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

Unidades propulsoras híbrido elétricas são uma alternativa em potencial para a redução do consumo de combustível e emissões de poluentes, quando empregadas em veículos de transporte público. A configuração híbrido elétrica de interesse é a série, na qual as fontes de potência, para o motor elétrico de tração, são compostas por um banco de baterias e uma unidade formada pela junção entre um motor à combustão interna e um gerador. Na presente Dissertação foi realizada a modelagem de um veículo elétrico híbrido série na qual diferentes estratégias de gerenciamento de potência foram investigadas. Dentre as estratégias de interesse, duas são fundamentadas em regras e a terceira em sistemas fuzzy. Resultados obtidos comprovaram que a fundamentada em sistemas fuzzy possibilita maior economia de combustível, permitindo que o motor à combustão interna forneça menos potência, face o emprego das baseadas em regras. Dessa forma, a utilização de sistemas fuzzy no gerenciamento de potência do veículo, permite o emprego de um motor à combustão menos potente, de menor custo, sem o comprometimento do desempenho do veículo. As simulações do presente modelo de veículo híbrido foram realizadas no ambiente Matlab/Simulink® 7.3.0. / Hybrid electric propulsion units are a potential alternative to the reduction of fuel consumption and pollutant emissions, when used in public transport vehicles. The electric hybrid configuration of interest is the series, in which the energy supplies to the traction electric motor are composed of batteries and a unit represented by the connection of an internal combustion engine and a generator. This Dissertation presents the modeling of a series hybrid electric vehicle in which different energy management strategies were investigated. Among the strategies of interest, two are based on rules and one on fuzzy systems. The obtained results proved that the strategy based on fuzzy systems improved the fuel economy, allowing the internal combustion engine to supply less power than the use of the strategies based on rules. Therefore, the use of fuzzy systems in the energy management of the vehicle allows for the adoption of a less potent and cheaper internal combustion engine, without compromising the vehicles performance. The simulations of the present model of the hybrid electric vehicle were performed in the Matlab/Simulink® 7.3.0 environment.

Unidades propulsoras híbrido elétricas são uma alternativa em potencial para a redução do consumo de combustível e emissões de poluentes, quando empregadas em veículos de transporte público. A configuração híbrido elétrica de interesse é a série, na qual as fontes de potência, para o motor elétrico de tração, são compostas por um banco de baterias e uma unidade formada pela junção entre um motor à combustão interna e um gerador. Na presente Dissertação foi realizada a modelagem de um veículo elétrico híbrido série na qual diferentes estratégias de gerenciamento de potência foram investigadas. Dentre as estratégias de interesse, duas são fundamentadas em regras e a terceira em sistemas fuzzy. Resultados obtidos comprovaram que a fundamentada em sistemas fuzzy possibilita maior economia de combustível, permitindo que o motor à combustão interna forneça menos potência, face o emprego das baseadas em regras. Dessa forma, a utilização de sistemas fuzzy no gerenciamento de potência do veículo, permite o emprego de um motor à combustão menos potente, de menor custo, sem o comprometimento do desempenho do veículo. As simulações do presente modelo de veículo híbrido foram realizadas no ambiente Matlab/Simulink® 7.3.0. / Hybrid electric propulsion units are a potential alternative to the reduction of fuel consumption and pollutant emissions, when used in public transport vehicles. The electric hybrid configuration of interest is the series, in which the energy supplies to the traction electric motor are composed of batteries and a unit represented by the connection of an internal combustion engine and a generator. This Dissertation presents the modeling of a series hybrid electric vehicle in which different energy management strategies were investigated. Among the strategies of interest, two are based on rules and one on fuzzy systems. The obtained results proved that the strategy based on fuzzy systems improved the fuel economy, allowing the internal combustion engine to supply less power than the use of the strategies based on rules. Therefore, the use of fuzzy systems in the energy management of the vehicle allows for the adoption of a less potent and cheaper internal combustion engine, without compromising the vehicles performance. The simulations of the present model of the hybrid electric vehicle were performed in the Matlab/Simulink® 7.3.0 environment.

