Optimizing the Electrolyte Mixing in Industrial Tanks of Vanadium Redox Flow Batteries

Abstract

Redox flow batteries (RFB) are an emerging technology for large-scale grid energy storage. Different approaches have been adopted to investigate the operation of RFBs, such as experimental testing and mathematical modelling of the electrochemical cells [1]. However, the effect of the flow and mixing in the electrolyte tanks on the performance of RFBs has been largely overlooked and only recently has started to receive some attention [2]. In a recent work, the authors used numerical simulations to show how the fluid dynamics of the electrolytes within small lab-scale tanks can lead to different flow behaviors depending on whether forced or natural convection is dominant. The irregular transient mixing of the electrolyte in the tanks modifies both the cell potential and battery capacity with respect to those predicted under the widely used perfect mixing assumption (i.e., continuous stirred-tank reactor) [3]. However, this first attempt relied on several simplifying assumptions, such as two-dimensional geometries under small-cell (i.e., laminar) flow conditions, and thus provided only a qualitatively understanding of the flow in the tanks.This work exploits the idea of using CFD simulations to investigate the effect of operating conditions on the electrolyte flow in the tanks and its impact on battery response, focusing on high capacity industrial tanks with high (i.e., turbulent) flow rates and less restrictive simplifying assumptions. Our numerical simulations show how turbulence improves mixing, but also produces quasi-steady flows where a turbulent jet crosses the tank leaving dead electrolyte regions. Different tank solutions are presented aiming to improve the mixing and increase the capacity of the battery, playing both with the tank geometry and the operating conditions. Results on the charging and discharging operations, cell potential and electrolyte mixing index are presented and discussed. Acknowledgments This work has been partially funded by FEDER/Ministerio de Ciencia, Innovación y Universidades – Agencia Estatal de Investigación Projects PID2019-106740RB-I00 and RTC-2017-5955-3/AEI/10.13039/501100011033, and by Grant IND2019/AMB-17273 of the Comunidad de Madrid.