Implementation for a cloud battery management system based on the CHAIN framework
File(s)
Accepted version
Author(s)
Type
Journal Article
Abstract
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.
Date Issued
2021-09-01
Date Acceptance
2021-05-01
Citation
Energy and AI, 2021, 5, pp.100088-100088
ISSN
2666-5468
Publisher
Elsevier BV
Start Page
100088
End Page
100088
Journal / Book Title
Energy and AI
Volume
5
Copyright Statement
©2021 Published by Elsevier Ltd.This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/)
Shichun YangZhengjie ZhangRui CaoMingyue WangHanchao ChengLisheng ZhangYinan JiangYonglin LiBinbin ChenHeping LingYubo LianBilly WuXinhua Liu
Shichun YangZhengjie ZhangRui CaoMingyue WangHanchao ChengLisheng ZhangYinan JiangYonglin LiBinbin ChenHeping LingYubo LianBilly WuXinhua Liu
Sponsor
The Faraday Institution
Identifier
https://www.sciencedirect.com/science/article/pii/S2666546821000422?via%3Dihub
Grant Number
FIRG025
Publication Status
Published
Article Number
ARTN 100088
Date Publish Online
2021-05-19