基于神经网络和多目标优化的增磁永磁电机优化设计

Qiang Ai, Hongqian Wei, Youtong Zhang
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引用次数: 1

摘要

对电动汽车用反凸转子增磁内嵌式永磁电机的优化问题进行了研究和说明。首先选取初始电机的尺寸参数,然后根据参数变量建立电机的有限元模型;其次,为避免有限元分析的频繁使用,采用训练良好的反向传播神经网络模型代替有限元模型;第三,将序列无约束最小化技术与非支配排序遗传算法- ii算法相结合,求解具有不等式约束的多目标优化解。最后,根据从Pareto前提取的最优参数对电机进行重构。仿真结果验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal design for Flux-intensifying Permanent Magnet Machine Based on Neural Network and Multi-objective optimization
The optimization of flux-intensifying interior permanent magnet motor with the reverse salient rotor for electric vehicles is considered and explained. Firstly, the size parameters of an initial motor are selected and then the finite element model is established based on parametric variables. Secondly, to avoid the frequent usage of finite element analysis, a well-trained back propagation neural network model is used to replace the finite element model. Thirdly, the sequential unconstrained minimization technique and non-dominated sorting genetic algorithm-II algorithm are combined together to solve the multi-objective optimization solution with inequality constraints. Finally, the electric machine is reconstructed based on the optimal parameters extracted from Pareto front. The effectiveness of proposed approach is verified by the simulation results.
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