Guidelines for designing a digital twin for Li-ion battery: A reference methodology

Concetta Semeraro, Haya Aljaghoub, Mohammad Ali Abdelkareem, Abdul Hai Alami, Michele Dassisti and A.G. Olabi

Energy, 2023, vol. 284, issue C

Abstract: The integration of digital technologies is causing a significant change in the energy sector. These innovations have transformed traditional energy grids into intelligent grids. As a result, the digital replica of Battery Energy Storage Systems (BESS) has become one of the most crucial components in the energy sector. Digital twin technology enables the seamless integration of BESS into intelligent grids and offers numerous benefits, such as easy identification and prediction of faults, real-time system monitoring, optimization, temperature regulation, and estimation of parameters. As a result, the overall performance of BESS is improved by the digital twin technology. Consequently, this paper discusses the general guidelines that must be followed to develop a digital twin for a Li-ion BESS successfully. The main function is to define how to design a digital twin able to optimize the system and facilitate early and predictive fault detection and diagnosis.

Keywords: Digital twin; Lithium battery; Battery energy storage systems; Unsupervised machine learning; Formal concept analysis (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544223020935
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:284:y:2023:i:c:s0360544223020935

DOI: 10.1016/j.energy.2023.128699

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().