Core Temperature Estimation for Lithium-Ion Batteries Based on Extended Kalman Filter

Lithium-ion batteries have become the preferred energy storage component for electric vehicles due to their excellent overall performance. However, during use, they generate heat, causing the battery temperature to rise and the internal and surface temperatures to be inconsistent, affecting the battery’s performance and even leading to thermal safety issues. It is difficult to obtain real-time internal temperature measurements in actual vehicles. Therefore, accurately estimating the internal temperature of the battery, promptly detecting thermal faults, and ensuring efficient and safe operation of the battery are of great importance. This paper establishes a dual-state thermal model based on extended Kalman filtering for a square ternary lithium battery, which achieves real-time updating of external thermal resistance and online estimation of core battery temperature. For this type of lithium battery and its battery module, an experimental platform was set up, and basic performance experiments were designed to identify the thermal physical parameters of the battery and dynamic condition experiments to evaluate the performance of the model. The results show that the established dual-state thermal model has an absolute temperature error of less than 0.7°C under constant power conditions. Under different operating conditions and temperature conditions, the absolute error in the estimated core temperature of the battery pack is within 1.3°C, and the relative error is within 2.822%, proving that the method has high accuracy and good robustness.