Lithium-Ion Battery Prognostics with Hybrid Gaussian Process Function Regression

Yu Peng, Yandong Hou, Yuchen Song, Jingyue Pang and Datong Liu
Additional contact information
Yu Peng: Department of Automatic Test and Control, Harbin Institute of Technology, Harbin 150080, China
Yandong Hou: Department of Automatic Test and Control, Harbin Institute of Technology, Harbin 150080, China
Yuchen Song: Department of Automatic Test and Control, Harbin Institute of Technology, Harbin 150080, China
Jingyue Pang: Department of Automatic Test and Control, Harbin Institute of Technology, Harbin 150080, China
Datong Liu: Department of Automatic Test and Control, Harbin Institute of Technology, Harbin 150080, China

Energies, 2018, vol. 11, issue 6, 1-20

Abstract: The accurate prognostics of lithium-ion battery state of health (SOH) and remaining useful life (RUL) have great significance for reducing the costs of maintenance. The methods based on the physical models cannot perform satisfactorily as the systems become more and more complex. With the development of digital acquisition and storage technology, the data of battery cells can be obtained. This makes the data-driven methods get more and more attention. In this paper, to overcome the problem that the trend fitting deteriorates rapidly when test data are far from the training data for multiple-step-ahead estimation, a prognostic method fusing the wavelet de-noising (WD) method and the hybrid Gaussian process function regression (HGPFR) model for predicting the RUL of the lithium-ion battery is proposed. Gaussian process regression (GPR) is a typical representative for the Bayesian structure with non-parameter expression and uncertainty presentation. In this case, the effects on predictive results are compared and analyzed using the proposed method and the HGPFR model with different lengths of training data. Besides, in consideration of the degradation characteristics for the lithium-ion battery data set, the selections of the wavelet de-noising method are performed with corresponding experimental analyses. Furthermore, we set the hype-parameter for the mean function and co-variance function, and then develop a method for parameter optimization to make the proposed model suitable for the data. Moreover, a numerical simulation based on the data repository of Department of Engineering Science (DES) university of Oxford and Center for Advanced Life Cycle Engineering (CALCE) of University of Maryland is carried out, and the results are analyzed. For the data repository, an accuracy of 2.2% is obtained compared with the same value of 6.7% for the HGPFR model. What is more, the applicability and stability are verified with the prognostic results by the proposed method.

Keywords: lithium-ion battery; RUL prediction; Gaussian process function regression; wavelet de-noising (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: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
https://www.mdpi.com/1996-1073/11/6/1420/pdf (application/pdf)
https://www.mdpi.com/1996-1073/11/6/1420/ (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:11:y:2018:i:6:p:1420-:d:150213

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 ().