Cross-conditions capacity estimation of lithium-ion battery with constrained adversarial domain adaptation
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DOI: 10.1016/j.energy.2023.127559
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- Chen, Lin & Wang, Huimin & Liu, Bohao & Wang, Yijue & Ding, Yunhui & Pan, Haihong, 2021. "Battery state-of-health estimation based on a metabolic extreme learning machine combining degradation state model and error compensation," Energy, Elsevier, vol. 215(PA).
- He, Jiabei & Tian, Yi & Wu, Lifeng, 2022. "A hybrid data-driven method for rapid prediction of lithium-ion battery capacity," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
- Ma, Yan & Shan, Ce & Gao, Jinwu & Chen, Hong, 2022. "A novel method for state of health estimation of lithium-ion batteries based on improved LSTM and health indicators extraction," Energy, Elsevier, vol. 251(C).
- Wang, Ya-Xiong & Chen, Zhenhang & Zhang, Wei, 2022. "Lithium-ion battery state-of-charge estimation for small target sample sets using the improved GRU-based transfer learning," Energy, Elsevier, vol. 244(PB).
- Li, Yuanyuan & Sheng, Hanmin & Cheng, Yuhua & Stroe, Daniel-Ioan & Teodorescu, Remus, 2020. "State-of-health estimation of lithium-ion batteries based on semi-supervised transfer component analysis," Applied Energy, Elsevier, vol. 277(C).
- Zhu, Rong & Chen, Yuan & Peng, Weiwen & Ye, Zhi-Sheng, 2022. "Bayesian deep-learning for RUL prediction: An active learning perspective," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
- Zhang, Yu & Peng, Zhen & Guan, Yong & Wu, Lifeng, 2021. "Prognostics of battery cycle life in the early-cycle stage based on hybrid model," Energy, Elsevier, vol. 221(C).
- Shen, Sheng & Sadoughi, Mohammadkazem & Li, Meng & Wang, Zhengdao & Hu, Chao, 2020. "Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 260(C).
- Ma, Zeyu & Yang, Ruixin & Wang, Zhenpo, 2019. "A novel data-model fusion state-of-health estimation approach for lithium-ion batteries," Applied Energy, Elsevier, vol. 237(C), pages 836-847.
- Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
- Chen, Zhang & Shen, Wenjing & Chen, Liqun & Wang, Shuqiang, 2022. "Adaptive online capacity prediction based on transfer learning for fast charging lithium-ion batteries," Energy, Elsevier, vol. 248(C).
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Keywords
Battery management systems; Cross-conditions capacity estimation; Unsupervised domain adaptation; Long short-term memory network; Self-supervised learning;All these keywords.
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