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.

This master‘s thesis pertains to the design of a cargo truck with battery electric drivetrain. The presented design offers a solution which respects the technical requirements of such a vehicle, ergonomic needs of its crew, and demands on the aesthetics of a modern commercial vehicle. In consideration of the chosen drivetrain, this solution is proposed as a concept with an outlook of 10 to 15 years in the future. In keeping with the specified goals, this vehicle offers a novel appearance which attempts to characterize the electric drivetrain with certain visual elements, includes solutions that are readily available or currently in development, and optimizes ergonomics for maximum user comfort and safety.

The Well-To-Wheels (WTW) methodology is widely used for policy making in the transportation sector. In this paper updated WTW calculations are provided, relying on 2013 statistic data, for the carbon intensity (CI) of the European electricity mix; detail is provided for electricity consumed in each EU Member State (MS). An interesting aspect presented is the calculation of the GHG content of electricity traded between Countries, affecting the carbon intensity of the electricity consumed at national level. The amount and CI of imported electricity is a key aspect: a Country importing electricity from another Country with a lower CI of electricity will lower, after the trade, its electricity CI, while importing electricity from a Country with a higher CI will raise the CI of the importing Country. In average, the CI of electricity used in EU at low voltage in 2013 was 447 gCO2eq/kWh, which is the 17% less compared to 2009. Then, some examples of calculation of GHG emissions from the use of electric vehicles (EVs) compared to internal combustion engine vehicles are provided. The use of EVs instead of gasoline vehicles can save (about 60% of) GHG in all or in most of the EU MSs, depending on the estimated consumption of EVs. Compared with diesel, EVs show average GHG savings of around 50% and not savings at all in some EU MS.

This thesis explores the possibility of using an optimal control scheme called Model Predictive Control (MPC), to control climatization systems for electric vehicles. Some components of electric vehicles, for example the batteries and power electronics, are sensitive to temperature and for this reason it is important that their temperature is well regulated. Furthermore, like all vehicles, the cab also needs to be heated and cooled. One of the weaknesses of electric vehicles is their range, for this reason it is important that the temperature control is energy efficient. Once the range of electric vehicles is increased the down sides compared to traditional combustion engine vehicles decrease, which could lead to an increase in the usage of electric vehicles. This could in turn lead to a decrease of greenhouse gas emission in the transportation sector. With the help of MPC it is possible for the controller to take more factors into consideration when controlling the system than just temperature and in this thesis the power consumption and noise are also taken into consideration. A simple model where parts of the climate system’s circuits were seen as point masses was developed, with nonlinear heat transfers occurring between them, which in turn were controlled by actuators such as fans, pumps and valves. The model was created using Simulink and MATLAB, and the MPC toolbox was used to develop nonlinear MPC controllers to control the climate system. A standard nonlinear MPC, a nonlinear MPC with custom cost functions and a PI controller where all developed and compared in simulations of a cooling scenario. The controllers were designed to control the temperatures of the battery, power electronics and the cab of an electric vehicle. The results of the thesis indicate that MPC could reduce power consumption for the climate control system, it was however not possible to draw any final conclusions as the PI controller that the MPC controllers were compared to was not well optimized for the system. The MPC controllers could benefit from further work, most importantly by applying a more sophisticated tuning method to the controller weights. What was certain was that it is possible to apply this type of centralized controller to very complex systems and achieve robustness without external logic. Even with the controller keeping track of six different temperatures and controlling 15 actuators, the control loop runs much faster than real time on a modern computer which shows promise with regard to implementing it on an embedded system.

Recently in more or all electric vehicles, higher voltage batteries are used which employ large number of cells in series. Series connection among cells may lead to single point of failures, safety and charge equalization issues that demand complex control and costly and/or lossy battery management methods. Most present day high voltage batteries use dissipative-charge balancing methods, which result in poor efficiency, additional thermal management burden and lower overall vehicle range. Furthermore, the output voltages of such batteries remain unregulated and may widely change with load and environmental conditions, complicating the overall power pass design of the electrical power system. As a step forward to address these issues, this thesis studies a fault-tolerant regulated active high voltage electric accumulator with integrated power electronics for safe charge and discharge of the high voltage energy storage system. / Thesis / Master of Applied Science (MASc)

No description available.

