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The Hybrid Electric Vehicle Team (HEVT) of Virginia Tech is participating in the EcoCAR 3 Advanced Vehicle Technology Competition series organized by Argonne National Labs (ANL), and sponsored by General Motors (GM) and the U.S. Department of Energy (DOE). EcoCAR 3 is a 4-year collegiate competition that challenges student with redesigning a 2016 Chevrolet Camaro into a hybrid. The five main goals of EcoCAR 3 are to reduce petroleum energy use (PEU) and green house gas (GHG) emissions while maintaining safety, consumer acceptability, and performance, with an increased focus on cost and innovation. HEVT selected a P3 Plug-in Parallel hybrid electric vehicle (PHEV) to meet design goals and competition requirements. This study presents different stages of the vehicle development process (VDP) followed to integrate the HEVT Camaro. This work documents the control system development process up to Year 2 of EcoCAR 3. The modeling process to select a powertrain is the first stage in this research. Several viable powertrains and the respective vehicle technical specifications (VTS) are evaluated. The P3 parallel configuration with a V8 engine is chosen because it generated the set of VTS that best meet design goals and EcoCAR 3 requirements. The V8 engine also preserves the heritage of the Camaro, which is attractive to the established target market. In addition, E85 is chosen as the fuel for the powertrain because of the increased impact it has on GHG emissions compared to E10 and gasoline. The use of advanced methods and techniques like model based design (MBD), and rapid control prototyping (RCP) allow for faster development of engineering products in industry. Using advanced engineering techniques has a tremendous educational value, and these techniques can assist the development of a functional and safe hybrid control system. HEVT has developed models of the selected hybrid powertrain to test the control code developed in software. The strategy developed is a Fuzzy controller for torque management in charge depleting (CD) and charge sustaining (CS) modes. The developed strategy proves to be functional without having a negative impact of the energy consumption characteristics of the hybrid powertrain. Bench testing activities with the V8 engine, a low voltage (LV) motor, and high voltage (HV) battery facilitated learning about communication, safety, and functionality requirements for the three components. Finally, the process for parallel development of models and control code is presented as a way to implement more effective team dynamics. / Master of Science

Active Queue Management (AQM) schemes are used for ensuring the Quality of Service (QoS) in telecommunication networks. However, they are sensitive to parameter settings and have weaknesses in detecting and controlling congestion under dynamically changing network situations. Another drawback for the AQM algorithms is that they have been applied only on the Markovian models which are considered as Short Range Dependent (SRD) traffic models. However, traffic measurements from communication networks have shown that network traffic can exhibit self-similar as well as Long Range Dependent (LRD) properties. Therefore, it is important to design new algorithms not only to control congestion but also to have the ability to predict the onset of congestion within a network. An aim of this research is to devise some new congestion control methods for communication networks that make use of various traffic characteristics, such as LRD, which has not previously been employed in congestion control methods currently used in the Internet. A queueing model with a number of ON/OFF sources has been used and this incorporates a novel congestion prediction algorithm for AQM. The simulation results have shown that applying the algorithm can provide better performance than an equivalent system without the prediction. Modifying the algorithm by the inclusion of a sliding window mechanism has been shown to further improve the performance in terms of controlling the total number of packets within the system and improving the throughput. Also considered is the important problem of maintaining QoS constraints, such as mean delay, which is crucially important in providing satisfactory transmission of real-time services over multi-service networks like the Internet and which were not originally designed for this purpose. An algorithm has been developed to provide a control strategy that operates on a buffer which incorporates a moveable threshold. The algorithm has been developed to control the mean delay by dynamically adjusting the threshold, which, in turn, controls the effective arrival rate by randomly dropping packets. This work has been carried out using a mixture of computer simulation and analytical modelling. The performance of the new methods that have.

