Internal Short Circuit Detection in Lithium Ion Battery

Abstract

Early detection of an internal short circuit (ISCr) in a Li-ion battery can prevent it from undergoing thermal runaway, and thereby ensure battery safety. In this thesis, we propose an algorithm for estimating an internal short circuit (ISCr) resistance in a Li-ion battery. The open circuit voltage (OCV) and the state of charge (SOC) are estimated by applying the equivalent circuit model of the Li-ion battery with ISCr, and by using the recursive least squares algorithm and the relation between OCV and SOC. As a fault index, the ISCr resistance R_ISCf is estimated from the estimated OCVs and SOCs to detect the ISCr. To improve the accuracy of R_ISCf estimates, the switching model method (SMM) is also proposed. The R_ISCf is used to update the model; this process yields accurate estimates of OCV and R_ISCf. Then the next R_ISCf is estimated and used to update the model iteratively. To verify this algorithm, the simulation data from MATLAB/Simulink model and experimental data are used. The result shows that the proposed algorithm contributes to detect the ISCr fault in Li-ion battery, thereby helping the battery management system to fulfill early detection of the ISCr.