A Novel Method for Lithium-Ion Battery Fault Diagnosis of Electric Vehicle Based on Real-Time Voltage

Abstract

The cell faults of lithium-ion batteries will lead to the atypical deterioration of battery performance and even thermal runaway. In this paper, a novel fault diagnosis method for lithium-ion batteries of electric vehicles based on real-time voltage is proposed. Firstly, the voltage distribution of battery cells is confirmed in electric vehicles, and the reasons are analyzed. Furthermore, kurtosis is utilized to discover cell faults for the first time. After the kurtosis-based strategy alarm, the faulty cells in the battery pack are identified through multidimensional scaling and density-based spatial clustering of applications with noise. This method reduces the computational load of the data platform due to the characteristics of the sequential structure. Finally, the strategies to quantify the level of faulty cells and evaluate the safety of electric vehicles are presented. Through the real-time data collected by electric vehicles, it has been proven that this method can warn and locate faulty cells earlier than the original system method and has better robustness than other unsupervised fault diagnosis methods.