Machine-learning assisted analysis on coupled fluid-dynamics and electrochemical processes in interdigitated channel for iron-chromium flow batteries

Abstract

This study explores the impact of interdigitated flow channel spacing on electrolyte distribution and flow velocity in porous electrodes, thereby affecting pump consumption and system efficiency. Utilizing a 3D electrochemical flow-coupled model, the research investigates the electrochemical performance and energy efficiency across various channel spacings, specific flow rates, and current densities. It elucidates the mechanisms by which interdigitated channel spacing influences battery performance. Through controlled trial-and-error, the optimal spacing for flow channels in Iron-Chromium Redox Flow Batteries (ICRFBs) was determined to be 4 mm. At a current density of 140 mA/cm2, the voltage efficiency reached 86.3 %, and the pump-based voltage efficiency achieved 85.9 %. To quickly and efficiently explore the impact of each individual parameter on battery efficiency and identify the optimal operating conditions, this study further developed a multi-task machine learning (ML) model. Initially trained on simulation data, the model achieved high predictive accuracy (R2 > 0.88) and effectively linked the interdigitated flow field design of ICRFBs with current density, specific flow rate, and channel spacing. The ML model was then validated through further investigation to ensure its accuracy and to achieve optimum performance efficiency as recommended by the model. This integrated approach offers a comprehensive understanding of RFBs performance under varying conditions, driving advancements in RFBs technology.