Cited by 818Advanced Battery Technologies ResearchAdvancements in Battery MaterialsElectric and Hybrid Vehicle Technologies

State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach

Chemali, Ephrem, Kollmeyer, Phillip J., Preindl, Matthias, Emadi, Ali·2018·Journal of Power Sources

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Registry

Crossref · 10.1016/j.jpowsour.2018.06.104

Cites

35 of 35 references verified0 citation errors · 0 not checkable

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Authors
Chemali, E., Kollmeyer, P. J., Preindl, M., & Emadi, A.
Affiliations
  • McMaster University (CA)
  • Columbia University (US)
Corresponding author
Chemali, Ephrem
Venue
Journal of Power Sources
Publisher
Elsevier BV
Published
2018-10
Volume
400
Pages
242-255
ISSN
0378-7753 (ISSN-L)
OpenAlex
W2885578090
Cited by
818
References
35
Citations by year
2026: 91 · 2025: 128 · 2024: 139 · 2023: 135 · 2022: 116 · 2021: 104 · 2020: 68 · 2019: 37
FWCI
27.78
Type
journal article
Language
en
Open access
closed
Locations
Journal of Power Sources (publishedVersion)
Funders
  • Nvidia
  • Canada Research Chairs
Topics
  • Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
  • Physical Sciences › Engineering › Automotive Engineering › Electric and Hybrid Vehicle Technologies
Keywords
state of charge estimation, lithium-ion batteries, deep feedforward neural network, ambient temperature effects, electric vehicles, battery management systems

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