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Throughout the past several years, a major push has been made for the automotive industry to provide vehicles with lower environmental impacts while maintaining safety, performance, and overall appeal. Various legislation has been put into place to establish guidelines for these improvements and serve as a challenge for automakers all over the world. In light of these changes, hybrid technologies have been growing immensely on the market today as customers are seeing the benefits with lower fuel consumption and higher efficiency vehicles. With the need for hybrids rising, it is vital for the engineers of this age to understand the importance of advanced vehicle technologies and learn how and why these vehicles can change the world as we know it. To help in the education process, this thesis seeks to define a powertrain model created and developed to help users understand the basics behind hybrid vehicles and the effects of these advanced technologies. One of the main goals of this research is to maintain a simplified approach to model development. There are very complex vehicle simulation models in the market today, however these can be hard to manipulate and even more difficult to understand. The 1 Hz model described within this work aims to allow energy to be simply and understandable traced through a hybrid powertrain. Through the use of a 'backwards' energy tracking method, demand for a drive cycle is found using a drive cycle and vehicle parameters. This demand is then used to determine what amount of energy would be required at each component within the powertrain all the way from the wheels to the fuel source, taking into account component losses and accessory loads on the vehicle. Various energy management strategies are developed and explained including controls for regenerative braking, Battery Electric Vehicles, and Thermostatic and Load-following Series Hybrid Electric Vehicles. These strategies can be easily compared and manipulated to understand the tradeoffs and limitations of each. After validating this model, several studies are completed. First, an example of using this model to design a hybrid powertrain is conducted. This study moves from defining system requirements to component selection, and then finding the best powertrain to accomplish the given constraints. Next, a parameter known as Power Split Fraction is studied to provide insight on how it affects overall powertrain efficiency. Since the goal with advanced vehicle powertrains is to increase overall system efficiency and reduce overall energy consumption, it is important to understand how all of the factors involved affect the system as a whole. After completing these studies, this thesis moves on to discussing future work which will continue refining this model and making it more applicable for design. Overall, this work seeks to provide an educational tool and aid in the development of the automotive engineers of tomorrow. / Master of Science

Defining key parameters for a charge sustaining supervisory (torque split) control strategy as well as an engine and catalyst warm-up strategy for a Split Parallel Architecture Extended-Range Electric Vehicle (SPA E-REV) is accomplished through empirically and experimentally measuring vehicle tailpipe emissions and energy consumption for two distinct control strategies. The results of the experimental testing and analysis define how the vehicle reduces fuel consumption, petroleum energy use and greenhouse gas emissions while maintaining low tailpipe emissions. For a SPA E-REV operating in charge sustaining mode with the engine providing net propulsive energy, simply operating the engine in regions of highest efficiency does not equate to the most efficient operation of the vehicle as a system and can have adverse effects on tailpipe emissions. Engine and catalyst warm-up during the transition from all-electric charge depleting to engine-dominant charge sustaining modes is experimentally analyzed to evaluate tailpipe emissions. The results presented are meant to define key parameters for a high-level torque-split strategy and to provide an understanding of the tradeoffs between low energy consumption and low tailpipe emissions. The literature review gives a background of hybrid and plug-in hybrid vehicle control publications including tailpipe emissions studies, but does not include experimental results and comparisons of supervisory strategies designed for low fuel consumption and low tailpipe emissions the SPA E-REV architecture. This paper details the high-level control strategy chosen for balancing low energy consumption and low tailpipe emissions while the engine is operating. Vehicle testing data from a chassis dynamometer is presented in support of the research. / Master of Science

For years the trend in the automotive industry has been toward more complex electronic control systems. The number of electronic control units (ECUs) in vehicles is ever increasing as is the complexity of communication networks among the ECUs. Increasing fuel economy standards and the increasing cost of fuel is driving hybridization and electrification of the automobile. Achieving superior fuel economy with a hybrid powertrain requires an effective and optimized control system. On the other hand, mathematical modeling and simulation tools have become extremely advanced and have turned simulation into a powerful design tool. The combination of increasing control system complexity and simulation technology has led to an industry wide trend toward model based control design. Rather than using models to analyze and validate real world testing data, simulation is now the primary tool used in the design process long before real world testing is possible. Modeling is used in every step from architecture selection to control system validation before on-road testing begins. The Hybrid Electric Vehicle Team (HEVT) of Virginia Tech is participating in the 2011-2014 EcoCAR 2 competition in which the team is tasked with re-engineering the powertrain of a GM donated vehicle. The primary goals of the competition are to reduce well to wheels (WTW) petroleum energy use (PEU) and reduce WTW greenhouse gas (GHG) and criteria emissions while maintaining performance, safety, and consumer acceptability. This paper will present systematic methodology for using model based design techniques for architecture selection, control system design, control strategy optimization, and controller validation to meet the goals of the competition. Simple energy management and efficiency analysis will form the primary basis of architecture selection. Using a novel method, a series-parallel powertrain architecture is selected. The control system architecture and requirements is defined using a systematic approach based around the interactions between control units. Vehicle communication networks are designed to facilitate efficient data flow. Software-in-the-loop (SIL) simulation with Mathworks Simulink is used to refine a control strategy to maximize fuel economy. Finally hardware-in-the-loop (HIL) testing on a dSPACE HIL simulator is demonstrated for performance improvements, as well as for safety critical controller validation. The end product of this design study is a control system that has reached a high level of parameter optimization and validation ready for on-road testing in a vehicle. / Master of Science

