Design of Real Time Battery Management Unit for PV-Hybrid System by Application of Coulomb Counting Method

Abstract

This paper presents a real-time battery management unit designed by applying the Coulomb counting method and intended for use in an integrated renewable energy system for PV-Hybrid power supply. Battery management is required to stabilize hybrid systems and extend battery lifetimes. The battery management unit is divided into three main stages. Firstly, analysis of the basic components of the battery type used in the system is considered. Secondly, the state of charge (SOC) estimation method and the deterioration factor of the battery are analyzed. Finally, the overall battery management system, including a computer-based measurement and control unit, is constructed. The control system displays real-time information through LabVIEW 8.5 by estimating the state of charge through various measurements. The system will issue alerts when malfunctions are detected, and the operator can analyze and react to the system in real time to stabilize the system and extend the battery lifetime.

Share and Cite:

Ausswamaykin, A. and Plangklang, B. (2014) Design of Real Time Battery Management Unit for PV-Hybrid System by Application of Coulomb Counting Method. Energy and Power Engineering, 6, 186-193. doi: 10.4236/epe.2014.67017.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] Plangklang, B. (2010) Photovoltaic System Technology. Rajamangala University of Technology, Thanyaburi.
[2] http://en.wikipedia.org/wiki/Lead-acid_battery
[3] Ladener, H. (1996) Solare Stromversorgung: Grundlagen, Planung, Anwenddung. ÖkobuchVerlag, Freiburg, 79-103.
[4] Garche, J. and Harnisch, P. (1999) Batterien in PV-Anlagen. In: Schmid, J., Ed., Photovol-Taik: Strom aus der Sonne, Technologie, Wirtschaftlichkeit und Marktentwick-Lung, Müller, Heiderberg, 143-174.
[5] Chen, Z.H. (2011) Battery State of Charge Estimation Based on a Combined Model of Extended Kalman Filter and Neural Networks. The 2011 International Joint Conference on Neural Networks (IJCNN), San Jose, 31 July-5 August 2011, 2156-2163.
[6] Rodrigues, S., Munichandraiah, N. and Shukla, A.K. (2000) A Review of State-of-Charge Indication of Batteries by Means of A.C. Impedance Measurements. Journal of power Sources, 87, 12-20.
[7] Pascoe, P.E. and Anbuky, A.H. (2004) VRLA Battery Discharge Reserve Time Estimation. IEEE Transaction on Power Electronic, 19, 1515.
[8] Matthias, D. (2006) Dynamic Model of a Lead Acid Battery for Use in a Domestic Fuel Cell System. Journal of Power Sources, 161, 1400-1411.
http://dx.doi.org/10.1016/j.jpowsour.2005.12.075
[9] Olivier, T. and Louis, A.D. (2009) Experimental Validation of a Battery Dynamic Model for EV Applications. EVS24 International Battery. World Electric Vehicle Journal, 3, 1-10.
[10] Shepherd, C.M. (1965) Design of Primary and Secondary Cells—Part 2. An Equation Describing Battery Discharge. Journal of Electrochemical Society, 112, 657-664.
[11] Sabine, P., Marion, P. and Andresa, J. (2001) Method for State of Charge Determination and Their Application. Journal of Power Sources, 96, 113-120.
[12] Sukrrmoak, K. (2009) Deteioration Factor of Battery. PEC Technology (Thailand) ltd., Bangkok.
[13] IEEE (2005) Guide for Selection and Sizing of Batteries for Uninterruptible Power Supply. IEEE Std., 1184-1994.
[14] Jossen, A. (1999) Battery Management Systems (BMS) for Increasing Battery Life Time. Center for Solar Energy and Hydrogen Research (ZSW Ulm), Helmholtzstr.
[15] Atia, Y., Zahran, A.M. and Al-Hossain, A. (2010) Solar Cell Curves Measurement Based on LabVIEW Microcontroller Interfacing. Proceedings of the 12th WSEAS International Conference on Automatic Control Modeling & Simulation, 59-64.

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.