SOC Estimation of Li-ion Batteries With Learning Rate-Optimized Deep Fully Convolutional Network
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In this letter, we train deep learning (DL) models to estimate the state-of-charge (SOC) of lithium-ion (Li-ion) battery directly from voltage, current, and battery temperature values. The deep fully convolutional network model is proposed for its novel architecture with learning rate optimization strategies. The proposed model is capable of estimating SOC at constant and varying ambient temperature on different drive cycles without having to be retrained. The model also outperformed other commonly used DL models such as the LSTM, GRU, and CNN on an open source Li-ion battery dataset. The model achieves 0.85% root mean squared error (RMSE) and 0.7% mean absolute error (MAE) at 25 °C and 2.0% RMSE and 1.55% MAE at varying ambient temperature (-20-25 °C).
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Hannan et al. replaced the MLP with a full CNN for the output layer, creating a SOC estimator based on a fully convolutional network (FCN); it surpasses LSTM, GRU, and CNN-MLP methods in estimation accuracy, generalization ability, FLOPs, and runtime speed.
“Hannan et al. replaced the MLP with a full CNN for the output layer, creating a SOC estimator based on a fully convolutional network (FCN)[67]. Resultantly, it surpasses LSTM, GRU, and CNN-MLP methods in estimation accuracy, generalization ability, floating point operations per second (FLOPs), and runtime speed.”
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 [67]
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- Authors
- Hannan, M. A., How, D. N. T., Lipu, M. S. H., Ker, P. J., Dong, Z. Y., Mansur, M., & Blaabjerg, F.
- Affiliations
- Universiti Tenaga Nasional (MY)
- National University of Malaysia (MY)
- UNSW Sydney (AU)
- Aalborg University (DK)
- Venue
- IEEE Transactions on Power Electronics
- Published
- 2021-07
- Volume
- 36
- Issue
- 7
- Pages
- 7349-7353
- ISSN
- 0885-8993 (ISSN-L)
- 1941-0107
- OpenAlex
- W3107692354
- Cited by
- 233
- References
- 14
- Citations by year
- 2026: 26 · 2025: 53 · 2024: 56 · 2023: 47 · 2022: 33 · 2021: 18
- FWCI
- 9.06
- Type
- journal article
- Language
- en
- Open access
- green
- vbn.aau.dk
- Licence
- Locations
- IEEE Transactions on Power Electronics (publishedVersion)
- VBN Forskningsportal (Aalborg Universitet) (submittedVersion, other-oa)
- Funders
- Ministry of Higher Education, Malaysia (20190101LRGS)
- 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, learning rate optimization, battery management systems, driving cycles, voltage and current measurement, battery temperature
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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