Digital Twin of Solid Oxide Electrochemical Cells: From 3D Microstructure Reconstruction to Multiphysics Modeling

Abstract

Solid oxide electrochemical cells (SOCs) are promising electrochemical devices offering high-efficiency energy conversion and storage. Their performance and durability, however, are critically governed by their underlying microstructure. Recent advancements in high-resolution imaging and computational modeling have enabled the development of digital twin frameworks, which integrate detailed microstructural analysis with predictive multiphysics simulations- delivering insights beyond the reach of conventional experimental techniques. This review provides a comprehensive overview of digital twin approaches for SOCs at the micrometer-scale, encompassing 3D microstructure reconstruction, quantitative descriptor extraction, and microstructure-resolved multiphysics simulations. Progress is summarized in both tomography-based and synthetic reconstruction techniques, the quantification of key microstructural parameters such as particle size, tortuosity, and triple-phase boundary length, and the simulation of electrochemical, thermal, and mechanical behavior based on realistic architectures. These digital twin developments have enabled a wide range of applications, including process optimization, composition design, performance prediction, and degradation analysis. Finally, current key challenges and emerging opportunities are discussed, highlighting the potential of integrating artificial intelligence into digital twin workflows to realize real-time feedback, adaptive modeling, and accelerated, microstructure-informed SOC design.