Online Lifetime Prediction for Lithium-Ion Batteries with Cycle-by-Cycle Updates, Variance Reduction, and Model Ensembling
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Calum Strange & Shawn Li & Richard Gilchrist & Gonçalo dos Reis, 2021. "Elbows of Internal Resistance Rise Curves in Li-Ion Cells," Energies, MDPI, vol. 14(4), pages 1-15, February.
- Hui, Hongxun & Ding, Yi & Shi, Qingxin & Li, Fangxing & Song, Yonghua & Yan, Jinyue, 2020. "5G network-based Internet of Things for demand response in smart grid: A survey on application potential," Applied Energy, Elsevier, vol. 257(C).
- Peter M. Attia & Aditya Grover & Norman Jin & Kristen A. Severson & Todor M. Markov & Yang-Hung Liao & Michael H. Chen & Bryan Cheong & Nicholas Perkins & Zi Yang & Patrick K. Herring & Muratahan Ayko, 2020. "Closed-loop optimization of fast-charging protocols for batteries with machine learning," Nature, Nature, vol. 578(7795), pages 397-402, February.
- Li Zhang & Min Zheng & Dajun Du & Yihuan Li & Minrui Fei & Yuanjun Guo & Kang Li, 2020. "State-of-Charge Estimation of Lithium-Ion Battery Pack Based on Improved RBF Neural Networks," Complexity, Hindawi, vol. 2020, pages 1-10, December.
- Weiping Diao & Saurabh Saxena & Bongtae Han & Michael Pecht, 2019. "Algorithm to Determine the Knee Point on Capacity Fade Curves of Lithium-Ion Cells," Energies, MDPI, vol. 12(15), pages 1-9, July.
- 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).
- Reniers, Jorn M. & Howey, David A., 2023. "Digital twin of a MWh-scale grid battery system for efficiency and degradation analysis," Applied Energy, Elsevier, vol. 336(C).
- Sohn, Suyeon & Byun, Ha-Eun & Lee, Jay H., 2022. "Two-stage deep learning for online prediction of knee-point in Li-ion battery capacity degradation," Applied Energy, Elsevier, vol. 328(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).
- Shi You & Junjie Hu & Charalampos Ziras, 2016. "An Overview of Modeling Approaches Applied to Aggregation-Based Fleet Management and Integration of Plug-in Electric Vehicles †," Energies, MDPI, vol. 9(11), pages 1-18, November.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Ibraheem, Rasheed & Wu, Yue & Lyons, Terry & dos Reis, Gonçalo, 2023. "Early prediction of Lithium-ion cell degradation trajectories using signatures of voltage curves up to 4-minute sub-sampling rates," Applied Energy, Elsevier, vol. 352(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.- Hu, Chunsheng & Ma, Liang & Guo, Shanshan & Guo, Gangsheng & Han, Zhiqiang, 2022. "Deep learning enabled state-of-charge estimation of LiFePO4 batteries: A systematic validation on state-of-the-art charging protocols," Energy, Elsevier, vol. 246(C).
- Lyu, Guangzheng & Zhang, Heng & Miao, Qiang, 2023. "An interpretable state of health estimation method for lithium-ion batteries based on multi-category and multi-stage features," Energy, Elsevier, vol. 283(C).
- Gomez, William & Wang, Fu-Kwun & Chou, Jia-Hong, 2024. "Li-ion battery capacity prediction using improved temporal fusion transformer model," Energy, Elsevier, vol. 296(C).
- Wang, Qiao & Ye, Min & Cai, Xue & Sauer, Dirk Uwe & Li, Weihan, 2023. "Transferable data-driven capacity estimation for lithium-ion batteries with deep learning: A case study from laboratory to field applications," Applied Energy, Elsevier, vol. 350(C).
- Wang, Fujin & Zhao, Zhibin & Zhai, Zhi & Guo, Yanjie & Xi, Huan & Wang, Shibin & Chen, Xuefeng, 2023. "Feature disentanglement and tendency retainment with domain adaptation for Lithium-ion battery capacity estimation," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
- Che, Yunhong & Zheng, Yusheng & Forest, Florent Evariste & Sui, Xin & Hu, Xiaosong & Teodorescu, Remus, 2024. "Predictive health assessment for lithium-ion batteries with probabilistic degradation prediction and accelerating aging detection," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
- Matthieu Dubarry & David Beck, 2021. "Analysis of Synthetic Voltage vs. Capacity Datasets for Big Data Li-ion Diagnosis and Prognosis," Energies, MDPI, vol. 14(9), pages 1-24, April.
