abstract: This study evaluates the potential profitability and environmental benefit available by providing renewable energy from solar- or wind-generated sources to electric vehicle drivers at public charging stations, also known as electric vehicle service equipment (EVSE), in the U.S. Past studies have shown above-average interest in renewable energy by drivers of plug-in electric vehicles (PEVs), though no study has evaluated the profitability and environmental benefit of selling renewable energy to PEV drivers at public EVSE. Through an online survey of 203 U.S.-wide PEV owners and lessees, information was collected on (1) current PEV and EVSE usage, (2) potential willingness to pay (WTP) for upgrading their charge event to renewable energy, and (3) usage of public EVSE if renewable energy was offered. The choice experiment survey method was used to avoid bias known to occur when directly asking for WTP. Sixty percent of the participants purchased their PEVs due to environmental concerns. The survey results indicate a 506% increase in the usage of public pay-per-use EVSE if renewable energy was offered and a mean WTP to upgrade to renewable energy of $0.61 per hour for alternating current (AC) Level 2 EVSE and $1.82 for Direct Current (DC) Fast Chargers (DCFC). Based on data from the 2013 second quarter (2Q) report of The EV Project, which uses the Blink public EVSE network, this usage translates directly to an annual gross income increase of 668% from the original $1.45 million to $11.1 million. Blink would see an annual cost of $16,005 per year for the acquisition of the required renewable energy as renewable energy credits (RECs). Excluding any profit seen purely from the raise in usage, $3.8 million in profits would be gained directly from the sale of renewable energy. Relative to a gasoline-powered internal combustion engine passenger vehicle, greenhouse gas (GHG) emissions are 42% less for the U.S. average blend grid electricity-powered electric vehicle and 99.997% less when wind energy is used. Powering all Blink network charge events with wind energy would reduce the annualized 2Q 2013 GHG emissions of 1,589 metric tons CO2 / yr to 125 kg CO2 / yr, which is the equivalent of removing 334 average U.S. gasoline passenger cars from the road. At the increased usage, 8,031 metric tons CO2 / yr would be prevented per year or the equivalent of the elimination of 1,691 average U.S. passenger cars. These economic and environmental benefits will increase as PEV ownership increases over time. / Dissertation/Thesis / Masters Thesis Technology 2014

With the advent of smart sensor devices and Internet of Things (IoT) in the rapid urbanizing cities, data is being generated, collected and analyzed to solve urban problems in the areas of transportation, epidemiology, emergency management, economics, and sustainability etc. The work in this area basically involves analyzing one or more types of data to identify and characterize their impact on other urban phenomena like traffic speed and ride-sharing, spread of diseases, emergency evacuation, share market and electricity demand etc. In this work, we perform spatio-temporal analysis of various urban datasets collected from different urban application areas. We start with presenting a framework for predicting traffic demand around a location of interest and explain how it can be used to analyze other urban activities. We use a similar method to characterize and analyze spatio-temporal criminal activity in an urban city. At the end, we analyze the impact of nearby traffic volume on the electric vehicle charging demand at a charging station. / Master of Science / Because of the ubiquity of the Internet and smart devices, a tremendous amount of data has been collected from multiple sources like vehicles, purchasing details, online searches etc., which is being used to develop innovative applications. These applications aim to improve economic, social and personal lives of people through new start-of-the-art techniques like machine learning and data analytics. With this motivation in mind, we present three applications leveraging the data collected from urban cities to improve the life of people living in such cities. First, we start by using taxi trip data, collected around a given location, and use it to develop a model that can predict taxi demand for next half hour. This model can be used to schedule advertisements or dispatch taxis depending upon the demand. Second, using a similar mathematical approach, we propose a strategy to predict the number of crimes that can happen at a given location on the next day. This helps in maintaining law and order in the city. As our third and last application, we use the traffic and historical charging data to predict electric vehicle charging demand for the next day. Electricity generating power plants can use this model to prepare themselves for the higher demand emerged because of the increasing use of electric vehicles.