As the global demand for energy increases, the people of the United States are increasingly subject to high and ever-rising oil prices. Additionally, the U.S. transportation sector accounts for 27% of total nationwide Greenhouse Gas (GHG) emissions. In the U.S. transportation sector, light-duty passenger vehicles account for about 58% of energy use. Therefore incremental improvements in light-duty vehicle efficiency and energy use will significantly impact the overall landscape of energy use in America. A crucial step to designing and building more efficient vehicles is modeling powertrain energy consumption. While accurate modeling is indeed key to effective and efficient design, a fundamental understanding of the powertrain and auxiliary systems that contribute to energy consumption for a vehicle is equally as important if not more important. This thesis presents a methodology that has been packaged into a tool, called VTool, that can be used to estimate the energy consumption of a vehicle powertrain. The method is intrinsically designed to foster understanding of the vehicle powertrain as it relates to energy consumption while still providing reasonably accurate results. VTool explicitly calculates the energy required at the wheels of the vehicle to complete a prescribed drive cycle and then explicitly applies component efficiencies to find component losses and the overall energy consumption for the drive cycle. In calculating component efficiencies and losses, VTool offers several tunable parameters that can be used to calibrate the tool for a particular vehicle, compare powertrain architectures, or simply explore the tradeoffs and sensitivities of certain parameters. In this paper, the method is fully and explicitly developed to model Electric Vehicles (EVs), Series Hybrid Electric Vehicles (HEVs) and Parallel HEVs for various different drive cycles. VTool has also been validated for use in UDDS and HwFET cycles using on-road test results from the 2011 EcoCAR competition. By extension, the method could easily be extended for use in other cycles. The end result is a tool that can predict fuel consumption to a reasonable degree of accuracy for a variety of powertrains, calculate J1711 Utility Factor weighted energy consumption for Extended Range Electric Vehicles (EREVs) and determine the Well-to-Wheel impact of a given powertrain or fuel. VTool does all of this while performing all calculations explicitly and calculating all component losses to allow the user maximum access which promotes understanding and comprehension of the fundamental dynamics of automotive fuel economy and the powertrain as a system. / Master of Science

<p dir="ltr">The demand for sustainable and renewable energy sources has prompted significant research and development efforts in the field of biomass gasification. Biomass gasification technology holds significant promise for sustainable energy production, offering a renewable alternative to fossil fuels while mitigating environmental impact. This thesis presents a detailed study on the design, development, and implementation of a Plug-Flow Reactor Biomass Gasifier integrated with an Automated Auger Jam Detection System and a Blower Algorithm to maintain constant reactor pressure by varying blower speed with respect to changes in reactor pressure. The system is based on indirectly- heated pyrolytic gasification technology and is developed using Simulink™.</p><p dir="ltr">The proposed gasification system use the principles of pyrolysis and gasification to convert biomass feedstock into syngas efficiently. An innovative plug-flow reactor configuration ensures uniform heat distribution and residence time, optimizing gasification performance and product quality. Additionally, the system incorporates an automated auger jam detection system, which utilizes sensor data to detect and mitigate auger jams in real-time, thereby enhancing operational reliability and efficiency. By monitoring these parameters, the system detects deviations from normal operating conditions indicative of auger jams and initiates corrective actions automatically. The detection algorithm is trained using test cases and validated through detailed testing to ensure accurate and reliable performance.</p><p dir="ltr">The MATLAB™-based implementation offers flexibility, scalability, and ease of integration with existing gasifier control systems. The graphical user interface (GUI) provides operators with real-time monitoring and visualization of system status, auger performance, and detected jam events. Additionally, the system generates alerts and notifications to inform operators of detected jams, enabling timely intervention and preventive maintenance. </p><p dir="ltr">To maintain consistent gasification conditions, a blower algorithm is developed to regulate airflow and maintain constant reactor pressure within the gasifier. The blower algorithm dynamically adjusts blower speed based on feedback from differential pressure sensors, ensuring optimal gasification performance under varying operating conditions. The integration of the blower algorithm into the gasification system contributes to stable syngas production and improved process control. The development of the Plug-Flow Reactor Biomass Gasifier, Automated Auger Jam Detection System, and Blower Algorithm is accompanied by rigorous simulation studies and experimental validation.</p><p dir="ltr">Overall, this thesis contributes to the advancement of biomass gasification technology by presenting a detailed study on a plug flow reactor biomass gasifier with indirectly- heated pyrolytic gasification technology with an Automated Auger Jam Detection System and Blower Algorithm. The findings offer valuable insights for researchers, engineers, policymakers, and industry stakeholders supporting the transition towards cleaner and more renewable energy systems.</p>

