Abstract: To address the issue of frequent faults in direct current electric vehicle charging piles and the difficulty of precise diagnosis, a fault diagnosis method based on an improved BP(Back Propagation) neural network is proposed. Firstly, the operation data set of the charging pile is preprocessed, such as normalization and filling in missing values, and the processed data set is input into the BP model for training. Secondly, an optimization method based on the BOA-SSA ( Butterfly Optimization Algorithm improved Sparrow Search Algorithm) is introduced to optimize the weights and thresholds of the BP model to obtain the optimal model. Finally, the fault status of the charging pile is diagnosed based on the optimized BP model. The simulation results show that the proposed BP method has good computational advantages in terms of MAE(Mean Absolute Error), MAPE(Mean Absolute Percentage Error), and RMSE(Root Mean Square Error). Compared to the traditional BP algorithm, the diagnostic accuracy of the improved BP method has increased by 14. 85% , which can diagnose the state of the charging pile accurately, providing a strong guarantee for the fault diagnosis of electric vehicles.

Key words: charging pile, fault diagnosis, neural network, sparrow search algorithm, butterfly optimization algorithm