Cited by 203Battery Modeling and ManagementAdvancements in Battery MaterialsElectric Vehicles and Infrastructure

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Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies

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Wu, Lifeng, Fu, Xiaohui, Guan, Yong·2016·Applied Sciences

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Abstract

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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“Therefore, random-variable current RUL prediction is an enormous challenge [59].”
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Authors: Wu, L., Fu, X., & Guan, Y.
Affiliations: Beijing Advanced Innovation Center for Imaging Technology (CN)
Corresponding author: Wu, Lifeng
ORCID: Wu, Lifeng
Fu, Xiaohui
Guan, Yong
Venue: Applied Sciences
Publisher: MDPI AG
Published: 2016-05-27
Volume: 6
Issue: 6
Pages: 166
ISSN: 2076-3417 (ISSN-L)
DOI: 10.3390/app6060166
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: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
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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Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies

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