Short-Circuit Detection in Lithium-Ion Batteries Using Machine Learning: Analysis and Comparison with Physics-Based Method

Abstract Early detection of short circuits in battery-powered systems is critical in preventing potential catastrophic failures. However, nascent short-circuit signatures are extremely weak and challenging to detect using existing algorithms without compromising on prediction accuracy. Traditional physics-based approaches rely on hand-crafted models to establish relationships between battery operating parameters and short resistance, which limits their ability to capture all relevant details, resulting in sub-optimal accuracies. In this study, we present a machine learning-based approach that leverages rest period voltage data to detect short circuits. Our method employs a 1D convolutional neural network (CNN) classifier/estimator that extracts temporal dynamic features relevant to the short circuit prediction problem from both the long and short tails of the rest period voltage profile. The approach is validated using commercial battery data, generated at different conditions including temperatures, and short circuits of varying severities; with prediction accuracies greater than 90% even for soft shorts of 500Ω. The key performance parameters of the 1D CNN model are compared against a physics-based short detection approach, demonstrating its superior performance and cost-effectiveness. Overall, our work represents a significant advancement in the field of short circuit detection in battery-powered systems, offering improved accuracy, efficiency, and cost-effectiveness.