The independent control of power capability, which is affected by reactor design, and energy capacity, which is affected by stored electrolyte volume, enables redox flow batteries (RFBs) to be potential candidates for large-scale energy storage systems. Typical RFBs consist of two large tanks with positive and negative electrolyte pumped to an electrochemical reactor through a network of pipes, while the reactor consists of high surface area porous electrodes (such as carbon felt) separated by an ion exchange membrane (IEM) or a non-selective separator (NSS). Previous research has established inorganic (such as vanadium) and organic/semi-organic (such as TEMPO) molecules for use in RFBs with aqueous or non-aqueous electrolytes. IEMs are used to electronically insulate the two electrodes and reduce crossover of the redox species by charge selectivity. IEMs are expensive and can contribute as much as 20% to the total battery cost depending on the size of the reactor [1]. Reduction of RFB capital costs could incentivize the adoption of RFBs for grid-energy arbitrage [2], and, hence, the development of RFBs with inexpensive NSSs with minimal crossover could enhance RFB use. In this regard, redox active polymers (RAPs) have been developed in both aqueous and non-aqueous systems to replace IEMs with NSSs. Here, NSSs mitigate crossover by size exclusion of RAPs [3,4]. However, the viscosity of such polymer based redox molecules increases with concentration leading to higher pumping pressures required to maintain the flow rate, thus adding to the pumping costs. In addition, higher pumping pressures increase electrolyte crossover which induces RFB capacity fade from cycle-to-cycle.

The objectives of the present work are (1) to model multi-component transport during simultaneous electrochemical reactions within RFBs using NSSs, (2) to assess the tradeoffs in rate capability and cycle life incurred when replacing IEMs with NSSs, and (3) to predict performance as a function NSS design (including pore size and thickness). In this context, we are developing a two-dimensional model using porous electrode theory that explicitly captures porous-media flow, electronic current conservation, and conservation of molecular species (typically four redox-active species and two supporting ionic species that are inert) with simultaneous electrochemical reactions. These processes produce coordinated molecular fluxes can result in crossover and shuttling of redox species. In this model, molecular fluxes arise due to (1) pressure differences across the membrane (advection) (2) concentration gradient between the two electrolytes in the reactor (diffusion) and (3) migration due to the gradient in the electrolyte potential. In addition, we observe electrolyte volume losses which arises due to bulk electrolyte flow through the porous membrane because of viscosity (and therefore pressure) difference between the two electrolytes.

Each electrode experiences primary reactions due to the major redox couple in the electrolyte and secondary reactions arising from the species that crossover from the counter electrode. We find that the secondary reactions experience a much higher overpotential than the primary reactions. The shuttling process occurs when the products of the secondary reactions diffuse back into the parent electrode. This shuttling process is used in bio sensing devices to amplify and detect the amount of catechol in human nervous system [5] and for overcharge protection in Li-ion batteries [6]. In the context of RFBs, this must be controlled to minimize capacity fade. The present porous-electrode model and associated reduced-order models will enable mechanistic interpretation of experimental data from which performance will be correlated as a function of several non-dimensional parameters that will aid in the design of NSSs. Further, we will validate our porous electrode model with experiments using aqueous RFBs having interdigitated flow fields using various NSSs and IEMs.

We gratefully acknowledge the financial support of the Joint Center for Energy Storage Research (JCESR).

References

1. Arora, P., Zhang, Z. M., Chem. Rev., 2004, 104, 4419−4462.
2. Darling et al., Energy Environ. Sci., 7 ,2014.
3. Gavvalapalli et al., Am. Chem. Soc., 136 (46),2014
4. Janoschka et al., Nature 527, 78–81, 2015
5. Bernhard Wolfrum et al., Analytical Chemistry, Vol. 80, No. 4, 2008
6. Jun Chen et al., Solid-State Lett., volume 8, issue 1, A59-A62, 2005