Remaining Useful Life Prediction of Lithium Batteries Based on Extended Kalman Particle Filter | |
Zhang N(张凝)2,3; Xu AD(徐皑冬)2,3; Wang K(王锴)2,3; Han XJ(韩晓佳)2,3; Hong WH(洪文焕)2,3; Hong, Seung Ho1 | |
刊名 | IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING |
2021 | |
卷号 | 16期号:2页码:206-214 |
关键词 | lithium‐ ion battery remaining useful life extended Kalman particle filter double exponential empirical degradation model |
ISSN号 | 1931-4973 |
产权排序 | 1 |
英文摘要 | 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. |
WOS研究方向 | Engineering |
语种 | 英语 |
WOS记录号 | WOS:000610818400004 |
内容类型 | 期刊论文 |
源URL | [http://ir.sia.cn/handle/173321/28314] |
专题 | 沈阳自动化研究所_工业控制网络与系统研究室 |
通讯作者 | Xu AD(徐皑冬) |
作者单位 | 1.Department of Electronic Engineering, Hanyang University, Ansan 15588, South Korea 2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China 3.Key Laboratory of Networked Control System, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China |
推荐引用方式 GB/T 7714 | Zhang N,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 N,Xu AD,Wang K,Han XJ,Hong WH,&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 N,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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