Renewable and green energy development is vigorously supported by most countries to suppress the continuously increasing greenhouse gas (GHG) emissions. However, as the total renewable capacity expands, the growth rate of emissions is not effectively restrained. An unforeseen factor contributing to this growth is the regulation service, which aims to mitigate power frequency deviations caused by the intermittent renewable power generation and unbalanced power supply and demand. Regulation services, normally issued by supply-side balancing authorities, leads to inefficient operations of regulating generators, thus directly contributing to the emissions growth. Therefore, it is urged to find solutions that can stabilize the power frequency with an increased energy using efficiency. Demand response (DR) is an ideal candidate to solve this problem. The current smart grid infrastructure enables a high penetration of smart residential electric loads, including heating, ventilation, and air conditioning systems (HVACs), air conditioners (A/Cs), electric water heaters (EWHs), and plug-in hybrid electric vehicles (PHEVs). Beyond simply drawing power from the grid for local electric demand, those loads can also adjust their power consumption patterns by responding to the control signals sent to them. It has been proved that, if appropriately aggregated and controlled, power consumption of demand-side residential loads possesses a huge potential for providing regulation services. The research of DR is pivotal from the the application perspective due to the efficient usage of renewable energy generation and the high power quality. However, many problems remain open in this area due to the load heterogeneity, device physical constraints, and computational and communication restrictions. In order to move one step further toward industry applications, this PhD thesis is concerned with two cruxes in DR program design: Aggregation Modeling and Control; it deals with two main types of terminal loads: Thermostatically Controlled Appliances (TCAs) (Chapters 2-4) and PHEVs (Chapter 5). This thesis proceeds with Chapter 1 by reviewing the state-of-the-art of DR. Then in Chapter 2, the focus is put on modeling and control of TCAs for secondary frequency control. In order to explicitly describe local TCA dynamics and to provide the aggregator a clear global view, TCAs are aggregated by directly stacking their individual dynamics. Terminal TCAs are assumed in a general case that an arbitrary number of TCAs are equipped with varying frequency drives (VFDs). A centralized model predictive control (MPC) scheme is firstly constructed. In the design, to tackle the TCA lockout effect and to facilitate the MPC scheme, a novel approach for converting time-integrated interdependent logic constraints into inequality constraints are proposed. Since a centralized MPC scheme may introduce non-trivial computational load by using this aggregation model, especially when the number of TCAs increases, a distributed MPC (DMPC) scheme is proposed. This DMPC scheme is validated through a more practical case study that all TCAs are subject to pure ON/OFF control. Chapter 3 targets on aggregation modeling and control of TCAs for the provision of primary frequency control. To efficiently reduce the computational load to facilitate the primary frequency control, the explicit monitoring of terminal TCAs must be compromised. To this end, a 2-D population-based model is proposed, in which TCAs are clustered into state bins according to their temperature information and running status. Within the proposed aggregation framework, individual TCA dynamics' evolutions develop into TCA population migration probabilities, thus the computational load of the centralized controller is dramatically reduced. Based on this model, a centralized MPC scheme is proposed for the primary frequency control. The previously proposed population-based model provides a promising direction for the centralized control. However, in traditional population-based model, TCA lockout effect can only be considered when implementing the control signals. This will cause a mismatch between the nominal control signals and the actually implemented ones. To conquer this, in Chapter 4, an improved population-based model is studied to explicitly formulate the TCA lockout effect in the aggregation model. A DMPC scheme is firstly constructed based on this model. Furthermore, since the predictions of regulation signals may not be available or they may include severe disturbances, a control scheme that does not require future regulation signals is urged. To this end, an optimal control scheme, in which a novel penalty is included to maximize the regulation capability, is proposed to facilitate the most practical scenario. Another type of terminal loads that has a huge potential in providing grid services is PHEV. At this point, Chapter 5 presents the aggregation and charging control of heterogeneous PHEVs for the provision of DR. In contrast to using battery state-of-charge (SOC) solely as the system state, a new aggregation model is proposed by introducing a novel concept, i.e., charging requirement index. This index combines the SOC with drivers' specified charging requirements, thus inherently providing the aggregation model with richer information. A centralized MPC scheme is proposed based on this novel model. Both of the model and controller are validated through an overnight valley-filling case study. Finally, the conclusions of the thesis are summarized and future research topics are presented. / Graduate / 0537 / 0544 / 0548 / mingxiliu419@gmail.com

The fuel usage of a hybrid electric vehicle can be reduced by strategically combining the usage of the combustion engine with the electric motor. One method to determine an optimal split between the two is to use dynamic programming. However, the amount of computations grows exponentially with the amount of states which makes its usage difficult on sequential hardware. This thesis project explores the usage of FPGAs for speeding up the required computations to possibly allow the optimisation to run in real time in the vehicle. A tool to convert a vehicle model to a hardware description language was developed and evaluated. The current version does not run fast enough to run in real time, but some optimisations which would allow that are proposed.

Um problema que as empresas distribuidoras de energia elétrica convivem são as quedas repentinas no fornecimento, causando inúmeros prejuízos tanto para essas empresas quanto para seus consumidores. Essa dissertação apresentará uma ferramenta que utilizará conhecimentos de sistemas de informações geográficas junto com o uso de inferência nebulosa para orientar a disposição de veículos híbridos (elétricos e à combustão) que podem operar como mini-usinas elétricas no abastecimento de localidades que esteja necessitando de energia em um determinado momento. Para isso, será levantada uma base de dados com características dos veículos híbridos e locais necessitados, dados esses que alimentarão um sistema nebuloso agregado à ferramenta MapServer e a um SIG (Sistema de Informações Geográficas) para, dessa forma, mostrar como saída do sistema qual veículo estará mais apto naquele instante para abastecer o local da demanda de energia. / A problem that electric energy companies have to face is the power outage, which causes innumerable damages for both companies and consumers. This dissertation describes a tool that joins Geographic Information Systems knowledge to Fuzzy Logic knowledge to guide the distribution of hybrid vehicles (electric and combustion) that can operate as mini electric power plants to supply places that need electrical power. For this, a database will be built with the characteristics of vehicles and places in need. The data will supply a Fuzzy system joined to a MapServer tool and a GIS (Geographic Information System), showing which vehicle will be more capable in that moment to supply the place in energy demand.

