Investigation on Internal Short Circuit Identification of Lithium-Ion Battery Based on Mean-Difference Model and Recursive Least Square Algorithm

Abstract

Electric vehicles powered by lithium-ion batteries take advantages for urban transportation. However, the safety of lithium-ion battery needs to be improved. Self-induced internal short circuit of lithium-ion batteries is a serious problem which may cause battery thermal runaway. Accurate and fast identification of internal short circuit is critical, while difficult for lithium-ion battery management system. In this study, the influences of the parameters of significance test on the performance of an algorithm for internal short circuit identification are evaluated experimentally. The designed identification is based on the mean-difference model and the recursive least square algorithm. First, the identification method is presented. Then, two characteristic parameters are determined. Subsequently, the parameters of the significance calculation are optimized based on the measured data. Finally, the effectiveness of the method for the early stage internal short circuit detection is studied by an equivalent experiment. The results indicate that the detection time can be shortened significantly via a proper configuration of the parameters for the significance test