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Bridging Multiscale Characterization Technologies and Digital Modeling to Evaluate Lithium Battery Full Lifecycle (Adv. Energy Mater. 33/2022).

Authors :
Liu, Xinhua
Zhang, Lisheng
Yu, Hanqing
Wang, Jianan
Li, Junfu
Yang, Kai
Zhao, Yunlong
Wang, Huizhi
Wu, Billy
Brandon, Nigel P.
Yang, Shichun
Source :
Advanced Energy Materials. Sep2022, Vol. 12 Issue 33, p1-1. 1p.
Publication Year :
2022

Abstract

Characterization, digital twins, machine learning, simulation Bridging Multiscale Characterization Technologies and Digital Modeling to Evaluate Lithium Battery Full Lifecycle (Adv. Keywords: characterization; digital twins; machine learning; simulation EN characterization digital twins machine learning simulation 1 1 1 09/05/22 20220901 NES 220901 B Lithium Batteries b In article number 2200889, Kai Yang, Shichun Yang and co-workers demonstrate the potential of cyber hierarchy and interactional network framework (CHAIN) to bridge multi-scale characterization and modeling technologies to evaluate lithium battery overall lifecycle. [Extracted from the article]

Details

Language :
English
ISSN :
16146832
Volume :
12
Issue :
33
Database :
Academic Search Index
Journal :
Advanced Energy Materials
Publication Type :
Academic Journal
Accession number :
158867972
Full Text :
https://doi.org/10.1002/aenm.202270144