The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
State-of-charge prediction of batteries and battery–supercapacitor hybrids using artificial neural networks
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Citations
Cites
Life prediction of batteries for selecting the technically most suitable and cost effective battery
Wenzl, H., Baring-Gould, I., Kaiser, R., Liaw, B. Y., Lundsager, P., Manwell, J., Ruddell, A., & Svoboda, V. (2005) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.11.045 · Cited by 183
Cited by
Shrivastava, P., Soon, T. K., Idris, M. Y. I. B., & Mekhilef, S. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.06.040 · Cited by 704
The State of Charge Estimating Methods for Battery: A Review
Chang, W.-Y. (2013) · ISRN Applied Mathematics · doi:10.1155/2013/953792 · Cited by 650
Eddahech, A., Briat, O., Bertrand, N., Delétage, J.-Y., & Vinassa, J.-M. (2012) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2012.04.050 · Cited by 462
A new neural network model for the state-of-charge estimation in the battery degradation process
Kang, L., Zhao, X., & Ma, J. (2014) · Applied Energy · doi:10.1016/j.apenergy.2014.01.066 · Cited by 325
Renxin, X., Yanwen, H., Wei, Z., & Zhaohui, C. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.107509 · Cited by 61
Metadata
- Authors
- Weigert, T., Tian, Q., & Lian, K.
- Venue
- Journal of Power Sources
- Publisher
- Elsevier BV
- Published
- 2011-04
- Volume
- 196
- Issue
- 8
- Pages
- 4061-4066
- ISSN
- 0378-7753 (ISSN-L)
- OpenAlex
- W2060889730
- Cited by
- 200
- References
- 1
- FWCI
- 11.38
- Type
- journal article
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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- The paper
- Weigert, T., Tian, Q., & Lian, K. (2011). State-of-charge prediction of batteries and battery–supercapacitor hybrids using artificial neural networks. Journal of Power Sources. https://doi.org/10.1016/j.jpowsour.2010.10.075
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.
