Digital Twin-Driven Innovations in SOFC Electrode Virtual Engineering

Abstract

This study presents a digital twin-based virtual laboratory designed to optimize the manufacturing processes of electrode materials for Solid Oxide Fuel Cells (SOFC). Leveraging artificial intelligence models (R2=0.89, RMSE=0.03), the system accurately predicts critical material properties such as electrode thickness, dispersion stability, and polarization resistance. The virtual simulation of key processes—material selection, combining, coating, and sintering—significantly enhances process accuracy, efficiency, and product quality, effectively reducing experimental time and costs.