An LSTM-Based Encoder-Decoder Model for State-of-Charge Estimation of Lithium-Ion Batteries
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Cui et al. applied an LSTM-based ED model to learn the nonlinear relationship between SOC and measurable current and voltage.
“Cui et al. applied an LSTM-based ED model to learn the nonlinear relationship between SOC and measurable current and voltage[113].”
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 [113]
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Deep Learning in the State of Charge Estimation for Li-Ion Batteries of Electric Vehicles: A Review
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- Authors
- Cui, S., Yong, X., Kim, S., Hong, S., & Joe, I.
- Affiliations
- Hanyang University (KR)
- Corresponding author
- Joe, Inwhee
- Venue
- Advances in Intelligent Systems and Computing
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 178-188
- ISSN
- 2194-5357 (ISSN-L)
- 2194-5365
- OpenAlex
- W3048097465
- Cited by
- 8
- References
- 19
- Citations by year
- 2025: 2 · 2024: 2 · 2023: 1 · 2022: 2 · 2021: 1
- FWCI
- 0.82
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Advances in intelligent systems and computing (publishedVersion)
- 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, encoder-decoder architecture, long short-term memory, battery management systems, voltage and current measurement, electric vehicles
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