  Battery models generally fall into two categories: physics-based models and
ECM models. Physics-based Doyle-Fuller-Newman (DFN) models can accurately
simulate the battery internal electrochemical processes, but to properly
account for thermal effects requires a strong coupling between a DFN model and
a 3D thermal model, which is computationally unaffordable. Distributed
Equivalent Circuit Network (ECN) models can perform simulations with high speed
and reasonable accuracy. However, these models rely heavily on the
characterisation experiments for ECN parameter identification, which is
resource-intensive and can lead to inaccurate parametrisation outcomes due to
internal thermal inhomogeneity. To harness the strengths of both models, we
propose a computational framework to integrate electrochemical DFN model and 3D
distributed ECN model together. Using this framework, we simulate constant
current discharge experiments of Kokam 7.5 Ah pouch cell (Model SLPB75106100)
and compare the simulations with the commonly-used lumped DFN-thermal model.
The computational model outperforms the lumped DFN model at low-temperature
and/or high C-rate scenarios significantly. 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. By integrating DFN and 3D-distributed ECN together, the
computational framework can simulate the complicated interplay between
electrochemistry, thermal process, and electricity within a cell fast and
accurately. We anticipate this computational framework to be a valuable toolset
to assist researchers and engineers in the design and control of Li-ion
batteries.
