Data-driven Prognostics and Remaining Useful Life Estimation for Lithium-ion Battery: A Review

被引：10

作者：

LIU Datong ^{[1
]}

ZHOU Jianbao ^{[1
]}

PENG Yu ^{[1
]}

机构：

[1] Department of Automatic Test and Control,Harbin Institute of Technology

来源：

Instrumentation | 2014年 / 01卷 / 01期

基金：

中国国家自然科学基金; 高等学校博士学科点专项科研基金;

关键词：

lithium-ion battery; remaining useful life; data-driven prognostics; hybrid approach;

D O I：

10.15878/j.cnki.instrumentation.2014.01.007

中图分类号：

TM912 [蓄电池];

学科分类号：

0808 ;

摘要：

As an important and necessary part in the intelligent battery management systems(BMS),the prognostics and remaining useful life(RUL) estimation for lithiumion batteries attach more and more attractions. Especially,the data-driven approaches use only the monitoring data and historical data to model the performance degradation and assess the health status,that makes these methods flexible and applicable in actual lithiumion battery applications. At first,the related concepts and definitions are introduced. And the degradation parameters identification and extraction is presented,as the health indicator and the foundation of RUL prediction for the lithiumion batteries. Then,data-driven methods used for lithiumion battery RUL estimation are summarized,in which several statistical and machine learning algorithms are involved. Finally,the future trend for battery prognostics and RUL estimation are forecasted.

引用

页码：59 / 70

页数：12

共 25 条

[11] Remaining Useful Life Estimation with Dynamic Grey Relevance Vector Machine for Lithium-ion Battery[J] . Jianbao Zhou,Yuntong Ma,Yu Peng,Xiyuan Peng.International Journal of Advancements in Computin . 2013 (6)
[12] Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life[J] . Chao Hu,Byeng D. Youn,Pingfeng Wang,Joung Taek Yoon.Reliability Engineering and System Safety . 2012
[13] A data-model-fusion prognostic framework for dynamic system state forecasting
Liu, J.
Wang, W.
Ma, F.
Yang, Y. B.
Yang, C. S.
[J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2012, 25 (04) : 814 - 823
[14] Comparative study of a structured neural network and an extended Kalman filter for state of health determination of lithium-ion batteries in hybrid electric vehicles[J] . D. Andre,A. Nuhic,T. Soczka-Guth,D.U. Sauer.Engineering Applications of Artificial Intelligence . 2012
[15] An adaptive recurrent neural networkfor remaining useful life prediction of lithium-ion batteries .2 LIU J,SAXENA A,GOEBEL K,et al. Annualconference of the Prognostics and Health Management Society . 2010
[16] Intelligent prognostics for battery health monitoring based on sample entropy
Widodo, Achmad
Shim, Min-Chan
Caesarendra, Wahyu
Yang, Bo-Suk
[J]. EXPERT SYSTEMS WITH APPLICATIONS, 2011, 38 (09) : 11763 - 11769
[17] Prognostics of lithium-ion batteries based on Dempster–Shafer theory and the Bayesian Monte Carlo method[J] . Wei He,Nicholas Williard,Michael Osterman,Michael Pecht.Journal of Power Sources . 2011 (23)
[18] A review on prognostics and health monitoring of Li-ion battery
Zhang, Jingliang
Lee, Jay
[J]. JOURNAL OF POWER SOURCES, 2011, 196 (15) : 6007 - 6014
[19] Equivalent circuit model parameters of a high-power Li-ion battery: Thermal and state of charge effects[J] . Jamie Gomez,Ruben Nelson,Egwu E. Kalu,Mark H. Weatherspoon,Jim P. Zheng.Journal of Power Sources . 2011 (10)
[20] A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation[J] . Chao Hu,Byeng D. Youn,Jaesik Chung.Applied Energy . 2011

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