to meet with FReedomCAR specifications as detailed by UQM Technologies Inc. In this paper, a relatively new modern evolutionary optimization technique known as the Bees Algorithm (BA) employed to search for the optimum design parameters according to a pre-defined multi-objective function. The results from BA have been compared with the other results obtained from Genetic Algorithm (GA). Therefore, both optimization methods have been compared in terms of computational efficiency and ability to find optimal solution for BLPM. However, the comparison results show that BA has better computational ability to search for optimal parameters and achieves lower average value for the objective function than the GA.