Predicting the state of health of VRLA batteries in UPS using data-driven method-Reference-Cited by-同舟云学术

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

Published:2023-09 Issue: Volume:9 Page:184-190
ISSN:2352-4847
Container-title:Energy Reports
language:en
Short-container-title:Energy Reports

Author:

Shang Yitong^ORCID,Zheng Weike,Yan Xiaoyun,Nguyen Dinh Hoa,Jian Linni^ORCID

Publisher

Elsevier BV

Subject

General Energy

Reference21 articles.

1. Data center energy consumption modeling: A survey;Dayarathna;IEEE Commun Surv Tutor,2015

2. Predictive maintenance of VRLA batteries in UPS towards reliable data centers;Tang;IFAC-PapersOnLine,2020

3. Battery monitoring and prognostics optimization techniques: Challenges and opportunities;Semeraro;Energy,2022

4. Machine learning pipeline for battery state-of-health estimation;Roman;Nat Mach Intell,2021

5. A review on the state of health estimation methods of lead–acid batteries;Jiang;J Power Sources,2022

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. High precision state of health estimation of lithium-ion batteries based on strong correlation aging feature extraction and improved hybrid kernel function least squares support vector regression machine model;Journal of Energy Storage;2024-06

2. Non-Invasive Method-Based Estimation of Battery State-of-Health with Dynamical Response Characteristics of Load Surges;Energies;2024-01-25

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