AI-Guided High-Throughput Experimentation to Accelerate Redox Flow Battery Electrolyte Optimization

Abstract

Redox flow batteries (RFBs) have garnered increasing interest for grid-scale energy storage applications due to their modularity, scalability, and ability to partially or fully decouple energy and power. Iron-based RFBs are of particular interest due to the low cost and earth abundance of iron. However, many iron-based RFB systems suffer from solubility-related limitations, resulting in limited volumetric power density or hindered performance due to precipitation of crossover species. Despite active research, a comprehensive, structured dataset of iron-based electrolyte formulations and properties remains limited. To establish a systematic understanding of electrolyte additive effects, we leverage the capabilities of the Material Innovation through Robotics & AI Laboratory (MIRAL) to develop a standardized protocol for RFB electrolyte formulation and evaluation. This work employs AI-guided high-throughput experimentation to systematically assess the impact of a library of organic and inorganic electrolyte additives. By employing a closed-loop workflow that couples researcher domain expertise with AI-driven predictions, the protocol accelerates identification and down-selection of ferrocyanide electrolyte formulations with enhanced solubility and ferrous chloride electrolyte formulations with improved pH stability in the presence of crossover species.