@inproceedings{1538670469764c89916e96f6e9d8aed6,
title = "An intelligence-based state of charge prediction for VRLA batteries",
abstract = "A battery management system (BMS) has three main functions, voltage monitoring, current discharge monitoring and remaining life monitoring. This paper primarily focuses on remaining life monitoring through the estimation of battery's state of charge (SOC). An Experimental set-up was prepared to measure the Valve-Regulated Lead-Acid (VRLA) battery's SOC under different operating conditions. Backpropagation (BP) neural network to estimate the battery's SOC using the experimental data. The results showed a satisfactory estimation of battery's SOC with a small (4.25%) root mean square perdition error (RMS).",
keywords = "Neural Network, SOC, SOC estimation, state of charge",
author = "Scott, {De Shaunna} and Jide Lu and Haneen Aburub and Aditya Sundararajan and Sarwat, {Arif I.}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE Transportation Electrification Conference, ITEC-India 2017 ; Conference date: 13-12-2017 Through 15-12-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/ITEC-India.2017.8333847",
language = "English",
series = "2017 IEEE Transportation Electrification Conference, ITEC-India 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--4",
booktitle = "2017 IEEE Transportation Electrification Conference, ITEC-India 2017",
}