A Robust Approach to Internal Short-Circuit Detection in Lithium-Ion Batteries With a Mechanistic Model for Cylindrical Cells

To guarantee the safety of electric vehicles (EVs), it is crucial to gain a comprehensive understanding of internal short circuits (ISC) and to develop effective early detection methods within battery management systems (BMS). This study presents a detailed analysis of ISC detection in cylindrical lithium-ion battery (LIB) cells, utilizing a mechanistic coupled electrochemical-thermal model to replicate various ISC scenarios. The proposed approach integrates an extended Kalman filter (EKF) with a coupled lumped-parameter electro-thermal model, enabling real-time ISC detection leveraging both electrical and thermal measurements. Extensive evaluations demonstrate that the coupled model significantly improves detection accuracy and timeliness, reducing detection times by 36% and errors by 44% on average compared to uncoupled models in different ISC scenarios. These findings are particularly significant for preventing with thermal runaway (TR), especially in the presence of severe ISC. robustness analysis further highlights the impact of ISC location on detection accuracy. The study concludes that the proposed ISC detection framework offers an efficient, accurate, and reliable solution to detect ISC faults in cylindrical battery cells, contributing to safer operation. By mitigating TR risks and extending battery lifespan, this work supports the development of sustainable energy systems and improves the reliability of energy storage technologies.