Résumé : Depuis la fin du XXème siècle, l’augmentation du prix du pétrole brut et les problématiques environnementales poussent l’industrie automobile à développer des technologies plus économes en carburant et générant moins d’émissions de gaz à effet de serre. Parmi ces technologies, les véhicules électriques hybrides constituent une solution viable et performante. En alliant un moteur électrique et un moteur à combustion, ces véhicules possèdent un fort potentiel de réduction de la consommation de carburant sans sacrifier son autonomie. La présence de deux moteurs et de deux sources d’énergie requiert un contrôleur, appelé système de gestion d’énergie, responsable de la commande simultanée des deux moteurs. Les performances du véhicule en matière de consommation dépendent en partie de la conception de ce contrôleur. Les véhicules électriques hybrides rechargeables, plus récents que leur équivalent non rechargeable, se distinguent par l’ajout d’un chargeur interne permettant la recharge de la batterie pendant l’arrêt du véhicule et par conséquent la décharge de celle-ci au cours d’un trajet. Cette particularité ajoute un degré de complexité pour ce qui est de la conception du système de gestion d’énergie. Dans cette thèse, nous proposons un modèle complet du véhicule dédié à la conception du contrôleur. Nous étudions ensuite la dépendance de la commande optimale des deux moteurs par rapport au profil de vitesse suivi au cours d’un trajet ainsi qu’à la quantité d’énergie électrique disponible au début d’un trajet. Cela nous amène à proposer une technique d’auto-apprentissage visant l’amélioration de la stratégie de gestion d’énergie en exploitant un certain nombre de données enregistrées sur les trajets antérieurs. La technique proposée permet l’adaptation de la stratégie de contrôle vis-à-vis du trajet en cours en se basant sur une pseudo-prédiction de la totalité du profil de vitesse. Nous évaluerons les performances de la technique proposée en matière de consommation de carburant en la comparant avec une stratégie optimale bénéficiant de la connaissance exacte du profil de vitesse ainsi qu’avec une stratégie de base utilisée couramment dans l’industrie. // Abstract : Since the end of the XXth century, the increase in crude oil price and the environmental concerns lead the automotive industry to develop technologies that can improve fuel savings and decrease greenhouse gases emissions. Among these technologies, the hybrid electric vehicles stand as a reliable and efficient solution. By combining an electrical motor and an internal combustion engine, these vehicles can bring a noticeable improvement in terms of fuel consumption without sacrificing the vehicle autonomy. The two motors and the two energy storage systems require a control unit, called energy management system, which is responsible for the command decision of both motors. The vehicle performances in terms of fuel consumption greatly depend on this control unit. The plug-in hybrid electric vehicles are a more recent technology compared to their non plug-in counterparts. They have an extra internal battery charger that allows the battery to be charged during OFF state, implying a possible discharge during a trip. This particularity adds complexity when it comes to the design of the energy management system. In this thesis, a complete vehicle model is proposed and used for the design of the controller. A study is then carried out to show the dependence between the optimal control of the motors and the speed profile followed during a trip as well as the available electrical energy at the beginning of a trip. According to this study, a self-learning optimization technique that aims at improving the energy management strategy by exploiting some driving data recorded on previous trips is proposed. The technique allows the adaptation of the control strategy to the current trip based on a pseudo-prediction of the total speed profile. Fuel consumption performances for the proposed technique will be evaluated by comparing it with an optimal control strategy that benefits from the exact a priori knowledge of the speed profile as well as a basic strategy commonly used in industry.

