Multi-parameter Optimization Strategy for Vanadium Redox Flow Battery Operation Based on Genetic Algorithm

Vanadium redox flow battery (VRB) is considered to be the most promising large-scale energy storage technology because of its high design flexibility and good reversibility. Operating characteristics of VRB are affected by various parameters including temperature, electrolyte flow rate, and current. Problems such as premature voltage cut-off and low energy efficiency may be caused by improper settings of operational parameters. Previous operation strategy only optimizes single variable, such as temperature or flow rate, neglecting the coupled effect of multi variables on battery operating characteristics. This paper proposed a dynamic multi-parameter operation strategy to improve the energy efficiency of battery systems. The parameters are such as temperature, flow rate and current density are optimized holistically. Based on relevant experiments, a 3kW/5kWh VRB electrochemical model is established for the implementation of the strategy. The real time operation objective is to reach maximum energy efficiency and genetic algorithm is applied for solving nonlinear objective. The results show that multi-parameter optimization strategy achieves high instantaneous energy efficiency compared with other optimization strategies, reaching up to 93% as the SOC increases whereas the applied current decreases. The simulation shows that the energy efficiency of the proposed strategy was improved 1% compared with that of the other strategies.