L'apparition de convertisseurs multicellulaires parallèles entrelacés et magnétiquement couplés a conduit ces dernières années à améliorer les performances des convertisseurs (en termes de densité de puissance, d'efficacité, de dynamique,...). Le pendant de ces améliorations successives résulte en une nécessité d'équilibrage précis des courants de phase, ce qui entraîne une complexification de la commande des ces convertisseurs. Une première étape de détermination de la fonction de transfert d'une boucle d'équilibrage des courants nous permet de déterminer la nature des correcteurs d'équilibrage de ces courants. Cette étude nous permet d'appréhender des systèmes plus complexes avec différentes topologies de couplage magnétique entre les bras du convertisseur parallèle. Suite à une étude bibliographique mettant en avant le manque de précision des techniques actuelles de mesure des courants de bras, nous proposons une technique d'émulation analogique précise de ces courants ne nécessitant qu'un seul capteur. Deux prototypes ont été réalisés et permettent de valider cette technique. Enfin, face à l'intérêt grandissant que portent les industriels pour des architectures modulaires, deux innovations permettant de s'affranchir d'un circuit spécifique de supervision sont proposées. Dans un premier temps, une technique modulaire d'équilibrage des courants est proposée et validée expérimentalement : elle permet, entre autres, une mesure différentielle précise des courants de bras. Ensuite, une méthode de génération modulaire de porteuses triangulaires auto-alignées est proposée et validée grâce à la réalisation d'une maquette de test. L'association de ces deux techniques nous permet de proposer une architecture entièrement modulaire ne nécessitant plus de circuit de commande superviseur. / Development of interleaved power converters with coupled inductors has enhanced converters performances (better power density, eciency, transient response. . .). Such improvements lead to the necessity of a precise current-sharing in the converter legs, and consequently to much more complex control strategy for those converters. First step is to determine current sharing loop transfer function in order to choose the kind of sharing corrector and calculate its parameters. State-space representation is used to consider any coupling topology. Because ux induced in coupled inductors must be controlled with accuracy, a bibliography study emphasizes the lack of precision in present current-sensing techniques. Then, a precise analogical emulation of currents in every leg, based on only one current sensor, is proposed. Two prototypes have been developed and validate this approach. Finally, because of growing interest of industrial in modular architectures, two innovations which avoid the use of central specic circuit are presented. First, a masterless and modular current sharing technique is proposed and tested : it allows a very precise dierential current measurement and regulation. Then a modular generation of self-aligned triangular carrier for interleaved converters is proposed and conrmed by test. The association of both techniques leads to a full masterless and modular approach for the control circuit of parallel converter with coupled inductors.

<p>U ovoj doktorskoj disertaciji razvijena je tehnika upravljanja za<br />pretvarač energetske elektronike pri nesimetričnim naponskim<br />prilikama u elektroenergetskom sistemu. Kao što je pokazano,<br />primenom tehnike poništavanja signala kašnjenjem moguće je<br />izdvajanje komponenti struje inverznog redosleda i njihovo<br />potpuno poništenje, što će omogućiti pouzdanu kontrolu<br />komponenti struje direktnog redosleda upotrebom klasičnih<br />tehnika upraljanja, uz adekvatno unapređenje tehnike za<br />sinhronizaciju sa vektorskim reprezentom napona. Predložena<br />je i upotreba algoritama za poboljšanje parametara kvaliteta<br />električne energije bez dodatnih pasivnih elemenata.</p> / <p>This PhD thesis presents an improved control technique for grid<br />connected converter under asymmetrical voltages at the point of<br />common coupling. As presented, using delay signal cancellation<br />technique it is possible to differentiate and completely mitigate the<br />negative sequence current, offering the possibility of reliable positive<br />sequence current control using classical control algorithms. The<br />improvements made in synchronization offered adequate<br />phase angle estimation under voltage asymmetry. Furthermore, a<br />technique for the improvement of power quality indices without<br />passive elements between the grid and</p>

In this thesis, the effect of pitch based optimisation was explored for a 80 turbine wind farm. Using a modified Jensen wake model and the Particle Swarm Optimisation (PSO) model, a pitch optimisation strategy was created for the dominant turbulence and atmospheric condition for the wind farm. As the wake model was based on the FLORIS model developed by P.M.O Gebraad et. al., the wake and power model was compared with the FLORIS model and a -0.090% difference was found. To determine the dynamic predictive capability of the wake model, measurement values across a 10 minute period for a 19 wind turbine array were used and the wake model under predicted the power production by 17.55%. Despite its poor dynamic predictive capability, the wake model was shown to accurately match the AEP production of the wind farm when compared to a CFD simulation done in FarmFlow and only gave a 3.10% over-prediction. When the optimisation model was applied with 150 iterations and particles, the AEP production of the wind farm increased by 0.1052%, proving that the pitch optimisation method works for the examined wind farm. When the iterations and particles used for the optimisation was increased to 250, the power improvement between optimised results improved by 0.1144% at a 222.5% increase in computational time, suggesting that the solution has yet to fully converge. While the solutions did not fully converge, they converged sufficiently and an increase in iterations gave diminishing results. From the results, the pitch optimisation model was found to give a significant increase in power production, especially in wake intensive wind directions. However, the dynamic predictive capabilities will have be improved upon before the control strategy can be applied to an operational wind farm.

