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Appl. Sci. 2016, 6(6), 166; doi:10.3390/app6060166

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

1,2,3,* , 1,2,3
and
1,2,3
1
College of Information Engineering, Capital Normal University, Beijing 100048, China
2
Beijing Engineering Research Center of High Reliable Embedded System, Capital Normal University, Beijing 100048, China
3
Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, China
*
Author to whom correspondence should be addressed.
Academic Editors: Frede Blaabjerg and Yongheng Yang
Received: 23 March 2016 / Revised: 12 May 2016 / Accepted: 24 May 2016 / Published: 27 May 2016
(This article belongs to the Special Issue Advancing Grid-Connected Renewable Generation Systems)
View Full-Text   |   Download PDF [465 KB, uploaded 27 May 2016]   |  

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. View Full-Text
Keywords: data-driven; vehicle lithium-ion batteries; degradation modeling; remaining useful life (RUL) data-driven; vehicle lithium-ion batteries; degradation modeling; remaining useful life (RUL)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Wu, L.; Fu, X.; Guan, Y. Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies. Appl. Sci. 2016, 6, 166.

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