Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Parametrisation and Use of a Predictive DFN Model for a High-Energy NCA/Gr-SiOx Battery
AU - Zülke, Alana
AU - Korotkin, Ivan
AU - Foster, Jamie M.
AU - Nagarathinam, Mangayarkarasi
AU - Hoster, Harry
AU - Richardson, Giles
PY - 2021/12/10
Y1 - 2021/12/10
N2 - 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 ∼95% of a full galvanostatic discharge, whilst the drive cycle discharge is matched to a ∼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.
AB - 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 ∼95% of a full galvanostatic discharge, whilst the drive cycle discharge is matched to a ∼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.
KW - Materials Chemistry
KW - Electrochemistry
KW - Surfaces, Coatings and Films
KW - Condensed Matter Physics
KW - Renewable Energy, Sustainability and the Environment
KW - Electronic, Optical and Magnetic Materials
U2 - 10.1149/1945-7111/ac3e4a
DO - 10.1149/1945-7111/ac3e4a
M3 - Journal article
VL - 168
JO - Journal of The Electrochemical Society
JF - Journal of The Electrochemical Society
SN - 0013-4651
IS - 12
M1 - 120522
ER -