Internal Short-Circuit Fault Diagnosis for Batteries of Energy Storage Stations Based on Multivariate Multiscale Sample Entropy

The safety of lithium-ion batteries (LIBs) in the battery energy storage station (BESS) is attracting increasing attention. To ensure the safe operation of BESS, it is necessary to detect the battery internal short circuit (ISC) fault which may lead to fire or explosion. This article proposes an early battery ISC fault diagnosis method based on the multivariate multiscale sample entropy (MMSE). The voltage, current, and temperature of the battery are utilized to extract the fault feature. The wavelet denoising method are employed to improve the MMSE performance. The adaptable threshold is proposed to diagnose the early ISC fault and prevent misdiagnosis. Finally, the ISC fault experiment and the battery overcharge experiment are used to validate the efficiency of the proposed early ISC diagnosis method. The real BESS operation data are used to validate the robustness of the proposed method. The results shows that the proposed method is effective in diagnosing the early ISC fault.