volume | PIER Journals

Abstract

As a new type of motor, axial flux permanent magnet synchronous motor has the advantages of compact structure and high power density. It shows good application prospects in new energy vehicles, unmanned aerial vehicles, and other fields. However, axial flux permanent magnet synchronous motor needs to consider the balance of multiple objectives during the design process, which makes its optimal design a complex multi-objective optimization problem. Therefore, the study proposes a motor optimization method based on multi-objective genetic algorithm. The method optimizes the rotor and stator parameters of the motor by establishing an analytical model of the motor's magnetic field and combining it with a multi-objective genetic algorithm. The experimental results indicated that the optimized motor with multi-objective genetic algorithm reached 92% in terms of efficiency, 25% in terms of power density; energy consumption was reduced to 2.5 kWh; failure rate was reduced to 1.5%; and noise level was reduced to 65 dB. In addition, the multi-objective genetic algorithm significantly improved the control stability index, which increased to 98%, indicating a more stable motor response under varying loads. The disturbance rejection capability was enhanced to 99%, demonstrating strong resistance to external noise and parameter fluctuations. Furthermore, the system response frequency reached 100 Hz, reflecting a faster dynamic response to input variations. It is indicated that the optimization method based on multi-objective genetic algorithm can effectively enhance the comprehensive performance of axial flux permanent magnet synchronous motor and significantly improve its competitiveness in high power density and high efficiency applications.

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Dr. Huijun Liu