Charging pile fault diagnosis method based on BOA-SSA-BP neural network
Fengkun Yang, Jie Qiu Bao, Liangliang Chen, Yue Zhao, Ke Xu · 2024
To address the issue of frequent faults in direct current electric vehicle charging piles and the difficulty of precise diagnosis, presenting an enhanced Back Propagation neural network (BP) as a basis for a fault diagnosis approach in charging equipment. Firstly, the operation data set of the charging pile is preprocessed, such as normalization and filling in missing values, and the preprocessed dataset is fed into the BP model for training purposes. Secondly, an enhanced optimization technique combining the Butterfly Optimization Algorithm(BOA) and the Sparrow Search Algorithm(SSA) is introduced to optimize the weights and thresholds of the BP model, resulting in the acquisition of an 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 improved BP method has good computational advantages in terms of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE). Compared with the diagnostic accuracy of the traditional BP algorithm, the improved BP method 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.