Integrating Machine Learning and Characterization in Battery Research: Toward Cognitive Digital Twins with Physics and Knowledge

Abstract The rapid advancement of battery technology has driven the need for innovative approaches to enhance battery management systems. In response, the concept of a cognitive digital twin has been developed to serve as a sophisticated virtual model that dynamically simulates, predicts, and optimizes battery behavior. These models integrate real‐time data with in‐depth physical insights, offering a comprehensive solution for battery management. Fundamental to this development are advanced characterization techniques such as microscopy, spectroscopy, tomography, and electrochemical methods—that provide critical insights into the underlying physics of batteries. Additionally, machine learning (ML) extends beyond predictive analytics to enhance the analytical capabilities. By uncovering deep physical insights, ML significantly improving the accuracy, reliability, and interpretability of these techniques. This review explores how integrating ML with traditional battery characterization techniques bridges the gap between deep physical insights and data‐driven analysis. The synergy not only enhances precision and computational efficiency but also minimizes human intervention, thereby paving the way for more robust and transparent digital twin technologies in battery research.