Citation

BibTex format

@article{Liu:2022:10.1002/aenm.202200889,
author = {Liu, X and Zhang, L and Yu, H and Wang, J and Li, J and Yang, K and Zhao, Y and Wang, H and Wu, B and Brandon, N and Yang, S},
doi = {10.1002/aenm.202200889},
journal = {Advanced Energy Materials},
title = {Bridging multiscale characterization technologies and digital modeling to evaluate lithium battery full lifecycle},
url = {http://dx.doi.org/10.1002/aenm.202200889},
volume = {12},
year = {2022}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - 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.
AU - Liu,X
AU - Zhang,L
AU - Yu,H
AU - Wang,J
AU - Li,J
AU - Yang,K
AU - Zhao,Y
AU - Wang,H
AU - Wu,B
AU - Brandon,N
AU - Yang,S
DO - 10.1002/aenm.202200889
PY - 2022///
SN - 1614-6832
TI - Bridging multiscale characterization technologies and digital modeling to evaluate lithium battery full lifecycle
T2 - Advanced Energy Materials
UR - http://dx.doi.org/10.1002/aenm.202200889
UR - https://onlinelibrary.wiley.com/doi/10.1002/aenm.202200889
UR - http://hdl.handle.net/10044/1/97546
VL - 12
ER -