DocumentCode :
1902368
Title :
Power Battery Charging State-of-Charge Prediction Based on Genetic Neural Network
Author :
Zhou, Yongqin ; Sun, Jinlei ; Wang, Xudong
Author_Institution :
Coll. of Electr. & Electron. Eng., Harbin Univ. of Sci. & Technol., Harbin, China
fYear :
2010
fDate :
25-26 Dec. 2010
Firstpage :
1
Lastpage :
4
Abstract :
The problem of power battery state of charge estimation for hybrid vehicle directly affects the vehicle performance and driving distance. Considering there exists nonlinear relationship between the battery state of charge and the observable external characteristics, this paper presents a kind of algorithm which is based on the combination of genetic algorithm and back-propagation neural network namely GA-BP algorithm, taking full advantage of the strong capability of global search of genetic algorithm and the remarkable generalization performance of back-propagation neural network,the hybrid vehicle Ni-MH power battery GA-BP charging model is designed for the charging process.The simulation analysis shows that the network training speed is superior to the traditional BP network, after training the optimal solutions can be approximated in short time on the basis of real-time battery external characteristics being collected.
Keywords :
backpropagation; battery chargers; battery powered vehicles; electric machine analysis computing; genetic algorithms; hybrid electric vehicles; nickel compounds; secondary cells; GA-BP algorithm; NiJkH; back-propagation neural network; genetic neural network; hybrid vehicle; hybrid vehicle Ni-MH power battery GA-BP charging model; network training speed; power battery charging state-of-charge prediction; simulation analysis; Analytical models; Artificial neural networks; Batteries; Data models; Estimation; System-on-a-chip; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Engineering and Computer Science (ICIECS), 2010 2nd International Conference on
Conference_Location :
Wuhan
ISSN :
2156-7379
Print_ISBN :
978-1-4244-7939-9
Electronic_ISBN :
2156-7379
Type :
conf
DOI :
10.1109/ICIECS.2010.5678405
Filename :
5678405
Link To Document :
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