Long haulage of heavy-duty trucks weighing over 15-ton stands for nearly 50% of the fuelconsumption among trucks, making them the most fuel consuming category. This indicatesthe potential benefits in improving the fuel efficiency for said category. Hybridization is onepossible solution.Hybrid vehicles are vehicles with two or more power sources in the powertrain. Differentpowertrain configurations, hybridization levels and hybrid concepts are best suitedfor different applications. With prices for fossil fuels constantly rising hybridization is animportant technology to improve fuel efficiency.Different variations of configurations and concepts enables many choices when decidingon a hybrid driveline. A simulation tool for efficiently comparing various hybrid drivelineswould be a great asset when deciding on a configuration for a certain vehicle application. Forthis thesis the application in focus is the previously mentioned category, a heavy duty longhauler weighing 36-ton.The modeling approach used for the simulation tool is called quasistatic modeling or"backward modeling". This name comes from, based on a chosen drive cycle, the resistingforces which act on the vehicle can statically be calculated at each step from the velocityprofile. The required power to drive along the drive cycle can then be calculated backwardswithin the powertrain resulting in a fuel consumption for the combustion engine. For thisthe free QSS-toolbox for Matlab Simulink has been used as a base and modified when needed.The configuration chosen to be implemented is a parallel electric hybrid and was chosenfor its good characteristics for the type of driving highways provide. For this configurationtwo types of controllers are used, one being an Equivalent Consumption Minimization Strategycontroller and the other a simple, rule based heuristic controller.The results for both controllers show small benefits with hybridization of the longhauler compared with the conventional which in the long run would make bigger differencebecause of the large consumption in whole. A sensitivity analysis was also done showingthat improving conventional vehicle parameters can be as beneficial as hybridization.

<p>Hybrid electric vehicles are considered to be an important part of the future vehicle industry, since they decrease fuel consumption without decreasing the performance compared to a conventional vehicle. They use two or more power sources to propel the vehicle, normally one combustion engine and one electric machine. These power sources can be arranged in different topologies and can cooporate in different ways. In this thesis, dynamic models of parallel and series hybrid powertrains are developed, and different strategies for how to control them are compared.An optimization algorithm for decreasing fuel consumption and utilize the battery storage capacity as much as possible is also developed, implemented and tested.</p>

<p>Hybrid electric vehicles have two power sources - an internal combustion engine and an electric motor. These vehicles are of great interest because they contribute to a decreasing fuel consumption and air pollution and still maintain the performance of a conventional car. Different topologies are described in this thesis and especially the series and parallel hybrid electric vehicle and Toyota Prius have been studied. </p><p>This thesis also depicts modelling of a reference car and a series hybrid electric vehicle in Modelica. When appropriate, models from the Modelica standard library have been used. Models for a manual gearbox, final drive, wheel, chassis, air drag and a driver have been developed for the reference car. </p><p>For the hybrid electric vehicle a continuously variable transmission, battery, an electric motor, fuel cut-off function for the internal combustion engine and a converter that distributes the current between generator, electric motor and internal combustion engine have been designed. </p><p>These models have been put together with models from the Modelica standard library to a reference car and a series hybrid electric vehicle which follows the NEDC driving cycle. A sketch for the parallel hybrid electric vehicle and Toyota Prius have also been made in Modelica. </p><p>Developed models have been introduced into the Modelica library VehProLib, which is a vehicle propulsion library under development by Vehicular Systems, Linköpings universitet.</p>

Performance of two different waste heat recovery systems (one based on Rankine cycle and the other one using thermoelectricity) combined with non-hybrid, mild-hybrid and full hybrid systems are investigated. The vehicle under investigation was a 440hp Scania truck, loaded by 40 tons. Input data included logged data from a long haulage drive test in Sweden.All systems (waste heat recovery as well as hybrid) are implemented and simulated in Matlab/Simulink. Almost all systems are modeled using measured data or performance curves provided by one manufacturer. For Rankine system results from another investigation were used.Regardless of practical issues in implementing systems, reduction in fuel consumption for six different combination of waste heat recovery systems and hybrid systems with different degrees of hybridization are calculated. In general Rankine cycle shows a better performance. However, due to improvements achieved in laboratories, thermoelectricity could also be an option in future.This study focuses on “system” point of view and therefore high precision calculations is not included. However it can be useful in making decisions for further investigations.