State of charge prediction framework for lithium-ion batteries incorporating long short-term memory network and transfer learning
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This method reduced the required data volume by 70%, significantly enhancing the practicality of deep estimators.
“This method reduced the required data volume by 70%, significantly enhancing the practicality of deep estimators.”
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 [92]
Liu et al. employed transfer learning to fine-tune LSTM output layer parameters using minimal data from the NCM battery, achieving accurate SOC estimations.
“Liu et al. employed transfer learning to fine-tune LSTM output layer parameters using minimal data from the NCM battery, achieving accurate SOC estimations[92].”
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 [92]
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References 36 · Cited by 90
- Authors
- Liu, Y., Shu, X., Yu, H., Shen, J., Zhang, Y., Liu, Y., & Chen, Z.
- Affiliations
- Chongqing University (CN)
- Kunming University of Science and Technology (CN)
- China Automotive Technology and Research Center (CN)
- Queen's University Belfast (GB)
- Corresponding author
- Shu, Xing
- Chen, Zheng
- Venue
- Journal of Energy Storage
- Publisher
- Elsevier BV
- Published
- 2021-05
- Volume
- 37
- Pages
- 102494
- ISSN
- 2352-152X (ISSN-L)
- OpenAlex
- W3148608221
- Cited by
- 90
- References
- 36
- Citations by year
- 2026: 9 · 2025: 11 · 2024: 17 · 2023: 27 · 2022: 19 · 2021: 7
- FWCI
- 5.68
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Energy Storage (publishedVersion)
- Research Portal (Queen's University Belfast) (submittedVersion)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion)
- Funders
- Horizon 2020 Marie Skłodowska-Curie Actions
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Keywords
- state of charge estimation, lithium-ion batteries, long short-term memory networks, transfer learning, fine-tuning, battery capacity degradation, battery degradation, temperature effects, limited training data
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