Online Monitoring Method for Internal Short-Circuit Faults in Lithium-Ion Batteries Caused by Low-Temperature Conditions

Early warning of internal short-circuit faults in lithium-ion batteries is an effective way to prevent thermal runaway. Addressing the issues of high computational complexity and limited real-time fault detection in the existing early detection methods for internal short circuits, this paper presents an online monitoring method for lithium-ion battery internal short circuits caused by lithium dendrites at low temperatures. Based on the waveform characteristics of internal short circuits in individual cells, the monitoring accuracy is improved using moving average filters and active noise reduction methods. Additionally, a statistical method for internal short-circuit frequency based on battery correlation characteristic values is used to classify and count the frequency of voltage dips caused by internal short-circuits during different evolution processes, and an early warning scheme is designed. An electro-thermal coupled batteries simulation model was built, and the simulation results verify the effectiveness and accuracy of the proposed monitoring method.