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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.”
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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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