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