The BMS Tool Monitoring Vertiv UPS and Vision Lithium-Ion Battery System

  • Tshepo Sithole University of South Africa, South Africa
  • Vasudeva Rao Veerdhi University of South Africa, South Africa
  • Thembelani Sithebe University of South Africa, South Africa
DOI: https://doi.org/10.31258/ijeepse.7.1.55-66
Abstract viewed: 131 times
pdf downloaded: 45 times
Keywords: BMS Tool, Soweto Small Wind Turbine Implementation, TP Lithium Ion Battery, Vertiv UPS

Abstract

In my previous work published in an acknowledged publication, titled "Factory test of a TP-100 Lithium Ion Vision Battery System for possible Implementation in Soweto, Johannesburg, South Africa," According to the findings from the study report, the TP-100 Vision Lithium-Ion battery system was found to be suitable for implementing a wind turbine system in Soweto. Furthermore, the study's findings indicated that Lithium-Ion batteries were the optimal choice for storing energy in wind turbines. These batteries provided significant total cost of ownership (TCO) reductions over a 10-year period, without the inconvenience and expenses associated with replacing lead-acid batteries. This research paper aims to validate the precision of parameters obtained from the integration of a 160 kVA Vetiv Three Phase UPS to a TP-100 Vision Lithium Ion Battery system in contrast to those obtained from the Battery Monitoring System (BMS) while using Tool BMS Version 1.3 Software. The results validation was evident as the parameters acquired from the UPS and Battery system were found to be accurate when compared to those observed through the BMS tool. Finally, utilizing the Smart Cloud Management System (SCMS), validity was shown by the ability to remotely monitor the operation of both the UPS and the Li-ion Battery system.

References

M. Giegerich et al., Open, flexible and extensible battery management system for lithium-ion batteries in mobile and stationary applications, IEEE 25th International Symposium on Industrial Electronics (ISIE), Santa Clara, CA, USA, pp. 991-996, 2016.

K.W., Wang, G., Zhang, Y. et al. Critical review and functional safety of a battery management system for large-scale lithium-ion battery pack technologies. Int J Coal Sci Technol 9, 36, 2022.

Y. Liao., and D. Fu, Design and development of teaching tool for lithium-ioN management system. Proceedings of 2015 ASEE Conference for Industry and Education Collaboration (CIEC), 2015.

G., Isaias, A.J., Calderón, and F.G., Folgado. IoT real time system for monitoring lithium-ion battery long-term operation in microgrids, Journal of Energy Storage 51: 104596, 2022.

M. Rusul. and A. Wahhab. Enhancement the performance of the wind energy conversion system based PMSG with lithium battery. AIP Conf.; 2977 (1): 020047. Dec. 2022.

N.K.L. Dantas., A.C.M. Souza, A.S.M. Vasconcelos., J. WdAS, R.G. Dall’Orto, J.F.C. Castro., Y. Liu and p. Rosas. Impact Analysis of a Battery Energy Storage System Connected in Parallel to a Wind Farm. Energies. 15(13):4586, 2022.

M.K. Tran. Et. al. “Concept Review of a Cloud-Based Smart Battery Management System for Lithium-Ion Batteries: Feasibility, Logistics, and Functionality”., Vo.8, No 2, 2022.

S. Madhankumar, S. Dharshini, N. Rohit Vignesh, P. Amrutha, and J. Dhanaselvam, Cloud computing-based Li-Ion Battery-BMS design for constant DC load applications." In Soft Computing for Security Applications: Proceedings of ICSCS 2021, pp. 299-312. Springer Singapore, 2022.

T. S. Sithole, V. R. Veeredhi, and T. Sithebe, Factory Test of a TP-100 Lithium-Ion Vision Battery System for Possible Implementation in Soweto, Johannesburg, South Africa, Eng. Technol. Appl. Sci. Res., vol. 13, no. 3, pp. 10984–10988, Jun, 2023.

S.M. Qaisar, and A. Maram, An Effective Li‐Ion Battery State of Health Estimation Based on Event‐Driven Processing. Green Energy: Solar Energy, Photovoltaics, and Smart Cities: PP.167-190, 2020.

O. Long, A. Ammar, D. Timothy, and H. Ryan, Mobile On/Off Grid Battery Energy Storage System (MOGBESS), 2021.

G. Anandhakumar, M. Lavanya, G. B. Santhi, and B. Chidambaranathan, Intelligent Control-Based Effective Utilization of Renewable Energy Sources." In Energy and Exergy for Sustainable and Clean Environment, Volume 2, pp. 71-81. Singapore: Springer Nature Singapore, 2022.

V. Myilsamy, S. Sudhakar, A. Roobaea, and A. Majed, State-of-Health Prediction for Li-ion Batteries for Efficient Battery Management System Using Hybrid Machine Learning Model, Journal of Electrical Engineering & Technology: pp.1-16, 2023.

Published
2024-03-01
How to Cite
[1]
T. Sithole, V. R. Veerdhi, and T. Sithebe, “The BMS Tool Monitoring Vertiv UPS and Vision Lithium-Ion Battery System ”, IJEEPSE, vol. 7, no. 1, pp. 55-66, Mar. 2024.