Predicting Cell Cycling Performance in Redox Flow Batteries Using Reduced-Order Analytical Models

Abstract

Achieving decarbonization across multiple sectors (e.g., electricity generation, transportation, manufacturing) requires widespread adoption of renewable energy technologies, which demand energy storage solutions to enable sustainable, reliable, and resilient power delivery.1 To this end, redox flow batteries (RFBs) are a promising grid-scale energy storage platform, owing to their improved scalability, simplified manufacturing, and long service life.2 However, state-of-the-art RFBs remain too expensive for broad adoption, motivating the development of novel electrolyte formulations and reactor designs to meet performance, cost, and scale targets for emerging applications.3 While many recently-reported next-generation materials offer short-term performance improvements and the potential for cost reductions when produced at-scale, they often complicate system operation over extended durations due to a multitude of interrelated parasitic processes (e.g., side reactions, crossover, species decomposition) which lead to capacity fade and efficiency losses.3,4 Such processes challenge the establishment of quantitative and unambiguous connections between individual component properties and overall cell behavior. Here, we aim to develop mathematical models that translate fundamental material properties to cell performance metrics, enabling more informed design criteria for system engineering.In this presentation, we introduce an analytically-derived, zero-dimensional modeling framework to predict cell cycling behavior in RFBs. While previously-developed zero- and one-dimensional models demonstrate accurate performance predictions when compared to experimental systems, they must solve coupled differential equations using numerical methods.5,6 As a result, these approaches become computationally expensive for multi-cycle simulations (i.e., 10s – 100s of cycles), frustrating their implementation in system design and optimization. By deriving analytical solutions to these models, we can markedly reduce computation times and enable analyses hitherto unachievable.To demonstrate the utility of this modeling framework, we explore several representative scenarios that examine the connection between RFB material properties, operating conditions, and performance (i.e., power output, accessible capacity, efficiency). Additionally, we investigate the impact of different parasitic processes on capacity fade, highlighting the effects of species decomposition and crossover in durational cell cycling. Finally, we discuss several modalities for expanding this framework to include additional sources of performance losses and for integrating these models into larger computational schemes (e.g., optimization, parameter estimation, techno-economic assessments). The mathematical models developed in this work have potential to advance foundational understanding in RFB design, leading to quantitatively informed selection criteria for emerging candidate materials. 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. J.L gratefully acknowledges support from the MIT Materials Research Laboratory REU Program, as part of the MRSEC Program of the NSF under grant number DMR-14-19807. 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 S. Chu and A. Majumdar, Nature, 488, 294–303 (2012).M. L. Perry and A. Z. Weber, J. Electrochem. Soc., 163, A5064–A5067 (2016).F. R. Brushett, M. J. Aziz, and K. E. Rodby, ACS Energy Lett., 5, 879–884 (2020).M. L. Perry, J. D. Saraidaridis, and R. M. Darling, Current Opinion in Electrochemistry, 21, 311–318 (2020).M. Pugach, M. Kondratenko, S. Briola, and A. Bischi, Applied Energy, 226, 560–569 (2018).S. Modak and D. G. Kwabi, J. Electrochem. Soc., 168, 080528 (2021).