DocumentCode
271312
Title
A Variable Effective Capacity Model for 
Traction Batteries Using Computational Intelligence Techniques
Author
SaÌnchez, Luciano ; Blanco, Cristian ; AntoÌn, Juan C. ; GarciÌa, Victor ; GonzaÌlez, Manuela ; Viera, Juan C.
Author_Institution
Dept. of Comput. Sci., Univ. of Oviedo, Gijon, Spain
Volume
62
Issue
1
fYear
2015
fDate
Jan. 2015
Firstpage
555
Lastpage
563
Abstract
Computational intelligence techniques are used to approximate the nonlinear operation of LiFePO4 batteries using rule-based systems. In this paper, rule-based systems are not directly fitted to data, but comprise constructive blocks in a differential-equation-based dynamical model that is numerically integrated to infer battery voltage, charge, and temperature. The design methodology has been validated with three different LiFePO4 batteries, and the results were found to be more accurate than those of a selection of statistical models and state-of-the-art artificial intelligence techniques.
Keywords
differential equations; equivalent circuits; iron compounds; knowledge based systems; lithium compounds; phosphorus compounds; secondary cells; LiFePO4 batteries; LiFePO4; computational intelligence techniques; constructive blocks; differential-equation-based dynamical model; nonlinear operation; rule-based systems; variable effective capacity model; Batteries; Computational modeling; Data models; Discharges (electric); Integrated circuit modeling; Mathematical model; Numerical models; $hbox{LiFePO}_{4}$ batteries; Computational intelligence; equivalent circuit model; semiphysical intelligent systems;
fLanguage
English
Journal_Title
Industrial Electronics, IEEE Transactions on
Publisher
ieee
ISSN
0278-0046
Type
jour
DOI
10.1109/TIE.2014.2327552
Filename
6824225
Link To Document