We consider a facility location problem with uncertainty flow customers' demands, which we refer to as stochastic flow capturing location allocation problem (SFCLAP). Potential applications include siting farmers' market, emergency shelters, convenience stores, advertising boards and so on. For this dissertation, electric vehicle charging stations siting with maximum accessibility at lowest cost would be studied. We start with placing charging stations under the assumptions of pre-determined demands and uniform candidate facilities. After this model fails to deal with different scenarios of customers' demands, a two stage flow capturing location allocation programming framework is constructed to incorporate demand uncertainty as SFCLAP. Several extensions are built for various situations, such as secondary coverage and viewing facility's capacity as variables. And then, more capacitated stochastic programming models are considered as systems optimal and user oriented optimal cases. Systems optimal models are introduced with variations which include outsourcing the overflow and alliance within the system. User oriented optimal models incorporate users' choices with system's objectives. After the introduction of various models, an approximation method for the boundary of the problem and also the exact solution method, the L-Shaped method, are presented. As the computation time in the user oriented case surges with the expansion of the network, scenario reduction method is introduced to get similar optimal results within a reasonable time. And then, several cases including testing with different number of scenarios and different sample generating methods are operated for model validation. In the last part, simulation method is operated on the authentic network of the state of Arizona to evaluate the performance of this proposed framework.

At the time of this writing, increasing pressure for fuel efficient passenger vehicles has prompted automotive manufactures to invest in the research and development of electrically propelled vehicles. This includes vehicles of strictly electric drive and hybrid electric vehicles with internal combustion engines. To investigate some of the many technological innovations possible with electric power trains, the AUTO21 network of centres of excellence funded project E301-EHV; a project to convert a Chrysler Pacifica into a hybrid electric vehicle. The converted vehicle is intended for use as a test-bed in the research and development of a variety of advances pertaining to electric propulsion. Among these advances is hybrid energy storage, the focus of this investigation. A key difficulty of electric propulsion is the portable storage or provision of electricity, challenges are twofold; (1) achieving sufficient energy capacity for long distance driving and (2) ample power delivery to sustain peak driving demands. Where gasoline is highly energy dense and may be burned at nearly any rate, storing large quantities of electrical energy and supplying it at high rate prove difficult. Furthermore, the demands of regenerative braking require the storage system to undergo frequent current reversals, reducing the service life of some electric storage systems. A given device may be optimized for one of either energy storage or power delivery, at the sacrifice of the other. A hybrid energy storage system (HESS) attempts to address the storage needs of electric vehicles by combining two of the most popular storage technologies; lithium ion batteries, ideal for high energy capacity, and ultracapacitors, ideal for high power discharge and frequent cycles. Two types of HESS are investigated in this study; one using energy-dense lithium ion batteries paired with ultracapacitors and the other using energy-dense lithium ion batteries paired with ultra high powered batteries. These two systems are compared against a control system using only batteries. Three sizes of each system are specified with equal volume in each size. They are compared for energy storage, energy efficiency, vehicle range, mass and relative demand fluctuation when simulated for powering a model Pacifica through each of five different drive cycles. It is shown that both types of HESS reduce vehicle mass and demand fluctuation compared to the control. Both systems have reduced energy efficiency. In spite of this, a battery-battery system increases range with greater storage capacity, but battery-capacitor systems have reduced range. It is suggested that further work be conducted to both optimize the design of the hybrid storage systems, and improve the control scheme allocating power demand across the two energy sources.

The current stimuli of climate change and rising oil prices have spurred the development of hybrid electric (HEV), and battery electric vehicles (BEV): collectively termed EVs. However, the battery technology needs much development: at the time of writing, the range of a BEV is too low to be practical in many situations. A critical limitation is the sensitivity of batteries to temperature: the heat generated during operation affects their performance and reduces the lifetime. This study investigates battery cooling using cooling plates: thin rectangular fabrications inserted between battery cells. A coolant pumped through internal channels absorbs heat and transports it away from the battery. Previous studies of liquid heat exchangers have indicated that the geometry of the channels plays a significant role in the performance; however, there is a lack of rigorous numerical optimization applied to EV cooling plates. By developing a numerical optimization framework utilizing parametric geometry generation and computational fluid dynamics, this research has investigated the characteristics of optimum cooling plate geometry with respect to three objectives: average temperature, temperature uniformity, and coolant pressure drop. By applying each objective separately, improvements of up to 70% have been made compared to a reference design. The influence of boundary conditions on performance and optimum design has been assessed, and multi-objective optimization has investigated the trade-off between competing objective functions. Although care should be taken when extrapolating the results beyond the geometry and conditions in the study, some general design principles can be proposed. Objectives of average temperature and pressure drop can both be satisfied by a common design with wide cooling channels, but different characteristics are needed for temperature uniformity. Additional assessments have revealed that optimizations of temperature uniformity are especially sensitive to the boundary conditions, whereas the other objective functions are largely insensitive. The optimization process developed in this work can be applied to any potential cooling plate design and will lead to gains in the targeted performance measure. In doing so, the performance of the EV will be incrementally improved, thereby advancing the day when an EV is not only an environmental choice, but also a practical choice. / Thesis (Master, Mechanical and Materials Engineering) -- Queen's University, 2011-09-07 16:24:14.6

