Constant current charging time based fast state-of-health estimation for lithium-ion batteries
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DOI: 10.1016/j.energy.2022.123556
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- Meng, Jinhao & Cai, Lei & Stroe, Daniel-Ioan & Ma, Junpeng & Luo, Guangzhao & Teodorescu, Remus, 2020. "An optimized ensemble learning framework for lithium-ion Battery State of Health estimation in energy storage system," Energy, Elsevier, vol. 206(C).
- Zhang, Yajun & Liu, Yajie & Wang, Jia & Zhang, Tao, 2022. "State-of-health estimation for lithium-ion batteries by combining model-based incremental capacity analysis with support vector regression," Energy, Elsevier, vol. 239(PB).
- Sui, Xin & He, Shan & Vilsen, Søren B. & Meng, Jinhao & Teodorescu, Remus & Stroe, Daniel-Ioan, 2021. "A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery," Applied Energy, Elsevier, vol. 300(C).
- Jiang, Yihui & Xu, Jun & Hou, Wenlong & Mei, Xuesong, 2021. "A stack pressure based equivalent mechanical model of lithium-ion pouch batteries," Energy, Elsevier, vol. 221(C).
- Son, Seho & Jeong, Siheon & Kwak, Eunji & Kim, Jun-hyeong & Oh, Ki-Yong, 2022. "Integrated framework for SOH estimation of lithium-ion batteries using multiphysics features," Energy, Elsevier, vol. 238(PA).
- Li, Xiaoyu & Yuan, Changgui & Li, Xiaohui & Wang, Zhenpo, 2020. "State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression," Energy, Elsevier, vol. 190(C).
- Deng, Yuanwang & Ying, Hejie & E, Jiaqiang & Zhu, Hao & Wei, Kexiang & Chen, Jingwei & Zhang, Feng & Liao, Gaoliang, 2019. "Feature parameter extraction and intelligent estimation of the State-of-Health of lithium-ion batteries," Energy, Elsevier, vol. 176(C), pages 91-102.
- Kong, Jin-zhen & Yang, Fangfang & Zhang, Xi & Pan, Ershun & Peng, Zhike & Wang, Dong, 2021. "Voltage-temperature health feature extraction to improve prognostics and health management of lithium-ion batteries," Energy, Elsevier, vol. 223(C).
- Li, Yi & Zou, Changfu & Berecibar, Maitane & Nanini-Maury, Elise & Chan, Jonathan C.-W. & van den Bossche, Peter & Van Mierlo, Joeri & Omar, Noshin, 2018. "Random forest regression for online capacity estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 232(C), pages 197-210.
- Chang, Chun & Wu, Yutong & Jiang, Jiuchun & Jiang, Yan & Tian, Aina & Li, Taiyu & Gao, Yang, 2022. "Prognostics of the state of health for lithium-ion battery packs in energy storage applications," Energy, Elsevier, vol. 239(PB).
- Ospina Agudelo, Brian & Zamboni, Walter & Monmasson, Eric, 2021. "Application domain extension of incremental capacity-based battery SoH indicators," Energy, Elsevier, vol. 234(C).
- Khaleghi, Sahar & Karimi, Danial & Beheshti, S. Hamidreza & Hosen, Md. Sazzad & Behi, Hamidreza & Berecibar, Maitane & Van Mierlo, Joeri, 2021. "Online health diagnosis of lithium-ion batteries based on nonlinear autoregressive neural network," Applied Energy, Elsevier, vol. 282(PA).
- Wang, Zengkai & Zeng, Shengkui & Guo, Jianbin & Qin, Taichun, 2019. "State of health estimation of lithium-ion batteries based on the constant voltage charging curve," Energy, Elsevier, vol. 167(C), pages 661-669.
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- Feng, Juqiang & Cai, Feng & Zhao, Yang & Zhang, Xing & Zhan, Xinju & Wang, Shunli, 2024. "A novel feature optimization and ensemble learning method for state-of-health prediction of mining lithium-ion batteries," Energy, Elsevier, vol. 299(C).
