The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks
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Citations
Cites
Data Mining Techniques: For Marketing, Sales, and Customer Support
Berry, M. J., & Linoff, G. S. (1997) · University of Maribor digital library (University of Maribor) · Cited by 1,736
He, W., Williard, N., Osterman, M., & Pecht, M. (2011) · Journal of Power Sources · doi:10.1016/j.jpowsour.2011.08.040 · Cited by 1,105
Waag, W., Fleischer, C., & Sauer, D. U. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.02.064 · Cited by 1,059
An ensemble model for predicting the remaining useful performance of lithium-ion batteries
Xing, Y., Ma, E. W. M., Tsui, K.-L., & Pecht, M. (2013) · Microelectronics Reliability · doi:10.1016/j.microrel.2012.12.003 · Cited by 673
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
Remaining useful life prediction of lithium-ion battery with unscented particle filter technique
Miao, Q., Xie, L., Cui, H., Liang, W., & Pecht, M. (2013) · Microelectronics Reliability · doi:10.1016/j.microrel.2012.12.004 · Cited by 481
Kim, I.-S. (2010) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2009.2034966 · Cited by 438
A naive Bayes model for robust remaining useful life prediction of lithium-ion battery
Ng, S. S. Y., Xing, Y., & Tsui, K. L. (2014) · Applied Energy · doi:10.1016/j.apenergy.2013.12.020 · Cited by 345
Liu, D., Zhou, J., Liao, H., Peng, Y., & Peng, X. (2015) · IEEE Transactions on Systems, Man, and Cybernetics: Systems · doi:10.1109/tsmc.2015.2389757 · Cited by 313
Tang, S., Yu, C., Wang, X., Guo, X., & Si, X. (2014) · Energies · doi:10.3390/en7020520 · Cited by 277
Research works printed but not linked: 2
- Sadowsky, 1990, A new method for Viterbi decoder simulation using importance sampling, IEEE Trans Commun, 38, 1341, 10.1109/26.61375 · doi:10.1109/26.61375
- Biondini, 2015, An introduction to rare event simulation and importance sampling, 29, 10.1016/B978-0-444-63492-4.00002-2 · doi:10.1016/b978-0-444-63492-4.00002-2
Cited by
Hu, X., Xu, L., Lin, X., & Pecht, M. (2020) · Joule · doi:10.1016/j.joule.2019.11.018 · Cited by 1,320
Data-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
“[127] used FFNN to simulate the relationship between battery charge curves at a constant current and battery RUL.”
MethodYang, D., Zhang, X., Pan, R., Wang, Y., & Chen, Z. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2018.03.015 · Cited by 765
Self-citationWei, J., Dong, G., & Chen, Z. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2782224 · Cited by 643
LSTM-Based battery remaining useful life prediction with multi-channel charging profiles
Park, K., Choi, Y., Choi, W. J., Ryu, H.-Y., & Kim, H. (2020) · IEEE Access · doi:10.1109/access.2020.2968939 · Cited by 397
Metadata
- Authors
- Wu, J., Zhang, C., & Chen, Z.
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2016-07
- Volume
- 173
- Pages
- 134-140
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W2342265232
- Cited by
- 528
- References
- 22
- FWCI
- 28.77
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- Funders
- National Natural Science Foundation of China-Yunnan Joint Fund (61375079)
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- CoChat. (2026). Citation check: An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks. https://app.cochat.ai/papers/10.1016/j.apenergy.2016.04.057
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- Wu, J., Zhang, C., & Chen, Z. (2016). An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks. Applied Energy. https://doi.org/10.1016/j.apenergy.2016.04.057
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.
