A Voltage-Temperature Co-Estimation Method of Lithium Batteries Based on Extended Kalman Filter

Accurate and timely estimation of a lithium battery's status during operation is essential for ensuring both safety and performance. Traditional estimation methods typically rely on individual parameters such as voltage, current, or temperature, making it difficult to comprehensively assess the battery's overall safety. In this paper, we propose a voltagetemperature co-estimation method based on the Extended Kalman Filter (EKF). Utilize the least squares method to perform battery parameter fitting, and establish a second-order RC model as well as an electro-thermal coupling model. By developing the two models, this approach enables the simultaneous prediction of voltage and temperature. The feasibility of the proposed method is validated using battery data from the University of WisconsinMadison. The RMSE values for voltage and temperature predictions were 0.061 V and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.0994^{\circ} \mathrm{C}$</tex> respectively. The estimated results provide a valuable reference for battery safety assessment, enabling the early and comprehensive detection of potential safety hazards.