This dissertation formulates and develops models and solution approaches for plug-in electric vehicle (PEV) charging station installation. The models are formulated in the form of bilevel programming and stochastic programming problems, while a meta-heuristic method, genetic algorithm, and Monte Carlo bounding techniques are used to solve the problems. Demand for PEVs is increasing with the growing concerns about environment pollution, energy resources, and the economy. However, battery capacity in PEVs is still limited and represents one of the key barriers to a more widespread adoption of PEVs. It is expected that drivers who have long-distance commutes hesitate to replace their internal combustion engine vehicles with PEVs due to range anxiety. To address this concern, PEV infrastructure can be developed to provide re-fully status when they are needed. This dissertation is primarily focused on the development of mathematical models that can be used to support decisions regarding a charging station location and installation problem. The major parts of developing the models included identification of the problem, development of mathematical models in the form of bilevel and stochastic programming problems, and development of a solution approach using a meta-heuristic method. PEV parking building problem was formulated as a bilevel programming problem in order to consider interaction between transportation flow and a manager decisions, while the charging station installation problem was formulated as a stochastic programming problem in order to consider uncertainty in parameters. In order to find the best-quality solution, a genetic algorithm method was used because the formulation problems are NP-hard. In addition, the Monte Carlo bounding method was used to solve the stochastic program with continuous distributions. Managerial implications and recommendations for PEV parking building developers and managers were suggested in terms of sensitivity analysis. First, in the planning stage, the developer of the PEV parking building should consider long-term changes in future traffic flow and locate a PEV parking building closer to the node with the highest destination trip rate. Second, to attract more parking users, the operator needs to consider the walkability of walking links.

Today uncertainties of petroleum supply and concerns over global warming call for further advancement of green vehicles with higher energy efficiency and lower green house gas (GHG) emissions. Development of advanced hybrid electric powertrain technology plays an important role in the green vehicle transformation with continuously improved energy efficiency and diversified energy sources. The added complexity of the multi-discipline based, advanced hybrid powertrain systems make traditional powertrain design method obsolete, inefficient, and ineffective. This research follows the industrial leading model-based design approach for hybrid electric vehicle powertrain development and introduces the optimization based methods to address several key design challenges in hybrid electric powertrain and its control system design. Several advanced optimization methods are applied to identify the proper hybrid powertrain architecture and design its control strategies for better energy efficiency. The newly introduced optimization based methods can considerably alleviate the design challenges, avoid unnecessary design iterations, and improve the quality and efficiency of the powertrain design. The proposed method is tested through the design and development of a prototype extended range electric vehicle (EREV), UVic EcoCAR. Developments of this advanced hybrid vehicle provide a valuable platform for verifying the new design method and obtaining feedbacks to guide the fundamental research on new hybrid powertrain design methodology. / Graduate

In an effort to reduce global emissions by electrifying vehicles and machines with internal combustion engines has led to the development of batteries that are more powerful and efficient than the common lead acid battery.  One of the most popular batteries being used for such an installation is lithium ion, but due to its short effective usable lifetime, charging time, and costs has driven researcher to other technologies to replace it.  Vanadium redox flow batteries have come into the spotlight recently as a means of replacing rechargeable batteries in electric vehicles and has previously be used mainly to store energy for load leveling.  It possesses many qualities that would be beneficial to electrify vehicles.  The battery has the ability for power and energy to be sized independently which is not dissimilar to internal combustion vehicles.  It also has the potential for a tolerance to low discharges, fast response time, and can quickly be refueled by replacing the electrolyte; just like is done when a car refuels at the gas station.  The purpose of the study is to determine the possibility of using vanadium redox flow batteries to power heavy construction equipment, a wheel loader, with a finite amount of space available for implementation.  A model has been designed in MATLAB to determine how long the battery could last under typically applications for the wheel loader which needs a peak power of 200 kW.  From the volume available it has been determined that the battery can be installed with an energy capacity of 148 kWh.  The results of the model show that vanadium redox flow batteries can be used to power a wheel loader but due to the limiting energy density and cell components it remains to be impractical.

