State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach
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An MLP-based estimator achieved high accuracy across various temperatures.
“developed an MLP-based estimator with high accuracy across various temperatures[62].”
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 [62]
Chemali et al. also used the mean to represent charging and discharging behaviors over time.
“This concept aligns with Chemali et al., both using the mean to represent the charging and discharging behaviors over time[62].”
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 [62]
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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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