DocumentCode
3564209
Title
State of charge estimation for a lead-acid battery using backpropagation neural network method
Author
Husnayain, F. ; Utomo, A.R. ; Priambodo, P.S.
Author_Institution
Dept. of Electr. Eng., Univ. Indonesia, Depok, Indonesia
fYear
2014
Firstpage
274
Lastpage
278
Abstract
An accurate battery State of Charge (SOC) method are essential for having optimum utilization of a battery. The SOC estimation in this research propose Back propagation Neural Network method, then the result compare with Open Circuit Voltage (OCV) prediction and coulometric counting method. Experiment results show that the SOC estimation shows accurate measurements with maximum average percentage error no more than 0.893%.
Keywords
backpropagation; battery charge measurement; lead acid batteries; neural nets; power engineering computing; OCV prediction; SOC estimation; back propagation neural network method; battery SOC method; battery state of charge method; coulometric counting method; lead-acid battery; open circuit voltage prediction; Batteries; Biological neural networks; Estimation; Lead; Mathematical model; System-on-chip; coulometric counting; lead-acid batteries; neural network; open circuit voltage; state-of-charge estimation;
fLanguage
English
Publisher
ieee
Conference_Titel
Electrical Engineering and Computer Science (ICEECS), 2014 International Conference on
Print_ISBN
978-1-4799-8477-0
Type
conf
DOI
10.1109/ICEECS.2014.7045261
Filename
7045261
Link To Document