DocumentCode :
2114153
Title :
A new method based on RBFNN in SOC estimation of HEV battery
Author :
Liu Yanwei ; Zhao Kegang ; Huang Xiangdong ; Pei Feng
Author_Institution :
Guangdong Key Lab. of Vehicle Eng., South China Univ. of Technol., Guangzhou, China
fYear :
2010
fDate :
29-31 July 2010
Firstpage :
4923
Lastpage :
4927
Abstract :
In order to mend the prediction of battery´s SOC in hybrid electric vehicles, by analyzing varying rule of electromotive force, residual capacity and equivalent inner resistance of battery during discharge, it has been put forward that neural network model should take time based characteristics into consideration. So the model could reflect the dynamic characteristics of battery more exactly. The battery model based on Radial Basis Function Neural Network is set up and trained with the curve altering with time. The pertinent experiments show that improved ability of the established model to estimate SOC of battery has been achieved.
Keywords :
hybrid electric vehicles; radial basis function networks; RBFNN; electromotive force; equivalent inner resistance; hybrid electric vehicles battery; radial basis function neural network; residual capacity; state of scharge; Artificial neural networks; Batteries; Discharges; Estimation; Hybrid electric vehicles; Resistance; System-on-a-chip; Hybrid Electric Vehicle; Neural Network; State of Charge(SOC);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Conference (CCC), 2010 29th Chinese
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-6263-6
Type :
conf
Filename :
5573690
Link To Document :
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