Proceedings of the 2017 International Conference on Computational Science and Engineering (ICCSE 2017)

Design of Analytical Model for Predicting the Remaining Battery Discharge Time

Authors
Zhong-hua Ling, Lu-ling Duan, Lei Zhang
Corresponding Author
Zhong-hua Ling
Available Online July 2017.
DOI
10.2991/iccse-17.2017.2How to use a DOI?
Keywords
Lead-acid battery, Battery remaining capacity, Mathematic model
Abstract

Based on the data of voltage, current intensity and remaining battery discharge time, a mathematical model of lead-acid battery discharge curve is established. The model was used to fit the sampling data of the curve about lead battery discharge with different current intensity. The nlinfit function of the optimization toolbox of MATLAB is applied along with calculation to obtain the model coefficient, results of which indicate the mean relative error (MRE) of the model fitting the curve about battery discharge time with different current intensity is 2.393%, 1.538%, 1.185%, 0.560%, 0.460%, 0.697, 0.745%, 0.642%, and 0.673% respectively. They are all lower than 3%, suggesting the model is with high accuracy.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

Volume Title
Proceedings of the 2017 International Conference on Computational Science and Engineering (ICCSE 2017)
Series
Advances in Computer Science Research
Publication Date
July 2017
ISBN
10.2991/iccse-17.2017.2
ISSN
2352-538X
DOI
10.2991/iccse-17.2017.2How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Zhong-hua Ling
AU  - Lu-ling Duan
AU  - Lei Zhang
PY  - 2017/07
DA  - 2017/07
TI  - Design of Analytical Model for Predicting the Remaining Battery Discharge Time
BT  - Proceedings of the 2017 International Conference on Computational Science and Engineering (ICCSE 2017)
PB  - Atlantis Press
SP  - 9
EP  - 13
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccse-17.2017.2
DO  - 10.2991/iccse-17.2017.2
ID  - Ling2017/07
ER  -