Digital twin in battery energy storage systems: Trends and gaps detection through association rule mining. (15th June 2023) - Semeraro, Concetta; Aljaghoub, Haya; Abdelkareem, Mohammad Ali; Alami, Abdul Hai; Olabi, A.G.
Abstract: Energy sector is being revolutionized with the introduction of digitalization technologies. Digitalization technologies converted conventional energy grids into smart grids. Therefore, the virtual representation of battery energy storage systems, known as a digital twin, has become a highly valuable tool in the energy industry. This technology seamlessly integrates battery energy storage systems into smart grids and facilitates fault detection and prognosis, real-time monitoring, temperature control, optimization, and parameter estimations. In general, the use of digital twin technology improves the efficiency of the battery system after a thorough assessment of the battery performance. Hence, this paper aims to review the advancements of digital twin technology in battery energy storage systems. In particular, this paper focuses on the different functions and architectures of the digital twin for battery energy storage systems. Then, this paper further analyzes the digital twin characteristics using the Formal Concept Analysis (FCA) algorithm. The FCA is run to find trends and gaps between the digital twin functions and architectures in the battery system. Exploring the trends and gaps from previous research associated with the integration of digital twin with battery energy systems is essential to pave the way for further enhancements in this field. Highlights: Review the application of digital twin in battery energy storage systems. Analyze the digital twin functions and architecture for battery energy storage systems. Association rule mining technique is employed to explore trends and gaps of integrating the digital twin in battery storage systems.