Application Research of Power Battery System Fault Identification Method Based on Multi-Classifier Fusion

Abstract

In this paper, a module fault identification method for a power battery system based on multi-classifier fusion is designed. Taking the inverter single tube open circuit fault of the power battery system as an example, the deep learning method of deep neural network and convolutional neural network is used to build the primary diagnosis layer. The fusion method of evidential reasoning rules is used to fuse the primary diagnosis results. The fault data are obtained through simulation of the simulation model, which verifies that the scheme avoids the disadvantage of unstable diagnosis results of a single classifier, and obtains more stable diagnostic results with higher diagnostic accuracy.