Tire performance over soft soil influences the performance of off-road vehicles on soft soil, as the tire is the only force transmitting element between the off-road vehicles and soil during the vehicle operation. One aspect of the tire performance over soft soil is the tire tractive performance on soft soil, and it attracts the attention of vehicle and geotechnical engineers. The vehicle engineer is interested in the tire tractive performance on soft soil because it is related to vehicle mobility and energy efficiency; the geotechnical engineer is concerned about the soil compaction, brought about by the tire traffic, which accompanies the tire tractive performance on soft soil. In order to improve the vehicle mobility and energy efficiency over soft soil and mitigate the soil compaction, it's essential to develop an in-depth understanding of tire tractive performance on soft soil. This study has enhanced the understanding of tire tractive performance on soft soil and promoted the development of terramechanics and tire model parameterization method through experimental tests. The experimental tests consisted of static tire deflection tests, static tire-soil tests, soil properties tests, and dynamic tire-soil tests. The series of tests (test program) presented herein produced parameterization and validation data that can be used in tire off-road traction dynamics modeling and terramechanics modeling. The 225/60R16 97S Uniroyal (Michelin) Standard Reference Test Tire (SRTT) and loamy sand were chosen to be studied in the test program. The tests included the quantification or/and measurement of soil properties of the test soil, pre-traffic soil condition, the pressure distribution in the tire contact patch, tire off-road tractive performance, and post-traffic soil compaction. The influence of operational parameters, e.g., tire inflation pressure, tire normal load, tire slip ratio, initial soil compaction, or the number of passes, on the measurement data of tire performance parameters or soil response parameters was also analyzed. New methods of the rolling radius estimation for a tire on soft soil and of the 3-D rut reconstruction were developed. A multi-pass effect phenomenon, different from any previously observed phenomenon in the available existing literature, was discovered. The test data was fed into optimization programs for the parameterization of the Bekker's model, a modified Bekker's model, the Magic Formula tire model, and a bulk density estimation model. The modified Bekker's model accounts for the slip sinkage effect which the original Bekker's pressure-sinkage model doesn't. The Magic Formula tire model was adapted to account for the combined influence of tire inflation pressure and initial soil compaction on the tire tractive performance and validated by the test data. The parameterization methods presented herein are new effective terramechanics model parameterization methods, can capture tire-soil interaction which the conventional parameterization methods such as the plate-sinkage test and shear test (not using a tire as the shear tool) cannot sufficiently, and hence can be used to develop tire off-road dynamics models that are heavily based on terramechanics models. This study has been partially supported by the U.S. Army Engineer Research and Development Center (ERDC) and by the Terramechanics, Multibody, and Vehicle (TMVS) Laboratory at Virginia Tech. / Doctor of Philosophy / Big differences exist between a tire moving in on-road conditions, such as asphalt lanes, and a tire moving in off-road conditions, such as soft soil. For example, for passenger cars commonly driven on asphalt lanes, normally, the tire inflation pressure is suggested to be between 30 and 35 psi; very low inflation pressure is also not suggested. By contrast, for off-road vehicles operated on soft soil, low inflation pressure is recommended for their tires; the inflation pressure of a tractor tire can be as low as 12 psi, for the sake of low post-traffic soil compaction and better tire traction. Besides, unlike the research on tire on-road dynamics, the research on off-road dynamics is still immature, while the physics behind the off-road dynamics could be more complex than the on-road dynamics. In this dissertation, experimental tests were completed to study the factors influencing tire tractive performance and soil behavior, and model parameterization methods were developed for a better prediction of tire off-road dynamics models. Tire or vehicle manufacturers can use the research results or methods presented in this dissertation to offer suggestions for the tire or vehicle operation on soft soil in order to maximize the tractive performance and minimize the post-traffic soil compaction.

