Micro-short circuit fault diagnosis of lithium-ion battery based on voltage curve similarity ranking volatility

An electric vehicle battery system often consists of multiple battery cells that are interconnected in series and parallel. If a micro-short circuit failure occurs in one of the battery cells, it may cause a serious internal short circuit or even thermal runaway. Consequently, it is extremely important to detect micro-short circuit faults in batteries. This paper presents a novel approach for diagnosing faults in lithium-ion batteries based on the similarity ranking fluctuation rate of voltage curve, and verify the feasibility of the method through a series of micro-short circuit experiments containing an external parallel variable resistance device. This approach does not require complete charge-discharge curves. Firstly, the charging voltage data is preprocessed using the Variational Mode Decomposition algorithm, after which the Dynamic Time Warping technique is employed to calculate the similarity between the charging voltage of each battery and a reference battery. Then a ranking fluctuation rate of voltage curve similarity is established, and the Grubbs criterion is utilized to identify batteries exhibiting abnormal fluctuation rate within the ranking. Finally, the effectiveness of the proposed method is validated by analyzing actual battery signals collected from faulty vehicles. The results demonstrate that the method accurately identifies batteries with micro-short circuit faults.