Life prediction of batteries for selecting the technically most suitable and cost effective battery

被引:132
作者
Wenzl, H
Baring-Gould, I
Kaiser, R
Liaw, BY
Lundsager, P
Manwell, J
Ruddell, A
Svoboda, V
机构
[1] Beratung Batterien & Energietech, D-37520 Osterode, Germany
[2] Natl Renewable Energy Lab, Golden, CO 80401 USA
[3] Fraunhofer Inst Solar Energy Syst, D-79110 Freiburg, Germany
[4] Univ Hawaii, Sch Ocean & Earth Sci & Technol, Electrochem Power Syst Lab, Hawaii Nat Energy Inst, Honolulu, HI 96822 USA
[5] Riso Natl Lab, DK-4000 Roskilde, Denmark
[6] Univ Massachusetts, Amherst, MA 01003 USA
[7] Rutherford Appleton Lab, Council Cent Lab, Res Councils, Didcot OX11 0QX, Oxon, England
关键词
battery life prediction; modelling; simulation; end-of-life criteria; electrochemical model; equivalent circuit model;
D O I
10.1016/j.jpowsour.2004.11.045
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
It is necessary to be able to predict the lifetime of a battery in target applications in order to make sound technical and commercial decisions at the system design stage. In general, accurate lifetime prediction requires more than knowledge of ageing processes and the availability of battery models. A concise procedure linking user requirements, operating regimes and operating conditions of batteries to ageing processes and loss of performance has to be used. Quantified end-of-life criteria have to be defined with the details of the application requirements in mind. To verify lifetime prediction models it is necessary to have data of the battery when new and immediately before replacement, results of post mortem analysis and detailed data of the operation. This paper describes a procedure that can be used for lifetime prediction, outlines some of the requirements for a prediction and discusses the principles of battery models and their potential use for lifetime prediction. (c) 2004 Elsevier B.V. All rights reserved.
引用
收藏
页码:373 / 384
页数:12
相关论文
共 23 条
[1]  
BOGEL W, 2001, Patent No. 2831670
[2]  
Caselitz P, 1996, VDI BERICHT, V1287, P219
[3]   The available capacity computation model based on artificial neural network for lead-acid batteries in electric vehicles [J].
Chan, CC ;
Lo, EWC ;
Shen, WX .
JOURNAL OF POWER SOURCES, 2000, 87 (1-2) :201-204
[4]  
DEMETTRE D, 2000, PUBLISHABLE FINAL RE, P24
[5]  
DROUILLHET S, 1997, BATTERY LIFE PREDICT
[6]   STROMVERTEILUNG UBER DIE LANGE VON AKKUMULATORENPLATTEN AUS ROHRFORMIGEN ELEMENTEN [J].
EULER, J ;
HORN, L .
ARCHIV FUR ELEKTROTECHNIK, 1965, 50 (02) :85-&
[7]   Numerical modeling of coupled electrochemical and transport processes in lead-acid batteries [J].
Gu, WB ;
Wang, CY ;
Liaw, BY .
JOURNAL OF THE ELECTROCHEMICAL SOCIETY, 1997, 144 (06) :2053-2061
[8]   Advanced integrated battery testing and simulation [J].
Liaw, BY ;
Bethune, KP ;
Yang, YG .
JOURNAL OF POWER SOURCES, 2002, 110 (02) :330-340
[9]  
LUNDSAGER P, 2003, DEV BATTERY LIFETIME
[10]  
MANWELL JF, 1998, THEORY MANUAL NATL