Understanding Capacity Fade in Organic Flow Batteries by Combining Experiments with Modeling and Uncertainty Quantification

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Redox-flow batteries (RFBs) are among the most promising future technologies for cheaply storing grid-scale electricity from intermittently available renewable (e.g. solar and wind) power. Hence, they are important for reducing dependence on energy from fossil fuels. These batteries require liquid electrolytes containing electro-active species. Organic molecules are attractive candidates as electro-active species because they may allow the overall cost of RFBs to be less than conventional (e.g. Li-ion) batteries. However, most laboratory-scale organic flow cells exhibit high rates of capacity loss over time during cycling, which render them unsuitable for commercial use. The project will advance knowledge of the origins of these capacity losses by combining experimental measurements with modeling and statistical methods. Also, in collaboration with the University of Michigan’s Museum of Natural History, the investigators will construct a research station highlighting the role of battery-based energy storage in decarbonizing the electric grid. Understanding capacity losses in organic flow cells is challenging, because candidate electrolyte reactants encompass a large range of molecular classes and are therefore susceptible to a wide variety of conversion and loss mechanisms that may mutually interact and/or operate on overlapping timescales. This project focuses on addressing this combinatorial problem via modeling and Bayesian statistical learning, which is capable of discerning how multiple, dynamically interacting inputs compose a given experimental output. The Bayesian statistical framework offers a rigorous mathematical characterization of noise and uncertainty, and therefore provides a pathway to weighing the probabilities of various hypothesized capacity loss mechanisms in light of cycling data and guiding future experimental designs that produce the most useful data. By analyzing organic flow cell performance on this basis and together with a physics-based electrochemical model of the cell, capacity loss will be described in terms of chemical and electrochemical mechanisms related to parameters such as the electrolyte acidity, the state of charge of the cell, and reactant concentration. The synergistic use of experiment, modeling, and data-informed statistical learning complements more traditional techniques and will inform synthetic chemical and electrochemical strategies for capacity loss mitigation. The research efforts will also lead to an improved understanding of RFB performance tradeoffs under realistic operating conditions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.