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Status | 已發表Published |
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![]() ![]() | |
2024-09-15 | |
Source Publication | Journal of Energy Storage
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ISSN | 2352-152X |
Volume | 98Pages: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. |
Keyword | Doping Strategies Electrochemical Performance Graph Neural Networks Lithium-ion Batteries Ternary Nickel–cobalt–manganese Cathode Materials |
DOI | 10.1016/j.est.2024.112982 |
URL | View the original |
Indexed By | SCIE |
Language | 英語English |
WOS Research Area | Energy & Fuels |
WOS Subject | Energy & Fuels |
WOS ID | WOS:001278235100001 |
Publisher | ELSEVIER, RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS |
Scopus ID | 2-s2.0-85199185046 |
Fulltext Access | |
Citation statistics | |
Document Type | Journal article |
Collection | INSTITUTE OF APPLIED PHYSICS AND MATERIALS ENGINEERING |
Corresponding Author | Lin, Zhan |
Affiliation | 1.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 Affilication | INSTITUTE 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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