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Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks
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“While conventional Backpropagation (BP) neural networks handle nonlinear inputs effectively, they struggle with temporal relationships in timeseries data due to gradient issues [29][30][31].”
From SOC and SOH Prediction of Lithium‐Ion Batteries Based on LSTM–AUKF Joint Algorithm (2025) · cites this paper as [31]
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Metadata
- Authors
- Li, X., Zhang, L., Wang, Z., & Dong, P.
- Affiliations
- Beijing Institute of Technology (CN)
- Corresponding author
- Zhang, Lei
- Wang, Zhenpo
- Venue
- Journal of Energy Storage
- Publisher
- Elsevier BV
- Published
- 2019-02
- Volume
- 21
- Pages
- 510-518
- ISSN
- 2352-152X (ISSN-L)
- OpenAlex
- W2907460853
- Cited by
- 445
- References
- 36
- Citations by year
- 2026: 29 · 2025: 59 · 2024: 71 · 2023: 85 · 2022: 66 · 2021: 75 · 2020: 38 · 2019: 22
- FWCI
- 17.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Energy Storage (publishedVersion)
- Funders
- National Natural Science Foundation of China (U1764258)
- 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 › Machine Fault Diagnosis Techniques
- Keywords
- lithium-ion batteries, remaining useful life prediction, long short-term memory, Elman neural network, battery capacity degradation, recurrent neural networks
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