Data-physics-driven estimation of battery state of charge and capacity
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DOI: 10.1016/j.energy.2024.130776
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Cited by:
- Zhang, Ran & Ji, ChunHui & Zhou, Xing & Liu, Tianyu & Jin, Guang & Pan, Zhengqiang & Liu, Yajie, 2024. "Capacity estimation of lithium-ion batteries with uncertainty quantification based on temporal convolutional network and Gaussian process regression," Energy, Elsevier, vol. 297(C).
- Yao, Jiachi & Chang, Zhonghao & Han, Te & Tian, Jingpeng, 2024. "Semi-supervised adversarial deep learning for capacity estimation of battery energy storage systems," Energy, Elsevier, vol. 294(C).
- Tang, Aihua & Xu, Yuchen & Hu, Yuanzhi & Tian, Jinpeng & Nie, Yuwei & Yan, Fuwu & Tan, Yong & Yu, Quanqing, 2024. "Battery state of health estimation under dynamic operations with physics-driven deep learning," Applied Energy, Elsevier, vol. 370(C).
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Keywords
Lithium-ion battery; State of charge estimation; Capacity estimation; Gaussian fusion distribution;All these keywords.
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