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 :