Significant advances have been made in the research and development of electric vehicles (EV’s). Along with the major challenge of energy storage, being also addressed is the efficient design of system energy transfer and consumption. This has had the effect of fundamentally changing perspectives across the mobility and transportation sector. Applied predominantly to road-going vehicles, the industrial context of non-road Electric Vehicles (nrEV’s) and specifically the use of manned electric forklift trucks integrated within the production related materials handling system has, to-date, received far less attention. The overarching aim of this research is to examine the impact and potential for the use of contactless occasional recharging of nrEV’s integrated within a manufacturing line, recognising the need to balance the (sometimes competing) demands of delivering sustainable production while exercising environmental responsibility. Meeting the objectives of this research resulted in the development of a location allocation model for electric charging station determination based on a fundamental understanding of the nature and quality of process inherent key performance indicators (KPI’s) as well as comprehensive process and energy monitoring while considering both Lean and Green Management perspectives. The integration of the generated knowledge and information into a generally valid simulation tool for occasional charging system implementation allows to more thoroughly investigate the impact from occasional charging to overall efficiency and sustainability to be realised. An investigation into relevant literature identified the need for specifically generated energy consumption data and confirmed the need for an energy optimisation model specific to the area of production related materials handling. Empirical data collected from repeated standardised materials handling operations within a selected production related materials handling environment resulted in the development of the Standard Energy Consumption Activity tool (SECA). Further work within this pilot study confirmed the tool as capable of generating reliable and valid data and confirmed the SECA tool as a generally applicable benchmark for energy consumption determination in material handling based on fractional process functions. Integrating this approach into a comprehensive process analysis and charging infrastructure optimisation resulted in the development of an Excel-based simulation model. The (Occasional Charging Station Location Model) OCSLM is based upon Maximal Covering Location Modelling and an endogenous covering distance definition in order to simulate process related potentials and optimal charging system implementation allocations, the target being to increase vehicles usable battery energy. A comprehensive case study based upon six individual and one combined data set confirmed the general and wider applicability of the OCSLM model while the application of the model provides a set of novel results. The application demonstrated a theoretical increase in usable battery energy of between 40% and 60% and within the same case study the impact of technology implementation identified that a reduction in battery and system cost of between 5% and 45% can be realised. However, the use of contactless power transfer resulted in an increase in CO2 emissions of up to 6.89% revealing a negative impact to overall ecology from the use of this energy transfer system. Depending on the availability of fast connecting, contact based energy transmission systems, the approach and results of OCSLM have shown to be directly applicable to contact based systems with resulting CO2 emissions decreasing by 0.94% at an energy transfer efficiency of 96%. Further novelty, of benefit to both academic and industry practice, was realised through the framework and information of the research with the provision of SECA as a process function-based and generally applicable energy consumption standard, OCSLM as a Maximal Covering Location Modell with a focus on occasional charging based on an endogenous covering distance and integrating detailed energy and process monitoring into electric charging station allocation, and the methodology for the application of this approach for fast connecting contactless and contact charging models and cases.

Technological improvements along with falling prices on photovoltaic (PV) panels and electric vehicles (EVs) suggest that they might become more common in the future. The introduction of distributed PV power production and EV charging has a considerable impact on the power system, in particular at the end-user in the electricity grid. In this PhD thesis PV power production, household electricity use and EV charging are investigated on different system levels. The methodologies used in this thesis are interdisciplinary but the main contributions are mathematical modeling, simulations and data analysis of these three components and their interactions. Models for estimating PV power production, household electricity use, EV charging and their combination are developed using data and stochastic modeling with Markov chains and probability distributions. Additionally, data on PV power production and EV charging from eight solar charging stations is analyzed. Results show that the clear-sky index for PV power production applications can be modeled via a bimodal Normal probability distribution, that household electricity use can be modeled via either Weibull or Log-normal probability distributions and that EV charging can be modeled by Bernoulli probability distributions. Complete models of PV power production, household electricity use and EV home-charging are developed with both Markov chain and probability distribution modeling. It is also shown that EV home-charging can be modeled as an extension to the Widén Markov chain model for generating synthetic household electricity use patterns. Analysis of measurements from solar charging stations show a wide variety of EV charging patterns. Additionally an alternative approach to modeling the clear-sky index is introduced and shown to give a generalized Ångström equation relating solar irradiation to the duration of bright sunshine. Analysis of the total power consumption/production patterns of PV power production, household electricity use and EV home-charging at the end-user in the grid highlights the dependency between the components, which quantifies the mismatch issue of distributed intermittent power production and consumption. At an aggregate level of households the level of mismatch is shown to be lower.

