State of Health Estimations for Lithium-Ion Batteries Based on MSCNN
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- S, Vignesh & Che, Hang Seng & Selvaraj, Jeyraj & Tey, Kok Soon & Lee, Jia Woon & Shareef, Hussain & Errouissi, Rachid, 2024. "State of Health (SoH) estimation methods for second life lithium-ion battery—Review and challenges," Applied Energy, Elsevier, vol. 369(C).
- Yang, Yixin, 2021. "A machine-learning prediction method of lithium-ion battery life based on charge process for different applications," Applied Energy, Elsevier, vol. 292(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.- 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.
- Liu, Yunpeng & Hou, Bo & Ahmed, Moin & Mao, Zhiyu & Feng, Jiangtao & Chen, Zhongwei, 2024. "A hybrid deep learning approach for remaining useful life prediction of lithium-ion batteries based on discharging fragments," Applied Energy, Elsevier, vol. 358(C).
- He, Ning & Wang, Qiqi & Lu, Zhenfeng & Chai, Yike & Yang, Fangfang, 2024. "Early prediction of battery lifetime based on graphical features and convolutional neural networks," Applied Energy, Elsevier, vol. 353(PA).
- Wei, Meng & Balaya, Palani & Ye, Min & Song, Ziyou, 2022. "Remaining useful life prediction for 18650 sodium-ion batteries based on incremental capacity analysis," Energy, Elsevier, vol. 261(PA).
- 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).
- J. N. Chandra Sekhar & Bullarao Domathoti & Ernesto D. R. Santibanez Gonzalez, 2023. "Prediction of Battery Remaining Useful Life Using Machine Learning Algorithms," Sustainability, MDPI, vol. 15(21), pages 1-28, October.
- Yongsheng Shi & Tailin Li & Leicheng Wang & Hongzhou Lu & Yujun Hu & Beichen He & Xinran Zhai, 2023. "A Method for Predicting the Life of Lithium-Ion Batteries Based on Successive Variational Mode Decomposition and Optimized Long Short-Term Memory," Energies, MDPI, vol. 16(16), pages 1-16, August.
- Tang, Aihua & Jiang, Yihan & Nie, Yuwei & Yu, Quanqing & Shen, Weixiang & Pecht, Michael G., 2023. "Health and lifespan prediction considering degradation patterns of lithium-ion batteries based on transferable attention neural network," Energy, Elsevier, vol. 279(C).
- Song, Dengwei & Cheng, Yujie & Zhou, An & Lu, Chen & Chong, Jin & Ma, Jian, 2024. "Remaining useful life prediction and cycle life test optimization for multiple-formula battery: A method based on multi-source transfer learning," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
- Pepe, Simona & Ciucci, Francesco, 2023. "Long-range battery state-of-health and end-of-life prediction with neural networks and feature engineering," Applied Energy, Elsevier, vol. 350(C).
- Ni, Yulong & Xu, Jianing & Zhu, Chunbo & Pei, Lei, 2022. "Accurate residual capacity estimation of retired LiFePO4 batteries based on mechanism and data-driven model," Applied Energy, Elsevier, vol. 305(C).
- Li, Marui & Dong, Chaoyu & Xiong, Binyu & Mu, Yunfei & Yu, Xiaodan & Xiao, Qian & Jia, Hongjie, 2022. "STTEWS: A sequential-transformer thermal early warning system for lithium-ion battery safety," Applied Energy, Elsevier, vol. 328(C).
- Huang, Zhiliang & Wang, Huaixing & Zou, Wei & Zhang, Rongchuan & Wang, Yuhan & Chen, Jie & Wu, Shengben, 2024. "An online evaluation model for mechanical/thermal states in prismatic lithium-ion batteries under fast charging/discharging," Energy, Elsevier, vol. 302(C).
- Catherine Rincón-Maya & Fernando Guevara-Carazas & Freddy Hernández-Barajas & Carmen Patino-Rodriguez & Olga Usuga-Manco, 2023. "Remaining Useful Life Prediction of Lithium-Ion Battery Using ICC-CNN-LSTM Methodology," Energies, MDPI, vol. 16(20), pages 1-20, October.
- Chuang Sun & An Qu & Jun Zhang & Qiyang Shi & Zhenhong Jia, 2022. "Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Improved Variational Mode Decomposition and Machine Learning Algorithm," Energies, MDPI, vol. 16(1), pages 1-15, December.
- Chen, Dinghong & Zhang, Weige & Zhang, Caiping & Sun, Bingxiang & Cong, XinWei & Wei, Shaoyuan & Jiang, Jiuchun, 2022. "A novel deep learning-based life prediction method for lithium-ion batteries with strong generalization capability under multiple cycle profiles," Applied Energy, Elsevier, vol. 327(C).
- Zhao, Wanjie & Ding, Wei & Zhang, Shujing & Zhang, Zhen, 2024. "Enhancing lithium-ion battery lifespan early prediction using a multi-branch vision transformer model," Energy, Elsevier, vol. 302(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).
- Wei, Meng & Ye, Min & Zhang, Chuanwei & Wang, Qiao & Lian, Gaoqi & Xia, Baozhou, 2024. "Integrating mechanism and machine learning based capacity estimation for LiFePO4 batteries under slight overcharge cycling," Energy, Elsevier, vol. 296(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).
More about this item
Keywords
lithium-ion battery; state of health; health indicator; MSCNN;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:17:y:2024:i:17:p:4220-:d:1462844. 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.