- Che, Yunhong & Zheng, Yusheng & Wu, Yue & Sui, Xin & Bharadwaj, Pallavi & Stroe, Daniel-Ioan & Yang, Yalian & Hu, Xiaosong & Teodorescu, Remus, 2022. "Data efficient health prognostic for batteries based on sequential information-driven probabilistic neural network," Applied Energy, Elsevier, vol. 323(C).
- Calum Strange & Shawn Li & Richard Gilchrist & Gonçalo dos Reis, 2021. "Elbows of Internal Resistance Rise Curves in Li-Ion Cells," Energies, MDPI, vol. 14(4), pages 1-15, February.
- Ibraheem, Rasheed & Wu, Yue & Lyons, Terry & dos Reis, Gonçalo, 2023. "Early prediction of Lithium-ion cell degradation trajectories using signatures of voltage curves up to 4-minute sub-sampling rates," Applied Energy, Elsevier, vol. 352(C).
- Wang, Fujin & Zhao, Zhibin & Zhai, Zhi & Shang, Zuogang & Yan, Ruqiang & Chen, Xuefeng, 2023. "Explainability-driven model improvement for SOH estimation of lithium-ion battery," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
- Fan, Wenjun & Zhu, Jiangong & Qiao, Dongdong & Jiang, Bo & Wang, Xueyuan & Wei, Xuezhe & Dai, Haifeng, 2024. "Prediction of nonlinear degradation knee-point and remaining useful life for lithium-ion batteries using relaxation voltage," Energy, Elsevier, vol. 294(C).
- Ma, Liya & Hui, Hongxun & Wang, Sheng & Song, Yonghua, 2024. "Coordinated optimization of power-communication coupling networks for dispatching large-scale flexible loads to provide operating reserve," Applied Energy, Elsevier, vol. 359(C).
- Cui, Binghan & Wang, Han & Li, Renlong & Xiang, Lizhi & Zhao, Huaian & Xiao, Rang & Li, Sai & Liu, Zheng & Yin, Geping & Cheng, Xinqun & Ma, Yulin & Huo, Hua & Zuo, Pengjian & Lu, Taolin & Xie, Jingyi, 2024. "Ultra-early prediction of lithium-ion battery performance using mechanism and data-driven fusion model," Applied Energy, Elsevier, vol. 353(PA).
- Pietro Iurilli & Luigi Luppi & Claudio Brivio, 2022. "Non-Invasive Detection of Lithium-Metal Battery Degradation," Energies, MDPI, vol. 15(19), pages 1-14, September.
- Wang, Jiajia & Yue, Xiyan & Wang, Peifen & Yu, Tao & Du, Xiao & Hao, Xiaogang & Abudula, Abuliti & Guan, Guoqing, 2022. "Electrochemical technologies for lithium recovery from liquid resources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
- Turki Alsuwian & Aiman Shahid Butt & Arslan Ahmed Amin, 2022. "Smart Grid Cyber Security Enhancement: Challenges and Solutions—A Review," Sustainability, MDPI, vol. 14(21), pages 1-21, October.
- Jeddi, Babak & Mishra, Yateendra & Ledwich, Gerard, 2021. "Distributed load scheduling in residential neighborhoods for coordinated operation of multiple home energy management systems," Applied Energy, Elsevier, vol. 300(C).
- Tan, Kang Miao & Padmanaban, Sanjeevikumar & Yong, Jia Ying & Ramachandaramurthy, Vigna K., 2019. "A multi-control vehicle-to-grid charger with bi-directional active and reactive power capabilities for power grid support," Energy, Elsevier, vol. 171(C), pages 1150-1163.
- 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).
More about this item
Keywords
remaining-useful-life; prediction of full degradation curve; machine learning; cloud computing; ensemble models;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:gam:jeners:v:16:y:2023:i:7:p:3273-:d:1117603. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.