Les prairies représentent 45% de la surface agricole en France et 40% en Europe, ce qui montre qu’il s’agit d’un secteur important particulièrement dans un contexte de changement climatique où les prairies contribuent d’un côté aux émissions de gaz à effet de serre et en sont impactées de l’autre côté. L’enjeu de cette thèse a été de contribuer à l’évaluation des incertitudes dans les sorties de modèles de simulation de prairies (et utilisés dans les études d’impact aux changements climatiques) dépendant du paramétrage du modèle. Nous avons fait appel aux méthodes de la statistique Bayésienne, basées sur le théorème de Bayes, afin de calibrer les paramètres d’un modèle référent et améliorer ainsi ses résultats en réduisant l’incertitude liée à ses paramètres et, par conséquent, à ses sorties. Notre démarche s’est basée essentiellement sur l’utilisation du modèle d’écosystème prairial PaSim, déjà utilisé dans plusieurs projets européens pour simuler l’impact des changements climatiques sur les prairies. L’originalité de notre travail de thèse a été d’adapter la méthode Bayésienne à un modèle d’écosystème complexe comme PaSim (appliqué dans un contexte de climat altéré et à l’échelle du territoire européen) et de montrer ses avantages potentiels dans la réduction d’incertitudes et l’amélioration des résultats, en combinant notamment méthodes statistiques (technique Bayésienne et analyse de sensibilité avec la méthode de Morris) et outils informatiques (couplage code R-PaSim et utilisation d’un cluster de calcul). Cela nous a conduit à produire d’abord un nouveau paramétrage pour des sites prairiaux soumis à des conditions de sécheresse, et ensuite à un paramétrage commun pour les prairies européennes. Nous avons également fourni un outil informatique de calibration générique pouvant être réutilisé avec d’autres modèles et sur d’autres sites. Enfin, nous avons évalué la performance du modèle calibré par le biais de la technique Bayésienne sur des sites de validation, et dont les résultats ont confirmé l’efficacité de cette technique pour la réduction d’incertitude et l’amélioration de la fiabilité des sorties. / Grasslands cover 45% of the agricultural area in France and 40% in Europe. Grassland ecosystems have a central role in the climate change context, not only because they are impacted by climate changes but also because grasslands contribute to greenhouse gas emissions. The aim of this thesis was to contribute to the assessment of uncertainties in the outputs of grassland simulation models, which are used in impact studies, with focus on model parameterization. In particular, we used the Bayesian statistical method, based on Bayes’ theorem, to calibrate the parameters of a reference model, and thus improve performance by reducing the uncertainty in the parameters and, consequently, in the outputs provided by models. Our approach is essentially based on the use of the grassland ecosystem model PaSim (Pasture Simulation model) already applied in a variety of international projects to simulate the impact of climate changes on grassland systems. The originality of this thesis was to adapt the Bayesian method to a complex ecosystem model such as PaSim (applied in the context of altered climate and across the European territory) and show its potential benefits in reducing uncertainty and improving the quality of model outputs. This was obtained by combining statistical methods (Bayesian techniques and sensitivity analysis with the method of Morris) and computing tools (R code -PaSim coupling and use of cluster computing resources). We have first produced a new parameterization for grassland sites under drought conditions, and then a common parameterization for European grasslands. We have also provided a generic software tool for calibration for reuse with other models and sites. Finally, we have evaluated the performance of the calibrated model through the Bayesian technique against data from validation sites. The results have confirmed the efficiency of this technique for reducing uncertainty and improving the reliability of simulation outputs.

The use of numerical weather prediction (NWP) has brought significant improvements to severe weather outbreak forecasting; however, determination of the primary mode of severe weather (in particular tornadic and nontornadic outbreaks) continues to be a challenge. Uncertainty in model runs contributes to forecasting difficulty; therefore it is beneficial to a forecaster to understand the sources and magnitude of uncertainty in a severe weather forecast. This research examines the impact of data assimilation, microphysics parameterizations, and planetary boundary layer (PBL) physics parameterizations on severe weather forecast accuracy and model variability, both at a mesoscale and synoptic-scale level. NWP model simulations of twenty United States tornadic and twenty nontornadic outbreaks are generated. In the first research phase, each case is modeled with three different modes of data assimilation and a control. In the second phase, each event is modeled with 15 combinations of physics parameterizations: five microphysics and three PBL, all of which were designed to perform well in convective weather situations. A learning machine technique known as a support vector machine (SVM) is used to predict outbreak mode for each run for both the data assimilated model simulations and the different parameterization simulations. Parameters determined to be significant for outbreak discrimination are extracted from the model simulations and input to the SVM, which issues a diagnosis of outbreak type (tornadic or nontornadic) for each model run. In the third phase, standard synoptic parameters are extracted from the model simulations and a k-means cluster analysis is performed on tornadic and nontornadic outbreak data sets to generate synoptically distinct clusters representing atmospheric conditions found in each type of outbreak. Variations among the synoptic features in each cluster are examined across the varied physics parameterization and data assimilation runs. Phase I found that conventional and HIRS-4 radiance assimilation performs best of all examined assimilation variations by lowering false alarm ratios relative to other runs. Phase II found that the selection of PBL physics produces greater spread in the SVM classification ability. Phase III found that data assimilation generates greater model changes in the strength of synoptic-scale features than either microphysics or PBL physics parameterization.

