<p>This thesis aims at formulating and validating models for electrochemical energy storage devices. More specifically, the devices under consideration are lithium ion batteries and polymer electrolyte fuel cells.</p><p>A model is formulated to describe an experimental cell setup consisting of a Li_xNi_0.8Co_0.15Al_0.05O₂ composite porous electrode with three porous separators and a reference electrode between a current collector and a pure Li planar electrode. The purpose of the study being the identification of possible degradation mechanisms in the cell, the model contains contact resistances between the electronic conductor and the intercalation particles of the porous electrode and between the current collector and the porous electrode. On the basis of this model formulation, an analytical solution is derived for the impedances between each pair of electrodes in the cell. The impedance formulation is used to analyse experimental data obtained for fresh and aged Li_xNi_0.8Co_0.15Al_0.05O₂ composite porous electrodes. Ageing scenarios are formulated based on experimental observations and related published electrochemical and material characterisation studies. A hybrid genetic optimisation technique is used to simultaneously fit the model to the impedance spectra of the fresh, and subsequently also to the aged, electrode at three states of charge. The parameter fitting results in good representations of the experimental impedance spectra by the fitted ones, with the fitted parameter values comparing well to literature values and supporting the assumed ageing scenario.</p><p>Furthermore, a steady state model for a polymer electrolyte fuel cell is studied under idealised conditions. The cell is assumed to be fed with reactant gases at sufficiently high stoichiometric rates to ensure uniform conditions everywhere in the flow fields such that only the physical phenomena in the porous backings, the porous electrodes and the polymer electrolyte membrane need to be considered. Emphasis is put on how spatially resolved porous electrodes and nonequilibrium water transport across the interface between the gas phase and the ionic conductor affect the model results for the performance of the cell. The future use of the model in higher dimensions and necessary steps towards its validation are briefly discussed.</p>

In recent years, uranium pollution in the environment has been recognized as a serious threat, and novel in situ microbial bioremediation strategies have been incorporated into field-scale contaminated sites. The Oak Ridge Integrated Field-scale Subsurface Research Challenge (IFC) site is one of the largest uranium contaminated areas in the United States, and a literature review has revealed the potential of uranium reduction by dominant Anaeromyxobacter dehalogenans species that respire during bioremediation. A genome-scale model of A. dehalogenans, a unique microbe with diverse metabolic capabilities that thrives in the natural environment, has been developed, and applied to an in silico field-scale computational setting for evaluation of the biotic uranium reduction in the Oak Ridge IFC site. The metabolic model of A. dehalogenans was integrated into an expanded microbial community framework for the prediction of chemical profiles, and subsequent scenario evaluation of in situ measured data.

In recent years, uranium pollution in the environment has been recognized as a serious threat, and novel in situ microbial bioremediation strategies have been incorporated into field-scale contaminated sites. The Oak Ridge Integrated Field-scale Subsurface Research Challenge (IFC) site is one of the largest uranium contaminated areas in the United States, and a literature review has revealed the potential of uranium reduction by dominant Anaeromyxobacter dehalogenans species that respire during bioremediation. A genome-scale model of A. dehalogenans, a unique microbe with diverse metabolic capabilities that thrives in the natural environment, has been developed, and applied to an in silico field-scale computational setting for evaluation of the biotic uranium reduction in the Oak Ridge IFC site. The metabolic model of A. dehalogenans was integrated into an expanded microbial community framework for the prediction of chemical profiles, and subsequent scenario evaluation of in situ measured data.

This thesis aims at formulating and validating models for electrochemical energy storage devices. More specifically, the devices under consideration are lithium ion batteries and polymer electrolyte fuel cells. A model is formulated to describe an experimental cell setup consisting of a LixNi0.8Co0.15Al0.05O2 composite porous electrode with three porous separators and a reference electrode between a current collector and a pure Li planar electrode. The purpose of the study being the identification of possible degradation mechanisms in the cell, the model contains contact resistances between the electronic conductor and the intercalation particles of the porous electrode and between the current collector and the porous electrode. On the basis of this model formulation, an analytical solution is derived for the impedances between each pair of electrodes in the cell. The impedance formulation is used to analyse experimental data obtained for fresh and aged LixNi0.8Co0.15Al0.05O2 composite porous electrodes. Ageing scenarios are formulated based on experimental observations and related published electrochemical and material characterisation studies. A hybrid genetic optimisation technique is used to simultaneously fit the model to the impedance spectra of the fresh, and subsequently also to the aged, electrode at three states of charge. The parameter fitting results in good representations of the experimental impedance spectra by the fitted ones, with the fitted parameter values comparing well to literature values and supporting the assumed ageing scenario. Furthermore, a steady state model for a polymer electrolyte fuel cell is studied under idealised conditions. The cell is assumed to be fed with reactant gases at sufficiently high stoichiometric rates to ensure uniform conditions everywhere in the flow fields such that only the physical phenomena in the porous backings, the porous electrodes and the polymer electrolyte membrane need to be considered. Emphasis is put on how spatially resolved porous electrodes and nonequilibrium water transport across the interface between the gas phase and the ionic conductor affect the model results for the performance of the cell. The future use of the model in higher dimensions and necessary steps towards its validation are briefly discussed.

