The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network
Abstract
Accurate estimation of the remaining useful life of lithium-ion batteries is critically important for electronic devices. In the existing literature, the widely applied model-based approaches for remaining useful battery life estimation are limited by the complexity of the electrochemical modeling required. In addition, data-driven approaches for remaining useful battery life estimation commonly define unreliable sliding window sizes empirically and the prediction accuracy of these approaches needs to be improved. To address the above issues, use of a hybrid neural network with the false nearest neighbors method is proposed in this paper. First, the false nearest neighbors method is used to calculate the sliding window size required for prediction. Second, a hybrid neural network that combines the advantages of a convolutional neural network with those of long short-term memory is designed for model training and prediction. Remaining useful life prediction experiments for batteries with various rated capacities are performed to verify the effectiveness of the proposed approach, and the results demonstrate that the proposed approach offers wide generality and reduced errors when compared with the other state-of-the-art methods.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A comparative study of equivalent circuit models for Li-ion batteries
Hu, X., Li, S., & Peng, H. (2012) · Journal of Power Sources · doi:10.1016/j.jpowsour.2011.10.013 · Cited by 2,179
He, W., Williard, N., Osterman, M., & Pecht, M. (2011) · Journal of Power Sources · doi:10.1016/j.jpowsour.2011.08.040 · Cited by 1,105
Yang, D., Zhang, X., Pan, R., Wang, Y., & Chen, Z. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2018.03.015 · Cited by 765
Wei, J., Dong, G., & Chen, Z. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2782224 · Cited by 643
Dong, H., Jin, X., Lou, Y., & Wang, C. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.07.176 · Cited by 310
A novel health indicator for on-line lithium-ion batteries remaining useful life prediction
Zhou, Y., Huang, M., Chen, Y., & Tao, Y. (2016) · Journal of Power Sources · doi:10.1016/j.jpowsour.2016.04.119 · Cited by 289
An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries
Liu, J., Saxena, A., Goebel, K., Saha, B., & Wang, W. (2010) · Annual Conference of the PHM Society · doi:10.36001/phmconf.2010.v2i1.1896 · Cited by 274
Wu, L., Fu, X., & Guan, Y. (2016) · Applied Sciences · doi:10.3390/app6060166 · Cited by 203
Qin, T., Zeng, S., Guo, J., & Skaf, Z. (2016) · Energies · doi:10.3390/en9110896 · Cited by 52
Cited by
Ma, Y., Shan, C., Gao, J., & Chen, H. (2022) · Energy · doi:10.1016/j.energy.2022.123973 · Cited by 351
Hybrid physics-informed neural networks for lithium-ion battery modeling and prognosis
Nascimento, R. G., Corbetta, M., Kulkarni, C. S., & Viana, F. A. C. (2021) · Journal of Power Sources · doi:10.1016/j.jpowsour.2021.230526 · Cited by 224
An overview of data-driven battery health estimation technology for battery management system
Chen, M., Ma, G., Liu, W., Zeng, N., & Luo, X. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.02.031 · Cited by 172
Self-citationA Hybrid Ensemble Deep Learning Approach for Early Prediction of Battery Remaining Useful Life
Xu, Q., Wu, M., Khoo, E., Chen, Z., & Li, X. (2023) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2023.123024 · Cited by 119
Early prediction of lithium-ion battery cycle life based on voltage-capacity discharge curves
Xiong, W., Xu, G., Li, Y., Zhang, F., Ye, P., & Li, B. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.106790 · Cited by 93
Metadata
- Authors
- Ma, G., Zhang, Y., Cheng, C., Zhou, B., Hu, P., & Yuan, Y.
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2019-11
- Volume
- 253
- Pages
- 113626
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W2966169983
- Cited by
- 342
- References
- 9
- FWCI
- 18.31
- Type
- journal article
- Language
- en
- Open access
- closed
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 ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network. https://app.cochat.ai/papers/10.1016/j.apenergy.2019.113626
- The paper
- Ma, G., Zhang, Y., Cheng, C., Zhou, B., Hu, P., & Yuan, Y. (2019). Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network. Applied Energy. https://doi.org/10.1016/j.apenergy.2019.113626
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
