A Hybrid Prognostic Approach for Remaining Useful Life Prediction of Lithium-Ion Batteries
Joint Authors
Yang, Wen-An
Xiao, Maohua
Zhou, Wei
Guo, Yu
Liao, Wenhe
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-04-11
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
Lithium-ion battery is a core component of many systems such as satellite, spacecraft, and electric vehicles and its failure can lead to reduced capability, downtime, and even catastrophic breakdowns.
Remaining useful life (RUL) prediction of lithium-ion batteries before the future failure event is extremely crucial for proactive maintenance/safety actions.
This study proposes a hybrid prognostic approach that can predict the RUL of degraded lithium-ion batteries using physical laws and data-driven modeling simultaneously.
In this hybrid prognostic approach, the relevant vectors obtained with the selective kernel ensemble-based relevance vector machine (RVM) learning algorithm are fitted to the physical degradation model, which is then extrapolated to failure threshold for estimating the RUL of the lithium-ion battery of interest.
The experimental results indicated that the proposed hybrid prognostic approach can accurately predict the RUL of degraded lithium-ion batteries.
Empirical comparisons show that the proposed hybrid prognostic approach using the selective kernel ensemble-based RVM learning algorithm performs better than the hybrid prognostic approaches using the popular learning algorithms of feedforward artificial neural networks (ANNs) like the conventional backpropagation (BP) algorithm and support vector machines (SVMs).
In addition, an investigation is also conducted to identify the effects of RVM learning algorithm on the proposed hybrid prognostic approach.
American Psychological Association (APA)
Yang, Wen-An& Xiao, Maohua& Zhou, Wei& Guo, Yu& Liao, Wenhe. 2016. A Hybrid Prognostic Approach for Remaining Useful Life Prediction of Lithium-Ion Batteries. Shock and Vibration،Vol. 2016, no. 2016, pp.1-15.
https://search.emarefa.net/detail/BIM-1119061
Modern Language Association (MLA)
Yang, Wen-An…[et al.]. A Hybrid Prognostic Approach for Remaining Useful Life Prediction of Lithium-Ion Batteries. Shock and Vibration No. 2016 (2016), pp.1-15.
https://search.emarefa.net/detail/BIM-1119061
American Medical Association (AMA)
Yang, Wen-An& Xiao, Maohua& Zhou, Wei& Guo, Yu& Liao, Wenhe. A Hybrid Prognostic Approach for Remaining Useful Life Prediction of Lithium-Ion Batteries. Shock and Vibration. 2016. Vol. 2016, no. 2016, pp.1-15.
https://search.emarefa.net/detail/BIM-1119061
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1119061