Modeling of temperature characteristics of lithium-ion batteries considering the state dependency and its robust estimation of internal temperature

Abstract

Accurate internal temperature estimation of lithium-ion batteries (LIBs) plays an important role in their safe and economical application. However, the traditional observer estimation method based on linear thermal models cannot accurately describe the nonlinear dynamics of the LIB, and the data-driven estimation approach based on an open loop structure makes it difficult to obtain satisfactory robustness. To bridge this research gap, we construct a state-dependent model to represent the LIB's nonlinear dynamics. The coefficients of this model are implemented by a set of radial basis function neural networks, thus the model considers the actual state of the LIB under different working conditions. Based on the identified state-dependent model offline, an online estimation method of the internal temperature is implemented using the extended Kalman filter (EKF). Furthermore, the robustness is further verified by the extended state observer-EKF. The validation results for different batteries and working conditions show that the root mean square error (RMSE) does not exceed 0.30 °C in the presence of wrong initial values, and it does not exceed 0.28 °C in the presence of bias noise.