Journal
APPLIED SCIENCES-BASEL
Volume 6, Issue 6, Pages -Publisher
MDPI
DOI: 10.3390/app6060166
Keywords
data-driven; vehicle lithium-ion batteries; degradation modeling; remaining useful life (RUL)
Categories
Funding
- National Natural Science Foundation of China [61202027]
- Beijing Natural Science Foundation of China [4122015]
- Project of Construction of Innovative Teams and Teacher Career Development for Universities and Colleges under Beijing Municipality [IDHT20150507]
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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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