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Remaining Useful Life Prediction of Lithium Batteries Based on Extended Kalman Particle Filter
Zhang, Ning1,2; Xu AD(徐皑冬)1,2; Wang K(王锴)1,2; Han XJ(韩晓佳)1,2; Hong, Wenhuan1,2; Hong, Seung Ho3
Department工业控制网络与系统研究室
Source PublicationIEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING
ISSN1931-4973
2021
Volume16Issue:2Pages:206-214
Indexed BySCI ; EI
EI Accession number20210609899582
WOS IDWOS:000610818400004
Contribution Rank1
Keywordlithium‐ ion battery remaining useful life extended Kalman particle filter double exponential empirical degradation model
Abstract

The prognosis of time-to-failure for a battery can avoid the failure caused by battery performance loss. In this paper, a novel and effective algorithm is proposed to predict the remaining useful life of lithium-ion batteries. The extended Kalman particle filter is used to improve particle degradation problem existing in standard particle filter algorithm. In order to fit battery capacity degradation, a transformed model is proposed based on double exponential empirical degradation model. It can reduce the number of parameters and the training difficulty of parameters; it also matches the form of state transfer equation. In order to improve prediction accuracy, the auto regression model is introduced to correct observation values produced by observation equation. Experimental results show that the proposed algorithm can effectively improve the accuracy of prediction compared with other algorithms. (c) 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Language英语
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/28314
Collection工业控制网络与系统研究室
Corresponding AuthorXu AD(徐皑冬)
Affiliation1.Key Laboratory of Networked Control System, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
3.Department of Electronic Engineering, Hanyang University, Ansan 15588, South Korea
Recommended Citation
GB/T 7714
Zhang, Ning,Xu AD,Wang K,et al. Remaining Useful Life Prediction of Lithium Batteries Based on Extended Kalman Particle Filter[J]. IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING,2021,16(2):206-214.
APA Zhang, Ning,Xu AD,Wang K,Han XJ,Hong, Wenhuan,&Hong, Seung Ho.(2021).Remaining Useful Life Prediction of Lithium Batteries Based on Extended Kalman Particle Filter.IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING,16(2),206-214.
MLA Zhang, Ning,et al."Remaining Useful Life Prediction of Lithium Batteries Based on Extended Kalman Particle Filter".IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 16.2(2021):206-214.
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