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 methods have enhanced SOC estimation.
“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]
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- 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 › Advanced Battery Technologies Research
- 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
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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