Model-Free Quantitative Diagnosis of Internal Short Circuit for Sodium-Ion Batteries With Charging Capacity Difference Analysis

Thermal runaway caused by the internal short circuit (ISC) poses a significant safety risk for sodium-ion batteries (SIBs) in electric vehicles and energy storage applications. Early detection of ISC faults is considered a potential way to reduce the risk associated with fire or explosion and can be identified via minor power leakage. However, diagnosing these leaks in the initial stages of ISC is quite challenging without battery modeling or preliminary experiments. Here, a model-free method based on charging capacity difference (CCD) analysis is proposed for quantitatively diagnosing ISC fault. First, a wavelet denoising algorithm with improved thresholding is presented to extract the real signals from the noise-containing voltage and current data. Subsequently, the ISC resistance is estimated by calculating the CCD variation between the ISC cell and the normal cell. Furthermore, the amount of power leakage during the charging process of ISC batteries is considered to improve the accuracy of the ISC diagnosis. Finally, experiments with elaborate ISC batteries are conducted to validate the method, achieving a resistance estimation error below 5% across different ISC severities while maintaining robustness to capacity inconsistencies. Compared to existing model-free methods, the proposed method significantly enhances diagnostic accuracy and broadens its applicability.