Data-driven prognostics and remaining useful life estimation for lithium-ion battery: A Review  

作　　者：LIU Datong ZHOU Jianbao PENG Yu 

机构地区：[1]Department of Automatic Test and Control,Harbin Institute of Technology,Harbin 150080,China

出　　处：《Instrumentation》2014年第1期59-70,共12页仪器仪表学报（英文版）

基　　金：supported partly by National Natural Science Foundation of China(Grant No.61301205);Research Fund for the Doctoral Program of Higher Education of China(Grant No.20112302120027);Natural Scientific Research Innovation Foundation in Harbin Institute of Technology(Grant No.HIT.NSRIF.2014017);China Scholarship Council.,2155-0875/Copyright C 2010 Binary Information Press July 2010

摘　　要：As an important and necessary part in the intelligent battery management systems(BMS),the prognostics and remaining useful life(RUL)estimation for lithium-ion 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 lithium-ion 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 lithium-ion batteries.Then,data-driven methods used for lithium-ion 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.

关 键 词：lithium-ion battery remaining useful life data-driven prognostics hybrid approach 

分 类 号：TM912[电气工程—电力电子与电力传动]