Permanent magnet motor multiobjective optimization using multiple runs of an evolutionary algorithm

This paper presents an original method of permanent magnet motor optimal design. The permanent magnet machines optimization must respect multiple constraints. Efficiency and weight have a large influence on the design. These two constraints can be found in several vehicular applications: propulsion motors, electrical fans for combustion engine, driving motors for ancillaries, driving motors for air-circuit fuel-cell compressor...Indeed, in all those embedded applications, the efficiency must be maximal to limit the energy consumption and the mass or the volume must be as low as possible. In this paper, the authors focus on an original multi-objective optimization algorithm well adapted to the previous problem. The method is based on multiplying runs of a new genetic algorithm specialized in broadly covering the solution space around target objectives. This algorithm is an improved variant of previously developed algorithms. The efficiency of these algorithms was proven by comparing with a deterministic algorithm (SQP) and a reference multi-objective genetic algorithm (NSGA-II). The presented algorithm is first validated on a study case from the literature: the dimensioning of a slotless permanent magnet machine. Then experimental results of the complete method applied on a permanent magnet motor are highlighted in a multi-objective point of view.