Affiliations: [a] CES Laboratory, Engineering School of Sfax, Sfax, Tunisia | [b] SOIE Laboratory, National School of Computer Science, Manouba, Tunisia
Correspondence:
[*]
Corresponding author: Ali Mansouri, CES Laboratory, Engineering School of Sfax, B.P. W 3038, Sfax, Tunisia. E-mail:[email protected]
Abstract: 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.
