Rapid diagnosis and assessment of lithium-ion battery short circuit in three seconds across a wide range of short-circuit resistances

Short circuits are a prevalent fault in lithium-ion battery applications, leading to severe safety consequences; therefore, the rapid diagnosis is imperative for improving battery safety. External and internal short circuits exhibit similar characteristics but have significantly different evolution results. This study introduces a diagnostic approach based on Discrete Wavelet Transform (DWT) that promptly identifies and categorizes short circuits using only three seconds of post-occurrence data. The diagnostic method encompasses a wide range of short-circuit resistances, from 10 mΩ to 10 Ω. Subsequently, a fault severity assessment method based on empirical formulas is established. For online applications, an enhanced extreme learning machine is utilized to estimate short-circuit resistance and to predict the potential peak temperature, failure time, and remaining capacity. The proposed method is evaluated through experiments, including internal short circuits, external short circuits, sensor faults, and normal battery conditions. The tests demonstrate that the method can achieve a diagnosis accuracy of 95.7 % while the estimation error for short-circuit resistance is less than 3.9 %.