Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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1 Online-Ressource.
- Language
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Englisch
- Bibliographic citation
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Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis ; volume:15 ; number:1 ; day:21 ; month:5 ; year:2024 ; pages:1-12 ; date:12.2024
Nature Communications ; 15, Heft 1 (21.5.2024), 1-12, 12.2024
- Classification
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Elektrotechnik, Elektronik
- Creator
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Wang, Fujin
Zhai, Zhi
Zhao, Zhibin
Di, Yi
Chen, Xuefeng
- Contributor
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SpringerLink (Online service)
- DOI
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10.1038/s41467-024-48779-z
- URN
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urn:nbn:de:101:1-2407311001268.843333137761
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 10:48 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Wang, Fujin
- Zhai, Zhi
- Zhao, Zhibin
- Di, Yi
- Chen, Xuefeng
- SpringerLink (Online service)
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Supply risks associated with lithium-ion battery materials
Development of environmentally friendly lithium-ion battery components
Review - "Knees" in Lithium-Ion Battery Aging Trajectories
Hybrid Modeling of Lithium-Ion Battery: Physics-Informed Neural Network for Battery State Estimation
Recyclable 3D‐Printed Aqueous Lithium‐Ion Battery
Investigation of nanostructured lithium-ion battery materials
Synthetic Data Generation for AI-Informed End-of-Line Testing for Lithium-Ion Battery Production
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Evaluation of Lithium-Ion Battery Cell Value Chain
Slot die coating of lithium-ion battery electrodes
Evaluation of Lithium-ion battery cell value chain
Virtual Electrode Design for Lithium‐Ion Battery Cathodes
Supply risks associated with lithium-ion battery materials
Development of environmentally friendly lithium-ion battery components