Klimatförändringar är en av vår tids största utmaningar och andelen växthusgaser ut i atmosfären måste minskas kraftigt. Sverige har som mål om en fossilfri bilflotta år 2030, vilken ska upprättas med hjälp av alternativa fordon och drivmedel. I och med detta har elektriskt drivna fordon kommit att bli attraktiva vilket föranleder nya utmaningar för elnätsägare. Denna studie har till syfte att verka som ett underlag för kommande investeringar i ett lokalt elnät. Målet är att med simuleringar av scenarier gällande laddningseffekt och andelen förekommande elbilsladdning, succesivt belasta elnätet för att på så vis utreda nätkapaciteten. Nätstationers transformatorer samt kablage skall även åldersbestämmas. Metoden är byggd utifrån litteraturstudier och simuleringar har gjorts i dpPower. Resultaten visar att nätet är gammalt men klarar ändock nästintill 100 % penetration elbilar med laddningseffekten 3,7 kW. Vid högre laddningseffekter börjar nätet ge vika vid ca 25-50% elbilspenetration. Nätstationernas transformatorer är komponenterna som belastas hårdast och spänningsfall är den mest förekommande kvalitetsbristen. Då matarkabelarean ökades mellan nätstation och kabelskåp, medförde detta avsevärda förbättringar gällande spänningsfall. Högspänningsnätet visade sig vara mycket starkt. Slutsatsen är att transformatorers märkeffekt samt spänningsfall blir begränsande faktorer för framtiden. Transformatorer och matarkablar kan behöva förstärkas alternativt att ytterligare nätstationer tillsätts nätet. Detta skulle med omkonstruktion av nätet fördela lasten, minska avstånd till kund och då stävja spänningsfallen. Högspänningsnätet bör i framtiden utnyttjas mer och på så vis avlasta lågspänningsnätet. / Climate change is one of today's biggest challenges, and the amount of greenhouse gases into the atmosphere has to be greatly reduced. Sweden aims at a fossil-free car fleet by 2030, using alternative vehicles and fuels. Therefore electrically driven vehicles have become attractive, causing new challenges for power grid owners. This study aims to serve as a basis for future investments in a local electricity grid. By simulating scenarios regarding electrical vehicle charging power in relation to the number of vehicles involved, the electricity grid will be loaded to investigate its capacity. Transformers and cables in the grid will be determined by age. The method is based on literature studies and simulations have been made in dpPower. The results show that the grid is old, but yet capable of a nearly 100 % of electric cars charged with 3.7 kW, with higher charging powers the grid starts to yield at about 25-50 % of electric car penetration. The substations are the components that are subjected the most, problems with voltage drops is the most common quality shortage. The cross section of the feeding cable between substations and the nearest cable distribution cabinet was increased which resulted in significant improvements in voltage drop. The high voltage network is considered as very strong and can handle all scenarios. The conclusion is that transformer power rating and voltage drops will become limiting factors in the future. Transformers and power cables may need to be reinforced alternatively additional substations to the grid put in place. Additional substations with the redesign of the grid may distribute the load, reduce the distance to customers and with that reduce the voltage drop. The high-voltage grid should be utilized more in the future, thus unburdening the low voltage grid.

abstract: A principal goal of this dissertation is to study stochastic optimization and real-time scheduling in cyber-physical systems (CPSs) ranging from real-time wireless systems to energy systems to distributed control systems. Under this common theme, this dissertation can be broadly organized into three parts based on the system environments. The first part investigates stochastic optimization in real-time wireless systems, with the focus on the deadline-aware scheduling for real-time traffic. The optimal solution to such scheduling problems requires to explicitly taking into account the coupling in the deadline-aware transmissions and stochastic characteristics of the traffic, which involves a dynamic program that is traditionally known to be intractable or computationally expensive to implement. First, real-time scheduling with adaptive network coding over memoryless channels is studied, and a polynomial-time complexity algorithm is developed to characterize the optimal real-time scheduling. Then, real-time scheduling over Markovian channels is investigated, where channel conditions are time-varying and online channel learning is necessary, and the optimal scheduling policies in different traffic regimes are studied. The second part focuses on the stochastic optimization and real-time scheduling involved in energy systems. First, risk-aware scheduling and dispatch for plug-in electric vehicles (EVs) are studied, aiming to jointly optimize the EV charging cost and the risk of the load mismatch between the forecasted and the actual EV loads, due to the random driving activities of EVs. Then, the integration of wind generation at high penetration levels into bulk power grids is considered. Joint optimization of economic dispatch and interruptible load management is investigated using short-term wind farm generation forecast. The third part studies stochastic optimization in distributed control systems under different network environments. First, distributed spectrum access in cognitive radio networks is investigated by using pricing approach, where primary users (PUs) sell the temporarily unused spectrum and secondary users compete via random access for such spectrum opportunities. The optimal pricing strategy for PUs and the corresponding distributed implementation of spectrum access control are developed to maximize the PU's revenue. Then, a systematic study of the nonconvex utility-based power control problem is presented under the physical interference model in ad-hoc networks. Distributed power control schemes are devised to maximize the system utility, by leveraging the extended duality theory and simulated annealing. / Dissertation/Thesis / Ph.D. Electrical Engineering 2012

