State-of-charge sequence estimation of lithium-ion battery based on bidirectional long short-term memory encoder-decoder architecture
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Bian et al. employed BiLSTM as a recursive unit to capture temporal dependencies of sequence data in both the Encoder and Decoder, enhancing and improving estimation accuracy.
“Bian et al. employed BiLSTM as a recursive unit to capture temporal dependencies of sequence data in both the Encoder and Decoder[114], enhancing and improving estimation accuracy, as depicted in Figure 7A.”
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 [114]
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References 30 · Cited by 143
- Authors
- Bian, C., He, H., Yang, S., & Huang, T.
- Affiliations
- Beihang University (CN)
- Corresponding author
- Yang, Shunkun
- Venue
- Journal of Power Sources
- Publisher
- Elsevier BV
- Published
- 2020-02
- Volume
- 449
- Pages
- 227558
- ISSN
- 0378-7753 (ISSN-L)
- OpenAlex
- W2992785100
- Cited by
- 143
- References
- 30
- Citations by year
- 2026: 12 · 2025: 19 · 2024: 28 · 2023: 38 · 2022: 21 · 2021: 20 · 2020: 5
- FWCI
- 5.25
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Power Sources (publishedVersion)
- Funders
- National Natural Science Foundation of China (61672080, 41402020502)
- 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, bidirectional LSTM, encoder-decoder architecture, temperature variation, battery management systems, sequence estimation, long-range dependencies
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