A Computationally-Efficient, Zero-Dimensional Stack Model for Simulating Redox Flow Battery Performance

Abstract

Redox flow batteries (RFBs) are an electrochemical energy storage platform with potential to support grid-scale decarbonization and resiliency efforts. The RFB architecture allows for decoupled energy and power, long lifetimes with simplified maintenance, and a range of potential chemistries. Despite their promising characteristics, present embodiments are too expensive for widespread adoption.1 , 2 Significant research efforts have focused on advancing new redox couples and constituent materials (e.g., electrodes, flow fields, membranes), but these contributions primarily focus on individual lab-scale cells, due, at least in part, to the time, material, and expertise required to investigate performance and durability in larger device formats (e.g., multi-cell stacks). This results in knowledge gaps within the field—specifically, electrochemical reactor scaling relationships are not yet well-understood and the relative importance of many tunable molecular and material properties at the stack level remains unclear.To aid with RFB stack design and understanding, computational models of varying dimensionality and complexity have been developed to simulate charge-discharge cycling.3 Zero- and one-dimensional models, which make simplifying assumptions about underlying electrochemistry and fluid dynamics, are particularly useful for capturing general trends in cell performance at a much lower computational cost than comprehensive multi-dimensional frameworks. However, previous models still employ numerical methods to solve complex systems of differential equations, which slows simulation time and impedes broader inquiry (e.g., durational cycling, parametric property sweeps).4 ,5 Further, existing models are typically designed to interrogate specific chemistries and, as such, cannot easily incorporate the array of properties and operating conditions possible for different redox couples and constituent materials.In this presentation, we will discuss the formulation and application of a computationally-lightweight zero-dimensional RFB stack model suitable for linking stack-level electrochemical and fluid dynamic performance characteristics to molecular- and cell-level property sets. By constructing analytical expressions for mass balances and cell voltages under galvanostatic cycling conditions,6 this framework facilitates connections between system design, component material properties, operating conditions, and cycling performance at the stack level with little computational expense. To validate the functionality of the model, we simulate durational performance (i.e., capacity fade, energy efficiency, power density) across a range of different representative chemistries, operating conditions, and reactor scales. Additionally, we evaluate the impact of typical parasitic losses—including shunt currents, crossover, and species decay —on cycling characteristics over hundreds of charge-discharge cycles. Finally, we will discuss pathways to integrate this framework into techno-economic assessments to enable physics-informed cost predictions that support more holistic technology comparisons. More broadly, the tools described here have the potential to advance understanding of cell-to-stack design principles by enabling assessments of system viability early in the innovation pipeline. Acknowledgments This work was supported by the Joint Center for Energy Storage Research, an Energy Innovation Hub funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences. B.J.N gratefully acknowledges the NSF Graduate Research Fellowship Program under Grant Number 1122374. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. References B. R. Chalamala et al., Proceedings of the IEEE, 102, 976–999 (2014).Z. Yang et al., Chem. Rev., 111, 3577–3613 (2011).B. Kumar Chakrabarti et al., Sustainable Energy & Fuels, 4, 5433–5468 (2020).M. Pugach, M. Kondratenko, S. Briola, and A. Bischi, Applied Energy, 226, 560–569 (2018).J. L. Barton and F. R. Brushett, Batteries, 5, 25 (2019).B. J. Neyhouse, J. Lee, and F. R. Brushett, J. Electrochem. Soc., 169, 090503 (2022).