Bridging Multiscale Characterization Technologies and Digital Modeling to Evaluate Lithium Battery Full Lifecycle (Adv. Energy Mater. 33/2022). Issue 33 (1st September 2022)

• Record Type: Journal Article
• Authors: Liu, Xinhua; Zhang, Lisheng; Yu, Hanqing; Wang, Jianan; Li, Junfu; Yang, Kai; Zhao, Yunlong; Wang, Huizhi; Wu, Billy; Brandon, Nigel P.; Yang, Shichun
• Is Part Of: Advanced energy materials. Volume 12:Issue 33(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-09-01
• Subjects: characterization -- digital twins -- machine learning -- simulation; Energy harvesting -- Materials -- Periodicals; Energy conversion -- Materials -- Periodicals; Energy storage -- Materials -- Periodicals; Photovoltaics -- Periodicals; Fuel cells -- Periodicals; Thermoelectric materials -- Periodicals; 621.31
• DOI: 10.1002/aenm.202270144
• Languages: English
• ISSNs: 1614-6832

Abstract

Abstract : Lithium Batteries 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. CHAIN combing digital twin, cyber twin and artificial intelligence can enable the digitalization of cyber‐physical space, further encouraging battery full‐lifespan management from new material discovery, intelligent manufacturing and cloud management system design.