Optimal Supervisory Control of Hybrid Vehicles

Hybrid vehicles (HV), comprising a conventional ICE-based powertrain and a secondary energy source, to be converted into mechanical power as well, represent a well-established alternative to substantially reduce both fuel consumption and tailpipe emissions of passenger cars. Several HV architectures are either being studied or already available on market, e.g. Mechanical, Electric, Hydraulic and Pneumatic Hybrid Vehicles. Among the others, Electric (HEV) and Mechanical (HSF-HV) parallel Hybrid configurations are examined throughout this Thesis. To fully exploit the HVs potential, an optimal choice of the hybrid components to be installed must be properly designed, while an effective Supervisory Control must be adopted to coordinate the way the different power sources are managed and how they interact. Real-time controllers can be derived starting from the obtained optimal benchmark results. However, the application of these powerful instruments require a simplified and yet reliable and accurate model of the hybrid vehicle system. This can be a complex task, especially when the complexity of the system grows, i.e. a HSF-HV system assessed in this Thesis. The first task of the following dissertation is to establish the optimal modeling approach for an innovative and promising mechanical hybrid vehicle architecture. It will be shown how the chosen modeling paradigm can affect the goodness and the amount of computational effort of the solution, using an optimization technique based on Dynamic Programming. The second goal concerns the control of pollutant emissions in a parallel Diesel-HEV. The emissions level obtained under real world driving conditions is substantially higher than the usual result obtained in a homologation cycle. For this reason, an on-line control strategy capable of guaranteeing the respect of the desired emissions level, while minimizing fuel consumption and avoiding excessive battery depletion is the target of the corresponding section of the Thesis.

Modeling and simulations of nanoparticles in liquid crystalline systems

The aim of this work is to investigate, using extensive Monte Carlo computer simulations, composite materials consisting of liquid crystals doped with nanoparticles. These systems are currently of great interest as they offer the possibility of tuning the properties of liquid crystals used in displays and other devices as well as providing a way of obtaining regularly organized systems of nanoparticles exploiting the molecular organization of the liquid crystal medium. Surprisingly enough, there is however a lack of fundamental knowledge on the properties and phase behavior of these hybrid materials, making the route to their application an essentially empirical one. Here we wish to contribute to the much needed rationalization of these systems studying some basic effects induced by different nanoparticles on a liquid crystal host. We investigate in particular the effects of nanoparticle shape, size and polarity as well as of their affinity to the liquid crystal solvent on the stability of the system, monitoring phase transitions, order and molecular organizations. To do this we have proposed a coarse grained approach where nanoparticles are modelled as a suitably shaped (spherical, rod and disk like) collection of spherical Lennard-Jones beads, while the mesogens are represented with Gay-Berne particles. We find that the addition of apolar nanoparticles of different shape typically lowers the nematic–isotropic transition of a non-polar nematic, with the destabilization being greater for spherical nanoparticles. For polar mesogens we have studied the effect of solvent affinity of the nanoparticles showing that aggregation takes places for low solvation values. Interestingly, if the nanoparticles are polar the aggregates contribute to stabilizing the system, compensating the shape effect. We thus find the overall effects on stability to be a delicate balance of often contrasting contributions pointing to the relevance of simulations studies for understanding these complex systems.

Kinetic modeling and experiments on gas uptake and chemical transformation of organic aerosol in the atmosphere

