Parametrisation and Use of a Predictive DFN Model for a High-Energy NCA/Gr-SiOx Battery. Issue 12 (10th December 2021)

• Record Type: Journal Article
• Authors: Zülke, Alana; Korotkin, Ivan; Foster, Jamie M.; Nagarathinam, Mangayarkarasi; Hoster, Harry; Richardson, Giles
• Is Part Of: Journal of the Electrochemical Society. Volume 168:Issue 12(2021)
• Publication Date: 2021-12-10
• Subjects: Li-ion battery modelling -- Drive-cycles simulation -- Newman-type modelling; Electrochemistry -- Periodicals; 541.3705
• DOI: 10.1149/1945-7111/ac3e4a
• Languages: English
• ISSNs: 0013-4651

Abstract

Abstract : 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.