@inproceedings{cda24b6722b6466a88ade9d7bed960fb,
title = "State of charge estimation for a lead-acid battery using backpropagation neural network method",
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 = "coulometric counting, lead-acid batteries, neural network, open circuit voltage, state-of-charge estimation",
author = "F. Husnayain and Utomo, {Agus R.} and Priambodo, {Purnomo Sidi}",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 1st International Conference on Electrical Engineering and Computer Science, ICEECS 2014 ; Conference date: 24-11-2014 Through 25-11-2014",
year = "2014",
month = feb,
day = "18",
doi = "10.1109/ICEECS.2014.7045261",
language = "English",
series = "Proceedings of 2014 International Conference on Electrical Engineering and Computer Science, ICEECS 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "274--278",
booktitle = "Proceedings of 2014 International Conference on Electrical Engineering and Computer Science, ICEECS 2014",
address = "United States",
}