Enhanced Model-Based Algorithm for Early Internal Short Circuit Detection in Lithium-Ion Batteries

Abstract

Electric vehicles (EVs) are becoming more popular due to concerns about fuel shortages and environmental pollution. Lithium-ion batteries are the preferred power source for EVs because they have high energy and power densities. Ensuring the efficient, safe, and reliable operation of these batteries has been a significant focus of research in recent decades. One major concern that can affect Li-ion battery performance is thermal runaway, which can cause dangerous battery fires. Internal short circuits (ISCs) are believed to be the root cause of thermal runaway incidents in batteries, making early detection of spontaneous ISCs a critical diagnostic task. This study presents a new and simple early ISC detection method for a Li-ion cell based on the augmentation of the state space of an Extended Kalman Filter (EKF) that includes voltage and surface temperature observations. The framework allows for an estimation of the cell's internal ISC state while remaining computationally efficient. The proposed approach is demonstrated in a simulated environment using dynamic stress tests that reflect a practical battery working cycle. The results demonstrate that the method can promptly detect ISC occurrences.