Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies
Source abstract
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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Research context
OpenAlex topics: Advanced Battery Technologies Research · Reliability and Maintenance Optimization · Fault Detection and Control Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Information Engineering, Capital Normal University, Beijing 100048, China
Beijing Engineering Research Center of High Reliable Embedded System, Capital Normal University, Beijing 100048, China
Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, China
References (64)
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