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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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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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