The automotive industry is facing some of the most difficult design challenges in industry history. Developing innovative methods to reduce fossil fuel dependence is imperative for maintaining compliance with government regulations and consumer demand. In addition to powertrain design, route selection contributes to vehicle environmental impact. The objective of this thesis is to develop a methodology for evaluating the energy consumption of each route option for a specific vehicle. A 'backwards' energy tracking method determines tractive demand at the wheels from route requirements and vehicle characteristics. Next, this method tracks energy quantities at each powertrain component. Each component model is scalable such that different vehicle powertrains may be approximated. Using an 'ecorouting' process, the most ideal route is selected by weighting relative total energy consumption and travel time. Only limited powertrain characteristics are publicly available. As the future goal of this project is to apply the model to many vehicle powertrain types, the powertrain model must be reasonably accurate with minimal vehicle powertrain characteristics. Future work expands this model to constantly re-evaluate energy consumption with real-time traffic and terrain information. While ecorouting has been applied to conventional vehicles in many publications, electrified vehicles are less studied. Hybrid vehicles are particularly complicated to model due to additional components, systems, and operation modes. This methodology has been validated to represent conventional, battery electric, and parallel hybrid electric vehicles. A sensitivity study demonstrates that the model is capable of differentiating powertrains with different parameters and routes with different characteristics. / Master of Science

This thesis develops a time-domain integrated modeling approach for design of hybrid-electric marine propulsion systems that enables co-simulation of powertrain dynamics along with ship hydrodynamics. This work illustrates the model-based design and analysis methodology by performing a case study for an EV conversion of a short-cross ferry using the BC Ferries’ M.V. Klitsa. A data acquisition study was performed to establish the typical mission cycle of the ship for its crossing route between Brentwood Bay and Mill Bay, across the Saanich Inlet near Victoria, BC Canada. The data provided by the data acquisition study serves as the primary means of validation for the model’s ability to accurately predict powertrain loads over the vessel’s standard crossing. This functionality enables model-based powertrain and propulsion system design optimization through simulation to intelligently deploy hybrid-electric propulsion architectures. The ship dynamics model is developed using a Newton-Euler approach which incorporates hydrodynamic coefficient data produced by potential flow solvers. The radiation forces resulting from vessel motion are fit to continuous time-domain transfer functions for computational efficiency. The ship resistance drag matrix is parameterized using results from uRANS CFD studies that span the operating range of the vessel. A model of the existing well-mounted azimuthing propeller is developed to predict thrust production and mechanical torque for pseudo-second quadrant operation to represent all operating conditions seen in real operation. The propeller model is parameterized from the results of a series of uRANS CFD on the propeller geometry. A full battery-electric powertrain model is produced to study the accuracy of the model in predicting the drivetrain loads, as well as assessing the technological feasibility of an EV conversion for this particular vessel. A dual-polarization equivalent circuit model is created for a large-scale LTO battery pack. An average value model with MTPA control and dynamics loss model is developed for a commercially available electric drive system. Power loss models were developed for required converter topologies for computational efficiency. The model results for load prediction are compared to data acquired, and results indicate that the approach is effective for enabling the study of various powertrain architecture alternatives. / Graduate

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

Syfte & forskningsfråga: Syftet med den här uppsatsen är att utreda och analysera hur olika faktorer kan påverka efterfrågan och försäljning av elbilen. Syftet är också att identifiera förutsättningar för elbilens framtid och betydelse inom bilsektorn. Uppsatsens forskningsfråga formulerades utifrån uppsatsens syfte och frågan är följande: Vad krävs för att elbilen ska nå ut till en bredare målgrupp i framtiden? Metod: Vår uppsats är baserad på en kvalitativ forskningsmetod för att vi ska få en djupare förståelse kring det valda forskningsområdet. Vi har strävat efter en induktiv ansats i studien men vi är medvetna om att vi har deduktiva inslag, vilket har bidragit till att uppsatsen har en abduktiv karaktär. Vi valde att ta med respondenter med olika positioner från både bilföretag, bilmagasin samt en intresseorganisation för att få en mer bredare syn på forskningsområdet. Sammanlagt har vi sju respondenter samt två fokusgrupper som har bidragit med information till det empiriska materialet i uppsatsen. Slutsatser: I vårt avslutande kapitel besvarar vi våra delsyften som vi har i studien samt vår forskningsfråga om vad som krävs för att elbilen ska nå ut till en bredare marknad i framtiden. Vi anser att elbilen har goda förutsättningar till att utvecklas ännu mer samt spridas till fler kundsegment. Vi har genom vår teoretiska och empiriska analys identifierat att statliga incitament och förmåner, en ökad medvetenhet bland kunderna samt en sänkning av priset är de främsta faktorerna som kan bidra till en utökning av elbilen på marknaden.