Digital twin in battery energy storage systems: Trends and gaps detection through association rule mining

Energy sector is being revolutionized with the introduction of digitalization technologies. Digitalization tech-nologies 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 asso-ciated with the integration of digital twin with battery energy systems is essential to pave the way for further enhancements in this field.

Automated summary

The energy sector is witnessing a revolutionary change through the adoption of digitalization technologies, transforming conventional energy grids into smart grids. A digital twin, a virtual representation of battery energy storage systems, has emerged as a valuable tool in the energy industry. It seamlessly integrates battery energy storage systems into smart grids, enabling fault detection, real-time monitoring, and optimization. This paper reviews the advancements of digital twin technology in battery energy storage systems, focusing on its functions, architectures, and characteristics analyzed using the Formal Concept Analysis (FCA) algorithm.