Depuis une dizaine d’années, les constructeurs et les grands groupesdu secteur de l’automobile se sont mobilisés autour de la recherche et dudéveloppement de nouveaux prototypes de véhicules économes (moins consommateursd’énergie) et propres (moins de rejets de polluants) tels queles véhicules hybrides et tout électriques. C’est une nouvelle mutation. Ellefait profondément évoluer l’automobile, d’une architecture de propulsionthermique, devenue maîtrisée mais fortement polluante, vers une tractionélectrique ou hybride plus complexe et peu, voire pas du tout, maîtrisée ;le nombre de composants (sources d’énergie, actionneurs, contrôleurs, calculateurs,...) devient important, de nature multidisciplinaire et possédantbeaucoup de non linéarités. De plus, faute de maturité dans ce domaine, àce jour l’industrie de l’automobile ne possède pas encore les connaissancessuffisantes nécessaires à la modélisation, à la simulation et à la conceptionde ces nouveaux véhicules et plus particulièrement les dispositifs relatifs auxsources d’énergie et aux différents actionneurs de propulsion.Les travaux de cette thèse visent à donner des méthodes de conceptiond’une chaine de traction hybride et d’en gérer la gestion de l’énergie. Lathèse s’appuie sur l’exemple de la conception et la gestion de l’énergie d’unvéhicule hybride basé sur une pile à combustible et des batteries.Dans un premier temps, un méthode de dimensionnement des composantsde la chaine de traction est présentée : Elle consiste en l’étude statistique decycle de conduite générés pseudo aléatoirement représentatif de la conduiteen condition réelle de véhicule. Un générateur de cycle de conduite à été créeet est présenté, et la méthode de dimensionnement de la source primaire, iciune pile a combustible, ainsi que le source secondaire de puissance, ici desbatteries, est détaillée. Un exemple est pris pour illustrer cette méthode avecla conception d’un véhicule de type camion poubelle décrivant des cycles deconduites urbains à arrêts fréquents.Dans un second temps, la gestion de l’énergie de la chaine de traction hybridesérie est étudiée : une gestion de l’énergie “offline” est présentée, basé surl’optimisation par programmation dynamique. Cette optimisation permetd’avoir le découpage de la puissance par les deux sources de la chaine detraction de manière optimal pour un cycle précis. De part l’aspect déterministede la programmation dynamique, les résultats servent de référence quant aufuturs développements de gestion temps réel.Un contrôleur temps réel basé sur la logique floue est ainsi exposé et lesrésultats sont comparés par rapport à la gestion “offline”. Le contrôleurest ensuite optimisé et rendu adaptatif par un algorithme génétique et unalgorithme de reconnaissance de type de profil routier.Enfin, une introduction à la gestion de l’énergie dans les véhicules hybrides de type : “plug in” est présentée : Elle repose sur le principe de la déterminationde la distance restante à parcourir par la reconnaissance de la destination àl’aide d’une matrice de probabilité de Markov. / Hybrid electric vehicle have known a quickly grow in the last 10 years.Between conventional vehicles which are criticized for their CO2 emissionand electric vehicles which have a big issue about autonomy, hybrid electricones seems to be a good trade of. No standard has been set yet, and the architecturesresulting of theses productions vary between brands. Nevertheless,all of them are design as a thermal vehicle with battery added which leadsto bad sizing of the component, specially internal combustion engine andbattery capacity. Consequently, the control strategy applied to its componentshas a lot of constraints and cannot be optimal.This thesis investigate a new methodology to design and control a hybridelectric vehicle. Based on statistical description of driving cycle and the generationof random cycle, a new way of sizing component is presented. Thecontrol associate is then determined and apply for different scenarios : firstlya heavy vehicle : A truck and then a lightweight vehicle. An offline controlbased on the optimization of the power split via a dynamic programmingalgorithm is presented to get the optimal results for a given driving cycle.A real time control strategy is then define with its optimization for a givenpatterns and compared to the offline results. Finally, a new control of plug inhybrid electric vehicle based on destination predictions is presented.

