Fault Identification and Quantitative Diagnosis Method for Series-Connected Lithium-Ion Battery Packs Based on Capacity Estimation

Abstract

Internal short circuit is considered as one of the general causes that may lead to battery thermal runaway. The capacity of cells ages with the effect of working conditions. Hence, both micro-short circuit (MSC) and low-capacity cells may exist in a battery pack. However, both two faults perform the same features in the discharging process: state of charge (SOC) deviation increases continuously. If we diagnosed the abnormal states only based on the discharging data, it would misdiagnose these two faults. A fault identification method based on capacity estimation is proposed to distinguish MSC and low-capacity cells in the article. Furthermore, the capacity of the low-capacity cell and the MSC cell could be quantitatively estimated. In the proposed method, a mean-difference model and extended Kalman filter algorithm are used to calculate the cell SOCs in the battery pack. An online capacity estimation method is adopted to estimate the cell capacities in the charging and discharging process. A reasonable threshold considering capacity change characteristics is established to initially identify the fault and for further quantitative diagnosis. The experimental results show that a coexisting MSC fault and low-capacity fault in the battery packs could be diagnosed effectively by using the proposed method.