Abstract Electric Vehicles (EVs) are considered a more sustainable alternative vehicle because of their efficient electric motor when compared to internal combustion engines (ICE), and thus help to mitigate environmental problems and reduce fossil fuel dependency. In or-der to support drivers of plug-in hybrid electrical vehicles (PEVs), the installation and adequate distribution of Electric Vehicle Service Equipment (EVSE) is a major factor. The availability of EVSE is a vital requirement in order to charge the vehicle’s battery pack through connection to the electricity grid. This thesis evaluates the likely distribu-tion of a sufficient number of charging stations, measured as the demand of EVSE, for initial deployment in the E14 highway. This highway is also known as the Green High-way region, where a plan has been outlined with the aim to create a fleet of 15% EVs in the area by 2020.In order to model EVSE distribution, the first step was to complete a survey in 2012 on the population density and location of cities, along with the location of already estab-lished charging station locations on the Green Highway. The survey was done with ge-ography information survey (GIS) software. The second step was to create a map of the region. Based on the map, the initial estimate of EVSE locations on the Green Highway project plan was analyzed, as the third step. This was used as an initial analysis. The forth step was to use the location of current gasoline stations to provide as alternative pattern for the EVSE sites.It was observed that the network of gasoline stations correlates positively with population density. Through using these stations, the optimal location of the EVSEs was proposed. However, the model results do not provide for sufficient placement of EVSE sites where the population density is very low. In order to assess the different potential options, it was necessary to create analytical models in Arc-GIS, in which buffer zones were created with a variable size of 10, 15, 20 and 31 miles. This permitted allocation of a geographical area to estimate the optimum sites for charging stations. The resultsiiishowed that for a buffer zone of 10 miles, 28 charging stations were calculated, using buffer zone of 15 miles gives 18 stations, and a buffer zone of 20 miles results in 13 charging station sites. Notably, the estimate of the 20-mile buffer zone gives the same results as for the 50 km (31 miles) buffer zone for residential areas along E14. Therefore, the results show that the optimal design is to deploy 14 fast charging stations with three-phase DC, or 14 fast charging stations with three-phase AC, installed adjacent to the E14 road.

The main objective of this research is the development of a flexible test-bench for evaluation of hybrid electric powertrain controllers. As a case study, a real-time near-optimal powertrain controller for a series hybrid electric vehicle (HEV) has been designed and tests. The designed controller, like many other optimal controllers, is based on a simple model. This control-oriented model aims to be as simple as possible in order to minimize the controller computational effort. However, a simple model may not be able to capture the vehicle's dynamics accurately, and the designed controller may fail to deliver the anticipated behavior. Therefore, it is crucial that the controller be tested in a realistic environment. To evaluate the performance of the designed model-based controller, it is first applied to a high-fidelity series HEV model that includes physics-based component models and low-level controllers. After successfully passing this model-in-the-loop test, the controller is programmed into a rapid-prototyping controller unit for hardware-in-the-loop simulations. This type of simulation is mostly intended to consider controller computational resources, as well as the communication issues between the controller and the plant (model solver). As the battery pack is one of the most critical components in a hybrid electric powertrain, the component-in-the-loop simulation setup is used to include a physical battery in the simulations in order to further enhance simulation accuracy. Finally, the driver-in-the-loop setup enables us to receive the inputs from a human driver instead of a fixed drive cycle, which allows us to study the effects of the unpredictable driver behavior. The developed powertrain controller itself is a real-time, drive cycle-independent controller for a series HEV, and is designed using a control-oriented model and Pontryagin's Minimum Principle. Like other proposed controllers in the literature, this controller still requires some information about future driving conditions; however, the amount of information is reduced. Although the controller design procedure is based on a series HEV with NiMH battery as the electric energy storage, the same procedure can be used to obtain the supervisory controller for a series HEV with an ultra-capacitor. By testing the designed optimal controller with the prescribed simulation setups, it is shown that the controller can ensure optimal behavior of the powertrain, as the dominant system behavior is very close to what is being predicted by the control-oriented model. It is also shown that the controller is able to handle small uncertainties in the driver behavior.

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This work aims to analyze the current situation of electric vehicles in the world, considering the factors that assisted their insertion in the world market and the public policies that are used to encourage this technology. Based on this analysis, is performed a prediction of a scenario in which electric vehicles are inserted in the Brazilian market through simulation results using the Monte Carlo Method a known and widespread mathematical method. Therefore, two cities are compared, a national pole of development and diversity, Sao Paulo, and the city known as the Capital of Electric Vehicle, Oslo, which will provide the information that will be used as a basis for the simulation. With this forecast, it is possible to predict an estimative of the environmental impact that replacing gasoline-powered vehicles for electric vehicles would bring to the environment. Furthermore, an analysis of energy impact in the Brazilian matrix is discussed and evaluated. / Este trabalho visa analisar a situação atual dos veículos elétricos no mundo, considerando os fatores que auxiliaram sua inserção no mercado mundial e as principais políticas de incentivo a esta tecnologia. Com base nesta análise, é realizada uma previsão na qual os veículos elétricos são inseridos no mercado brasileiro, através de resultados de simulação utilizando o Método de Monte Carlo, conhecido e difundido. Para tanto, duas cidades serão comparadas, um pólo de desenvolvimento e diversidade nacional, São Paulo, e a cidade conhecida como a Capital do Veículo Elétrico, Oslo, da qual serão extraídas as informações que servirão de base para a simulação e estimativa do impacto na matriz energética brasileira. Com esta previsão, também é possível avaliar o impacto que a substituição dos veículos movidos à gasolina por veículos elétricos traria para o meio ambiente. Além disso, uma análise de impacto energético na matriz brasileira é discutida e avaliada.