Ce mémoire est consacré à la modélisation des condensateurs électrolytiques aluminium dédiés à des applications à hautes températures ainsi qu’à la compréhension de leur vieillissement lors d’utilisations réalistes. En effet, dans le cas d’onduleur de traction de véhicule électrique, les sollicitations, notamment en température, peuvent être parfois très variables. Or, il se trouve que pour ce type d’applications, ces derniers sont la plupart du temps de type électrolytiques aluminium, technologie étant parmi les plus fragiles. Par conséquent, ce manuscrit propose tout d’abord une nouvelle modélisation électrique s’appuyant des phénomènes de diffusion permettant d’obtenir une représentation très précise de l’impédance de ces condensateurs. Compte-tenu de leur forte dépendance en température, la modélisation thermique couplée au modèle électrique est également traitée. Le but premier est de développer un outil permettant d’estimer précisément les pertes à chaque instant pour permettre au contrôleur d’ajuster la température de ce dernier par une modification de la stratégie MLI. Une méthode d’identification en ligne est alors proposée par l’utilisation de filtres de Kalman conjoints avec de très bons résultats obtenus en simulation. Le dimensionnement ainsi que la création d’un banc de cyclage accéléré est développé et une comparaison du vieillissement obtenu après 12 000 heures entre des composants cyclés thermiquement et d’autres non cyclés est donnée. Les résultats montrent une très bonne tenue dans le temps des condensateurs étudiés que ce soit au niveau de l’impédance ou bien visuellement avec néanmoins un impact du cyclage thermique non négligeable / This thesis is devoted to the modeling of aluminum electrolytic capacitors dedicatedto high temperatures. The purpose is also to understand their ageing while submitted to realistic use. Indeed, in the case of electric vehicle traction inverter, solicitations like temperature can vary a lot. This type of stress has already been studied for active components, but not yet on passive ones such as decoupling capacitors. However, it turns out that for this kind of application, they are most of the time aluminum electrolytic capacitors which is among the weakest technology. Consequently, this manuscript proposes at first a new electric model based on a diffusion phenomena which leads to a very accurate impedance description. It permits also a better understanding of the physical phenomena involved in these components. Because of their important temperature dependence, thermal modeling coupled to the electric model is also discussed. The very first purpose is to develop a tool that is able to estimate losses accurately at every moment. Thanks to it, the controller could so change the PWM strategy in order to act on the temperature. An online identification method is then proposed with the use of joint Kalman filters which led to very good results in simulation. The design and the creation of an accelerated cycling bench is developed and comparisons about the ageing obtained after 12 000 hours between thermally cycled components and others non-cycled are given. Results show a very good stability over time of the studied capacitors (PEG225MF470Q Kemet©) either on the impedance or visually. Nevertheless a significant impact can be observed on the cycled ones.

Perceptual decision making can be described as a process of accumulating evidence to a bound which has been formalized within drift-diffusion models (DDMs). Recently, an equivalent Bayesian model has been proposed. In contrast to standard DDMs, this Bayesian model directly links information in the stimulus to the decision process. Here, we extend this Bayesian model further and allow inter-trial variability of two parameters following the extended version of the DDM. We derive parameter distributions for the Bayesian model and show that they lead to predictions that are qualitatively equivalent to those made by the extended drift-diffusion model (eDDM). Further, we demonstrate the usefulness of the extended Bayesian model (eBM) for the analysis of concrete behavioral data. Specifically, using Bayesian model selection, we find evidence that including additional inter-trial parameter variability provides for a better model, when the model is constrained by trial-wise stimulus features. This result is remarkable because it was derived using just 200 trials per condition, which is typically thought to be insufficient for identifying variability parameters in DDMs. In sum, we present a Bayesian analysis, which provides for a novel and promising analysis of perceptual decision making experiments.

Perceptual decision making can be described as a process of accumulating evidence to a bound which has been formalized within drift-diffusion models (DDMs). Recently, an equivalent Bayesian model has been proposed. In contrast to standard DDMs, this Bayesian model directly links information in the stimulus to the decision process. Here, we extend this Bayesian model further and allow inter-trial variability of two parameters following the extended version of the DDM. We derive parameter distributions for the Bayesian model and show that they lead to predictions that are qualitatively equivalent to those made by the extended drift-diffusion model (eDDM). Further, we demonstrate the usefulness of the extended Bayesian model (eBM) for the analysis of concrete behavioral data. Specifically, using Bayesian model selection, we find evidence that including additional inter-trial parameter variability provides for a better model, when the model is constrained by trial-wise stimulus features. This result is remarkable because it was derived using just 200 trials per condition, which is typically thought to be insufficient for identifying variability parameters in DDMs. In sum, we present a Bayesian analysis, which provides for a novel and promising analysis of perceptual decision making experiments.