Aerosolpartikel beeinflussen das Klima durch Streuung und Absorption von Strahlung sowie als Nukleations-Kerne für Wolkentröpfchen und Eiskristalle. Darüber hinaus haben Aerosole einen starken Einfluss auf die Luftverschmutzung und die öffentliche Gesundheit. Gas-Partikel-Wechselwirkunge sind wichtige Prozesse, weil sie die physikalischen und chemischen Eigenschaften von Aerosolen wie Toxizität, Reaktivität, Hygroskopizität und optische Eigenschaften beeinflussen. Durch einen Mangel an experimentellen Daten und universellen Modellformalismen sind jedoch die Mechanismen und die Kinetik der Gasaufnahme und der chemischen Transformation organischer Aerosolpartikel unzureichend erfasst. Sowohl die chemische Transformation als auch die negativen gesundheitlichen Auswirkungen von toxischen und allergenen Aerosolpartikeln, wie Ruß, polyzyklische aromatische Kohlenwasserstoffe (PAK) und Proteine, sind bislang nicht gut verstanden.rn Kinetische Fluss-Modelle für Aerosoloberflächen- und Partikelbulk-Chemie wurden auf Basis des Pöschl-Rudich-Ammann-Formalismus für Gas-Partikel-Wechselwirkungen entwickelt. Zunächst wurde das kinetische Doppelschicht-Oberflächenmodell K2-SURF entwickelt, welches den Abbau von PAK auf Aerosolpartikeln in Gegenwart von Ozon, Stickstoffdioxid, Wasserdampf, Hydroxyl- und Nitrat-Radikalen beschreibt. Kompetitive Adsorption und chemische Transformation der Oberfläche führen zu einer stark nicht-linearen Abhängigkeit der Ozon-Aufnahme bezüglich Gaszusammensetzung. Unter atmosphärischen Bedingungen reicht die chemische Lebensdauer von PAK von wenigen Minuten auf Ruß, über mehrere Stunden auf organischen und anorganischen Feststoffen bis hin zu Tagen auf flüssigen Partikeln. rn Anschließend wurde das kinetische Mehrschichtenmodell KM-SUB entwickelt um die chemische Transformation organischer Aerosolpartikel zu beschreiben. KM-SUB ist in der Lage, Transportprozesse und chemische Reaktionen an der Oberfläche und im Bulk von Aerosol-partikeln explizit aufzulösen. Es erforder im Gegensatz zu früheren Modellen keine vereinfachenden Annahmen über stationäre Zustände und radiale Durchmischung. In Kombination mit Literaturdaten und neuen experimentellen Ergebnissen wurde KM-SUB eingesetzt, um die Effekte von Grenzflächen- und Bulk-Transportprozessen auf die Ozonolyse und Nitrierung von Protein-Makromolekülen, Ölsäure, und verwandten organischen Ver¬bin-dungen aufzuklären. Die in dieser Studie entwickelten kinetischen Modelle sollen als Basis für die Entwicklung eines detaillierten Mechanismus für Aerosolchemie dienen sowie für das Herleiten von vereinfachten, jedoch realistischen Parametrisierungen für großskalige globale Atmosphären- und Klima-Modelle. rn Die in dieser Studie durchgeführten Experimente und Modellrechnungen liefern Beweise für die Bildung langlebiger reaktiver Sauerstoff-Intermediate (ROI) in der heterogenen Reaktion von Ozon mit Aerosolpartikeln. Die chemische Lebensdauer dieser Zwischenformen beträgt mehr als 100 s, deutlich länger als die Oberflächen-Verweilzeit von molekularem O3 (~10-9 s). Die ROIs erklären scheinbare Diskrepanzen zwischen früheren quantenmechanischen Berechnungen und kinetischen Experimenten. Sie spielen eine Schlüsselrolle in der chemischen Transformation sowie in den negativen Gesundheitseffekten von toxischen und allergenen Feinstaubkomponenten, wie Ruß, PAK und Proteine. ROIs sind vermutlich auch an der Zersetzung von Ozon auf mineralischem Staub und an der Bildung sowie am Wachstum von sekundären organischen Aerosolen beteiligt. Darüber hinaus bilden ROIs eine Verbindung zwischen atmosphärischen und biosphärischen Mehrphasenprozessen (chemische und biologische Alterung).rn Organische Verbindungen können als amorpher Feststoff oder in einem halbfesten Zustand vorliegen, der die Geschwindigkeit von heterogenen Reaktionenen und Mehrphasenprozessen in Aerosolen beeinflusst. Strömungsrohr-Experimente zeigen, dass die Ozonaufnahme und die oxidative Alterung von amorphen Proteinen durch Bulk-Diffusion kinetisch limitiert sind. Die reaktive Gasaufnahme zeigt eine deutliche Zunahme mit zunehmender Luftfeuchte, was durch eine Verringerung der Viskosität zu erklären ist, bedingt durch einen Phasenübergang der amorphen organischen Matrix von einem glasartigen zu einem halbfesten Zustand (feuchtigkeitsinduzierter Phasenübergang). Die chemische Lebensdauer reaktiver Verbindungen in organischen Partikeln kann von Sekunden bis zu Tagen ansteigen, da die Diffusionsrate in der halbfesten Phase bei niedriger Temperatur oder geringer Luftfeuchte um Größenordnungen absinken kann. Die Ergebnisse dieser Studie zeigen wie halbfeste Phasen die Auswirkung organischeer Aerosole auf Luftqualität, Gesundheit und Klima beeinflussen können. rn

