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What if the Doyle-Fuller-Newman model fails? A new macroscale modeling framework

IEEE Conference on Decision and Control

The Doyle-Fuller-Newman (DFN) model is generally considered the modeling standard to assess the worthiness of reduced-order electrochemical models. An aspect of such a macroscale model which has often been overlooked is that they are approximate representations of pore-scale transport dynamics and their predictive ability is hence susceptible to certain operating conditions. In this paper, we identify battery operating conditions that lead to loss of accuracy and root mean square error as high as 83.9 mV in the voltage prediction of the DFN model, and interpret our observations using a phase diagram analysis. Under the same scenarios, we simulate the performance of a full-homogenized macroscale (FHM) model developed by applying multiple-scale expansions to the Poisson-Nernst-Planck (PNP) transport equations. The performance of both models is assessed against experiments conducted on 18650 cylindrical lithium-ion cells. Results infer that the DFN model fails to predict battery voltage accurately towards the end of discharge at temperatures higher than 40◦C. The FHM model accurately predicts measured battery terminal voltage with less than 22 mV RMS error for the evaluated conditions.