南方科技大学知识苑(SUSTech KC): Predicting the state of health of VRLA batteries in UPS using data-driven method

题名	Predicting the state of health of VRLA batteries in UPS using data-driven method
作者	Shang, Yitong1,2,3 ; Zheng, Weike 1; Yan, Xiaoyun 1; Nguyen, Dinh Hoa 4,5; Jian, Linni2,3
通讯作者	Jian, Linni
发表日期	2023-09-01
DOI	10.1016/j.egyr.2023.04.264
发表期刊	ENERGY REPORTS 影响因子和分区
ISSN	2352-4847
卷号	9页码:184-190
摘要	Uninterruptible power battery (UPS) is an important part to ensure the stable operation of data center. Its security is related to the reliability and stability of power system. Among them, the state of health (SOH) prediction is a key issue of the valve regulated lead-acid (VRLA) battery operation and maintenance in data center. In this work, the battery SOH is predicted by the correlation between the nadir voltage value of Coup De Fouet (CDF) phenomenon and SOH. Then, the CDF phenomenon is combined with popular data-driven methods, such as linear regression, regression tree, support-vector machine, gaussian process, neural network, to predict battery SOH through 215 features. Finally, the above method is verified with the real discharge dataset of UPS battery in data center. The experimental results show that the data-driven method combining big data has higher accuracy than the simple prediction of battery SOH based on the nadir voltage value of CDF phenomenon and its variants. (c) 2023 The Authors. 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/).
关键词	Data center Battery state of health Coup De Fouet phenomenon Data -driven method
相关链接	[来源记录]
收录类别	SCI ; EI ; CPCI-S
语种	英语
学校署名	通讯
资助项目	Department of Education of Guangdong Province,China[2020ZDZX3002] ; Guangzhou Municipal Science and Technology Bureau[202102010416] ; Science and Technology Innovation Committee of Shenzhen, China[JCYJ20220530113008019]
WOS研究方向	Energy & Fuels
WOS类目	Energy & Fuels
WOS记录号	WOS:000988867400001
出版者	ELSEVIER
EI入藏号	20231714020055
EI主题词	Support vector machines ; Trees (mathematics) ; Uninterruptible power systems
EI分类号	Computer Software, Data Handling and Applications:723 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4
来源库	Web of Science
引用统计	被引频次[WOS]：2
成果类型	期刊论文
条目标识符	http://sustech.caswiz.com/handle/2SGJ60CL/536286
专题	工学院_电子与电气工程系
作者单位	1.Tencent Inc, Shenzhen 518052, Peoples R China 2.Southern Univ Sci & Technol, Jiaxing Res Inst, Jiaxing 314050, Peoples R China 3.Southern Univ Sci & Technol, Dept Elect & Elect Engn, Shenzhen 518055, Peoples R China 4.Kyushu Univ, WPI I2CNER, Fukuoka 8190395, Japan 5.Kyushu Univ, IMI, Fukuoka 8190395, Japan
第一作者单位	南方科技大学; 电子与电气工程系
通讯作者单位	南方科技大学; 电子与电气工程系
推荐引用方式 GB/T 7714	Shang, Yitong,Zheng, Weike,Yan, Xiaoyun,et al. Predicting the state of health of VRLA batteries in UPS using data-driven method[J]. ENERGY REPORTS,2023,9:184-190.
APA	Shang, Yitong,Zheng, Weike,Yan, Xiaoyun,Nguyen, Dinh Hoa,&Jian, Linni.(2023).Predicting the state of health of VRLA batteries in UPS using data-driven method.ENERGY REPORTS,9,184-190.
MLA	Shang, Yitong,et al."Predicting the state of health of VRLA batteries in UPS using data-driven method".ENERGY REPORTS 9(2023):184-190.