Cited by 793Advanced Battery Technologies ResearchAdvancements in Battery MaterialsMachine Learning in Materials Science

Predicting the state of charge and health of batteries using data-driven machine learning

Open access
Ng, Man-Fai, Zhao, Jin, Yan, Qingyu, Conduit, Gareth J., Seh, Zhi Wei·2020·Nature Machine Intelligence

Integrity signals

Registry

Crossref · 10.1038/s42256-020-0156-7

Cites

121 of 121 references verified0 citation errors · 0 not checkable

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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
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
Open access
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

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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.

Statistics and arithmetic are recomputed, not judged.

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