Ensemble Learning-Based Correlation Coefficient Method for Robust Diagnosis of Voltage Sensor and Short-Circuit Faults in Series Battery Packs

Accurate and reliable multifault diagnosis of battery packs is crucial to the safe operation of electric vehicles. To this end, this article proposes a systematically improved correlation coefficient (CC) method by utilizing multivariate statistical analysis and Bayesian probability theory under the framework of ensemble learning. Specifically, different window widths are first selected in an appropriate value range, and the CC signals between cross-cell voltages for each fixed window width are calculated to create different local submodels. Then, in each submodel, an independent component analysis-based fault diagnosis is implemented to obtain the local diagnostic result, and the results of all submodels are integrated as an ensemble fault probability (EFP) and an ensemble contribution rate (ECR) through Bayesian probabilistic ensemble interface. Once the EFP exceeds its threshold, a fault is considered to be detected and the ECR is subsequently applied along with the cross-cell sensor topology to identify the fault type (short-circuit or sensor fault) and locate the accurate position of the failed battery cell/sensor. In-depth theoretical analysis and sufficient comparative experiments on a real Lithium-ion battery packs test platform demonstrate that the proposed approach becomes more robust and reliable than the conventional CC-based methods, and also has better physical interpretability.

Automated summary

This article proposes an improved correlation coefficient method for multifault diagnosis of battery packs in electric vehicles. It utilizes multivariate statistical analysis and Bayesian probability theory under the framework of ensemble learning. The method creates local submodels based on cross-cell voltage correlation signals and implements fault diagnosis using independent component analysis. Results are integrated using Bayesian probabilistic ensemble interface, and accurate fault type identification and localization are achieved using ensemble fault probability and ensemble contribution rate.