Bridging multiscale characterization technologies and digital modeling to evaluate lithium battery full lifecycle
File(s)
Accepted version
Author(s)
Type
Journal Article
Abstract
The safety, durability and power density of lithium-ion batteries (LIBs) are currently inadequate to satisfy the continuously growing demand of the emerging battery markets. Rapid progress has been made from material engineering to system design, combining experimental results and simulations to enhance LIB performance. Limited by spatial and temporal resolution, state-of-the-art advanced characterization techniques fail to fully reveal the complex multi-scale degradation mechanism in LIBs. Strengthening interaction and iteration between characterization and modeling improves the understanding of reaction mechanisms as well as design and management of LIBs. Herein, a seed cyber hierarchy and interactional network framework is demonstrated to evaluate the overall lifecycle of LIBs. The typical examples of bridging the characterization techniques and modeling are discussed. The critical parameters extracted from multi-scale characterization can serve as digital inputs for modeling. Furthermore, advanced computational techniques including cloud computing, big data, machine learning, and artificial intelligence can also promote the comprehensive understanding and precise control of the whole battery lifecycle. Digital twins techniques will be introduced enabling the real-time monitoring and control of LIBs, autonomous computer-assisted characterizations and intelligent manufacturing. It is anticipated that this work will provide a roadmap for further intensive research on developing high-performance LIBs and intelligent battery management.
Date Issued
2022-09-01
Date Acceptance
2022-06-15
Citation
Advanced Energy Materials, 2022, 12 (33)
ISSN
1614-6832
Publisher
Wiley-VCH Verlag
Journal / Book Title
Advanced Energy Materials
Volume
12
Issue
33
Copyright Statement
© 2022 Wiley-VCH GmbH. This is the accepted version of the following article: Liu, X., Zhang, L., Yu, H., Wang, J., Li, J., Yang, K., Zhao, Y., Wang, H., Wu, B., Brandon, N. P., Yang, S., Bridging Multiscale Characterization Technologies and Digital Modeling to Evaluate Lithium Battery Full Lifecycle. Adv. Energy Mater. 2022, which has been published in final form at https://doi.org/10.1002/aenm.202200889
Identifier
https://onlinelibrary.wiley.com/doi/10.1002/aenm.202200889
Subjects
Science & Technology
Physical Sciences
Technology
Chemistry, Physical
Energy & Fuels
Materials Science, Multidisciplinary
Physics, Applied
Physics, Condensed Matter
Chemistry
Materials Science
Physics
characterization
digital twins
machine learning
simulation
ELECTROCHEMICAL IMPEDANCE SPECTROSCOPY
ELECTRODE-ELECTROLYTE INTERFACE
DENSITY-FUNCTIONAL THEORY
LAYERED OXIDE CATHODES
INTERNAL SHORT-CIRCUIT
HIGH-ENERGY-DENSITY
X-RAY-DIFFRACTION
ION-BATTERY
IN-SITU
THERMAL-STABILITY
0303 Macromolecular and Materials Chemistry
0912 Materials Engineering
0915 Interdisciplinary Engineering
Publication Status
Published
Article Number
ARTN 2200889
Date Publish Online
2022-06-15