(423b) Going Faster: Analytical Models for Predicting Cell Cycling Performance in Redox Flow Batteries | AIChE

(423b) Going Faster: Analytical Models for Predicting Cell Cycling Performance in Redox Flow Batteries


Neyhouse, B. J. - Presenter, Massachusetts Institute of Technology
Lee, J., University of Michigan
Brushett, F., Massachusetts Institute of Technology

Global decarbonization of the energy sector necessitates development of storage technologies to mediate the inherent intermittency of renewable resources. Electrochemical systems are well-positioned to support this transition, with redox flow batteries (RFBs) emerging as a promising platform due to their scalability, simplified manufacturing, and long service lifetimes.1 However, current embodiments are too expensive for broad adoption, as they rely on costly charge-storage species and reactor components, motivating investigations into alternative material sets. For example, molecular engineering offers an expansive design space for redox species, enabling a myriad of potential combinations.2 Despite this versatility, next-generation materials must carefully balance complex tradeoffs between power / energy density, cycling stability, energy efficiency, and capital costs. Navigating this multifaceted parameter space requires suitable models to unambiguously connect underlying constituent material properties to cell performance metrics.

Numerous modeling efforts of varying degrees of complexity and detail have sought to assess the electrochemical and fluid dynamic processes that govern cell behavior, providing insight into features which govern capacity fade and cycling efficiencies for certain systems. In particular, zero-dimensional models are typically used to describe time-dependent changes in redox species concentrations while ignoring spatial variations,3–5 as such frameworks can more easily facilitate simulations of durational cycling (tens to hundreds of cycles), which are necessary to understand the long-term impact of parasitic losses (e.g., crossover, species decay). However, these approaches still rely on numerical solutions to the governing mass balances, making them computationally inefficient for multi-cycle simulations and thus challenging their use in broader parametric studies. By instead developing analytical models, we can markedly reduce computation times and enable analyses that were previously unachievable for systematic RFB diagnostics.

In this presentation, we introduce a zero-dimensional modeling framework to describe galvanostatic cycling in redox flow cells. Using a generalized set of constitutive equations, we derive analytical solutions for mass balances, charge / discharge behavior, and performance metrics (e.g., capacity fade, energy efficiency). To illustrate their utility, we explore several representative scenarios that highlight key relationships between material properties, operating conditions, and cycling performance. Importantly, the rapid computation times associated with analytical models facilitate their integration into more complex mathematical schemes; to this end, we discuss applications of this framework in techno-economic assessments, process control and optimization, parameter estimation from experimental studies, and synthetic data generation. Ultimately, this modeling toolkit provides a platform for exploring the impact of specific cell components whose properties govern multiple processes, helping to elucidate key performance descriptors and to identify favorable materials combinations for targeted energy storage applications.


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.


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