Estimating Parameters for a Doyle Fuller Newman Model of a Graphite Half Cell Battery

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Chipman, Gregory Dan
Fuller, Thomas F.
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Electrochemical research involves the modeling of electrochemical systems using various types of models. Models use adjustable parameters to be able to fit experimental data. Parameters of physics-based models are actual properties of the electrochemical system; and, if determined accurately, can reveal more about the inner physics of the system. Further physics-based models can be extrapolated with more confidence to other experimental conditions. Because insights obtained and ability to extrapolate from a physics-based model is based on the accuracy of the parameters obtained, the main objective of adjusting the parameters to fit the experimental data should be finding the most accurate parameter set, not the best fitting parameter set. Because of the complexity of physics-based models, adjusting parameters to fit experimental data without forethought and estimates may lead to inaccurate parameter sets. This thesis focused on laying out a procedure for estimating parameters for a physics-based model to increase the probability of obtaining an accurate parameter set for the electrochemical system. As an example, parameters were obtained for a Doyle Fuller Newman model for a graphite vs. lithium coin cell battery. These estimates were obtained from scanning electron microscopy images, the galvanostatic intermittent titration technique, and electrochemical impedance spectroscopy. These estimates were put into a Doyle Fuller Newman model in gPROMs and the simulation output was compared to experimental discharge data. These estimates can be used as a starting point for fitting the model to experimental data to find a final set of parameters for the model.
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