This master thesis deals with design of DC-DC converter for onboard charging of electric vehicle. Developed converter will mainly be used for charging stationary traction battery in laboratory. Output voltage of this charger will be adjustable by user in between 200 V and 450 V depending on the current charged battery configuration. Output current limit is set at 8 A. Since the converter will be supplied from standard household socket, the problem of power factor correction must be solved during the design. That is because a large part of this thesis is focused on describing the problematics of power factor correction. After that, active PFC module is designed, completed and performance of this module is verified. To achieve low overall losses and thus be able to keep small volume of the system, modern switching components based on Silicon Carbide were preferred. Beside laboratory use, completed system will be used to emphasize volumetric difference between onboard chargers based on old versus modern switching components.

<div>Electric vehicles (EVs) face a major issue before becoming the norm of society, that is, their lack of range when it comes to long trips. Fast charging stations are a good step forward to help make it simpler for EVs, but it is still not as convenient when compared to vehicles with an internal combustion engine (ICE). Plenty of infrastructure changes have been proposed in the literature attempting to tackle this issue, but they typically tend to be either an expensive solution or a difficult practical implementation.</div><div> </div><div> This dissertation presents two solutions to help increase the range of EVs: a novel wireless charging method and a multi-motor architecture for EVs. The first proposed solution involves the ability for EVs to charge while en route from another vehicle, which will be referred to from here on as vehicle-to-vehicle recharging (VVR). The aim of this system is to bring an innovative way for EVs to charge their battery without getting off route on a highway. The electric vehicle can request such a service from a designated charger vehicle on demand and receive electric power wirelessly while en route. The vehicles that provide energy (charger vehicles) through wireless power transfer (WPT) only need to be semi-autonomous in order to ``engage'' or ``disengage'' during a trip. Also, a novel method for wireless power transfer will be presented, where the emitter (TX) or receiver (RX) pads can change angles to improve the efficiency of power transmission. This type of WPT system would be suitable for the VVR system presented in this dissertation, along with other applications.</div><div> </div><div> The second solution presented here will be an architecture for EVs with three or more different electric motors to help prolong the state of charge (SOC) of the battery. The key here is to use motors with different high efficiency regions. The proposed control algorithm optimizes the use of the motors on-board to keep them running in their most efficient regions. With this architecture, the powertrain would see a combined efficiency map that incorporates the best operating points of the motors. Therefore, the proposed architecture will allow the EV to operate with a higher range for a given battery capacity.</div><div> </div><div> The state-of-the-art is divided into four subsections relevant to the proposed solutions and where most of the innovations to reduce the burden of charging EVs can be found: (1) infrastructure changes, (2) device level innovations, (3) autonomous vehicles, and (4) electric vehicle architectures. The infrastructure changes highlight some of the proposed systems that aim to help EVs become a convenient solution to the public. Device level innovations covers some of the literature on technology that addresses EVs in terms of WPT. The autonomous vehicle subsection covers the importance of such technology in terms of safety and reliability, that could be implemented on the VVR system. Finally, the EV architectures covers the current typologies used in EVs. Furthermore, modeling, analysis, and simulation is presented to validate the feasibility of the proposed VVR system, the WPT system, and the multi-motor architecture for EVs.</div>

No description available.

Εlectric vehicles are replacing the internal combustion engine vehicles rapidly and they will dominate the market completely in the next years. The amount of energy and power needed to support this new technology is huge. This will increase the already high electricity demand of our societies. The electric vehicles can provide a solution by using them to transfer energy to any other vehicles or infrastructure in combination with electricity management. This can be achieved by controlling the electric vehicle chargers and by knowing the exact consumption of the other vehicles or infrastructures. In Dansmästaren, Uppsala, there is a parking garage with 30 Charge Amps Aura charging stations. The same type of charger has been used in order to examine if it is possible to extract and update data through programming, as well as its functions regarding Vehicle-to-everything (V2X). This thesis presents two Python scriptswhere the first is used to update different functions of the charger and the secondto get high resolution electricity data and the energy consumption of the charger.The collected data is stored in two MySQL database every 30 seconds for future use. The data that can be updated by the user immediately, from anywhere and at any time. Similarly, the data collection has shown that different charging patterns exist and they can be observed by using the data that are generated and saved in the databases.