Peak-tracking method to quantify degradation modes in lithium-ion batteries via differential voltage and incremental capacity
File(s)
Supporting information
Author(s)
Chen, Jingyi
Naylor Marlow, Max
Jiang, Qianfan
Wu, Billy
Type
Journal Article
Abstract
Incremental capacity (IC) and differential voltage (DV) analyses are effective for monitoring battery health, however, the diagnosis often requires considerable parameterisation efforts and a low scan rate. In this work, a simple-to-parameterise quantitative diagnostic approach is presented, which differentiates between loss of lithium inventory and loss of active materials in the anode and cathode. With an open-circuit voltage model and a genetic algorithm optimisation routine, peak signatures in voltage and capacity differentials are used to quantify degradation modes as opposed to traditional approaches of matching the whole voltage and capacity spectra. The outputs are validated with synthetic IC-DV spectra and achieve a low root-mean-square error of ± 2.0 %. A similar level of accuracy is achieved when heterogeneity is introduced in the synthetic degradation data and also with partial discharge data. Experiments from pouch cells under 5 C discharge and 0.3 C charge cycling at 25 °C and 45 °C, together with post-mortem measurements, confirm the accuracy of this approach with diagnosis scan taken at 0.3 C. The IC-DV peak-tracking quantitative diagnostic code demonstrates a reliable and easy-to-implement means of extracting deeper insights into battery degradation and is shared alongside this manuscript to help academia and industry develop better lifetime predictions.
Date Issued
2022-01
Date Acceptance
2021-11-20
Citation
Journal of Energy Storage, 2022, 45, pp.1-12
ISSN
2352-152X
Publisher
Elsevier
Start Page
1
End Page
12
Journal / Book Title
Journal of Energy Storage
Volume
45
Copyright Statement
© 2021 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/
Sponsor
Innovate UK
The Faraday Institution
The Faraday Institution
The Faraday Institution
Identifier
https://www.sciencedirect.com/science/article/pii/S2352152X2101344X?via%3Dihub
Grant Number
104428
FIRG003
FIRG004
FIRG025
Publication Status
Published
Date Publish Online
2021-12-11