Global and regional sea level variations in the recent past and future: insight from observations and modeling

This work is focused on the analysis of sea–level change (last century), based mainly on instrumental observations. During this period, individual components of sea–level change are investigated, both at global and regional scales. Some of the geophysical processes responsible for current sea-level change such as glacial isostatic adjustments and current melting terrestrial ice sources, have been modeled and compared with observations. A new value of global mean sea level change based of tide gauges observations has been independently assessed in 1.5 mm/year, using corrections for glacial isostatic adjustment obtained with different models as a criterion for the tide gauge selection. The long wavelength spatial variability of the main components of sea–level change has been investigated by means of traditional and new spectral methods. Complex non–linear trends and abrupt sea–level variations shown by tide gauges records have been addressed applying different approaches to regional case studies. The Ensemble Empirical Mode Decomposition technique has been used to analyse tide gauges records from the Adriatic Sea to ascertain the existence of cyclic sea-level variations. An Early Warning approach have been adopted to detect tipping points in sea–level records of North East Pacific and their relationship with oceanic modes. Global sea–level projections to year 2100 have been obtained by a semi-empirical approach based on the artificial neural network method. In addition, a model-based approach has been applied to the case of the Mediterranean Sea, obtaining sea-level projection to year 2050.

Design of a semi-implantable hearing device for direct acoustic cochlear stimulation

A new hearing therapy based on direct acoustic cochlear stimulation was developed for the treatment of severe to profound mixed hearing loss. The device efficacy was validated in an initial clinical trial with four patients. This semi-implantable investigational device consists of an externally worn audio processor, a percutaneous connector, and an implantable microactuator. The actuator is placed in the mastoid bone, right behind the external auditory canal. It generates vibrations that are directly coupled to the inner ear fluids and that, therefore, bypass the external and the middle ear. The system is able to provide an equivalent sound pressure level of 125 dB over the frequency range between 125 and 8000 Hz. The hermetically sealed actuator is designed to provide maximal output power by keeping its dimensions small enough to enable implantation. A network model is used to simulate the dynamic characteristics of the actuator to adjust its transfer function to the characteristics of the middle ear. The geometry of the different actuator components is optimized using finite-element modeling.

An ONIOM Study of the Bergman Reaction: A Computationally Efficient and Accurate Method for Modeling the Enediyne Anticancer Antibiotics

The Bergman cyclization of large polycyclic enediyne systems that mimic the cores of the enediyne anticancer antibiotics was studied using the ONIOM hybrid method. Tests on small enediynes show that ONIOM can accurately match experimental data. The effect of the triggering reaction in the natural products is investigated, and we support the argument that it is strain effects that lower the cyclization barrier. The barrier for the triggered molecule is very low, leading to a reasonable half-life at biological temperatures. No evidence is found that would suggest a concerted cyclization/H-atom abstraction mechanism is necessary for DNA cleavage.

Modeling multilevel sleep transitional data via Poisson log-linear multilevel models

This paper proposes Poisson log-linear multilevel models to investigate population variability in sleep state transition rates. We specifically propose a Bayesian Poisson regression model that is more flexible, scalable to larger studies, and easily fit than other attempts in the literature. We further use hierarchical random effects to account for pairings of individuals and repeated measures within those individuals, as comparing diseased to non-diseased subjects while minimizing bias is of epidemiologic importance. We estimate essentially non-parametric piecewise constant hazards and smooth them, and allow for time varying covariates and segment of the night comparisons. The Bayesian Poisson regression is justified through a re-derivation of a classical algebraic likelihood equivalence of Poisson regression with a log(time) offset and survival regression assuming piecewise constant hazards. This relationship allows us to synthesize two methods currently used to analyze sleep transition phenomena: stratified multi-state proportional hazards models and log-linear models with GEE for transition counts. An example data set from the Sleep Heart Health Study is analyzed.

