Online Internal Temperature Estimation for Lithium-Ion Batteries Based on Kalman Filter

Abstract

The battery internal temperature estimation is important for the thermal safety in applications, because the internal temperature is hard to measure directly. In this work, an online internal temperature estimation method based on a simplified thermal model using a Kalman filter is proposed. As an improvement, the influences of entropy change and overpotential on heat generation are analyzed quantitatively. The model parameters are identified through a current pulse test. The charge/discharge experiments under different current rates are carried out on the same battery to verify the estimation results. The internal and surface temperatures are measured with thermocouples for result validation and model construction. The accuracy of the estimated result is validated with a maximum estimation error of around 1 K.