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Predicting doping strategies for ternary nickel–cobalt–manganese cathode materials to enhance battery performance using graph neural networks
Zhao, Zirui1; Luo, Dong2,4; Wu, Shuxing3,4; Sun, Kaitong1; Lin, Zhan3,4; Li, Hai Feng1
2024-09-15
Source PublicationJournal of Energy Storage
ISSN2352-152X
Volume98Pages:112982
Abstract

The exceptional electrochemical performance of lithium-ion batteries has spurred considerable interest in advanced battery technologies, particularly those utilizing ternary nickel–cobalt–manganese (NCM) cathode materials, which are renowned for their robust electrochemical performance and structural stability. Building upon this research, investigators have explored doping additional elements into NCM cathode materials to further enhance their electrochemical performance and structural integrity. However, the multitude of doping strategies available for NCM battery systems presents a challenge in determining the most effective approach. In this study, we elucidate the potential of ternary NCM systems as cathode materials for lithium-ion batteries. We compile a comprehensive database of lithium-ion batteries employing NCM systems from various sources of prior research and develop a corresponding data-driven model utilizing graph neural networks to predict optimal doping strategies. Our aim is to provide insights into the NCM-based battery systems for both fundamental understanding and practical applications.

KeywordDoping Strategies Electrochemical Performance Graph Neural Networks Lithium-ion Batteries Ternary Nickel–cobalt–manganese Cathode Materials
DOI10.1016/j.est.2024.112982
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEnergy & Fuels
WOS SubjectEnergy & Fuels
WOS IDWOS:001278235100001
PublisherELSEVIER, RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
Scopus ID2-s2.0-85199185046
Fulltext Access
Citation statistics
Document TypeJournal article
CollectionINSTITUTE OF APPLIED PHYSICS AND MATERIALS ENGINEERING
Corresponding AuthorLin, Zhan
Affiliation1.Institute of Applied Physics and Materials Engineering, University of Macau, Taipa, Avenida da Universidade, 999078, Macao
2.Hunan Provincial Key Laboratory of Advanced Materials for New Energy Storage and Conversion, School of Materials Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China
3.Jieyang Branch of Chemistry and Chemical Engineering Guangdong Laboratory, Jieyang, 515200, China
4.School of Chemical Engineering and Light Industry, Guangdong University of Technology, Guangzhou, 510006, China
First Author AffilicationINSTITUTE OF APPLIED PHYSICS AND MATERIALS ENGINEERING
Recommended Citation
GB/T 7714
Zhao, Zirui,Luo, Dong,Wu, Shuxing,et al. Predicting doping strategies for ternary nickel–cobalt–manganese cathode materials to enhance battery performance using graph neural networks[J]. Journal of Energy Storage, 2024, 98, 112982.
APA Zhao, Zirui., Luo, Dong., Wu, Shuxing., Sun, Kaitong., Lin, Zhan., & Li, Hai Feng (2024). Predicting doping strategies for ternary nickel–cobalt–manganese cathode materials to enhance battery performance using graph neural networks. Journal of Energy Storage, 98, 112982.
MLA Zhao, Zirui,et al."Predicting doping strategies for ternary nickel–cobalt–manganese cathode materials to enhance battery performance using graph neural networks".Journal of Energy Storage 98(2024):112982.
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