Core Temperature Estimation Method for Lithium-Ion Battery Based on Long Short-Term Memory Model With Transfer Learning

Temperature is a crucial parameter that determines the safety and reliability of lithium-ion batteries (LIBs) in electric vehicles and energy storage systems. Estimating LIBs temperature for battery management system state monitoring and thermal control, especially the core temperature (CT), is essential. However, the CT cannot be obtained directly and must be estimated via other measurable variables. To this end, this article proposes a method to estimate the CT of LIBs based on the long short-term memory (LSTM) method combined with transfer learning (TL). Through this method, the relationship between CT and measured variables can be obtained. Moreover, the TL procedure, with its fine-tuning strategy, can substantially reduce the computation burden when used to estimate the CT of other LIBs via a reduced training set. In addition, the applicability of the proposed method is verified via a long period cycle test. The experimental results demonstrate that the proposed method can precisely estimate the LIB CT in ambient temperatures of between −10 °C and 55 °C in highly dynamic driving cycles and aging cycle tests, and the maximum root-mean-square error (RMSE) is 0.3302 °C. The LSTM-TL method has a high accuracy compared with other widely used methods.