Temperature state prediction for lithium-ion batteries based on improved physics informed neural networks

Heat generation significantly influences the performance of lithium-ion batteries and also hinders the application of them. Precise prediction of battery temperature can offer feedbacks to monitor system so as to enable safe and efficient operation of batteries. However, battery temperature prediction remains extremely challenging due to the increase of irreversible heat caused by aging across the life cycle. To tackle this problem, we propose a novel framework named battery informed neural network (BINN). In this paper, we incorporate battery physical models into long short-term memory (LSTM)-based networks that is trainable in an end -to-end manner for battery temperature prediction. Multi-head attention mechanism is introduced to attend to information from longer time series. Physical parameters of the battery electrical model, heat generation model, and thermal model are automatically learnt during training. The irreversible heat changes brought on by aging is considered and represented by physical parameters. Temperature prediction for the full life cycle of lithium-ion batteries using BINN is tested under different working conditions. It is shown that BINN is interpretable and has better generalization and transferability than traditional learning-based methods.