基于DBO-BP的永磁同步电机损耗预测模型  

A loss prediction model for PMSM based on BP neural network optimizedby dung beetle optimizer

作　　者：李良辉 李乐[1,2] 王茜 张喜明 LI Lianghui;LI Le;WANG Qian;ZHANG Ximing(Mechanical Electrical Engineering School,Beijing Information Science&Technology University,Beijing 100192,China;Key Laboratory of Modern Measurement&Control Technology,Ministry of Education,Beijing Information Science&Technology University,Beijing 100192,China;China North Vehicle Research Institute,Beijing 100072,China)

机构地区：[1]北京信息科技大学机电工程学院,北京100192 [2]北京信息科技大学现代测控技术教育部重点实验室,北京100192 [3]中国北方车辆研究所,北京100072

出　　处：《现代制造工程》2025年第2期130-137,共8页Modern Manufacturing Engineering

基　　金：北京信息科技大学基础产品创新项目(MKF20240018)。

摘　　要：针对有限元法计算永磁同步电机损耗的实时性问题,提出了一种采用蜣螂优化(Dung Beetle Optimizer,DBO)算法优化BP神经网络的永磁同步电机损耗预测模型。以一台额定功率为40 kW的车用永磁同步电机为研究对象,首先,在有限元分析软件Maxwell中建立了电机的电磁场损耗求解模型;其次,通过最佳空间填充试验设计方法,选取了600组控制参数组合(电枢电流、内功率因数角和转速)进行电机损耗求解,得到训练神经网络所需的数据集;最后,利用DBO算法对BP神经网络进行优化,构建了基于DBO-BP神经网络的永磁同步电机损耗预测模型,并与传统的BP神经网络、遗传算法优化的BP神经网络模型的预测效果进行对比。结果表明,DBO-BP神经网络预测模型在预测精度上优于其他2种神经网络模型,预测误差控制在5.86%以内,且计算速度是有限元模型的1267倍,能有效替代耗时较多的有限元模型,提高了损耗预测的实时性和准确性,为电机损耗预测提供了一种有效的方法。To address the real-time issue of loss calculation in Permanent Magnet Synchronous Motor(PMSM)using the finite element method,a loss prediction model for PMSM based on BP neural network optimized by Dung Beetle Optimizer(DBO)algorithm was proposed.The study focuses on a 40 kW automotive PMSM.Firsty,the electromagnetic field loss solution model of the motor was established in the Finite Element Analysis(FEA)software Maxwell.Next,600 sets of control parameter combinations(armature current,internal power factor angle,speed)were selected for the motor loss solution through the optimal space-filling experimental design method to get the data set required for training the neural network.Finally,the DBO algorithm was utilized to optimize the BP neural network and a loss prediction model for PMSM based on the DBO-BP neural network was constructed.The predictive performance was compared with traditional BP neural networks model and BP neural network model optimized by genetic algorithms.The results indicate that the DBO-BP neural network prediction model surpasses the other two neural network models in prediction accuracy,with the prediction error controlled within 5.86%,and the computation speed was 1267 times faster than the finite element model.This effectively replaces the time-consuming finite element model,enhancing the real-time capability and accuracy of loss prediction,thus providing an effective method for motor loss prediction.

关 键 词：永磁同步电机 损耗预测 有限元分析 蜣螂优化算法 神经网络 

分 类 号：TM341[电气工程—电机]