Torque ripple reduction by optimizing magnet shape using GA in axial gap motor

Abstract

This paper proposes a method to reduce torque ripple in axial gap motors by multi objective optimization of permanent magnets (PMs) shape using genetic algorithm (GA). Torque ripple is a problem because it causes vibration and noise. Conventionally, torque ripple has been reduced by quantitatively designing the PMs in the shape of multiplicative wave. However, it is difficult to optimize the objective function only by quantitative evaluation through sensitivity analysis. Therefore, in this study, the functions constituting the PMs interface shape are expressed as a Fourier-based series. The PMs is optimized by optimizing combination of their coefficients with GA. As a result, the proposed model is almost equal to the average torque of the basic model and the torque ripple is significantly reduced. Furthermore, fillets are applied and the effect on each characteristic is verified.