Techniques for monitoring and predicting the OCV for VRLA battery systems

Alessandro Mariani; Kary Thanapalan; Peter Stevenson; Thomas Stockley; Jonathan Williams

doi:10.1109/IConAC.2015.7313982

Techniques for monitoring and predicting the OCV for VRLA battery systems

Alessandro Mariani, Kary Thanapalan, Peter Stevenson, Thomas Stockley, Jonathan Williams

Faculty of Computing, Engineering and Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

This paper provides a simple and advanced technique to monitor and predict the state of health (SOH) and the open circuit voltage (OCV) for valve regulated lead acid (VRLA) battery cells. The underlying principal of the technique described in this paper employs a mathematical model that can simulate different pore geometries shape and a simple equation to predict the equilibrated cell voltage after a small rest period. The technique was tested and analyzed using results obtained from experiments conducted at the Yuasa Battery laboratories. Lead-acid battery system analysis was carried out with reference to the standard battery system models available in the literature. The results indicate that by using this technique, appreciable benefits can be accrued and it is possible to maintain high standard products in safe operating conditions.

Original language	English
Title of host publication	2015 21st International Conference on Automation and Computing: Automation, Computing and Manufacturing for New Economic Growth, ICAC 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9780992680107
DOIs	https://doi.org/10.1109/IConAC.2015.7313982
Publication status	Published - 30 Oct 2015
Event	21st International Conference on Automation and Computing, ICAC 2015 - Glasgow, United Kingdom Duration: 11 Sep 2015 → 12 Sep 2015

Conference

Conference	21st International Conference on Automation and Computing, ICAC 2015
Country/Territory	United Kingdom
City	Glasgow
Period	11/09/15 → 12/09/15

Keywords

battery system
cell relaxation
Lead-acid technology
prediction mechanism
system analysis

Access to Document

10.1109/IConAC.2015.7313982Licence: CC BY-NC-ND

Cite this

Mariani, A., Thanapalan, K., Stevenson, P., Stockley, T., & Williams, J. (2015). Techniques for monitoring and predicting the OCV for VRLA battery systems. In 2015 21st International Conference on Automation and Computing: Automation, Computing and Manufacturing for New Economic Growth, ICAC 2015 [7313982] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IConAC.2015.7313982

@inproceedings{0b44369fdcf140c69aa0a37bff29819a,

title = "Techniques for monitoring and predicting the OCV for VRLA battery systems",

abstract = "This paper provides a simple and advanced technique to monitor and predict the state of health (SOH) and the open circuit voltage (OCV) for valve regulated lead acid (VRLA) battery cells. The underlying principal of the technique described in this paper employs a mathematical model that can simulate different pore geometries shape and a simple equation to predict the equilibrated cell voltage after a small rest period. The technique was tested and analyzed using results obtained from experiments conducted at the Yuasa Battery laboratories. Lead-acid battery system analysis was carried out with reference to the standard battery system models available in the literature. The results indicate that by using this technique, appreciable benefits can be accrued and it is possible to maintain high standard products in safe operating conditions.",

keywords = "battery system, cell relaxation, Lead-acid technology, prediction mechanism, system analysis",

author = "Alessandro Mariani and Kary Thanapalan and Peter Stevenson and Thomas Stockley and Jonathan Williams",

year = "2015",

month = oct,

day = "30",

doi = "10.1109/IConAC.2015.7313982",

language = "English",

booktitle = "2015 21st International Conference on Automation and Computing: Automation, Computing and Manufacturing for New Economic Growth, ICAC 2015",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "21st International Conference on Automation and Computing, ICAC 2015 ; Conference date: 11-09-2015 Through 12-09-2015",

}

Mariani, A, Thanapalan, K, Stevenson, P, Stockley, T & Williams, J 2015, Techniques for monitoring and predicting the OCV for VRLA battery systems. in 2015 21st International Conference on Automation and Computing: Automation, Computing and Manufacturing for New Economic Growth, ICAC 2015., 7313982, Institute of Electrical and Electronics Engineers Inc., 21st International Conference on Automation and Computing, ICAC 2015, Glasgow, United Kingdom, 11/09/15. https://doi.org/10.1109/IConAC.2015.7313982

Techniques for monitoring and predicting the OCV for VRLA battery systems. / Mariani, Alessandro; Thanapalan, Kary; Stevenson, Peter et al.

2015 21st International Conference on Automation and Computing: Automation, Computing and Manufacturing for New Economic Growth, ICAC 2015. Institute of Electrical and Electronics Engineers Inc., 2015. 7313982.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Techniques for monitoring and predicting the OCV for VRLA battery systems

AU - Mariani, Alessandro

AU - Thanapalan, Kary

AU - Stevenson, Peter

AU - Stockley, Thomas

AU - Williams, Jonathan

PY - 2015/10/30

Y1 - 2015/10/30

N2 - This paper provides a simple and advanced technique to monitor and predict the state of health (SOH) and the open circuit voltage (OCV) for valve regulated lead acid (VRLA) battery cells. The underlying principal of the technique described in this paper employs a mathematical model that can simulate different pore geometries shape and a simple equation to predict the equilibrated cell voltage after a small rest period. The technique was tested and analyzed using results obtained from experiments conducted at the Yuasa Battery laboratories. Lead-acid battery system analysis was carried out with reference to the standard battery system models available in the literature. The results indicate that by using this technique, appreciable benefits can be accrued and it is possible to maintain high standard products in safe operating conditions.

AB - This paper provides a simple and advanced technique to monitor and predict the state of health (SOH) and the open circuit voltage (OCV) for valve regulated lead acid (VRLA) battery cells. The underlying principal of the technique described in this paper employs a mathematical model that can simulate different pore geometries shape and a simple equation to predict the equilibrated cell voltage after a small rest period. The technique was tested and analyzed using results obtained from experiments conducted at the Yuasa Battery laboratories. Lead-acid battery system analysis was carried out with reference to the standard battery system models available in the literature. The results indicate that by using this technique, appreciable benefits can be accrued and it is possible to maintain high standard products in safe operating conditions.

KW - battery system

KW - cell relaxation

KW - Lead-acid technology

KW - prediction mechanism

KW - system analysis

U2 - 10.1109/IConAC.2015.7313982

DO - 10.1109/IConAC.2015.7313982

M3 - Conference contribution

AN - SCOPUS:84964091237

BT - 2015 21st International Conference on Automation and Computing: Automation, Computing and Manufacturing for New Economic Growth, ICAC 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 21st International Conference on Automation and Computing, ICAC 2015

Y2 - 11 September 2015 through 12 September 2015

ER -

Mariani A, Thanapalan K, Stevenson P, Stockley T, Williams J. Techniques for monitoring and predicting the OCV for VRLA battery systems. In 2015 21st International Conference on Automation and Computing: Automation, Computing and Manufacturing for New Economic Growth, ICAC 2015. Institute of Electrical and Electronics Engineers Inc. 2015. 7313982 doi: 10.1109/IConAC.2015.7313982

Techniques for monitoring and predicting the OCV for VRLA battery systems

Abstract

Conference

Keywords

Access to Document

Fingerprint

Cite this