Research on the identification method of internal short circuit in large-sized lithium-ion batteries based on surface temperature diversity analysis

Abstract With the increasing size of lithium-ion batteries (LIBs), more temperature sensing points can be deployed to capture a comprehensive thermal profile, enabling more accurate monitoring of battery states. However, traditional internal short circuit (ISC) recognition methods often exhibit limited sensitivity to early-stage micro short circuits, delayed response, and vulnerability to external thermal disturbances, leading to false alarms or missed detections. To address these issues, this study proposes an early ISC identification method that integrates a thermal resistance network model with a cosine similarity-based diversity analysis, and the model-predicted temperature field is used as a reference baseline. Then, a dissimilarity index based on cosine similarity is introduced to dynamically monitor local deviations in the measured temperature matrix, thereby enabling early identification of micro short circuits. Meanwhile, by analyzing the evolution trend of discrepancies between the measured and predicted temperature fields, the method can effectively identify internal fault symptoms even under weak external thermal disturbances. Furthermore, by simulating the initial thermal behavior of an ISC through local heating experiments using electric heating wire, the results show that the proposed method achieves higher accuracy, robustness, and response speed than conventional temperature difference or gradient-based approaches, particularly for micro short circuits. This method not only improves the sensitivity and reliability of micro short circuit recognition but also provides a promising solution for digital thermal safety monitoring of large-sized LIBs.