The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
State of health estimation of lithium-ion battery based on improved ant lion optimization and support vector regression
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Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Mirjalili, S. (2015) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2015.01.010 · Cited by 3,233
Yang, 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
A review of the state of health for lithium-ion batteries: Research status and suggestions
Tian, H., Qin, P., Li, K., & Zhao, Z. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.120813 · Cited by 651
Li, X., Yuan, C., Li, X., & Wang, Z. (2020) · Energy · doi:10.1016/j.energy.2019.116467 · Cited by 482
Wang, X.-X., Yu, G.-F., Zhang, J., Yu, M., Ramakrishna, S., & Long, Y.-Z. (2021) · Progress in Materials Science · doi:10.1016/j.pmatsci.2020.100704 · Cited by 447
A Novel Deep Learning Framework for State of Health Estimation of Lithium-Ion Battery
Fan, Y., Xiao, F., Li, C., Yang, G., & Tang, X. (2020) · Journal of Energy Storage · doi:10.1016/j.est.2020.101741 · Cited by 415
Galeotti, M., Cinà, L., Giammanco, C., Cordiner, S., & Di Carlo, A. (2015) · Energy · doi:10.1016/j.energy.2015.05.148 · Cited by 412
Cui, Z., Wang, L., Li, Q., & Wang, K. (2022) · International Journal of Energy Research · doi:10.1002/er.7545 · Cited by 384
Self-citationMa, G., Zhang, Y., Cheng, C., Zhou, B., Hu, P., & Yuan, Y. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.113626 · Cited by 342
Cited by
A Data-Driven Method for Extracting Aging Features to Accurately Predict the Battery Health
Xiong, R., Sun, Y., Wang, C., Tian, J., Chen, X., Li, H., & Zhang, Q. (2023) · Energy storage materials · doi:10.1016/j.ensm.2023.02.034 · Cited by 190
A review on rapid state of health estimation of lithium-ion batteries in electric vehicles
Wang, Z., Zhao, X., Fu, L., Zhen, D., Gu, F., & Ball, A. D. (2023) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2023.103457 · Cited by 107
State of health estimation for lithium battery random charging process based on CNN-GRU method
Zheng, Y., Hu, J., Chen, J., Deng, H., & Hu, W. (2023) · Energy Reports · doi:10.1016/j.egyr.2022.12.093 · Cited by 107
Ansari, S., Hannan, M. A., Ayob, A., Abdolrasol, M. G. M., & Dar, M. A. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2024.114711 · Cited by 20
Artificial Intelligence in Electric Vehicle Battery Disassembly: A Systematic Review
Ai, Z., Nee, A. Y. C., & Ong, S. K. (2024) · Automation · doi:10.3390/automation5040028 · Cited by 16
Metadata
- Authors
- Li, Q., Li, D., Zhao, K., Wang, L., & Wang, K.
- Venue
- Journal of Energy Storage
- Publisher
- Elsevier BV
- Published
- 2022-06
- Volume
- 50
- Pages
- 104215
- ISSN
- 2352-152X (ISSN-L)
- OpenAlex
- W4212889492
- Cited by
- 331
- References
- 36
- FWCI
- 20.01
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: State of health estimation of lithium-ion battery based on improved ant lion optimization and support vector regression. https://app.cochat.ai/papers/10.1016/j.est.2022.104215
- The paper
- Li, Q., Li, D., Zhao, K., Wang, L., & Wang, K. (2022). State of health estimation of lithium-ion battery based on improved ant lion optimization and support vector regression. Journal of Energy Storage. https://doi.org/10.1016/j.est.2022.104215
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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.
