A realistic model for battery state of charge prediction in energy management simulation tools

Bart Homan, Marnix V. ten Kortenaar, Johann L. Hurink and Gerard J.M. Smit

Energy, 2019, vol. 171, issue C, 205-217

Abstract: In this paper, a comprehensive model for the prediction of the state of charge of a battery is presented. This model has been specifically designed to be used in simulation tools for energy management in (smart) grids. Hence, this model is a compromise between simplicity, accuracy and broad applicability. The model is verified using measurements on three types of Lead-acid (Pb-acid) batteries, a Lithium-ion Polymer (Li-Poly) battery and a Lithium Iron-phosphate (LiFePo) battery. For the Pb-acid batteries the state of charge is predicted for typical scenarios, and these predictions are compared to measurements on the Pb-acid batteries and to predictions made using the KiBaM model. The results show that it is possible to accurately model the state of charge of these batteries, where the difference between the model and the state of charge calculated from measurements is less than 5%. Similarly the model is used to predict the state of charge of Li-Poly and LiFePo batteries in typical scenarios. These predictions are compared to the state of charge calculated from measurements, and it is shown that it is also possible to accurately model the state of charge of both Li-Poly and LiFePo batteries. In the case of the Li-Poly battery the difference between the measured and predicted state of charge is less than 5% and in the case of the LiFePo battery this difference is less than 3%.

Keywords: Pb-acid battery; Li-poly battery; LiFePo battery; State-of-Charge; Prediction; Energy management (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544218325064
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:171:y:2019:i:c:p:205-217

DOI: 10.1016/j.energy.2018.12.134

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().