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Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation

Zhu, Jiangong 1; Wang, Yixiu; Huang, Yuan 1; Bhushan Gopaluni, R.; Cao, Yankai; Heere, Michael 1; Mühlbauer, Martin J. 1; Mereacre, Liuda 1; Dai, Haifeng ; Liu, Xinhua; Senyshyn, Anatoliy; Wei, Xuezhe; Knapp, Michael ORCID iD icon 2; Ehrenberg, Helmut 1
1 Institut für Angewandte Materialien – Energiespeichersysteme (IAM-ESS), Karlsruher Institut für Technologie (KIT)
2 Institut für Angewandte Materialien (IAM), Karlsruher Institut für Technologie (KIT)

Abstract:

Accurate capacity estimation is crucial for the reliable and safe operation of lithium-ion batteries. In particular, exploiting the relaxation voltage curve features could enable battery capacity estimation without additional cycling information. Here, we report the study of three datasets comprising 130 commercial lithium-ion cells cycled under various conditions to evaluate the capacity estimation approach. One dataset is collected for model building from batteries with LiNi$_{0.86}$Co$_{0.11}$Al$_{0.03}$O$_{2}$-based positive electrodes. The other two datasets, used for validation, are obtained from batteries with LiNi$_{0.83}$Co$_{0.11}$Mn$_{0.07}$O$_{2}$-based positive electrodes and batteries with the blend of Li(NiCoMn)O$_{2}$ - Li(NiCoAl)O$_{2}$ positive electrodes. Base models that use machine learning methods are employed to estimate the battery capacity using features derived from the relaxation voltage profiles. The best model achieves a root-mean-square error of 1.1% for the dataset used for the model building. A transfer learning model is then developed by adding a featured linear transformation to the base model. This extended model achieves a root-mean-square error of less than 1.7% on the datasets used for the model validation, indicating the successful applicability of the capacity estimation approach utilizing cell voltage relaxation.


Verlagsausgabe §
DOI: 10.5445/IR/1000148920
Veröffentlicht am 25.07.2022
Originalveröffentlichung
DOI: 10.1038/s41467-022-29837-w
Scopus
Zitationen: 305
Web of Science
Zitationen: 224
Dimensions
Zitationen: 301
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Materialien – Energiespeichersysteme (IAM-ESS)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 12.2022
Sprache Englisch
Identifikator ISSN: 2041-1723
KITopen-ID: 1000148920
HGF-Programm 38.02.03 (POF IV, LK 01) Batteries in Application
Erschienen in Nature Communications
Verlag Nature Research
Band 13
Heft 1
Seiten Art.Nr. 2261
Vorab online veröffentlicht am 27.04.2022
Nachgewiesen in Scopus
Dimensions
Web of Science
Globale Ziele für nachhaltige Entwicklung Ziel 7 – Bezahlbare und saubere Energie
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