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...

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Bibliographic Details
Published in:IOP conference series. Earth and environmental science 2023-12, Vol.1281 (1), p.12065
Main Authors: Shahroom, Afif Firdaus, Mansor, Muhammad, Ying, Yong Jia, Abd. Rahman, Muhamad Safwan
Format: Article
Language:English
Subjects:
Energy storage
Life span
Lithium
Lithium-ion batteries
Rechargeable batteries
Risk assessment
State of charge
Statistical analysis
Statistics
Thermal management
Thermodynamic properties
Trend analysis
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Summary: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.
ISSN:1755-1307
1755-1315
DOI:10.1088/1755-1315/1281/1/012065