Data-driven lithium-ion batteries capacity estimation based on deep transfer learning using partial segment of charging/discharging data
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DOI: 10.1016/j.energy.2023.127033
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Cited by:
- Nak-Hun Choi & Jung Woo Sohn & Jong-Seok Oh, 2023. "Defect Detection Model Using CNN and Image Augmentation for Seat Foaming Process," Mathematics, MDPI, vol. 11(24), pages 1-13, December.
- Wang, Huan & Li, Yan-Fu & Zhang, Ying, 2023. "Bioinspired spiking spatiotemporal attention framework for lithium-ion batteries state-of-health estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
- Bao, Xinyuan & Chen, Liping & Lopes, António M. & Li, Xin & Xie, Siqiang & Li, Penghua & Chen, YangQuan, 2023. "Hybrid deep neural network with dimension attention for state-of-health estimation of Lithium-ion Batteries," Energy, Elsevier, vol. 278(C).
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Keywords
Lithium-ion batteries; Capacity estimation; Transfer learning; Convolutional neural network; Partial segment;All these keywords.
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