Temperature enhanced early detection of internal short circuits in lithium-ion batteries using an extended Kalman filter

Fuel shortages and environmental concerns primarily drive the recent growth in electrification. Lithium-ion batteries are the preferred power source for electric vehicles and other applications due to their high energy and power densities. Among potential issues that can affect Li-ion batteries, thermal runaway is a significant concern, believed to be primarily caused by internal short circuits. Therefore, early internal short circuit detection has become a critical task for any Li-ion battery-powered engineering system prioritizing safety. This paper presents a novel online internal short circuit detection method based on the state vector augmentation of an extended Kalman filter with: (i) voltage and surface temperature observations, (ii) a hysteresis state, and (iii) a state related to the internal short circuit. The proposed method is assessed numerically, mimicking an electrical vehicle battery working cycle. The framework allows for an online estimation of the internal short circuit state while remaining computationally lean, thus potentially allowing for implementation into commercial BMSs, and it is proven to capture the internal short circuit occurrence within safety limits. Additionally, the advantages of including both voltage and surface temperature observations have been highlighted. Future work envisaged towards field implementation of the technique in BMS is eventually and briefly discussed.