The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation
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Citations
Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
A review on the key issues for lithium-ion battery management in electric vehicles
Lu, L., Han, X., Li, J., Hua, J., & Ouyang, M. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2012.10.060 · Cited by 4,837
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs
Plett, G. L. (2004) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.02.033 · Cited by 1,768
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs
Plett, G. L. (2004) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.02.031 · Cited by 1,385
Prognostics methods for battery health monitoring using a bayesian framework
Saha, B., Goebel, K., Poll, S., & Christophersen, J. (2009) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2008.2005965 · Cited by 831
Liu, D., Pang, J., Zhou, J., Peng, Y., & Pecht, M. (2013) · Microelectronics Reliability · doi:10.1016/j.microrel.2013.03.010 · Cited by 443
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
Dong, H., Jin, X., Lou, Y., & Wang, C. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.07.176 · Cited by 310
A novel Gaussian model based battery state estimation approach: State-of-Energy
He, H., Zhang, Y., Xiong, R., & Wang, C. (2015) · Applied Energy · doi:10.1016/j.apenergy.2015.04.062 · Cited by 127
Cited by
Zhang, Y., Xiong, R., He, H., & Pecht, M. G. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2805189 · Cited by 1,326
Hu, X., Xu, L., Lin, X., & Pecht, M. (2020) · Joule · doi:10.1016/j.joule.2019.11.018 · Cited by 1,320
Gaussian process regression for forecasting battery state of health
Richardson, R. R., Osborne, M. A., & Howey, D. A. (2017) · Journal of Power Sources · doi:10.1016/j.jpowsour.2017.05.004 · Cited by 732
Wei, J., Dong, G., & Chen, Z. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2782224 · Cited by 643
Wu, J., Zhang, C., & Chen, Z. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.04.057 · Cited by 528
Metadata
- Authors
- Patil, M. A., Tagade, P., Hariharan, K. S., Kolake, S. M., Song, T., Yeo, T., & Doo, S.
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2015-12
- Volume
- 159
- Pages
- 285-297
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W1628954589
- Cited by
- 626
- References
- 11
- FWCI
- 16.05
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation. https://app.cochat.ai/papers/10.1016/j.apenergy.2015.08.119
- The paper
- Patil, M. A., Tagade, P., Hariharan, K. S., Kolake, S. M., Song, T., Yeo, T., & Doo, S. (2015). A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation. Applied Energy. https://doi.org/10.1016/j.apenergy.2015.08.119
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.
