Imperial College London
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Dr Billy Wu

Faculty of Engineering, Dyson School of Design Engineering

Reader in Electrochemical Design Engineering
 
 
 
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Contact

 

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

 
 
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Location

 

1M04Royal College of ScienceSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Yang:2021:10.1016/j.egyai.2021.100088,
author = {Yang, S and Zhang, Z and Cao, R and Wang, M and Cheng, H and Zhang, L and Jiang, Y and Li, Y and Chen, B and Ling, H and Lian, Y and Wu, B and Liu, X},
doi = {10.1016/j.egyai.2021.100088},
journal = {Energy and AI},
pages = {100088--100088},
title = {Implementation for a cloud battery management system based on the CHAIN framework},
url = {http://dx.doi.org/10.1016/j.egyai.2021.100088},
volume = {5},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - An intelligent battery management system is a crucial enabler for energy storage systems with high power output, increased safety and long lifetimes. With recent developments in cloud computing and the proliferation of big data, machine learning approaches have begun to deliver invaluable insights, which drives adaptive control of battery management systems (BMS) with improved performance. In this paper, a general framework utilizing an end-edge-cloud architecture for a cloud-based BMS is proposed, with the composition and function of each link described. Cloud-based BMS leverages from the Cyber Hierarchy and Interactional Network (CHAIN) framework to provide multi-scale insights, more advanced and efficient algorithms can be used to realize the state-of-X estimation, thermal management, cell balancing, fault diagnosis and other functions of traditional BMS system. The battery intelligent monitoring and management platform can visually present battery performance, store working-data to help in-depth understanding of the microscopic evolutionary law, and provide support for the development of control strategies. Currently, the cloud-based BMS requires more effects on the multi-scale integrated modeling methods and remote upgrading capability of the controller, these two aspects are very important for the precise management and online upgrade of the system. The utility of this approach is highlighted not only for automotive applications, but for any battery energy storage system, providing a holistic framework for future intelligent and connected battery management.
AU  - Yang,S
AU  - Zhang,Z
AU  - Cao,R
AU  - Wang,M
AU  - Cheng,H
AU  - Zhang,L
AU  - Jiang,Y
AU  - Li,Y
AU  - Chen,B
AU  - Ling,H
AU  - Lian,Y
AU  - Wu,B
AU  - Liu,X
DO  - 10.1016/j.egyai.2021.100088
EP  - 100088
PY  - 2021///
SN  - 2666-5468
SP  - 100088
TI  - Implementation for a cloud battery management system based on the CHAIN framework
T2  - Energy and AI
UR  - http://dx.doi.org/10.1016/j.egyai.2021.100088
UR  - https://www.sciencedirect.com/science/article/pii/S2666546821000422?via%3Dihub
UR  - http://hdl.handle.net/10044/1/89127
VL  - 5
ER  -
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