Parameter Identification of an Electrical Battery Model using DC-IR Data

Ayse Cisel Aras, Emre Yonel

2017

Abstract

Parameter identification of an electrical battery model is significant for the analysis of the performance of a battery. In order to obtain an accurate electrical battery model, a series of cell characterization tests should be conducted which will take a considerable amount of time. In this study, in order to identify the parameters of the electrical battery model in a short amount of time with an acceptable accuracy, DC-IR data is used. DC-IR test will take less time compared to the cell characterization tests. For the parameter identification, one of the most commonly used evolutionary algorithm (EA), Genetic Algorithm (GA) is used for the curve fitting problem and its performance is compared with the Levenberg-Marquardt algorithm.

Paper Citation

in Harvard Style

Aras A. and Yonel E. (2017). Parameter Identification of an Electrical Battery Model using DC-IR Data . In Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-263-9, pages 575-581. DOI: 10.5220/0006422705750581

in Bibtex Style

@conference{icinco17,
author={Ayse Cisel Aras and Emre Yonel},
title={Parameter Identification of an Electrical Battery Model using DC-IR Data},
booktitle={Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2017},
pages={575-581},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006422705750581},
isbn={978-989-758-263-9},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Parameter Identification of an Electrical Battery Model using DC-IR Data
SN - 978-989-758-263-9
AU - Aras A.
AU - Yonel E.
PY - 2017
SP - 575
EP - 581
DO - 10.5220/0006422705750581