Convolutional neural network based capacity estimation using random segments of the charging curves for lithium-ion batteries
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DOI: 10.1016/j.energy.2021.120333
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- Zhang, Meng & Hu, Tao & Wu, Lifeng & Kang, Guoqing & Guan, Yong, 2021. "A method for capacity estimation of lithium-ion batteries based on adaptive time-shifting broad learning system," Energy, Elsevier, vol. 231(C).
- Huang, Haichi & Bian, Chong & Wu, Mengdan & An, Dong & Yang, Shunkun, 2024. "A novel integrated SOC–SOH estimation framework for whole-life-cycle lithium-ion batteries," Energy, Elsevier, vol. 288(C).
- Ming Zhang & Dongfang Yang & Jiaxuan Du & Hanlei Sun & Liwei Li & Licheng Wang & Kai Wang, 2023. "A Review of SOH Prediction of Li-Ion Batteries Based on Data-Driven Algorithms," Energies, MDPI, vol. 16(7), pages 1-28, March.
- Yao, Jiachi & Han, Te, 2023. "Data-driven lithium-ion batteries capacity estimation based on deep transfer learning using partial segment of charging/discharging data," Energy, Elsevier, vol. 271(C).
- Jiang, Fusheng & Ren, Yi & Tang, Ting & Wu, Zeyu & Xia, Quan & Sun, Bo & Yang, Dezhen, 2024. "An adaptive semi-supervised self-learning method for online state of health estimation of lithium-ion batteries," Energy, Elsevier, vol. 305(C).
- Chen, Si-Zhe & Liang, Zikang & Yuan, Haoliang & Yang, Ling & Xu, Fangyuan & Fan, Yuanliang, 2023. "A novel state of health estimation method for lithium-ion batteries based on constant-voltage charging partial data and convolutional neural network," Energy, Elsevier, vol. 283(C).
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- Jiang, Bo & Zhu, Yuli & Zhu, Jiangong & Wei, Xuezhe & Dai, Haifeng, 2023. "An adaptive capacity estimation approach for lithium-ion battery using 10-min relaxation voltage within high state of charge range," Energy, Elsevier, vol. 263(PC).
- Meng, Huixing & Geng, Mengyao & Han, Te, 2023. "Long short-term memory network with Bayesian optimization for health prognostics of lithium-ion batteries based on partial incremental capacity analysis," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
- Qian, Cheng & Guan, Hongsheng & Xu, Binghui & Xia, Quan & Sun, Bo & Ren, Yi & Wang, Zili, 2024. "A CNN-SAM-LSTM hybrid neural network for multi-state estimation of lithium-ion batteries under dynamical operating conditions," Energy, Elsevier, vol. 294(C).
- Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
- Guarino, Antonio & Trinchero, Riccardo & Canavero, Flavio & Spagnuolo, Giovanni, 2022. "A fast fuel cell parametric identification approach based on machine learning inverse models," Energy, Elsevier, vol. 239(PC).
- Han, Dongho & Kwon, Sanguk & Lee, Miyoung & Kim, Jonghoon & Yoo, Kisoo, 2023. "Electrochemical impedance spectroscopy image transformation-based convolutional neural network for diagnosis of external environment classification affecting abnormal aging of Li-ion batteries," Applied Energy, Elsevier, vol. 345(C).
- Zhu, Yuli & Jiang, Bo & Zhu, Jiangong & Wang, Xueyuan & Wang, Rong & Wei, Xuezhe & Dai, Haifeng, 2023. "Adaptive state of health estimation for lithium-ion batteries using impedance-based timescale information and ensemble learning," Energy, Elsevier, vol. 284(C).
- Zhang, Ying & Li, Yan-Fu, 2022. "Prognostics and health management of Lithium-ion battery using deep learning methods: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
- Du, Jingcai & Zhang, Caiping & Li, Shuowei & Zhang, Linjing & Zhang, Weige, 2024. "Two-stage prediction method for capacity aging trajectories of lithium-ion batteries based on Siamese-convolutional neural network," Energy, Elsevier, vol. 295(C).
- Zhang, Meng & Kang, Guoqing & Wu, Lifeng & Guan, Yong, 2022. "A method for capacity prediction of lithium-ion batteries under small sample conditions," Energy, Elsevier, vol. 238(PC).
- Lin, Yan-Hui & Ruan, Sheng-Jia & Chen, Yun-Xia & Li, Yan-Fu, 2023. "Physics-informed deep learning for lithium-ion battery diagnostics using electrochemical impedance spectroscopy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
- Fan, Xinyuan & Zhang, Weige & Zhang, Caiping & Chen, Anci & An, Fulai, 2022. "SOC estimation of Li-ion battery using convolutional neural network with U-Net architecture," Energy, Elsevier, vol. 256(C).
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Keywords
Lithium-ion battery; Capacity estimation; One-dimensional convolutional neural network; Random segment;All these keywords.
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