Innovative fault diagnosis and early warning method based on multifeature fusion model for electric vehicles

Abstract

Fault diagnostics and early warning are crucial to the safe operation of lithium-ion batteries, and despite partial progress, it is still extremely difficult to solve the problem in a high-dimensional parameter space with complex failure causes. In this article, an innovative fault diagnosis and early warning method based on multifeature fusion model is designed for quantitative and qualitative comprehensive analysis and evaluation of the battery operating state information and the complex internal safety evolution trajectory. Firstly, to form a multi-dimensional fault feature matrix, a real vehicle dataset consisting of 400 vehicles is constructed using typical vehicle fault data from the cloud platform. Then, the original data is noise-reduced and pre-processed by applying initial bias correction and anomalous signal identification methods. Moreover, based on the equivalent circuit model and data-driven algorithm, 13 features including internal short circuit internal resistance, inconsistency accumulation risk, and principal element extraction index are designed from three dimensions: threshold, statistics and model. Finally, the multiple features are combined with random forest (RF) algorithm for hybrid fusion decision to achieve better generalization ability and suppress the overfitting risk of the algorithm. The results show that the proposed feature extraction and fusion decision methods can identify abnormal states and hazard levels in a timely and accurate manner, and this RF-based classification, warning and evaluation framework shows the promise of machine learning algorithms for interpretable early warning studies of battery failures, which can use more modal features to assess the hazard levels of actual operating battery systems in end-cloud collaboration scenarios.