Predicting the state of health of VRLA batteries in UPS using data-driven method

作     者：Shang, Yitong Zheng, Weike Yan, Xiaoyun Nguyen, Dinh Hoa Jian, Linni 

作者机构：Tencent Inc Shenzhen 518052 Peoples R China Southern Univ Sci & Technol Jiaxing Res Inst Jiaxing 314050 Peoples R China Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen 518055 Peoples R China Kyushu Univ WPI I2CNER Fukuoka 8190395 Japan Kyushu Univ IMI Fukuoka 8190395 Japan 

出 版 物：《ENERGY REPORTS》 (Energy Rep.)

年 卷 期：2023年第9卷

页      面：184-190页

核心收录：

基　　金：Tencent Rhino-Bird Research Elite Program Department of Education of Guangdong Province,China [2020ZDZX3002] Guangzhou Municipal Science and Technology Bureau Science and Technology Innovation Committee of Shenzhen, China [JCYJ20220530113008019] 

主　　题：Data center Battery state of health Coup De Fouet phenomenon Data -driven method 

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://***/licenses/by-nc-nd/4.0/).