Imperial College London
Portrait Picture

Dr Billy Wu

Faculty of Engineering, Dyson School of Design Engineering

Reader in Electrochemical Design Engineering
 
 
 
//

Contact

 

+44 (0)20 7594 6385billy.wu Website

 
 
//

Location

 

1M04Royal College of ScienceSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Gao:2021:10.1016/j.ensm.2021.01.007,
author = {Gao, X and Liu, X and He, R and Wang, M and Xie, W and Brandon, N and Wu, B and Ling, H and Yang, S},
doi = {10.1016/j.ensm.2021.01.007},
journal = {Energy Storage Materials},
pages = {435--458},
title = {Designed high-performance lithium-ion battery electrodes using a novel hybrid model-data driven approach},
url = {http://dx.doi.org/10.1016/j.ensm.2021.01.007},
volume = {36},
year = {2021}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Lithium-ion batteries (LIBs) have been widely recognized as the most promising energy storage technology due to their favorable power and energy densities for applications in electric vehicles (EVs) and other related functions. However, further improvements are needed which are underpinned by advances in conventional electrode designs. This paper reviews conventional and emerging electrode designs, including conventional LIB electrode modification techniques and electrode design for next-generation energy devices. Thick electrode designs with low tortuosity are the most conventional approach for energy density improvement. Chemistries such as lithium-sulfur, lithium-air and solid-state batteries show great potential, yet many challenges remain. Microscale structural modelling and macroscale functional modelling methods underpin much of the electrode design work and these efforts are summarized here. More importantly, this paper presents a novel framework for next-generation electrode design termed: Cyber Hierarchy And Interactional Network based Multiscale Electrode Design (CHAIN-MED), a hybrid solution combining model-based and data-driven techniques for optimal electrode design, which significantly shortens the development cycle. This review, therefore, provides novel insights into combining existing design approaches with multiscale models and machine learning techniques for next-generation LIB electrodes.
AU  - Gao,X
AU  - Liu,X
AU  - He,R
AU  - Wang,M
AU  - Xie,W
AU  - Brandon,N
AU  - Wu,B
AU  - Ling,H
AU  - Yang,S
DO  - 10.1016/j.ensm.2021.01.007
EP  - 458
PY  - 2021///
SN  - 2405-8297
SP  - 435
TI  - Designed high-performance lithium-ion battery electrodes using a novel hybrid model-data driven approach
T2  - Energy Storage Materials
UR  - http://dx.doi.org/10.1016/j.ensm.2021.01.007
UR  - http://hdl.handle.net/10044/1/86521
VL  - 36
ER  -
Download