Le challenge de ces prochaines années est de réduire le plus possible les émissions de CO2 qui la première cause du réchauffement climatique. L’émission de CO2 est principalement due à l’utilisation du moteur thermique dans le milieu du transport. Pour diminuer cette émission, la solution réside à utiliser des véhicules électriques qui sont non polluants et rechargés par des sources émettant le moins possible de CO2. Mais cela impliquerait une production supplémentaire d’énergie. Aujourd’hui l’énergie électrique est produite principalement par des centrales thermiques au niveau mondial, des centrales nucléaires enFrance et des centrales hydrauliques au Québec. Les pics d’utilisations et de productions restant une problématique posant encore beaucoup de problèmes.Une utilisation croissante de véhicules électriques ou hybrides rechargeables permettrait de pouvoir disposer de systèmes de stockage d’énergie, permettant à la fois d’alimenter le moteur électrique du véhicule ou d’aider le réseau électriques. Ce flux est appelé Vehicle-to-Grid ou plus précisément dans le travail présenté ici, ce flux s’appelle Vehicle-to-Home. Alimenter la maison via la batterie du véhicule, permet de diminuer le pic de consommation du foyer. De plus, la batterie du véhicule peut être chargée durant la nuit lorsque la production d’énergie est au plus bas et la moins chère.Ce document présente une optimisation offline du système qui inclut les différents flux d’énergie. Cette optimisation a été réalisée à l’aide de la programmation dynamique. L’objectif de cette optimisation est de minimiser le coût de l’énergie que ce soit le coût de l’essence ou de l’électricité ou encore des énergies renouvelables installées localement.Ensuite deux contrôleurs flous localisés dans le véhicule et dans la maison ont été dimensionnés, testés par simulation (simulation online) et validés expérimentalement.Finalement cette recherche a mis en avant deux cas d’études: un en hivers et l’autre en été. Le cas d’hiver présente une réduction budgétaire de 40% dans la simulation offline, 27% dans la simulation online et 29% en expérimentation. D’autre part, le cas d’été montre une réduction budgétaire de 62% dans la simulation offline, 60% dans la simulation online et 64% en expérimentation. / The challenge for the next few years is to reduce CO2 emissions, which are the cause of global climate warming. CO2 emissions are mainly due to thermal engines used in transportation. To decrease this emission, a viable solution lies in using non-polluting electric vehicles recharged by low CO2 emission energy sources. New transportation penetration has effected on energy production. Energy production has already reached peaks. At the same time, load demand has drastically increased. Hence, it has become imperative to increase daily energy production. It is well-known that world energy production is mainly produced thermal pollutant power plants, except in Québec, where energy is produced by hydro power plants.The more recent electricity utility trend is that electric, and plug-in hybrid electric vehicles (EV, PHEV) could allow storage and/or production of energy. EV/PHEV batteries can supply the electric motor of the vehicle, and act as an energy storage that assists the grid to supply household loads. This power flow is called vehicle-to-grid, V2G. In this dissertation, the V2G power flow is specifically called vehicle-to-home, V2H. That is battery is used during peak. Moreover, the EV battery is charged during the night, when energy production is low and cheap. This important aspect of V2H is that the vehicle battery is not connected to the grid, but is a part of a house micro-grid.This dissertation presents an offline optimization technique, which includes different energy flows, between the home, EV/PHEV, and a renewable energy source (such as photovoltaic - PV and/or wind) which forms the micro-grid. This optimization has been realized through the dynamic programming algorithm. The optimization objective is to minimize energy cost, including fuel cost, electricity cost, and renewable energy cost.Two fuzzy logic controllers, one located in the vehicle and the second one in the house, have been designed, tested by simulation (online simulation) and validated by experiments.The research analyses two seasonal case studies: one in winter and the other one in summer. In the winter case, a cost reduction of 40% for the offline simulation, 27% for the online simulation and 29% for the experiment is realized whereas in the summer case a cost reduction of 62% for the offline simulation, 60% for the online simulation and 64% for the experiment is presented.

