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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Conventional Backpropagation neural networks handle nonlinear inputs effectively but struggle with temporal relationships in time-series data due to gradient issues.
“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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- 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 › Advanced Battery Technologies Research
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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