Prognostics of Lithium-Ion Batteries Based on Battery Performance Analysis and Flexible Support Vector Regression

Shuai Wang, Lingling Zhao, Xiaohong Su and Peijun Ma
Additional contact information
Shuai Wang: Department of Computer Science and Technology, Harbin Institute of Technology, No. 92 West Dazhi Street, Nan Gang District, Harbin 150001, Heilongjiang, China
Lingling Zhao: Department of Computer Science and Technology, Harbin Institute of Technology, No. 92 West Dazhi Street, Nan Gang District, Harbin 150001, Heilongjiang, China
Xiaohong Su: Department of Computer Science and Technology, Harbin Institute of Technology, No. 92 West Dazhi Street, Nan Gang District, Harbin 150001, Heilongjiang, China
Peijun Ma: Department of Computer Science and Technology, Harbin Institute of Technology, No. 92 West Dazhi Street, Nan Gang District, Harbin 150001, Heilongjiang, China

Energies, 2014, vol. 7, issue 10, 1-17

Abstract: Accurate prediction of the remaining useful life ( RUL ) of lithium-ion batteries is important for battery management systems. Traditional empirical data-driven approaches for RUL prediction usually require multidimensional physical characteristics including the current, voltage, usage duration, battery temperature, and ambient temperature. From a capacity fading analysis of lithium-ion batteries, it is found that the energy efficiency and battery working temperature are closely related to the capacity degradation, which account for all performance metrics of lithium-ion batteries with regard to the RUL and the relationships between some performance metrics. Thus, we devise a non-iterative prediction model based on flexible support vector regression ( F-SVR ) and an iterative multi-step prediction model based on support vector regression ( SVR ) using the energy efficiency and battery working temperature as input physical characteristics. The experimental results show that the proposed prognostic models have high prediction accuracy by using fewer dimensions for the input data than the traditional empirical models.

Keywords: lithium-ion batteries; remaining useful life ( RUL ); energy efficiency; working temperature; flexible support vector (SV) (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: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

Downloads: (external link)
https://www.mdpi.com/1996-1073/7/10/6492/pdf (application/pdf)
https://www.mdpi.com/1996-1073/7/10/6492/ (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:7:y:2014:i:10:p:6492-6508:d:41058

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 ().