An On-Board Remaining Useful Life Estimation Algorithm for Lithium-Ion Batteries of Electric Vehicles

Xiaoyu Li, Xing Shu, Jiangwei Shen, Renxin Xiao, Wensheng Yan and Zheng Chen
Additional contact information
Xiaoyu Li: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China
Xing Shu: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China
Jiangwei Shen: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China
Renxin Xiao: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China
Wensheng Yan: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China
Zheng Chen: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China

Energies, 2017, vol. 10, issue 5, 1-15

Abstract: Battery remaining useful life (RUL) estimation is critical to battery management and performance optimization of electric vehicles (EVs). In this paper, we present an effective way to estimate RUL online by using the support vector machine (SVM) algorithm. By studying the characteristics of the battery degradation process, the rising of the terminal voltage and changing characteristics of the voltage derivative (DV) during the charging process are introduced as the training variables of the SVM algorithm to determine the battery RUL. The SVM is then applied to build the battery degradation model and predict the battery real cycle numbers. Experimental results prove that the built battery degradation model shows higher accuracy and less computation time compared with those of the neural network (NN) method, thereby making it a potential candidate for realizing online RUL estimation in a battery management system (BMS).

Keywords: mean square error (MSE); remaining useful life (RUL); support vector machine (SVM); voltage derivative (DV) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

Downloads: (external link)
https://www.mdpi.com/1996-1073/10/5/691/pdf (application/pdf)
https://www.mdpi.com/1996-1073/10/5/691/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:10:y:2017:i:5:p:691-:d:98640

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().