State of Temperature Estimation for a Lithium-ion Battery Cell using Adaptive Unsent Kalman Filter

Accurate state of temperature (SOT) estimation for lithium-ion battery is essential to guarantee the safety and long-term stable operation of electric vehicles. While the majority of current physical modeling techniques rely on various types of equivalent circuit models (ECM), challenges persist in accurately estimating with the electrochemical models. Therefore, this paper proposes a methodology for temperature estimation based on a restructured electrochemical-thermal coupling model, and employing the Adaptive Unscented Kalman Filter (AUKF) algorithm to enhance battery SOT estimation accuracy. This method initially establishes a Thermal Single Particle Model (TSPM) that integrates a Single Particle Model (SPM) with a Lumped Thermal Model (LTM). And the model's alignment with experimental data further reinforced the thermal model's precision. Then, to account for the impact of noise on computational accuracy, the AUKF algorithm is introduced to adaptively process noise and filter the temperature values by the model. Finally, the validation of the algorithm is conducted through a comparative analysis with the temperature predictions of an open-loop model. The results indicate that the reconstructed electrochemical-thermal coupling model demonstrates high effectiveness in battery temperature estimation. By integrating the AUKF, the accuracy of temperature predictions was significantly enhanced, with the temperature prediction error notably reduced from 0.28°C to 0.07°C, achieving a 61.74% improvement in estimation accuracy. This paper contributes to the research on optimizing the SOT estimation methods for lithium batteries.