Improving the accuracy of physics-based battery model simulations: Determination of solid-phase diffusion coefficients and reaction-rate constants

Physics-based models are important tools for simulating and optimizing the performance of Li-ion batteries, with the Doyle-Fuller-Newman (DFN) model being the most widely adopted [1]. However, the accuracy of this model depends heavily on its parameters. Two of the most critical parameters for the model are the solid-phase diffusion coefficient (D_s) and the reaction-rate coefficient (k_0) [2]. These parameters are critical because D_s controls the diffusion overpotential, while k_0 governs the charge-transfer overpotential. Together, these two overpotentials account for more than half of the total overpotential in a battery. Therefore, precise determination of D_s and k_0 will enhance battery state-estimation, improve fast-charging protocols, and provide deeper insights into battery degradation mechanisms, leading to more efficient and reliable Li-ion battery applications.Since D_s and k_0 cannot be directly measured, they must be estimated – a process that can be quite complex. A review of the literature reveals that many studies have inaccurately determined these parameters. In this work, we have re-evaluated the estimation methods to address these inaccuracies and improve the reliability of the estimation process. This work uses galvanostatic and potentiostatic intermittent titration techniques (GITT and PITT, respectively) to estimate these key parameters using half-cells with Li(Ni0.4Co0.6)O2 electrodes. The two compared estimation methods are the widely used analytical approach based on Weppner and Huggins's work [3] and a physics-based approach with the DFN model.Figure 1 shows DFN model simulation results under dynamic current-loading conditions with D_s and k_0 determined from various measurement and estimation techniques. The combination of GITT measurements with a newly proposed physics-based protocol for determining D_s and k_0 gives the highest simulation accuracy, achieving an average root mean square error (RMSE) of 5.5 mV, as shown in Figure 1a. In contrast, the analytical method in combination with GITT measurements for determining D_s and k_0, which is the most used in literature, shows the least accuracy, with an RMSE of 24.2 mV, as shown in Figure 1b. This higher error is attributed to the limitations inherent in the analytical approach's core assumptions.This study introduces a novel protocol for optimizing D_s and k_0 as a function of lithiation degree from a single measurement, eliminating the need for expensive and complex techniques such as electrochemical impedance spectroscopy (EIS).References:[1] M. Doyle, T.F. Fuller, J. Newman, Modeling of Galvanostatic Charge and Discharge of the Lithium/Polymer/Insertion Cell, J. Electrochem. Soc. 140 (1993) 1526. https://doi.org/10.1149/1.2221597.[2] H.A.A. Ali, L.H.J. Raijmakers, K. Chayambuka, D.L. Danilov, P.H.L. Notten, R.-A. Eichel, A comparison between physics-based Li-ion battery models, Electrochimica Acta (2024) 144360. https://doi.org/10.1016/j.electacta.2024.144360.[3] W. Weppner, R.A. Huggins, Determination of the Kinetic Parameters of Mixed‐Conducting Electrodes and Application to the System Li3Sb, J. Electrochem. Soc. 124 (1977) 1569. https://doi.org/10.1149/1.2133112.