Remaining useful life prediction for 18650 sodium-ion batteries based on incremental capacity analysis
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DOI: 10.1016/j.energy.2022.125151
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- Zhou, Zhihao & Zhang, Wei & Yao, Peng & Long, Zhenhua & Bai, Mingling & Liu, Jinfu & Yu, Daren, 2024. "More realistic degradation trend prediction for gas turbine based on factor analysis and multiple penalty mechanism loss function," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
- Wei, Meng & Ye, Min & Zhang, Chuanwei & Li, Yan & Zhang, Jiale & Wang, Qiao, 2023. "A multi-scale learning approach for remaining useful life prediction of lithium-ion batteries based on variational mode decomposition and Monte Carlo sampling," Energy, Elsevier, vol. 283(C).
- Wei, Meng & Ye, Min & Zhang, Chuanwei & Wang, Qiao & Lian, Gaoqi & Xia, Baozhou, 2024. "Integrating mechanism and machine learning based capacity estimation for LiFePO4 batteries under slight overcharge cycling," Energy, Elsevier, vol. 296(C).
- Lian, Gaoqi & Ye, Min & Wang, Qiao & Li, Yan & Xia, Baozhou & Zhang, Jiale & Xu, Xinxin, 2024. "Robust state-of-charge estimation for LiFePO4 batteries under wide varying temperature environments," Energy, Elsevier, vol. 293(C).
- Shabani, Masoume & Wallin, Fredrik & Dahlquist, Erik & Yan, Jinyue, 2023. "The impact of battery operating management strategies on life cycle cost assessment in real power market for a grid-connected residential battery application," Energy, Elsevier, vol. 270(C).
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Keywords
18650 sodium-ion batteries; Aging mechanism; Remaining useful life; Incremental capacity analysis; Gaussian process regression;All these keywords.
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