Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Conditional Variational Autoencoders-Particle Filter

Accurate prediction of remaining useful life (RUL) is of great significance to the safety and reliability of lithium-ion batteries, which is able to provide useful reference information for maintenance. Particle filter (PF)-based prognostic methods have been widely used in the RUL prediction of batt...

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Veröffentlicht in:	IEEE transactions on instrumentation and measurement Jg. 69; H. 11; S. 8831 - 8843
Hauptverfasser:	Jiao, Ruihua, Peng, Kaixiang, Dong, Jie
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.11.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Batteries Conditional variational autoencoder (CVAE) Data analysis Data models Degradation Estimation Life prediction Lithium Lithium-ion batteries lithium-ion batteries (LIBs) Mathematical model particle filter (PF) Predictive models Rechargeable batteries Reliability remaining useful life (RUL) Resampling resampling method Useful life
ISSN:	0018-9456, 1557-9662
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Abstract	Accurate prediction of remaining useful life (RUL) is of great significance to the safety and reliability of lithium-ion batteries, which is able to provide useful reference information for maintenance. Particle filter (PF)-based prognostic methods have been widely used in the RUL prediction of batteries. However, due to the degeneracy of particles, the prediction accuracy of the traditional PF is not high. In this article, a novel PF framework based on conditional variational autoencoder (CVAE) and a reweighting strategy is proposed to predict the RUL of batteries. First, the CVAE algorithm is described in detail and embedded into the PF framework to substitute the traditional prior distribution so as to alleviate particle degradation. Furthermore, a reweighting strategy is introduced during particle resampling to prevent the loss of particle diversity. Afterward, the state-space model of battery capacity is established on the basis of data analysis. In the end, the proposed CVAE-PF is employed to predict the degradation of the battery capacity, and the RUL can be obtained when the capacity drops to a predefined failure threshold. From the experimental results it can be concluded that the new method is able to achieve better prediction performance compared with some traditional methods.
AbstractList	Accurate prediction of remaining useful life (RUL) is of great significance to the safety and reliability of lithium-ion batteries, which is able to provide useful reference information for maintenance. Particle filter (PF)-based prognostic methods have been widely used in the RUL prediction of batteries. However, due to the degeneracy of particles, the prediction accuracy of the traditional PF is not high. In this article, a novel PF framework based on conditional variational autoencoder (CVAE) and a reweighting strategy is proposed to predict the RUL of batteries. First, the CVAE algorithm is described in detail and embedded into the PF framework to substitute the traditional prior distribution so as to alleviate particle degradation. Furthermore, a reweighting strategy is introduced during particle resampling to prevent the loss of particle diversity. Afterward, the state-space model of battery capacity is established on the basis of data analysis. In the end, the proposed CVAE-PF is employed to predict the degradation of the battery capacity, and the RUL can be obtained when the capacity drops to a predefined failure threshold. From the experimental results it can be concluded that the new method is able to achieve better prediction performance compared with some traditional methods.
Author	Peng, Kaixiang Dong, Jie Jiao, Ruihua
Author_xml	– sequence: 1 givenname: Ruihua orcidid: 0000-0002-5143-6061 surname: Jiao fullname: Jiao, Ruihua email: jorry0123@163.com organization: Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China – sequence: 2 givenname: Kaixiang orcidid: 0000-0001-8314-3047 surname: Peng fullname: Peng, Kaixiang email: kaixiang@ustb.edu.cn organization: Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China – sequence: 3 givenname: Jie orcidid: 0000-0001-7585-6637 surname: Dong fullname: Dong, Jie email: dongjie@ies.ustb.edu.cn organization: Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China
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SubjectTerms	Algorithms Batteries Conditional variational autoencoder (CVAE) Data analysis Data models Degradation Estimation Life prediction Lithium Lithium-ion batteries lithium-ion batteries (LIBs) Mathematical model particle filter (PF) Predictive models Rechargeable batteries Reliability remaining useful life (RUL) Resampling resampling method Useful life
Title	Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Conditional Variational Autoencoders-Particle Filter
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