Nature Inspired Optimal Design of Axial Flux Permanent Magnet Motor for Electric Vehicle

Optimal design of electrical machines generally is usually a complicated process with a lot of optimization parameters, constrains and complex objective functions. Standard deterministic methods can sometimes have difficulties to solve such problems, where on the other hand lately the stochastic methods have proved that they are quite powerful in solving such problems. There is large group of stochastic methods that are nature inspired and based on behavior of certain species in nature. The purpose of this research work is to implement several nature-based optimization methods in the optimal design of an electric motor and investigate the effectiveness of the implemented methods. The object of investigation is an axial flux permanent magnet motor. The objective function in the optimization process is defined to be the efficiency of the motor. The optimization methods that are implemented in the optimal design of the permanent magnet motor are Genetic Algorithm, Particle Swarm Optimization and Cuckoo Search. A comparative analysis of all the optimal solutions gained from each optimization method is performed, based on the values of the objective function, optimization parameters as well as several other specific motor parameters. For each optimized model a Finite Element Analysis is also performed in order to check and validate the quality of the results.