Digital Twin and AI Integration for Lifecycle Management of Grid-Scale Energy Storage

Abstract

The rapid growth of renewable energy use has increased the demand for reliable and efficient grid-scale BESS. However, lifecycle management of BESS remains a significant challenge due to performance degradation, high maintenance costs, safety issues, and uncertainty in remaining useful life (RUL). This article explores how Digital Twin technology, combined with AI, offers a transformative framework for managing the entire lifecycle of grid-scale energy storage. A digital twin is a virtual replica of a physical asset, allowing monitoring whenever needed. Parts of this twin stay current through continuous data streaming via an interface. The closed loop enables data-driven decision-making for installation, operation, maintenance, and end-of-life processes. This paper reviews current studies on BESS, lifecycle challenges, AI models, predictive maintenance, degradation forecasting with machine learning and deep learning, and the role of digital twins in developing adaptive and resilient energy systems. A conceptual framework is proposed to demonstrate how integrating digital twins (DT) and artificial intelligence (AI) can enhance reliability, extend asset lifespan, and lower total ownership costs. The study examines issues such as data interoperability, real-time processing, and data security, while also highlighting future research directions. Taking a holistic view, this article argues that combining digital twins and AI will ensure the sustainability, safety, and cost-effectiveness of energy storage systems in a low-carbon energy future.