Modeling of diesel particulate filter filtration and regeneration for transient driving schedules

Due to their high thermal efficiency, diesel engines have excellent fuel economy and have been widely used as a power source for many vehicles. Diesel engines emit less greenhouse gases (carbon dioxide) compared with gasoline engines. However, diesel engines emit large amounts of particulate matter (PM) which can imperil human health. The best way to reduce the particulate matter is by using the Diesel Particulate Filter (DPF) system which consists of a wall-flow monolith which can trap particulates, and the DPF can be periodically regenerated to remove the collected particulates. The estimation of the PM mass accumulated in the DPF and total pressure drop across the filter are very important in order to determine when to carry out the active regeneration for the DPF. In this project, by developing a filtration model and a pressure drop model, we can estimate the PM mass and the total pressure drop, then, these two models can be linked with a regeneration model which has been developed previously to predict when to regenerate the filter. There results of this project were: 1 Reproduce a filtration model and simulate the processes of filtration. By studying the deep bed filtration and cake filtration, stages and quantity of mass accumulated in the DPF can be estimated. It was found that the filtration efficiency increases faster during the deep-bed filtration than that during the cake filtration. A “unit collector” theory was used in our filtration model which can explain the mechanism of the filtration very well. 2 Perform a parametric study on the pressure drop model for changes in engine exhaust flow rate, deposit layer thickness, and inlet temperature. It was found that there are five primary variables impacting the pressure drop in the DPF which are temperature gradient along the channel, deposit layer thickness, deposit layer permeability, wall thickness, and wall permeability. 3 Link the filtration model and the pressure drop model with the regeneration model to determine the time to carry out the regeneration of the DPF. It was found that the regeneration should be initiated when the cake layer is at a certain thickness, since a cake layer with either too big or too small an amount of particulates will need more thermal energy to reach a higher regeneration efficiency. 4 Formulate diesel particulate trap regeneration strategies for real world driving conditions to find out the best desirable conditions for DPF regeneration. It was found that the regeneration should be initiated when the vehicle’s speed is high and during which there should not be any stops from the vehicle. Moreover, the regeneration duration is about 120 seconds and the inlet temperature for the regeneration is 710K.

Development of Hardware-In-the-Loop Simulation System for Hybrid Electric Vehicle Study

The development of embedded control systems for a Hybrid Electric Vehicle (HEV) is a challenging task due to the multidisciplinary nature of HEV powertrain and its complex structures. Hardware-In-the-Loop (HIL) simulation provides an open and convenient environment for the modeling, prototyping, testing and analyzing HEV control systems. This thesis focuses on the development of such a HIL system for the hybrid electric vehicle study. The hardware architecture of the HIL system, including dSPACE eDrive HIL simulator, MicroAutoBox II and MotoTron Engine Control Module (ECM), is introduced. Software used in the system includes dSPACE Real-Time Interface (RTI) blockset, Automotive Simulation Models (ASM), Matlab/Simulink/Stateflow, Real-time Workshop, ControlDesk Next Generation, ModelDesk and MotoHawk/MotoTune. A case study of the development of control systems for a single shaft parallel hybrid electric vehicle is presented to summarize the functionality of this HIL system.

Transformer modeling ATP : internal faults & high-frequency discretization

Power transformers are key components of the power grid and are also one of the most subjected to a variety of power system transients. The failure of a large transformer can cause severe monetary losses to a utility, thus adequate protection schemes are of great importance to avoid transformer damage and maximize the continuity of service. Computer modeling can be used as an efficient tool to improve the reliability of a transformer protective relay application. Unfortunately, transformer models presently available in commercial software lack completeness in the representation of several aspects such as internal winding faults, which is a common cause of transformer failure. It is also important to adequately represent the transformer at frequencies higher than the power frequency for a more accurate simulation of switching transients since these are a well known cause for the unwanted tripping of protective relays. This work develops new capabilities for the Hybrid Transformer Model (XFMR) implemented in ATPDraw to allow the representation of internal winding faults and slow-front transients up to 10 kHz. The new model can be developed using any of two sources of information: 1) test report data and 2) design data. When only test-report data is available, a higher-order leakage inductance matrix is created from standard measurements. If design information is available, a Finite Element Model is created to calculate the leakage parameters for the higher-order model. An analytical model is also implemented as an alternative to FEM modeling. Measurements on 15-kVA 240?/208Y V and 500-kVA 11430Y/235Y V distribution transformers were performed to validate the model. A transformer model that is valid for simulations for frequencies above the power frequency was developed after continuing the division of windings into multiple sections and including a higher-order capacitance matrix. Frequency-scan laboratory measurements were used to benchmark the simulations. Finally, a stability analysis of the higher-order model was made by analyzing the trapezoidal rule for numerical integration as used in ATP. Numerical damping was also added to suppress oscillations locally when discontinuities occurred in the solution. A maximum error magnitude of 7.84% was encountered in the simulated currents for different turn-to-ground and turn-to-turn faults. The FEM approach provided the most accurate means to determine the leakage parameters for the ATP model. The higher-order model was found to reproduce the short-circuit impedance acceptably up to about 10 kHz and the behavior at the first anti-resonant frequency was better matched with the measurements.

