Lithium-Ion Power Battery Grouping: A Multisource Data Fusion-Based Clustering Approach and Distributed Deployment

doi:10.1115/1.4053307

CASIA OpenIR > 中国科学院工业视觉智能装备工程实验室 > 工业智能技术与系统

	Lithium-Ion Power Battery Grouping: A Multisource Data Fusion-Based Clustering Approach and Distributed Deployment
	Wang, Yudong1 ; Bai, Xiwei2 ; Liu, Chengbao2 ; Tan, Jie2
发表期刊	JOURNAL OF ELECTROCHEMICAL ENERGY CONVERSION AND STORAGE
ISSN	2381-6872
	2022-05-01
卷号	19 期号:2 页码:12
通讯作者	Tan, Jie(tan.jie@tom.com)
摘要	Consistence of lithium-ion power battery significantly affects the life and safety of battery modules and packs. To improve the consistence, battery grouping is employed, assembling batteries with similar electrochemical characteristics to make up modules and packs. Therefore, grouping process boils down to unsupervised clustering problem. Current used grouping approaches include two aspects, static characteristics based and dynamic based. However, there are three problems. First, the common problem is under utilization of multi-source data. Second, for the static characteristics based, there is grouping failure over time. Third, for the dynamic characteristics based, there is high computational complexity. To solve these problems, we propose a distributed multisource data fusion based battery grouping approach. The proposed approach designs an effective network structure for multisource data fusing and feature extracting from both static and dynamic multisource data. We apply our approach on real battery modules and record state of health (SOH) during charging-discharging cycles. Experiments indicate that the proposed approach can increase SOH of modules by 3.89% and reduce the inconsistence by 68.4%. Meanwhile, with the distributed deployment the time cost is reduced by 87.9% than the centralized scheme.
关键词	analysis and design of components devices and systems batteries electrochemical storage reliability
DOI	10.1115/1.4053307
关键词[WOS]	CONSISTENCY ; DESIGN ; CELLS
收录类别	SCI
语种	英语
资助项目	National Key R&D Program of China[2020YFB1710600] ; National Natural Science Foundation of China[U62003344] ; National Natural Science Foundation of China[U1701262] ; National Natural Science Foundation of China[U1801263]
项目资助者	National Key R&D Program of China ; National Natural Science Foundation of China
WOS研究方向	Electrochemistry ; Energy & Fuels
WOS类目	Electrochemistry ; Energy & Fuels
WOS记录号	WOS:000778139700013
出版者	ASME
七大方向——子方向分类	人工智能+制造
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/48299
专题	中国科学院工业视觉智能装备工程实验室_工业智能技术与系统
通讯作者	Tan, Jie
作者单位	1.Chinese Acad Sci, Inst Automat, Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Wang, Yudong,Bai, Xiwei,Liu, Chengbao,et al. Lithium-Ion Power Battery Grouping: A Multisource Data Fusion-Based Clustering Approach and Distributed Deployment[J]. JOURNAL OF ELECTROCHEMICAL ENERGY CONVERSION AND STORAGE,2022,19(2):12.
APA	Wang, Yudong,Bai, Xiwei,Liu, Chengbao,&Tan, Jie.(2022).Lithium-Ion Power Battery Grouping: A Multisource Data Fusion-Based Clustering Approach and Distributed Deployment.JOURNAL OF ELECTROCHEMICAL ENERGY CONVERSION AND STORAGE,19(2),12.
MLA	Wang, Yudong,et al."Lithium-Ion Power Battery Grouping: A Multisource Data Fusion-Based Clustering Approach and Distributed Deployment".JOURNAL OF ELECTROCHEMICAL ENERGY CONVERSION AND STORAGE 19.2(2022):12.