A real-time thermal behaviour monitoring and analysis of a 500 kWh grid-connected battery energy storage system

Statistical analysis yields critical data for risk evaluation and management of a stationary battery energy storage system (BESS). Lithium-ion (Li-ion) batteries have attained huge attention for both stationary and non-stationary applications due to their lucrative features such as lightweight, high...

Full description

Saved in:
Bibliographic Details
Main Authors: Shahroom A.F., Mansor M., Ying Y.J., Rahman M.S.A.
Other Authors: 58812539700
Format: Conference Paper
Published: Institute of Physics 2024
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uniten.dspace-34488
record_format dspace
spelling my.uniten.dspace-344882024-10-14T11:20:07Z A real-time thermal behaviour monitoring and analysis of a 500 kWh grid-connected battery energy storage system Shahroom A.F. Mansor M. Ying Y.J. Rahman M.S.A. 58812539700 58845449800 58179118600 58811982000 Statistical analysis yields critical data for risk evaluation and management of a stationary battery energy storage system (BESS). Lithium-ion (Li-ion) batteries have attained huge attention for both stationary and non-stationary applications due to their lucrative features such as lightweight, high energy density efficiency, and long lifespan. However, detailed analysis and trends subjected to thermal behaviour of the device especially in grid-connected BESS application is still neglected. Therefore, this paper presents a statistical analysis for thermal behaviour of a grid connected BESS. The significance of Li-ion battery employing battery thermal management is presented, which can guarantee a reliable and safe operation as well as examining the effect of voltage, current and state of charge (SOC) on BESS operation. The large group of datasets recorded daily with five-minute intervals are difficult to be analysed numerically in a timely manner. Thus, the analysis can be made by visualizing the numerical data that was retrieved through representational state transfer (REST API) for easier interpretation and trend analysis. The visualization is made using Microsoft Power BI and presented in this paper. � 2023 Institute of Physics Publishing. All rights reserved. Final 2024-10-14T03:20:07Z 2024-10-14T03:20:07Z 2023 Conference Paper 10.1088/1755-1315/1281/1/012065 2-s2.0-85182373920 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182373920&doi=10.1088%2f1755-1315%2f1281%2f1%2f012065&partnerID=40&md5=c56ec8c8213b5bab45d15ec89b50a0f9 https://irepository.uniten.edu.my/handle/123456789/34488 1281 1 12065 All Open Access Gold Open Access Institute of Physics Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Statistical analysis yields critical data for risk evaluation and management of a stationary battery energy storage system (BESS). Lithium-ion (Li-ion) batteries have attained huge attention for both stationary and non-stationary applications due to their lucrative features such as lightweight, high energy density efficiency, and long lifespan. However, detailed analysis and trends subjected to thermal behaviour of the device especially in grid-connected BESS application is still neglected. Therefore, this paper presents a statistical analysis for thermal behaviour of a grid connected BESS. The significance of Li-ion battery employing battery thermal management is presented, which can guarantee a reliable and safe operation as well as examining the effect of voltage, current and state of charge (SOC) on BESS operation. The large group of datasets recorded daily with five-minute intervals are difficult to be analysed numerically in a timely manner. Thus, the analysis can be made by visualizing the numerical data that was retrieved through representational state transfer (REST API) for easier interpretation and trend analysis. The visualization is made using Microsoft Power BI and presented in this paper. � 2023 Institute of Physics Publishing. All rights reserved.
author2 58812539700
author_facet 58812539700
Shahroom A.F.
Mansor M.
Ying Y.J.
Rahman M.S.A.
format Conference Paper
author Shahroom A.F.
Mansor M.
Ying Y.J.
Rahman M.S.A.
spellingShingle Shahroom A.F.
Mansor M.
Ying Y.J.
Rahman M.S.A.
A real-time thermal behaviour monitoring and analysis of a 500 kWh grid-connected battery energy storage system
author_sort Shahroom A.F.
title A real-time thermal behaviour monitoring and analysis of a 500 kWh grid-connected battery energy storage system
title_short A real-time thermal behaviour monitoring and analysis of a 500 kWh grid-connected battery energy storage system
title_full A real-time thermal behaviour monitoring and analysis of a 500 kWh grid-connected battery energy storage system
title_fullStr A real-time thermal behaviour monitoring and analysis of a 500 kWh grid-connected battery energy storage system
title_full_unstemmed A real-time thermal behaviour monitoring and analysis of a 500 kWh grid-connected battery energy storage system
title_sort real-time thermal behaviour monitoring and analysis of a 500 kwh grid-connected battery energy storage system
publisher Institute of Physics
publishDate 2024
_version_ 1814061058960130048
score 13.214268