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A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM
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“The resulting PSO-LSTM network outperformed others, achieving a 0.43% reduction in MSE for isothermal estimation.”
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 [80]
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Metadata
- Authors
- Ren, X., Liu, S., Yu, X., & Dong, X.
- Affiliations
- Qilu University of Technology (CN)
- Corresponding author
- Liu, Shulin
- Venue
- Energy
- Publisher
- Elsevier BV
- Published
- 2021-11
- Volume
- 234
- Pages
- 121236
- ISSN
- 0360-5442 (ISSN-L)
- OpenAlex
- W3172280330
- Cited by
- 375
- References
- 27
- Citations by year
- 2026: 41 · 2025: 78 · 2024: 108 · 2023: 85 · 2022: 53 · 2021: 9
- FWCI
- 20.70
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Energy (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Natural Science Foundation of Shandong Province (ZR2020ME206, ZR2020QF064)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Battery Modeling and Management
- 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, battery management systems, long short-term memory neural network, particle swarm optimization, LSTM hyperparameter optimization, noise injection, interference suppression
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