Moreover, Wang et al. incorporated a hybrid approach of battery PBMs with a LSTM-based network to create a temperature prediction model for studying cells throughout their usable life . Parameters of the electrical, heat, and thermal models were automatically learnt during the training stage. They concluded that temperature prediction could be done at different operating conditions and that one of the advantages of the hybridization was the transferability and generalization of the model, due to the deeply embedded physics.