The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Lithium-ion battery capacity and remaining useful life prediction using board learning system and long short-term memory neural network
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Cites
Chen, C. L. P., & Liu, Z. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2716952 · Cited by 1,957
Wang, Y., Tian, J., Sun, Z., Wang, L., Xu, R., Li, M., & Chen, Z. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110015 · Cited by 1,377
Self-citationData-driven health estimation and lifetime prediction of lithium-ion batteries: A review
Li, Y., Liu, K., Foley, A. M., Zülke, A., Berecibar, M., Nanini-Maury, E., Van Mierlo, J., & Hoster, H. E. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109254 · Cited by 1,193
Waag, W., Fleischer, C., & Sauer, D. U. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.02.064 · Cited by 1,059
Zou, Y., Hu, X., Ma, H., & Li, S. E. (2015) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.09.146 · Cited by 723
A Data-Driven Auto-CNN-LSTM Prediction Model for Lithium-Ion Battery Remaining Useful Life
Ren, L., Dong, J., Wang, X., Meng, Z., Zhao, L., & Deen, M. J. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3008223 · Cited by 690
Patil, M. A., Tagade, P., Hariharan, K. S., Kolake, S. M., Song, T., Yeo, T., & Doo, S. (2015) · Applied Energy · doi:10.1016/j.apenergy.2015.08.119 · Cited by 626
Hu, X., Jiang, J., Cao, D., & Egardt, B. (2015) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2015.2461523 · Cited by 504
Xue, Z., Zhang, Y., Cheng, C., & Ma, G. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.09.074 · Cited by 440
Remaining Useful Life Prediction for Lithium-Ion Battery: A Deep Learning Approach
Ren, L., Zhao, L., Hong, S., Zhao, S., Wang, H., & Zhang, L. (2018) · IEEE Access · doi:10.1109/access.2018.2858856 · Cited by 433
Self-citation
Cited by
Huang, Y., Zhang, P., Lu, J., Xiong, R., & Cai, Z. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.122825 · Cited by 68
Liu, H., Li, Y., Luo, L., & Zhang, C. (2023) · Batteries · doi:10.3390/batteries9060323 · Cited by 47
Self-citationHossain Lipu, M. S., Rahman, M. S. A., Mansor, M., Rahman, T., Ansari, S., Fuad, A. M., & Hannan, M. A. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.113172 · Cited by 41
Dong, Y., Chen, K., Zhang, G., & Li, R. (2024) · World Electric Vehicle Journal · doi:10.3390/wevj15030075 · Cited by 11
Metadata
- Authors
- Zhao, S., Zhang, C., & Wang, Y.
- Venue
- Journal of Energy Storage
- Publisher
- Elsevier BV
- Published
- 2022-08
- Volume
- 52
- Pages
- 104901
- ISSN
- 2352-152X (ISSN-L)
- OpenAlex
- W4281674107
- Cited by
- 259
- References
- 46
- FWCI
- 15.50
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Lithium-ion battery capacity and remaining useful life prediction using board learning system and long short-term memory neural network. https://app.cochat.ai/papers/10.1016/j.est.2022.104901
- The paper
- Zhao, S., Zhang, C., & Wang, Y. (2022). Lithium-ion battery capacity and remaining useful life prediction using board learning system and long short-term memory neural network. Journal of Energy Storage. https://doi.org/10.1016/j.est.2022.104901
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
