Towards High Performance Non-Aqueous Flow Cells

Non-aqueous redox flow batteries (NRFB) have opened a new pathway towards achieving economically viable grid-level energy storage 1 . As such, a number of studies have reported on the electrochemical behavior of newly developed active species, salt, and solvent combinations (i.e. Brushett et al. 2 , Escalante-Garcia et al. 3 , etc.). Most of these redox chemistries, however, have been demonstrated only in beaker cells or un-optimized flow cells; system-level studies toward grid-scale applications have yet been realized. In order to meet aggressive grid storage cost targets, NRFB’s must meet the following power requirements: 3.5 V, 130 mA cm -2 , 91% voltaic efficiency 4 . Hence, designing high performance non-aqueous flow cells tailored for different chemistries becomes a daunting task given the breadth of available chemistries and lack of materials properties (i.e. viscosity) knowledge required for cell design. To ease the design process, this study demonstrates a chemistry-agnostic design cycle for non-aqueous redox flow batteries. Specifically, this design cycle will specify flow field geometries and porous electrode materials properties, which optimize the power output and voltaic efficiency of an unspecified NRFB chemistry. First, transport through interdigitated flow fields is modeled (Figure 1) to predict polarization as a function of various dimensionless groups. This modeling study develops guidelines for geometry and flow conditions as a function of materials properties in order to optimize electrochemical performance. Second, a single-electrolyte study 5 (Figure 1) is employed to verify the impact of cell design on the cell performance for model active compounds (i.e., TEMPO 6 ). The single-electrolyte configuration also allows us to study cell performance at fixed states of charge by collecting polarization and impedance data at varying flow rates. Ultimately, this experimental technique will verify trends discovered through the transport modeling. This modeling-experimental approach creates a tool for predicting optimized cell architectures given a set of input materials and chemical properties. Acknowledgments We gratefully acknowledge the financial support of the Joint Center for Energy Storage Research and the National Science Foundation Graduate Research Fellowship Program. References 1. R. M. Darling, K. G. Gallagher, J. A. Kowalski, S. Ha, and F. R. Brushett, Energy Environ. Sci. , 7 , 3459–3477 (2014). 2. F. R. Brushett, J.