Redox Flow Battery Flow Rate Optimization

Abstract

Redox Flow Batteries (RFBs), as they are considered promising for large scale energy storage as they are both scalable and efficient. Nevertheless, their performance and longevity depend greatly on the optimization of electrolyte flow rates. In this study, heuristic algorithms, namely Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) are combined with machine learning models to improve flow control. Through continuous monitoring on key parameters of voltage, current and flow rate, data driven insights into battery behavior have been achieved. Improvements in power output, energy efficiency, and operational stability were achieved with these methods. However, with obstacles like temperature fluctuations, electrolyte degradation, and computational complexity still significant, advanced optimization techniques and material developments have shown promise for solving these issues to provide cost effective, sustainable and scalable energy storage solutions.