Validity of solid-state Li+ diffusion coefficient estimation by electrochemical approaches for lithium-ion batteries

The solid-state diffusion coefficient of the electrode active material is one of the key parameters in lithium-ion battery modelling. Conventionally, this diffusion coefficient is estimated through the galvanostatic intermittent titration technique (GITT). In this work, the validity of GITT and a faster alternative technique, intermittent current interruption (ICI), are investigated regarding their effectiveness through a black-box testing approach. A Doyle-Fuller-Newman model with parameters for a LiNi0. 8Mn0. 1Co0. 1O2 electrode is used as a fairly faithful representation as a real battery system, and the GITT and ICI experiments are simulated to extract the diffusion coefficient. With the parameters used in this work, the results show that both the GITT and ICI methods can identify the solid-state diffusion coefficient very well compared to the value used as input into the simulation model. The ICI method allows more frequent measurement but the experiment time is 85% less than what takes to perform a GITT test. Different fitting approaches and fitting length affected the estimation accuracy, however not significantly. Moreover, a thinner electrode, a higher C-rate and a greater electrolyte diffusion coefficient will lead to an estimation of a higher solid-state diffusion coefficient, generally closer to the target value. (c) 2021 The Author(s). Published by Elsevier Ltd.This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Automated summary

The solid-state diffusion coefficient of the electrode active material is an important parameter in lithium-ion battery modeling. This study compares the effectiveness of the galvanostatic intermittent titration technique (GITT) and a faster alternative technique, intermittent current interruption (ICI). The results show that both methods can accurately determine the diffusion coefficient, with the ICI method allowing for more frequent measurements and shorter experiment time. Different fitting approaches and fitting length have minimal impact on the estimation accuracy.