A Physics-Informed TCN-LSTM Approach for Interpretable Internal Temperature Estimation of NMC811 Lithium-Ion Batteries

Accurate internal temperature estimation is of great significance to the safe and dependable working performance of lithium-ion batteries (LIBs). Nevertheless, conventional purely data-driven prediction models suffer from inherent limitations, including insufficient physical interpretability and degraded generalization capability under complex dynamic driving conditions. To tackle this challenge, the present study puts forward a physics-informed temporal convolutional network and long short-term memory (PI-TCN-LSTM) framework for high-precision LIB internal temperature estimation, which depends solely on readily obtainable external sensor measurements. The proposed hybrid architecture synergistically integrates the strengths of two temporal modules: TCN captures fast transient thermal dynamic characteristics, while LSTM effectively mines long-term thermal inertia dependencies of batteries. To make up for the absence of physical constraints in pure data-driven methods, a two-state lumped thermal model is adopted to characterize battery internal heat generation and heat transfer mechanisms. Specifically, the governing thermodynamic differential equations covering irreversible Joule heat, reversible entropic heat, and convective heat dissipation are directly embedded into the network loss function as physical constraints, achieving tight coupling of domain physical laws and data-driven learning. Comprehensive experimental validations on commercial NMC811 batteries demonstrate that the proposed PI-TCN-LSTM framework substantially outperforms conventional pure data-driven models. Notably, the developed approach demonstrates favorable generalization to the unseen FTP-75 driving cycle over a wide temperature range, maintaining RMSE values of 0.081 °C and 0.069 °C at 5 °C and 45 °C, respectively.