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A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique
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Cites
Accurate electrical battery model capable of predicting runtime and I-V performance
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Cited by
Vidal, C., Malysz, P., Kollmeyer, P., & Emadi, A. (2020) · IEEE Access · doi:10.1109/access.2020.2980961 · Cited by 512
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A Double-Scale, Particle-Filtering, Energy State Prediction Algorithm for Lithium-Ion Batteries
Xiong, R., Zhang, Y., He, H., Zhou, X., & Pecht, M. G. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2733475 · Cited by 285
Self-citationXiong, R., Yu, Q., Wang, L. Y., & Lin, C. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.05.136 · Cited by 293
Self-citation
Metadata
- Authors
- Sun, F., Xiong, R., & He, H.
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2016-01
- Volume
- 162
- Pages
- 1399-1409
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W2055531267
- Cited by
- 294
- References
- 2
- FWCI
- 42.48
- 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 (51276022)
- Beijing Institute of Technology Research Fund Program for Young Scholars
- National Science and Technology Infrastructure Program (2013BAG05B00)
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- The paper
- Sun, F., Xiong, R., & He, H. (2016). A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique. Applied Energy. https://doi.org/10.1016/j.apenergy.2014.12.021
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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.