A Fault Diagnosis Method for Lithium-Ion Battery Based on Kolmogorov Complexity

Battery is the key component and main trouble source in an electric vehicle (EV). With rapid growth of market share, thermal runaway caused by malfunction of batteries have been frequently reported, making fault diagnosis essential to ensure safety as well as to promote performance. Unfortunately, most of existing fault diagnosis methods focus solely on identification of abnormal single cell while ignoring characteristics of battery macro system, failing to catch error of certain types. In this paper, a novel fault diagnosis method based on Kolmogorov complexity theory is proposed and verified by the real vehicle fault data from China’s national big data monitoring platform for electric vehicles. On top of Kolmogorov complexity, degree of confusion inside a battery pack can be quantitatively described, where analysis results clearly show the correlation between increase of confusion and thermal runaway accident. As a brief conclusion, the proposed method can be an important complement to traditional methods.