The DOI matches its Crossref record; 140 of the 140 references it cites were found in a registry or an index.
A review on prognostics and health management (PHM) methods of lithium-ion batteries
Abstract
Abstract Batteries are prevalent energy providers for modern systems. They can also be regarded as storage units for renewable and sustainable energy. Failures of batteries can bring huge losses in terms of personnel, facility, environment, and reputation aspects. Therefore the accurate health estimation and high availability of batteries is urgently required by corresponding users, distributors, and manufactures. Fortunately, prognostics and health management (PHM) technique has been demonstrated the capability of supporting the improvement of the availability and reliability of batteries. In this paper, we gave a review on the state-of-the-art of the PHM study on batteries. We observed the increase of publication related to battery PHM in the past decade (2009–2018), especially in the past five years. Approaches related to battery performance prognostics are categorized into physics-based, data-driven and hybrid classes. Selection of the battery PHM approach requires to take the user requirement, data availability and degradation mechanisms attainability into consideration. Based on the survey, we also proposed research and development perspectives to conduct further studies on the battery PHM, including the approach selection, health management, performance evaluation, uncertainty treatment, application economics, as well as environmental issues. We focused on PHM of lithium-ion batteries, given the fact that several publications discussed other types of batteries (e.g., lead-acid batteries).
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
Data mining: concepts and techniques
(2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,531
Issues and challenges facing rechargeable lithium batteries
Tarascon, J.-M., & Armand, M. (2001) · Nature · doi:10.1038/35104644 · Cited by 21,130
A Practical Guide to Wavelet Analysis
Torrence, C., & Compo, G. P. (1998) · Bulletin of the American Meteorological Society · doi:10.1175/1520-0477(1998)079<0061:apgtwa>2.0.co;2 · Cited by 14,890
Extreme learning machine: Theory and applications
Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006) · Neurocomputing · doi:10.1016/j.neucom.2005.12.126 · Cited by 13,285
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002) · IEEE Transactions on Signal Processing · doi:10.1109/78.978374 · Cited by 11,502
New extension of the Kalman filter to nonlinear systems
Julier, S. J., & Uhlmann, J. K. (1997) · SPIE Proceedings · doi:10.1117/12.280797 · Cited by 5,331
A review on the key issues for lithium-ion battery management in electric vehicles
Lu, L., Han, X., Li, J., Hua, J., & Ouyang, M. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2012.10.060 · Cited by 4,837
Thermal runaway mechanism of lithium ion battery for electric vehicles: A review
Feng, X., Ouyang, M., Liu, X., Lu, L., Xia, Y., & He, X. (2018) · Energy Storage Materials · doi:10.1016/j.ensm.2017.05.013 · Cited by 4,182
Progress in electrical energy storage system: a critical review
Chen, H., Cong, T. N., Yang, W., Tan, C., Li, Y., & Ding, Y. (2009) · Progress in Natural Science · doi:10.1016/j.pnsc.2008.07.014 · Cited by 3,746
Self-citationData-driven prediction of battery cycle life before capacity degradation
Severson, K. A., Attia, P. M., Jin, N., Perkins, N., Jiang, B., Yang, Z., Chen, M. H., Aykol, M., Herring, P. K., Fraggedakis, D., et al. (2019) · Nature Energy · doi:10.1038/s41560-019-0356-8 · Cited by 3,228
Cited by
Tabelin, C. B., Dallas, J., Casanova, S., Pelech, T., Bournival, G., Saydam, S., & Canbulat, I. (2021) · Minerals Engineering · doi:10.1016/j.mineng.2020.106743 · Cited by 533
On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges
Achouch, M., Dimitrova, M., Ziane, K., Sattarpanah Karganroudi, S., Dhouib, R., Ibrahim, H., & Adda, M. (2022) · Applied Sciences · doi:10.3390/app12168081 · Cited by 528
Key challenges for a large-scale development of battery electric vehicles: A comprehensive review
Lebrouhi, B. E., Khattari, Y., Lamrani, B., Maaroufi, M., Zeraouli, Y., & Kousksou, T. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.103273 · Cited by 276
Dual-Aspect Self-Attention Based on Transformer for Remaining Useful Life Prediction
Zhang, Z., Song, W., & Li, Q. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3160561 · Cited by 269
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
Metadata
- Authors
- Meng, H., & Li, Y.-F.
- Venue
- Renewable and Sustainable Energy Reviews
- Publisher
- Elsevier BV
- Published
- 2019-12
- Volume
- 116
- Pages
- 109405
- ISSN
- 1364-0321 (ISSN-L)
- OpenAlex
- W2975620261
- Cited by
- 389
- References
- 140
- FWCI
- 15.34
- 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: A review on prognostics and health management (PHM) methods of lithium-ion batteries. https://app.cochat.ai/papers/10.1016/j.rser.2019.109405
- The paper
- Meng, H., & Li, Y.-F. (2019). A review on prognostics and health management (PHM) methods of lithium-ion batteries. Renewable and Sustainable Energy Reviews. https://doi.org/10.1016/j.rser.2019.109405
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.
