Parametrisation and Use of a Predictive DFN Model for a High-Energy NCA/Gr-SiOx Battery

We demonstrate the predictive power of a parametrised Doyle-Fuller-Newman (DFN) model of a commercial cylindrical (21700) lithium-ion cell with NCA/Gr-SiOx chemistry. Model parameters result from the deconstruction of a fresh commercial cell to determine/confirm chemistry and micro-structure, and also from electrochemical experiments with half-cells built from electrode samples. The simulations predict voltage profiles for (i) galvanostatic discharge and (ii) drive-cycles. Predicted voltage responses deviate from measured ones by <1% throughout at least similar to 95% of a full galvanostatic discharge, whilst the drive cycle discharge is matched to a similar to 1%-3% error throughout. All simulations are performed using the online computational tool DandeLiion, which rapidly solves the DFN model using only modest computational resources. The DFN results are used to quantify the irreversible energy losses occurring in the cell and deduce their location. In addition to demonstrating the predictive power of a properly validated DFN model, this work provides a novel simplified parametrisation workflow that can be used to accurately calibrate an electrochemical model of a cell.

Automated summary

In this study, the predictive power of a parametrised DFN model for a commercial cylindrical NCA/Gr-SiOx lithium-ion cell was demonstrated. The model parameters were determined by deconstructing a fresh commercial cell and conducting electrochemical experiments. The simulations accurately predicted voltage profiles for galvanostatic discharge and drive-cycles, with deviations from measured results staying within 1%-3%. This work also introduced a novel simplified parametrisation workflow for accurately calibrating an electrochemical cell model.