The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries
Open accessAbstract
Prognostics is an emerging science of predicting the health condition of a system (or its components) based upon current and previous system states. A reliable predictor is very useful to a wide array of industries to predict the future states of the system such that the maintenance service could be scheduled in advance when needed. In this paper, an adaptive recurrent neural network (ARNN) is proposed for system dynamic state forecasting. The developed ARNN is constructed based on the adaptive/recurrent neural network architecture and the network weights are adaptively optimized using the recursive Levenberg-Marquardt (RLM) method. The effectiveness of the proposed ARNN is demonstrated via an application in remaining useful life prediction of lithium-ion batteries.
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“Hu et al. [129] proposed to use novel genetic algorithm-based fuzzy C-means clustering technique to partition the training data sampled of a lithium-ion battery during the driving cycle-based test, and the clustering result was then applied to automatically learn the topology and antecedent parameters of the ANN model for battery state estimation.”
From Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019) · cites this paper as [129]
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Cites
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Zhang, Y., Xiong, R., He, H., & Pecht, M. G. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2805189 · Cited by 1,326
Hu, X., Xu, L., Lin, X., & Pecht, M. (2020) · Joule · doi:10.1016/j.joule.2019.11.018 · Cited by 1,320
Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review
Li, Y., Liu, K., Foley, A. M., Zülke, A., Berecibar, M., Nanini-Maury, E., Van Mierlo, J., & Hoster, H. E. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109254 · Cited by 1,193
Artificial Intelligence Applied to Battery Research: Hype or Reality?
Lombardo, T., Duquesnoy, M., El-Bouysidy, H., Årén, F., Gallo‐Bueno, A., Jørgensen, P. B., Bhowmik, A., Demortière, A., Ayerbe, E., Alcaide, F., et al. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00108 · Cited by 536
Practical options for selecting data-driven or physics-based prognostics algorithms with reviews
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Metadata
- Authors
- Liu, J., Saxena, A., Goebel, K., Saha, B., & Wang, W.
- Affiliations
- Carleton University (CA)
- Ames Research Center (US)
- Lakehead University (CA)
- Venue
- Annual Conference of the PHM Society
- Publisher
- PHM Society
- Published
- 2010-10-10
- Volume
- 2
- Issue
- 1
- ISSN
- 2325-0178 (ISSN-L)
- OpenAlex
- W2126450366
- Cited by
- 274
- References
- 29
- Citations by year
- 2026: 7 · 2025: 17 · 2024: 23 · 2023: 29 · 2022: 21 · 2021: 29 · 2020: 30 · 2019: 25 · 2018: 18 · 2017: 16 · 2016: 15 · 2015: 10 · 2014: 11 · 2013: 12 · 2012: 7
- FWCI
- 11.61
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Annual Conference of the PHM Society (publishedVersion, cc-by)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Battery Modeling and Management
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Reliability and Maintenance Optimization
- Keywords
- remaining useful life prediction, lithium-ion batteries, battery prognostics, maintenance scheduling
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