关键词: 不对称极内置式永磁同步电机, BP 神经网络, 多目标优化, 非支配排序遗传算法-Ⅱ

Abstract: Aiming at the problem of long time consuming in finite element optimization for the V-shaped interior permanent magnet synchronous motor with asymmetric poles, an optimization algorithm based on the surrogate model of BP neural network was proposed. The performance of the sampling points was calculated based on the finite element model, and the sample database was constructed. The rotor-related structural parameters were taken as the optimization variables,
and high average torque and low torque ripple were taken as the optimization objectives. Based on the sample data, the surrogate model was obtained by BP neural network, and then the NSGA-II algorithm was used for the structural optimization. The results show that the optimized structural parameters have high accuracy, and the sampling process reduces the number of finite element model calls by 90. 7% compared with the direct finite element-based optimization.

Key words: asymmetric-pole interior permanent magnet synchronous motor ( AIPMSM), BP neural network, multiobjective optimization, non-dominated sorting genetic algorithm Ⅱ( NSGA-Ⅱ)