Abstract: 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 1 267 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.

Key words: Permanent Magnet Synchronous Motor(PMSM), loss prediction, finite element analysis, Dung Beetle Optimizer (DBO) algorithm, neural network