DEVELOPMENT OF THE ECOCAR 3 PROPOSAL AND GUIDELINES FOR MODELING AND DESIGN IN YEAR ONE OF ECOCAR 3

This report summarizes the work done for the Vehicle Powertrain Modeling and Design Problem Proposal portion of the EcoCAR3 proposal as specified in the Request for Proposal from Argonne National Laboratory. The results of the modeling exercises presented in the proposal showed that: An average conventional vehicle powered by a combustion engine could not meet the energy consumption target when the engine was sized to meet the acceleration target, due the relatively low thermal efficiency of the spark ignition engine. A battery electric vehicle could not meet the required range target of 320 km while keeping the vehicle weight below the gross vehicle weight rating of 2000 kg. This was due to the low energy density of the batteries which necessitated a large, and heavy, battery pack to provide enough energy to meet the range target. A series hybrid electric vehicle has the potential to meet the acceleration and energy consumption parameters when the components are optimally sized. A parallel hybrid electric vehicle has less energy conversion losses than a series hybrid electric vehicle which results in greater overall efficiency, lower energy consumption, and less emissions. For EcoCAR3, Michigan Tech proposes to develop a plug-in parallel hybrid vehicle (PPHEV) powered by a small Diesel engine operating on B20 Bio-Diesel fuel. This architecture was chosen over other options due to its compact design, lower cost, and its ability to provide performance levels and energy efficiency that meet or exceed the design targets. While this powertrain configuration requires a more complex control system and strategy than others, the student engineering team at Michigan Tech has significant recent experience with this architecture and has confidence that it will perform well in the events planned for the EcoCAR3 competition.

Analytical Parametric Model Used to Study the Influence of Electrostatic Force on Surface Coverage During Electrospinning of Polymer Fibers

Electrospinning (ES) can readily produce polymer fibers with cross-sectional dimensions ranging from tens of nanometers to tens of microns. Qualitative estimates of surface area coverage are rather intuitive. However, quantitative analytical and numerical methods for predicting surface coverage during ES have not been covered in sufficient depth to be applied in the design of novel materials, surfaces, and devices from ES fibers. This article presents a modeling approach to ES surface coverage where an analytical model is derived for use in quantitative prediction of surface coverage of ES fibers. The analytical model is used to predict the diameter of circular deposition areas of constant field strength and constant electrostatic force. Experimental results of polyvinyl alcohol fibers are reported and compared to numerical models to supplement the analytical model derived. The analytical model provides scientists and engineers a method for estimating surface area coverage. Both applied voltage and capillary-to-collection-plate separation are treated as independent variables for the analysis. The electric field produced by the ES process was modeled using COMSOL Multiphysics software to determine a correlation between the applied field strength and the size of the deposition area of the ES fibers. MATLAB scripts were utilized to combine the numerical COMSOL results with derived analytical equations. Experimental results reinforce the parametric trends produced via modeling and lend credibility to the use of modeling techniques for the qualitative prediction of surface area coverage from ES. (Copyright: 2014 American Vacuum Society.)

From transient to steady state deformation and grain size: A thermodynamic approach using elasto-visco-plastic numerical modeling

Numerical simulation experiments give insight into the evolving energy partitioning during high-strain torsion experiments of calcite. Our numerical experiments are designed to derive a generic macroscopic grain size sensitive flow law capable of describing the full evolution from the transient regime to steady state. The transient regime is crucial for understanding the importance of micro structural processes that may lead to strain localization phenomena in deforming materials. This is particularly important in geological and geodynamic applications where the phenomenon of strain localization happens outside the time frame that can be observed under controlled laboratory conditions. Ourmethod is based on an extension of the paleowattmeter approach to the transient regime. We add an empirical hardening law using the Ramberg-Osgood approximation and assess the experiments by an evolution test function of stored over dissipated energy (lambda factor). Parameter studies of, strain hardening, dislocation creep parameter, strain rates, temperature, and lambda factor as well asmesh sensitivity are presented to explore the sensitivity of the newly derived transient/steady state flow law. Our analysis can be seen as one of the first steps in a hybrid computational-laboratory-field modeling workflow. The analysis could be improved through independent verifications by thermographic analysis in physical laboratory experiments to independently assess lambda factor evolution under laboratory conditions.