Optimized Design of Permanent Magnet Assisted Synchronous Reluctance Motor Using Oriented Auto-tuning Niching Algorithm

Abstract

In this paper, an oriented auto-tuning niching algorithm (OANA) is proposed for the design optimization of a permanent magnet assisted synchronous reluctance motor (PMa-SynRM) for a pedal-assist system electric bicycle (PAS-EB). The OANA is a fast and accurate optimization algorithm for finding the optimal points in multi-modal functions. Specifically, the OANA remedies the shortcomings of the conventional auto-tuning niching genetic algorithm by adopting a probability method that is based on the navigational pathways. The OANA showed excellent performance by utilizing mathematical multi-modal test functions. Then, the OANA was applied to cogging torque optimization for a PMa-SynRM design of the PAS-EB and found several optimal points. Finally, the optimum design is derived by comparing the average torque and torque ripple of the obtained design points.