Digital Twin Technology: An Overview of Digital Twin and Implementation Approaches for Battery Digital Twin

Abstract

Digital twin technology has recently garnered significant attention across multiple industry sectors. A digital twin is a virtual representation of physical entities including individuals, devices, systems, or processes mapped from the physical world into the digital domain. This digital counterpart enables various simulations that aid in addressing real-world challenges and optimizing operations. The digital twin paradigm integrates several advanced technologies, such as machine learning, data analytics, modeling, visualization, and simulation. This study outlines two primary implementation strategies for digital twins by introducing layered frameworks to support practical deployments. A detailed survey of machine learning algorithms such as linear regression, decision trees, random forest regression, Gaussian process regression, and support vector regression is conducted to assess their effectiveness in predicting the battery’s State of Charge (SOC).

In the data-driven approach, data is the cornerstone for constructing accurate virtual models. In contrast, the physics-based approach relies on techniques like Equivalent Circuit Models (ECM) and mathematical methods, including the Kalman Filter algorithm, to mirror real-world behavior. Additionally, this paper traces the developmental history of digital twin technology, which originated in 2002. By exploring different stages of its evolution, the study outlines potential future advancements. These evolutionary insights can be leveraged to create efficient and forward-looking digital twin models aligned with emerging technological trends.