Alternative energy resources (such as photovoltaics, fuel cells, wind turbines, micro-turbines, and internal combustion engines) and energy storage systems (such as batteries, supercapacitors, and flywheels) are increasingly being connected to the utility grid, creating distributed energy resources which require the implementation of an effective distributed power management strategy. Parallel-connected power converters form a critical component in such a distributed energy resources system. This dissertation addresses small-signal modeling and analysis of parallel-connected power converter systems operating in distributed energy environments. This work focuses on DC-DC and DC-AC power converters. First, this work addresses the small-signal modeling and analysis of parallel-connected power converters in a battery/supercapacitor hybrid energy storage system. The small-signal model considers variations in the current of individual energy storage devices and the DC bus voltage as state variables, variations in the power converter duty cycles as control variables, and variations in the battery and the supercapacitor voltages and the load current as external disturbances. This dissertation proposes several different control strategies and studies the effects of variations in controller and filter parameters on system performance. Simulation studies were carried out using the Virtual Test Bed (VTB) platform under various load conditions to verify the proposed control strategies and their effect on the final states of the energy storage devices. Control strategies for single DC-AC three-phase power converters are also identified and investigated. These include a novel PV (active power and voltage) control with frequency droop control loop, PQ (active power and reactive power) control, voltage control, PQ control with frequency droop control, and PQ control with voltage and frequency droop control. Small-signal models of a three-phase power converter system with these control strategies were developed, and the impact of parameter variations on the stability of a PV controlled converter were studied. Moreover, a small-signal model of parallel-connected three-phase DC-AC power converters with individual DC power supplies and network is proposed. The simulations carried out in stand-alone and grid-connected modes verify the combined control strategies that were developed. In addition, a detailed small-signal mathematical model that can represent the zero-sequence current dynamics in parallel-connected three-phase DC-AC power converters that share a single DC power source is presented. The effects of a variety of factors on the zero-sequence current are investigated, and a control strategy to minimize the zero-sequence current is proposed. Time-domain simulation studies verify the results. Simulations of a parallel-connected DC-AC power converter system with nonlinear load were carried out. The active power filter implemented in this system provides sharing of harmonic load between each power converter, and reduces harmonic distortion at the nonlinear load by harmonic compensation.

abstract: The environmental impact of the fossil fuels has increased tremendously in the last decade. This impact is one of the most contributing factors of global warming. This research aims to reduce the amount of fuel consumed by vehicles through optimizing the control scheme for the future route information. Taking advantage of more degrees of freedom available within PHEV, HEV, and FCHEV “energy management” allows more margin to maximize efficiency in the propulsion systems. The application focuses on reducing the energy consumption in vehicles by acquiring information about the road grade. Road elevations are obtained by use of Geographic Information System (GIS) maps to optimize the controller. The optimization is then reflected on the powertrain of the vehicle.The approach uses a Model Predictive Control (MPC) algorithm that allows the energy management strategy to leverage road grade to prepare the vehicle for minimizing energy consumption during an uphill and potential energy harvesting during a downhill. The control algorithm will predict future energy/power requirements of the vehicle and optimize the performance by instructing the power split between the internal combustion engine (ICE) and the electric-drive system. Allowing for more efficient operation and higher performance of the PHEV, and HEV. Implementation of different strategies, such as MPC and Dynamic Programming (DP), is considered for optimizing energy management systems. These strategies are utilized to have a low processing time. This approach allows the optimization to be integrated with ADAS applications, using current technology for implementable real time applications. The Thesis presents multiple control strategies designed, implemented, and tested using real-world road elevation data from three different routes. Initial simulation based results show significant energy savings. The savings range between 11.84% and 25.5% for both Rule Based (RB) and DP strategies on the real world tested routes. Future work will take advantage of vehicle connectivity and ADAS systems to utilize Vehicle to Vehicle (V2V), Vehicle to Infrastructure (V2I), traffic information, and sensor fusion to further optimize the PHEV and HEV toward more energy efficient operation. / Dissertation/Thesis / Masters Thesis Mechanical Engineering 2016