Multi-objective optimization of an in-wheel electric vehicle motor

The present work deals with the optimization of an in-wheel surface mounted permanent magnet motor (SMPM) with outer rotor and concentrated windings. The main objective of the optimization procedure is to find the optimum design geometry by maximizing the machine efficiency and minimizing its weight. To reach this goal, two objective functions are used. The first leads to a design with high output torque capability and then high efficiency. The second is a requirement imposed by the fact that the machine is directly integrated inside the vehicle wheel, and therefore a light weight is requested. In order to carry out this study, five multi-objective optimization algorithms were applied: the Genetic Algorithm (GA), the fast elitist multi-objective Genetic Algorithm (NSGA-II), the Adapting Scatter Search for Multi-objective Optimization algorithm (AbYSS), the improved PSO-based Multi-Objective Optimization and a new PSO-based Metaheuristic for Multi-objective Optimization. The reason of testing several intelligence artificial techniques is to find a fast and efficient technique for electrical machines optimization. Based on the objective functions design and the machine constraints, the results obtained by the five algorithms are compared and analyzed. In order to carry out this study, an analytical model describing the geometric, the magnetic and the electric properties of the studied design was firstly developed. Moreover, an optimized machine is chosen and studied by means of finite element analysis (FEA) tool. Then FEA results are compared with those obtained by the optimization procedure. Based on this comparison, a good concordance between the two results is shown.