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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 paper, a model-based switching model method (SMM) is proposed to detect the ISCr in the Li-ion battery. The SMM updates the model of the Li-ion battery with ISCr to improve the accuracy of ISCr resistance R ISC f estimates. The open circuit voltage (OCV) and the state of charge (SOC) are estimated by applying the equivalent circuit model, and by using the recursive least squares algorithm and the relation between OCV and SOC. As a fault index, the R ISC f is estimated from the estimated OCVs and SOCs to detect the ISCr, and used to update the model; this process yields accurate estimates of OCV and R ISC f . Then the next R ISC f is estimated and used to update the model iteratively. Simulation data from a MATLAB/Simulink model and experimental data verify that this algorithm shows high accuracy of R ISC f estimates to detect the ISCr, thereby helping the battery management system to fulfill early detection of the ISCr.

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We propose an algorithm for estimating an internal short circuit (ISC) resistance in a Li-ion battery. With a simple equivalent model of the Li-ion battery and recursive least square (RLS) algorithm, open circuit voltage (OCV) and State of Charge (SOC) are estimated. By using the estimated OCV and SOC, the ISC resistance can be calculated and used as ISC fault index. To verify this algorithm, the simulation data from MATLAB/Simulink model and experiment data are used. The result shows that the proposed algorithm contributes to detect the ISC fault in Li-ion battery by using the new ISC fault index.