- Zhang, Shuxin & Liu, Zhitao & Su, Hongye, 2023. "State of health estimation for lithium-ion batteries on few-shot learning," Energy, Elsevier, vol. 268(C).
- Ospina Agudelo, Brian & Zamboni, Walter & Postiglione, Fabio & Monmasson, Eric, 2023. "Battery State-of-Health estimation based on multiple charge and discharge features," Energy, Elsevier, vol. 263(PA).
- Liu, Mengmeng & Xu, Jun & Jiang, Yihui & Mei, Xuesong, 2023. "Multi-dimensional features based data-driven state of charge estimation method for LiFePO4 batteries," Energy, Elsevier, vol. 274(C).
- Wei, Meng & Ye, Min & Zhang, Chuanwei & Li, Yan & Zhang, Jiale & Wang, Qiao, 2023. "A multi-scale learning approach for remaining useful life prediction of lithium-ion batteries based on variational mode decomposition and Monte Carlo sampling," Energy, Elsevier, vol. 283(C).
- Shu, Xing & Shen, Jiangwei & Chen, Zheng & Zhang, Yuanjian & Liu, Yonggang & Lin, Yan, 2022. "Remaining capacity estimation for lithium-ion batteries via co-operation of multi-machine learning algorithms," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
- Changqing Du & Rui Qi & Zhong Ren & Di Xiao, 2023. "Research on State-of-Health Estimation for Lithium-Ion Batteries Based on the Charging Phase," Energies, MDPI, vol. 16(3), pages 1-14, February.
- Ko, Chi-Jyun & Chen, Kuo-Ching, 2024. "Using tens of seconds of relaxation voltage to estimate open circuit voltage and state of health of lithium ion batteries," Applied Energy, Elsevier, vol. 357(C).
- Li, Chuan & Zhang, Huahua & Ding, Ping & Yang, Shuai & Bai, Yun, 2023. "Deep feature extraction in lifetime prognostics of lithium-ion batteries: Advances, challenges and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(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).
- Hou, Jiayang & Xu, Jun & Lin, Chuanping & Jiang, Delong & Mei, Xuesong, 2024. "State of charge estimation for lithium-ion batteries based on battery model and data-driven fusion method," Energy, Elsevier, vol. 290(C).
- Jiang, Yihui & Xu, Jun & Liu, Mengmeng & Mei, Xuesong, 2022. "An electromechanical coupling model-based state of charge estimation method for lithium-ion pouch battery modules," Energy, Elsevier, vol. 259(C).
- Ko, Chi-Jyun & Chen, Kuo-Ching, 2024. "Constructing battery impedance spectroscopy using partial current in constant-voltage charging or partial relaxation voltage," Applied Energy, Elsevier, vol. 356(C).
- Wu, Ji & Fang, Leichao & Dong, Guangzhong & Lin, Mingqiang, 2023. "State of health estimation of lithium-ion battery with improved radial basis function neural network," Energy, Elsevier, vol. 262(PB).
- Huang, Kai & Yao, Kaixin & Guo, Yongfang & Lv, Ziteng, 2023. "State of health estimation of lithium-ion batteries based on fine-tuning or rebuilding transfer learning strategies combined with new features mining," Energy, Elsevier, vol. 282(C).
- Ji, Jie & Zhou, Mengxiong & Guo, Renwei & Tang, Jiankang & Su, Jiaoyue & Huang, Hui & Sun, Na & Nazir, Muhammad Shahzad & Wang, Yaodong, 2023. "A electric power optimal scheduling study of hybrid energy storage system integrated load prediction technology considering ageing mechanism," Renewable Energy, Elsevier, vol. 215(C).
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Keywords
Lithium-ion battery; Charging time; Incremental capacity peak; Random forest regression; State of health;All these keywords.
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