Multi-Objective Optimal Design and Fault-Tolerant Control of a Five-Phase Permanent Magnet Motor

In order to simultaneously improve the output torque and operating efficiency of the motor in the full torque range and achieve lightweight design, this paper proposes a model-data fusion optimization design method for a five-phase permanent magnet fault-tolerant motor. Firstly, the data samples are established by finite element calculation, and the structural parameters with higher sensitivity to the optimization objective are selected as optimization variables; secondly, the polynomial response surface model is established, and the optimal solution is obtained based on the data through the improved particle swarm algorithm by iterating repeatedly to obtain the global optimization design of the motor; the correctness of the model and the validity of the global optimization design of the motor with model-data fusion are verified through simulation, which validates that the proposed model-data fusion method is effective. Finally, the validity of the global optimal design of the motor model-data fusion and the reasonableness and validity of the adopted fault-tolerant control strategy for the five-phase permanent magnet motor under single-phase fault are verified through simulation.