A electrochemical-electro-thermal coupled computational framework to simulate the performance of Li-ion batteries at cell-level: Analysis on the thermal effects

Accurately predicting the performance of Li-ion batteries is of great importance for the global electric vehicle and energy storage industries. In this research, we propose a computational framework that integrates the electrochemical DFN model, ECM parametrisation, 3D distributed ECN model to simulate the performance of Li-ion cells. Using Kokam 7.5 Ah pouch cell (ModelSLPB75106100) as an example, we demonstrate the three-step workflow of the framework that consists of the characterisation data acquisition, parametrisation with BatPar, and 3D ECN-simulation with PyECN. With this framework, we simulate constant current discharge experiments in the literature and compare the simulations with DFN model that coupled with a classical lumped thermal model. With a better consideration of thermal process and its coupling effects with electrochemistry, the computational model outperforms DFN model, especially at low-temperature and/or high C-rate scenarios. The largest predicting error of the framework at 3 C-rate &Tam = 25oC and at 1 C-rate &Tam = 0 oC is approximately 1/3 of that for DFN model. At 3 C-rate &Tam = 5oC, the difference between these two can rise to 377 mV. Further analysis reveals that the lumped DFN + thermal model is unsuitable to simulate the performance of Li-ion batteries at a scale larger than cell level, due to significant internal heat generation and large Biot number. By integrating DFN and 3D-distributed ECN together, this proposed computational framework is electrochemical-electro-thermal coupled and can be used as a toolset by cell manufacturers and pack designers to predict, analyse, and optimise the performance of Li-based energy storage systems.