Two-stage deep learning for online prediction of knee-point in Li-ion battery capacity degradation
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DOI: 10.1016/j.apenergy.2022.120204
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Cited by:
- Fan, Wenjun & Zhu, Jiangong & Qiao, Dongdong & Jiang, Bo & Wang, Xueyuan & Wei, Xuezhe & Dai, Haifeng, 2024. "Prediction of nonlinear degradation knee-point and remaining useful life for lithium-ion batteries using relaxation voltage," Energy, Elsevier, vol. 294(C).
- Calum Strange & Rasheed Ibraheem & Gonçalo dos Reis, 2023. "Online Lifetime Prediction for Lithium-Ion Batteries with Cycle-by-Cycle Updates, Variance Reduction, and Model Ensembling," Energies, MDPI, vol. 16(7), pages 1-14, April.
- Lee, Jaewook & Lee, Jay H., 2024. "Simultaneous extraction of intra- and inter-cycle features for predicting lithium-ion battery's knees using convolutional and recurrent neural networks," Applied Energy, Elsevier, vol. 356(C).
- Maria Cortada-Torbellino & Abdelali El Aroudi & Hugo Valderrama-Blavi, 2023. "Outlook of Lithium-Ion Battery Regulations and Procedures to Improve Cell Degradation Detection and Other Alternatives," Energies, MDPI, vol. 16(5), pages 1-13, March.
- Che, Yunhong & Zheng, Yusheng & Forest, Florent Evariste & Sui, Xin & Hu, Xiaosong & Teodorescu, Remus, 2024. "Predictive health assessment for lithium-ion batteries with probabilistic degradation prediction and accelerating aging detection," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
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Keywords
Lithium-ion batteries; Knee-point; Convolutional neural networks; Feature extraction; Explainable artificial intelligence;All these keywords.
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