The DOI matches its Crossref record; 56 of the 58 references it cites were found in a registry or an index.
Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies
Open accessAbstract
Lithium-ion batteries are the primary power source in electric vehicles, and the prognosis of their remaining useful life is vital for ensuring the safety, stability, and long lifetime of electric vehicles. Accurately establishing a mechanism model of a vehicle lithium-ion battery involves a complex electrochemical process. Remaining useful life (RUL) prognostics based on data-driven methods has become a focus of research. Current research on data-driven methodologies is summarized in this paper. By analyzing the problems of vehicle lithium-ion batteries in practical applications, the problems that need to be solved in the future are identified.
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“Work in this area has focused on predicting battery state, degradation, and remaining lifetime using supervised learning techniques, fuzzy logic, and clustering [42,104,218,358,414,725,735,819,854].”
From Tackling climate change with machine learning (2023) · cites this paper as [854]
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“However, many models developed in academia are based on laboratory data that do not account for real-world factors such as environmental conditions [735,819,854].”
From Tackling climate change with machine learning (2023) · cites this paper as [854]
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Cites
Remaining useful life estimation – A review on the statistical data driven approaches
Si, X.-S., Wang, W., Hu, C.-H., & Zhou, D.-H. (2011) · European Journal of Operational Research · doi:10.1016/j.ejor.2010.11.018 · Cited by 2,162
Jaguemont, J., Boulon, L., & Dubé, Y. (2016) · Applied Energy · doi:10.1016/j.apenergy.2015.11.034 · Cited by 1,188
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
A review on prognostics and health monitoring of Li-ion battery
Zhang, J., & Lee, J. (2011) · Journal of Power Sources · doi:10.1016/j.jpowsour.2011.03.101 · Cited by 792
A review on electric vehicle battery modelling: From Lithium-ion toward Lithium–Sulphur
Fotouhi, A., Auger, D. J., Propp, K., Longo, S., & Wild, M. (2015) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.12.009 · Cited by 784
An ensemble model for predicting the remaining useful performance of lithium-ion batteries
Xing, Y., Eden, W. M., Tsui, K., & PECHT, M. G. (2013) · Microelectronics Reliability · doi:10.1016/j.microrel.2012.12.003 · Cited by 675
Nuhic, A., Terzimehic, T., Soczka-Guth, T., Buchholz, M., & Dietmayer, K. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2012.11.146 · Cited by 664
A Review of Accelerated Test Models
Escobar, L. A., & Meeker, W. Q. (2006) · Statistical Science · doi:10.1214/088342306000000321 · Cited by 639
Prognostics in Battery Health Management
Goebel, K., Saha, B., Saxena, A., Celaya, J., & Christophersen, J. (2008) · IEEE Instrumentation & Measurement Magazine · doi:10.1109/mim.2008.4579269 · Cited by 594
Battery management systems in electric and hybrid vehicles
Xing, Y., Ma, E. W. M., Tsui, K. L., & Pecht, M. (2011) · Energies · doi:10.3390/en4111840 · Cited by 540 · cited 1×
“Therefore, random-variable current RUL prediction is an enormous challenge [59].”
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Flagged first, then by citations
2 of the 58 references couldn’t be matched to a known paper.
Research works printed but not linked: 7
- Min, 2008, Lithium battery remaining capacity prediction method, Rural. Electr., 7, 15
- Wu, 2012, Research of Modified Elman Neural Network in the Lithium-ion Battery Capacity Prediction Method, J. Southwest Univ. Sci. Technol., 27, 65
- Chen, 2013, Probabilistc Residual life prediction for lithium-ion batteries based on Bayesian LS-SVR, Atca Aeronaut. Astron. Sin., 34, 2219
- Miao, 2005, Condition monitoring of rotating machinery hidden markov models, Acta Aeronaut. Astron. Sin., 26, 641
- Li, 2014, Temperature sensitivity of lithium-ion battery: A review, J Automot. Saf. Energy, 5, 224
- Ma, 2014, Research on Path Dependence of Large Format LiMn2O4 Battery Degradation in Thermal Aging, Trans. China Electrotech. Soc., 29, 221
- Yang, 2015, Robust Adaptive Observer for the Charge of Battery with Charging and Discharging Uncertainties, J. Chongqing Univ., 47, 1009
Cited by
Sorted by citations
Data-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
Hu, X., Xu, L., Lin, X., & Pecht, M. (2020) · Joule · doi:10.1016/j.joule.2019.11.018 · Cited by 1,320
Lipu, M. S. H., Hannan, M. A., Hussain, A., Hoque, M. M., Ker, P. J., Saad, M. H. M., & Ayob, A. (2018) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2018.09.065 · Cited by 755
A brief review on key technologies in the battery management system of electric vehicles
Liu, K., Li, K., Peng, Q., & Zhang, C. (2019) · Frontiers of Mechanical Engineering · doi:10.1007/s11465-018-0516-8 · Cited by 635
Xue, Z., Zhang, Y., Cheng, C., & Ma, G. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.09.074 · Cited by 450
Metadata
- Authors
- Wu, L., Fu, X., & Guan, Y.
- Affiliations
- Beijing Advanced Innovation Center for Imaging Technology (CN)
- Corresponding author
- Wu, Lifeng
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2016-05-27
- Volume
- 6
- Issue
- 6
- Pages
- 166
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W2405191498
- Cited by
- 203
- References
- 58
- Citations by year
- 2026: 9 · 2025: 20 · 2024: 23 · 2023: 31 · 2022: 33 · 2021: 31 · 2020: 13 · 2019: 18 · 2018: 16 · 2017: 8 · 2016: 1
- FWCI
- 12.49
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (4122015, 61202027, No. 61202027, IDHT20150507)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Battery Modeling and Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Keywords
- remaining useful life prediction, lithium-ion batteries, electric vehicles, data-driven prognostics, battery degradation modeling, battery life
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