Online core temperature estimation method for lithium-ion batteries over the entire lifecycle

Online monitoring of the core temperature in Lithium-ion batteries (LIBs) is essential for effective thermal management and risk prevention. Throughout the lifecycle of LIBs, battery aging and dynamic changes in the external environment complicate core temperature estimation. To address this challenge, this paper proposes a parameter and state-modified core temperature estimation method based on a simplified electro-thermal coupled model. The Thevenin model is coupled with the lumped equivalent thermal circuit (ETC) through temperature to establish a simplified electro-thermal coupled model. Then, A novel Dual AFFRLS-DEKF joint algorithm is introduced for the online identification of all parameters within the model and simultaneously estimates the battery's state of charge (SOC), state of health (SOH), and core temperature. The proposed method enhances adaptability to dynamic environmental changes by updating model parameters online and modifies SOC and SOH in real time to account for changes in heat generation due to battery aging. Finally, an experimental platform was established to validate the proposed method. The results demonstrate that the proposed method is robust and accurate in estimating core temperature over the entire lifecycle, with a root mean square error (RMSE) of core temperature estimation less than 0.5 degrees C and a maximum absolute error (MAE) less than 1 degrees C.