Quantitative diagnosis of internal short circuits in lithium-ion batteries based on terminal voltage during the charging stage

Abstract

Internal short-circuit (ISC) faults in lithium-ion batteries shorten service life and may cause severe safety issues such as thermal runaway. Therefore, this study proposes a purely data-driven method based on terminal voltage during charging. The analysis focuses on the stable mid-to-late stage of low-rate constant-current charging. Multiple discrete time instants within 4200 s–6000 s are selected, and the voltage difference between them is used as the diagnostic feature. A random forest (RF) model is developed, trained, and validated. To improve performance, Bayesian optimization tunes RF hyperparameters, and particle swarm optimization is applied for feature selection. Diagnostic thresholds are established based on model outputs to enable ISC fault identification and prediction. Support vector regression and Gaussian process regression are used for comparison. Results show that the optimized RF model achieves a mean relative error of 4.254%, a root mean square error of 32.688 Ω, and a coefficient of determination (R2) of 98.42%, significantly outperforming the comparison methods.