Battery capacity estimation using 10-second relaxation voltage and a convolutional neural network
Author
Abstract
Suggested Citation
DOI: 10.1016/j.apenergy.2022.120308
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Jiang, Bo & Dai, Haifeng & Wei, Xuezhe, 2020. "Incremental capacity analysis based adaptive capacity estimation for lithium-ion battery considering charging condition," Applied Energy, Elsevier, vol. 269(C).
- Yunwei Zhang & Qiaochu Tang & Yao Zhang & Jiabin Wang & Ulrich Stimming & Alpha A. Lee, 2020. "Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning," Nature Communications, Nature, vol. 11(1), pages 1-6, December.
- Jiangong Zhu & Yixiu Wang & Yuan Huang & R. Bhushan Gopaluni & Yankai Cao & Michael Heere & Martin J. Mühlbauer & Liuda Mereacre & Haifeng Dai & Xinhua Liu & Anatoliy Senyshyn & Xuezhe Wei & Michael K, 2022. "Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
- Kristen A. Severson & Peter M. Attia & Norman Jin & Nicholas Perkins & Benben Jiang & Zi Yang & Michael H. Chen & Muratahan Aykol & Patrick K. Herring & Dimitrios Fraggedakis & Martin Z. Bazant & Step, 2019. "Data-driven prediction of battery cycle life before capacity degradation," Nature Energy, Nature, vol. 4(5), pages 383-391, May.
- Richard Schmuch & Ralf Wagner & Gerhard Hörpel & Tobias Placke & Martin Winter, 2018. "Performance and cost of materials for lithium-based rechargeable automotive batteries," Nature Energy, Nature, vol. 3(4), pages 267-278, April.
- 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.
- Berecibar, M. & Gandiaga, I. & Villarreal, I. & Omar, N. & Van Mierlo, J. & Van den Bossche, P., 2016. "Critical review of state of health estimation methods of Li-ion batteries for real applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 572-587.
- Li, Yihuan & Li, Kang & Liu, Xuan & Wang, Yanxia & Zhang, Li, 2021. "Lithium-ion battery capacity estimation — A pruned convolutional neural network approach assisted with transfer learning," Applied Energy, Elsevier, vol. 285(C).
- Arnaud Devie & George Baure & Matthieu Dubarry, 2018. "Intrinsic Variability in the Degradation of a Batch of Commercial 18650 Lithium-Ion Cells," Energies, MDPI, vol. 11(5), pages 1-14, April.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- 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).
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Wang, Zhe & Yang, Fangfang & Xu, Qiang & Wang, Yongjian & Yan, Hong & Xie, Min, 2023. "Capacity estimation of lithium-ion batteries based on data aggregation and feature fusion via graph neural network," Applied Energy, Elsevier, vol. 336(C).
- Jiang, Bo & Zhu, Jiangong & Wang, Xueyuan & Wei, Xuezhe & Shang, Wenlong & Dai, Haifeng, 2022. "A comparative study of different features extracted from electrochemical impedance spectroscopy in state of health estimation for lithium-ion batteries," Applied Energy, Elsevier, vol. 322(C).
- Wang, Qiao & Ye, Min & Wei, Meng & Lian, Gaoqi & Li, Yan, 2023. "Random health indicator and shallow neural network based robust capacity estimation for lithium-ion batteries with different fast charging protocols," Energy, Elsevier, vol. 271(C).
- Xue, Qiao & Li, Junqiu & Xu, Peipei, 2022. "Machine learning based swift online capacity prediction of lithium-ion battery through whole cycle life," Energy, Elsevier, vol. 261(PA).
- Rauf, Huzaifa & Khalid, Muhammad & Arshad, Naveed, 2022. "Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
- Li, Guanzheng & Li, Bin & Li, Chao & Wang, Shuai, 2023. "State-of-health rapid estimation for lithium-ion battery based on an interpretable stacking ensemble model with short-term voltage profiles," Energy, Elsevier, vol. 263(PE).
- 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.
- Shahjalal, Mohammad & Roy, Probir Kumar & Shams, Tamanna & Fly, Ashley & Chowdhury, Jahedul Islam & Ahmed, Md. Rishad & Liu, Kailong, 2022. "A review on second-life of Li-ion batteries: prospects, challenges, and issues," Energy, Elsevier, vol. 241(C).
- Wang, Yixiu & Zhu, Jiangong & Cao, Liang & Gopaluni, Bhushan & Cao, Yankai, 2023. "Long Short-Term Memory Network with Transfer Learning for Lithium-ion Battery Capacity Fade and Cycle Life Prediction," Applied Energy, Elsevier, vol. 350(C).
- Penelope K. Jones & Ulrich Stimming & Alpha A. Lee, 2022. "Impedance-based forecasting of lithium-ion battery performance amid uneven usage," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
- 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, Alan G. & West, Alan C. & Preindl, Matthias, 2022. "Towards unified machine learning characterization of lithium-ion battery degradation across multiple levels: A critical review," Applied Energy, Elsevier, vol. 316(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).
- Jiangong Zhu & Yixiu Wang & Yuan Huang & R. Bhushan Gopaluni & Yankai Cao & Michael Heere & Martin J. Mühlbauer & Liuda Mereacre & Haifeng Dai & Xinhua Liu & Anatoliy Senyshyn & Xuezhe Wei & Michael K, 2022. "Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
- Su, Xiaojia & Sun, Bingxiang & Wang, Jiaju & Zhang, Weige & Ma, Shichang & He, Xitian & Ruan, Haijun, 2022. "Fast capacity estimation for lithium-ion battery based on online identification of low-frequency electrochemical impedance spectroscopy and Gaussian process regression," Applied Energy, Elsevier, vol. 322(C).
- Wang, Jinyu & Zhang, Caiping & Zhang, Linjing & Su, Xiaojia & Zhang, Weige & Li, Xu & Du, Jingcai, 2023. "A novel aging characteristics-based feature engineering for battery state of health estimation," Energy, Elsevier, vol. 273(C).
- Zhang, Qisong & Yang, Lin & Guo, Wenchao & Qiang, Jiaxi & Peng, Cheng & Li, Qinyi & Deng, Zhongwei, 2022. "A deep learning method for lithium-ion battery remaining useful life prediction based on sparse segment data via cloud computing system," Energy, Elsevier, vol. 241(C).
- 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).
- Zhang, Jianyu & Lu, Wei, 2022. "Sparse data machine learning for battery health estimation and optimal design incorporating material characteristics," Applied Energy, Elsevier, vol. 307(C).
- Wang, Fu-Kwun & Amogne, Zemenu Endalamaw & Chou, Jia-Hong & Tseng, Cheng, 2022. "Online remaining useful life prediction of lithium-ion batteries using bidirectional long short-term memory with attention mechanism," Energy, Elsevier, vol. 254(PB).
More about this item
Keywords
Lithium ion batteries; Capacity estimation; Relaxation voltage; Convolutional neural network;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:330:y:2023:i:pa:s0306261922015653. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.