Design and Optimization of Permanent-Magnet Fault-Tolerant Motors for Electric Aircraft Based on Multi-Objective Genetic Algorithm

As the core component of the electric-driven aircraft, the performance of the motors directly affects the operational performance and economic benefits of the aircraft. In this paper, a permanent-magnet fault-tolerant motors (PMFTM) for electric aircraft with a power of 10 kW and a speed of 2000 rpm is taken as the research object, and volume, loss and torque are selected as the optimization objectives. Based on the sensitivity analysis of the initial variables, the variables with higher correlation with the optimization objectives are selected. The sample space is constructed using central composite design (CCD) and the kriging response surface is built. Finally, the response surface is combined with the multi-objective genetic algorithm (MOGA) to obtain the Pareto solution set. The optimal points are selected based on parameter prioritization for simulation verification. After optimization, the motor volume is reduced by about 12.8% and the total motor loss is reduced by about 22%.