Renewable energy is key to a sustainable future. However, the intermittency of most renewable sources and lack of sufficient storage in the current power grid means that reliable integration of significantly more renewables will be a challenging task. Moreover, increased integration of renewables not only increases uncertainty, but also reduces the fraction of traditional controllable generation capacity that is available to cope with supply-demand imbalances and uncertainties. Less traditional generation also means less rotating mass that provides very short term, yet very important, kinetic energy storage to the system and enables mitigation of the frequency drop subsequent to major contingencies but before controllable generation can increase production. Demand, on the other side, has been largely regarded as non-controllable and inelastic in the current setting. However, there is strong evidence that a considerable portion of the current and future demand, such as electric vehicle load, is flexible. That is, the instantaneous power delivered to it needs not to be bound to a specific trajectory. In this thesis, we focus on harnessing demand flexibility as a key to enabling more renewable integration and cost reduction. We start with a data driven analysis of the potential of flexible demands, particularly plug-in electric vehicle (PEV) load. We first show that, if left unmanaged, these loads can jeopardize grid reliability by exacerbating the peaks in the load profile and increasing the negative correlation of demand with wind energy production. Then, we propose a simple local policy with very limited information and minimal coordination that besides avoiding undesired effects, has the positive side-effect of substantially increasing the correlation of flexible demand with wind energy production. Such local policies could be readily implemented as modifications to existing "grid friendly" charging modes of plug-in electric vehicles. We then propose improved localized charging policies that counter balance intermittency by autonomously responding to frequency deviations from the nominal frequency and show that PEV load can offer a substantial amount of such ancillary services. Next, we consider the case where real-time prices are employed to provide incentives for demand response. We consider a flexible load under such a pricing scheme and obtain the optimal policy for responding to stochastic price signals to minimize the expected cost of energy. We show that this optimal policy follows a multi-threshold form and propose a recursive method to obtain these thresholds. We then extend our results to obtain optimal policies for simultaneous energy consumption and ancillary service provision by flexible loads as well as optimal policies for operation of storage assets under similar real-time stochastic prices. We prove that the optimal policy in all these cases admits a computationally efficient form. Moreover, we show that while optimal response to prices reduces energy costs, it will result in increased volatility in the aggregate demand which is undesirable. We then discuss how aggregation of flexible loads can take us a step further by transforming the loads to controllable assets that help maintain grid reliability by counterbalancing the intermittency due to renewables. We explore the value of load flexibility in the context of a restructured electricity market. To this end, we introduce a model that economically incentivizes the load to reveal its flexibility and provides cost-comfort trade-offs to the consumers. We establish the performance of our proposed model through evaluation of the price reductions that can be provided to the users compared to uncontrolled and uncoordinated consumption. We show that a key advantage of aggregation and coordination is provision of "regulation" to the system by load, which can account for a considerable price reduction. The proposed scheme is also capable of preventing distribution network overloads. Finally, we extend our flexible load coordination problem to a multi-settlement market setup and propose a stochastic programming approach in obtaining day-ahead market energy purchases and ancillary service sales. Our work demonstrates the potential of flexible loads in harnessing renewables by affecting the load patterns and providing mechanisms to mitigate the inherent intermittency of renewables in an economically efficient manner. / text

Le bâtiment est un noeud énergétique important et un support idéal pour développer et analyser les effets d'un système de gestion optimisée d'énergie (SGEB) tant son impact potentiel sur la demande énergétique globale est important. Cependant, pour que ces objectifs soient atteints, plusieurs verrous doivent être levés. Au-delà des problématiques liées à l'architecture de distribution, aux modèles (y compris ceux relatifs au comportement des usagers), aux outils de dimensionnement, à la formalisation des paramètres, contraintes et critères, aux systèmes de production et aux modes de connexions au réseau de distribution, les problèmes liés à la mise en oeuvre d'un outil de gestion décentralisée et à sa validation sont centraux centrale. Ces travaux s'inscrivent directement dans cette optique. Ils portent en particulier sur l'élaboration de modèles énergétiques, de stratégies de gestion d'énergie dans une configuration multi-sources et multi-charges et surtout de mise en oeuvre de méthodes et d'outils de validation au travers de bancs tests variés où certains composants peuvent être réels. Ce travail analyse le gestionnaire énergétique " G-homeTech " comprenant plusieurs fonctionnalités de gestion testées sur des bancs d'essai virtuels et hybrides qui permettent de combiner à la fois des composants matériels et logiciels dans les simulations. Cela a permis d'insérer des actionneurs communicants pour tester leur pertinence. Les validations menées montrent que le gestionnaire énergétique permet l'effacement de pointes de consommation et des économies sur la facture énergétique globale tout en respectant les contraintes techniques et réglementaires. Les évènements prédits ne sont pas toujours ceux qui se produisent. Nous avons alors simulé de telles situations. La radiation solaire et la consommation totale des services non contrôlables sont différentes de celles prédites. Cette différence a conduit à des dépassements de puissance électrique souscrite qui a activé le mécanisme de gestion réactive du gestionnaire énergétique. Des ordres de délestage sont alors dynamiquement envoyés à certains équipements. Ces ordres alimentent directement les modèles des équipements électriques. Selon les importances relatives données au coût et au confort, nous avons montré que le gestionnaire énergétique permet de faire des économies substantielles en évitant les consommations durant les pics de prix et évitant les dépassements de souscription par effacement, par modulation du fonctionnement des systèmes de chauffage et par décalage de fonctionnement des services temporaires dans les périodes plus intéressante énergétiquement.