A novel Kalman-filter-based battery internal temperature estimation method based on an enhanced electro-thermal coupling model

Abstract

Estimating the internal temperature of lithium-ion batteries (LIBs) in battery management systems (BMS) is critical as it enables the system to monitor and manage the actual temperature of LIBs, thereby ensuring their long-term performance, safe operation and prevention of thermal runaway. However, the existing BMS can only monitor the surface temperature of the battery. To accurately estimate the internal temperature of LIBs during normal operation, this paper proposes an enhanced electro-thermal coupling model to calculate the internal temperature of LIBs. This model combines an improved second-order resistance-capacitance equivalent circuit model with a two-state thermal model to provide a more precise calculation of the internal temperature of LIBs. The model's parameters are determined using the Trust-Region Reflective based nonlinear least squares method, and the Kalman-filter algorithm is used to estimate the internal temperature of the battery based on the model. The proposed method has been demonstrated to be effective through experimental results, with a maximum root mean square error of 0.5705 °C at the battery testing conditions of Standard Charge, Federal Urban Driving Schedule (FUDS), Dynamic Stress Test (DST) and Constant Current Discharge (CC-Discharge). Due to its low cost and simplicity, this method is suitable for practical engineering applications.