The DOI matches its Crossref record; 121 of the 121 references it cites were found in a registry or an index.
Predicting the state of charge and health of batteries using data-driven machine learning
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“Recent advances in data-driven machine learning (ML) methods have enhanced SOC estimation[8,9], offering notable advantages.”
From Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches (2025) · cites this paper as [8]
Citations
Cites
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., et al. (2013) · APL Materials · doi:10.1063/1.4812323 · Cited by 13,094
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
Modeling of Galvanostatic Charge and Discharge of the Lithium/Polymer/Insertion Cell
Doyle, M., Fuller, T. F., & Newman, J. (1993) · Journal of The Electrochemical Society · doi:10.1149/1.2221597 · Cited by 4,328
Data-driven prediction of battery cycle life before capacity degradation
Severson, K. A., Attia, P. M., Jin, N., Perkins, N., Jiang, B., Yang, Z., Chen, M. H., Aykol, M., Herring, P. K., Fraggedakis, D., et al. (2019) · Nature Energy · doi:10.1038/s41560-019-0356-8 · Cited by 3,228
Rendle, S. (2010) · 2010 IEEE International Conference on Data Mining · doi:10.1109/icdm.2010.127 · Cited by 3,189
Saal, J. E., Kirklin, S., Aykol, M., Meredig, B., & Wolverton, C. (2013) · JOM · doi:10.1007/s11837-013-0755-4 · Cited by 2,584
A comparative study of equivalent circuit models for Li-ion batteries
Hu, X., Li, S., & Peng, H. (2012) · Journal of Power Sources · doi:10.1016/j.jpowsour.2011.10.013 · Cited by 2,179
Simulation and Optimization of the Dual Lithium Ion Insertion Cell
Fuller, T. F., Doyle, M., & Newman, J. (1994) · Journal of The Electrochemical Society · doi:10.1149/1.2054684 · Cited by 2,057
The high-throughput highway to computational materials design
Curtarolo, S., Hart, G. L. W., Nardelli, M. B., Mingo, N., Sanvito, S., & Levy, O. (2013) · Nature Materials · doi:10.1038/nmat3568 · Cited by 2,053
Hannan, M. A., Lipu, M. S. H., Hussain, A., & Mohamed, A. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.05.001 · Cited by 2,005
Cited by
Sorted by citations
An Overview of Artificial Intelligence Applications for Power Electronics
Zhao, S., Blaabjerg, F., & Wang, H. (2021) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2020.3024914 · Cited by 947
Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis
Wang, F., Zhai, Z., Zhao, Z., Di, Y., & Chen, X. (2024) · Nature Communications · doi:10.1038/s41467-024-48779-z · Cited by 897
Overview of batteries and battery management for electric vehicles
Liu, W., Placke, T., & Chau, K. T. (2022) · Energy Reports · doi:10.1016/j.egyr.2022.03.016 · Cited by 776
Vadhva, P., Hu, J., Johnson, M. J., Stocker, R., Braglia, M., Brett, D. J. L., & Rettie, A. J. E. (2021) · ChemElectroChem · doi:10.1002/celc.202100108 · Cited by 558
Machine learning for a sustainable energy future
Yao, Z., Lum, Y., Johnston, A. K., Mejía-Mendoza, L. M., Zhou, X., Wen, Y., Aspuru‐Guzik, A., Sargent, E. H., & Seh, Z. W. (2022) · Nature Reviews Materials · doi:10.1038/s41578-022-00490-5 · Cited by 571
Self-citation
Metadata
- Authors
- Ng, M.-F., Zhao, J., Yan, Q., Conduit, G. J., & Seh, Z. W.
- Affiliations
- Agency for Science, Technology and Research (SG)
- Nanyang Technological University (SG)
- University of Cambridge (GB)
- Corresponding author
- Yan, Qingyu
- Conduit, Gareth J.
- Seh, Zhi Wei
- Venue
- Nature Machine Intelligence
- Published
- 2020-03-02
- Volume
- 2
- Issue
- 3
- Pages
- 161-170
- ISSN
- 2522-5839 (ISSN-L)
- OpenAlex
- W3009652674
- Semantic Scholar
- 86788a38467e1f2f1df713f5d5694bfee9f8ae29
- Cited by
- 793
- References
- 120
- Citations by year
- 2026: 126 · 2025: 152 · 2024: 164 · 2023: 134 · 2022: 110 · 2021: 81 · 2020: 26
- FWCI
- 32.46
- Type
- journal article
- Language
- en
- Update notices
- correction (2020-06-12)
- Open access
- Licence
- Locations
- Nature Machine Intelligence (publishedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Apollo
- Funders
- National Research Foundation Singapore (NRF-NRFF2017-04)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Battery Modeling and Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- battery state of health, remaining useful life, battery modeling, equivalent circuit model, physics-based battery model, data-driven machine learning, high-throughput experimentation, explainable machine learning, energy storage, battery management
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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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