Optimal design of vanadium redox flow battery for large-scale energy storage

This study introduces a multi-objective optimization framework for vanadium redox flow batteries to enhance large-scale energy storage. The advanced model accounts for key losses, including vanadium crossover, pump work, and gas evolution, offering a realistic representation of battery performance. Utilizing the Non-dominated Sorting Genetic Algorithm II, this work captures complex trade-offs between energy efficiency and charge/discharge rates more effectively than single-variable approaches. Subsequently, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method identifies the most favorable operational parameters. Four distinct case studies demonstrate the framework’s capability to tailor performance: Case Study 1 maximizes power output, increasing charge and discharge rates by over 400 % and 325 %, respectively, at the cost of a 38 % efficiency reduction; Case Study 4 prioritizes efficiency, yielding a near 0.19 % gain alongside modest power improvements. Sensitivity analysis further reveals that current density, temperature, and electrolyte flow rate exhibit nonlinear, interdependent effects on system performance, emphasizing the need for adaptive parameter tuning. These results underscore the potential of optimized VRBs for renewable energy integration and grid stabilization, supporting their deployment in dynamic energy environments.