An efficient application and development of renewable energy sources is one of the most important contribution to the energetic balance of the human society. Anyhow, statistical model of the renewable energy market, which would fundamentally explain relevant economical rules related to these perspective energetic resources, is not clearly known up to now. Nevertheless, the relevant statistical data concerning application of solar energy (photovoltaic and thermo-solar heating) are available for the last twenty years. Based on the economic models, statistical data concerning sales of photovoltaic models and thermo-solar collectors sales have been analysed in this work. It has been shown that the model of constant elasticity predicts an exponential increase which will slow down when a certain level of annual cumulative sales was reached. The model of constant elasticity was found to be successful to interpret past sales data. In the approach of variable elasticity model the parameter of the elasticity has been modified as a function of variables such as market volume, price and time through the statistical evaluation. It enabled to calculate initial, saturation and competitive market conditions, as well. Whereas the constant elasticity demand model describes exponential growth of sales and installations, which was characteristic for the beginning of the application of these renewable resources of energy, the variable elasticity demand model describes a more realistic situation, where cumulative sales either increase or decrease and prices vary subsequently. Simple growth model of unlimited demand based on the growing sales is not realistic and could not be feasible in the long term. The market elasticity could be understood as a real economical parameter representing percentual market increase or decrease at a given time; in the variable demand elasticity model, the constant elasticity is replaced by a function of a market volume, price and time. In this case, we can estimate model parameters for the different market conditions: growth, saturation and decrease. The function representing the capital adequacy in the generalized market model has also been deliberated. Statistical models have been used to determine cumulative sales and market prices of photovoltaic modules and thermo-solar collectors. Moreover, model parameters have been used for the calculation of the realized photovoltaic and thermo solar projects' capital adequacy on the renewable energy market. By using model parameters, renewable energy market forecast up to 2020 has been estimated. We have used generalized market model to credibly estimate future renewable energy market until 2020; as well as extend model parameterization on other resources of renewable energy (water and wind, geothermal sources, biomass) and set prices of energy produced from these renewable sources. Potential energetic savings have been estimated for households (apartments and private houses), who can be relevant consumers of energy from renewable sources. We have performed statistical findings on randomly selected files, where we have reached a real energy consumption, to prove this. This research allowed us to perform a real estimate of a renewable energy contribution to the total energy balance. We have successfully proved that linearly growing capital adequacy function, with an annual growth between 2.5% and 3.0%, is reflecting the renewable energy market sufficiently and is fully in line with an average growth of the total energy consumption. Renewable energy share on the total energy balance will grow substantially to reach a level of 15% in 2015 on the world market and a level of 8% in the Czech Republic for the same period with a perspective to reach a level of 11% in 2020 respectively. Assuming this level of renewable energy on the total production will lead to a decrease of CO2 emissions by three million of tones in 2015 and by four million of tones in 2020. Final reach of this status quo is fully predicted by our statistical model for renewable energy market.

Lithium-ion (Li-Ion) batteries are widely used in various applications and are viable for automotive applications. The effective management of Li-Ion batteries in battery electric vehicles (BEV) plays a crucial role in performance and range. One can achieve good performance and range by using efficient battery models in battery management systems (BMS). Hence, these battery models play an essential part in the development process of battery electric vehicles. Physics-based battery models are used for design purposes, control, or to predict battery behaviour, and these require much information about materials and reaction and mass transport properties. Model parameterization, i.e., obtaining model parameters from different experimental sets (by fitting the model to experimental data sets), can be challenging depending on model complexity and the type and quality of experimental data. Based on the idea of parameter sensitivity, certain current/voltage data sets could be chosen that theoretically has a more considerable sensitivity for a given model parameter that is of interest to extract. In this thesis work, different methods for extracting model parameters for a Nickel-Manganese-Cobalt (NMC) battery composite electrode are experimentally tested and compared. Specifically, model parameterization using \emph{optimal experiments} based on performed parameter sensitivity analysis has been benchmarked against a 1C discharge test and low rate pulse tests. The different parameter sets obtained have then been validated on a drive cycle and 2C pulse tests. Comparing the methods show some promising results for the optimal experiment design (OED) method, but consideration regarding the state of charge (SOC) dependencies, the number of parameters has to be further evaluated. / Litiumjonbatterier (Li-jon) används i olika applikationer och är ett bra alternativ förfordonsapplikationer. Den effektiva hanteringen av litiumjonbatterier i elbilar har en viktigroll för fordonens prestanda och räckvidd. Man kan nå bra prestanda och räckviddgenom att använda bra batterimodeller i batteriets övervakningssystem (BMS). Därförspelar dessa batterimodeller en viktig roll i utvecklingen av elbilar. Fysikbaseradebatterimodeller används för design, reglering eller för att prediktera beteendet hos batteriet,vilket kräver mycket information om material samt dess reaktion och andra beskaffenheter.Modellparametrisering, dvs. att införskaffa modellparametrar från olika experiment (genom attanpassa modell till experimentella data) kan vara utmanande beroende på modellkomplexitetoch typen samt kvalitén på experimentell data. Baserat på idén om parametersensitivitet kan data om ström och spänning väljas så att de teoretiskt har mer sensitivitet för engiven modellparameter som är av intresse att extrahera. I detta examensarbete testas ochjämförs olika metoder för att extrahera modellparametrar för en Nickelmangankobolt (NMC)batterielektrod. Mer specifikt, modellparametrisering genom optimala experiment baseradepå genomförd parametersesitivitetsanalys jämförts med 1C urladdningstest och låg nivåpulstest. Jämförande av metoderna visar goda resultat för OED metoden men flera parametrarmåste fortsatt utvärderas gällande laddningstatusberoenden (SOC).