Improved Parameter Estimation of the Doyle-Fuller-Newman Model by Incorporating Temperature Dependence

Identifiability remains a key issue in estimating the model parameters of the Doyle-Fuller-Newman (DFN) model, which implements physics-based modeling of lithium-ion cells. This paper proposes the inclusion of physics-based temperature relations within the DFN model and the parameter estimation technique, in which model parameters are estimated over a wide temperature range. We evaluate the effect of including physics-based relations on the identifiability of the model, as well as its voltage prediction accuracy. The implementation of physics-based relations results in parameters that are physically meaningful, and comparable model accuracies to the original parameter estimation technique, in which the model parameters are identified at individual temperatures and physics-based temperature relations are not included. We further evaluate the robustness of the parameter estimation technique by perturbing initial conditions and compare its affect on the presened and the original parameter estimation technique. We find the presented parameter estimation technique to be more robust and reliable. Finally, we compare the DFN model to an equivalent-circuit model and find the DFN model to be comparable in accuracy whilst having a better representation of the internal states of the cell.