Digital twin for battery energy storage systems

Abstract

Battery Energy Storage Systems (BESSs) have become pivotal in modern energy infrastructures due to their critical role in balancing supply-demand dynamics, integrating renewable sources, and ensuring grid stability. However, the operational complexity, real-time variability, and data heterogeneity associated with BESS present significant challenges in their design, monitoring, and optimization. Digital Twin (DT) technology offers a promising paradigm to address these issues by enabling cyber-physical synchronization, predictive analytics, and intelligent control. Despite increasing research interest, a consolidated understanding of how DTs are applied to BESSs, including architecture, enabling technologies, and deployment challenges, remains lacking. This paper presents a comprehensive systematic literature review to bridge this gap by synthesizing DT research for BESSs in five key research questions. The study investigates DT applications, connectivity levels, enabling technologies, deployment challenges, and future directions. Through rigorous screening and quality assessment, relevant studies were analyzed to identify trends, gaps, and future opportunities. The paper introduces a multilayered architecture tailored for BESSs that spans the physical, control, preprocessing, monitoring, and optimization layers, facilitating robust integration of cloud edge devices. The findings highlight dominant use cases such as monitoring, prediction, and optimization, with increasing reliance on artificial intelligence, Internet of Things, and cloud-edge platforms. Common deployment challenges include data inconsistency, real-time synchronization, and scalability constraints. The proposed architecture and synthesized insights are expected to guide future research and industrial implementation, contributing to more adaptive, resilient, sustainable, and efficient BESS ecosystems.