Lithium-Ion Battery Thermal Parameter Identification and Core Temperature Estimation

Battery core and surface temperature are crucial for the thermal management and safety usage of Li-ion batteries. They affect the cell's physical properties and strongly correlate with some of its key states, such as the battery state of charge (SOC) and state of health (SOH). Therefore, an accurate estimate of the battery core and surface temperature will enhance the performance and prolong the battery's life. This study proposes an estimation system of the battery core and surface temperature. A simplified pseudo-two-dimensional model is introduced to capture the battery SOC, core and surface temperature that will be used later in this study to model and validate the results' accuracy. Then, a two-state thermal battery model (TSM) is presented and studied. The recursive least square (RLS) algorithm is adopted to identify the thermal parameters of the battery. Next, the TSM is validated using COMSOL Multiphysics simulation software and the thermal parameters are then fed to the Kalman filter (KF) to estimate the battery core temperature. Finally, the accuracy of the battery core temperature estimated results are validated with a root mean square error of 0.037K.