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This paper aims to further improve the efficiency of multi-objective optimization design of synchronous reluctance motors (SynRMs) using the level set (LS) method, which has the advantage of obtaining a practical shape. The solutions obtained by gradient methods tend to be local ones due to the multi-modality of the objective function, especially when multiple objective functions. A huge number of trial calculations are required to obtain a high-quality and broadly distributed Pareto front. Therefore, it is indispensable to effectively get out of the local solutions in the optimization process with the LS method.

The authors propose a novel method appropriately switching multiple objective functions with high independence of sensitivity information. The authors adopt highly independent mathematical expressions for the objective functions of the average torque and torque ripple. In addition, the authors repeatedly perform the optimization while appropriately selecting the sensitivity information of one objective function from multiple ones, which enables the authors to effectively break out of local solutions in the optimization process.

The proposed method was applied to the shape optimization of SynRM flux barriers and successfully searched a more extensive and advanced Pareto front in comparison with the conventional method.

The proposed method adopts search spaces with mathematical high independence for average torque and torque ripple. In the optimization process, when the solution search is judged to get stuck by several criteria, the search space is alternately switched to effectively get out of local solutions.