Prognostics for State of Health of Lithium-Ion Batteries Based on Gaussian Process Regression
Joint Authors
Zhou, Di
Yin, Hongtao
Fu, Ping
Lu, Wenbin
Song, Xianhua
Yuan, Lili
Fu, Zuoxian
Source
Mathematical Problems in Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-04-01
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Accurate estimation and prediction of the lithium-ion (Li-ion) batteries’ performance has important theoretical and practical significance to make better use of lithium-ion battery and to avoid unnecessary losses.
State of health (SOH) estimation is used as a qualitative measure of the capability of a lithium-ion battery to store and deliver energy in a system.
To evaluate and predict the SOH of batteries, the Gaussian process regression with neural network (GPRNN) as its variance function is proposed.
Experimental results confirm that the proposed method can be effectively applied to Li-ion battery monitoring and prognostics by quantitative comparison with basic GPR, combination LGPFR, combination QGPFR, and the multiscale GPR (SMK-GPR, P-MGPR, and SE-MGPR).
The criteria of RMSE and MAPE of the proposed three models are reduced significantly compared to those of other existing methods.
American Psychological Association (APA)
Zhou, Di& Yin, Hongtao& Fu, Ping& Song, Xianhua& Lu, Wenbin& Yuan, Lili…[et al.]. 2018. Prognostics for State of Health of Lithium-Ion Batteries Based on Gaussian Process Regression. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1209291
Modern Language Association (MLA)
Zhou, Di…[et al.]. Prognostics for State of Health of Lithium-Ion Batteries Based on Gaussian Process Regression. Mathematical Problems in Engineering No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1209291
American Medical Association (AMA)
Zhou, Di& Yin, Hongtao& Fu, Ping& Song, Xianhua& Lu, Wenbin& Yuan, Lili…[et al.]. Prognostics for State of Health of Lithium-Ion Batteries Based on Gaussian Process Regression. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1209291
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1209291