Application of BP neural network in on-board charger fault diagnosis

Abstract

With the accelerated growth of the number of electric vehicles, the frequent occurrence of electric vehicle charging safety accidents has brought some safety hazards to people. The fault diagnosis of on-board charger circuit has gradually become the focus of attention of scholars. This paper uses simulation software to conduct modeling simulation analysis, failure mode analysis and data acquisition for the on-board charger of electric vehicles, and establishes a fault diagnosis model based on BP neural network, which is applied to the fault diagnosis of on-board charger circuits. The optimal fault diagnosis model is obtained by continuously adjusting the number of neurons in the hidden layer of the BP neural network, and the open-circuit faults of diodes and MOSFETs are mainly analyzed experimentally. The simulation results show that the expected effect has been achieved in the fault diagnosis of the on-board charger circuit, and the established simple BP neural network model can be applied in practice. Some feasible solutions are provided for improving the charging safety of electric vehicles.