The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods
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Citations
Cites
Jaguemont, J., Boulon, L., & Dubé, Y. (2016) · Applied Energy · doi:10.1016/j.apenergy.2015.11.034 · Cited by 1,188
Nuhic, A., Terzimehic, T., Soczka-Guth, T., Buchholz, M., & Dietmayer, K. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2012.11.146 · Cited by 664
Xiong, R., Sun, F., Chen, Z., & He, H. (2014) · Applied Energy · doi:10.1016/j.apenergy.2013.07.061 · Cited by 529
Electric vehicle charging demand forecasting model based on big data technologies
Arias, M. B., & Bae, S. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.08.080 · Cited by 346
Hu, C., Jain, G., Zhang, P., Schmidt, C., Gomadam, P., & Gorka, T. (2014) · Applied Energy · doi:10.1016/j.apenergy.2014.04.077 · Cited by 302
Cai, H., Jia, X., Chiu, A. S. F., Hu, X., & Xu, M. (2014) · Transportation Research Part D: Transport and Environment · doi:10.1016/j.trd.2014.09.003 · Cited by 301
Sun, F., Xiong, R., & He, H. (2016) · Applied Energy · doi:10.1016/j.apenergy.2014.12.021 · Cited by 294
Adaptive Nonlinear Model-Based Fault Diagnosis of Li-Ion Batteries
Sidhu, A., Izadian, A., & Anwar, S. (2015) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2014.2336599 · Cited by 283
Liu, Z., & He, H. (2015) · Applied Energy · doi:10.1016/j.apenergy.2015.10.168 · Cited by 258
Chen, Z., Xiong, R., Tian, J., Shang, X., & Lu, J. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.10.026 · Cited by 247
Cited by
Theissler, A., Pérez-Velázquez, J., Kettelgerdes, M., & Elger, G. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.107864 · Cited by 581
Hu, X., Zhang, K., Liu, K., Lin, X., Dey, S., & Onori, S. (2020) · IEEE Industrial Electronics Magazine · doi:10.1109/mie.2020.2964814 · Cited by 580
He, H., Sun, F., Wang, Z., Lin, C., Zhang, C., Xiong, R., Deng, J., Zhu, X., Xie, P., Zhang, S., et al. (2022) · Green Energy and Intelligent Transportation · doi:10.1016/j.geits.2022.100020 · Cited by 334
Self-citationA Review of Lithium-Ion Battery Fault Diagnostic Algorithms: Current Progress and Future Challenges
Tran, M.-K., & Fowler, M. (2020) · Algorithms · doi:10.3390/a13030062 · Cited by 300
Intelligent state of health estimation for lithium-ion battery pack based on big data analysis
Song, L., Zhang, K., Liang, T., Han, X., & Zhang, Y. (2020) · Journal of Energy Storage · doi:10.1016/j.est.2020.101836 · Cited by 197
Metadata
- Authors
- Zhao, Y., Liu, P., Wang, Z., Zhang, L., & Hong, J.
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2017-12
- Volume
- 207
- Pages
- 354-362
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W2624543473
- Cited by
- 375
- References
- 26
- FWCI
- 12.25
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods. https://app.cochat.ai/papers/10.1016/j.apenergy.2017.05.139
- The paper
- Zhao, Y., Liu, P., Wang, Z., Zhang, L., & Hong, J. (2017). Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods. Applied Energy. https://doi.org/10.1016/j.apenergy.2017.05.139
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.
