The thesis develops a model for deployment of public charging stations for electric vehicles and hybrids in the city of Uppsala in relation to the electricity demand. Areas for deployment of public charging stations has been determined through traffic flow analysis and is displayed in a map over the city of Uppsala. The model was constructed using three different prognoses for an increase of electric vehicles and hybrids. Through the prognoses the electricity demand has been determined and the number of public charging stations in relation to the electricity demand. It could be concluded that future public charging stations is difficult to predict due to uncertainties concerning the future electric vehicle market. In this study it is concluded that the number of public charging stations would not have a significant impact on the total electricity use in the city of Uppsala.

The municipal company Sportfastigheter AB is currently renovating and developing Gränby sportfält, a sports field in Uppsala. Adjacent to the sports field, a parking lot for 700 vehicles is located, where Sportfastigheter AB is preparing to install charging points for electric vehicles (EVs) at some of the places. This bachelor thesis aims to investigate how a public charging solution should be modeled, with the parking lot at Gränby sportfält as a case study. The investigation involves estimating energy demand of visiting EVs, optimizing the ability to satisfy the estimated energy demand, and proposals of business models. A computer-based simulation of a representative week at Gränby sportfält was created as a decision basis for modeling the charging solution and what power capacity to dimension for. The results of this investigation indicates that the most suitable charging solution for Gränby sportfält is a solution with semi-fast chargers and load balancing, which is a type of controlled charging. With load balancing, a lower power capacity can be dimensioned for compared with the same solution without load balancing with savings in costs as a consequence. When investigating for 50 charging points the power capacity proposed to dimension for is 200 kW, which would lead to the possibility of meeting 98.7 % of the total energy demand of connected EVs. However, this study proposes to build the charging points gradually, with an initial installation of 12 charging points. Lastly, this study proposes to use a business model involving sponsoring, and offer the charging for free.

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.

This thesis explores barriers to widespread adoption of electric vehicles and proposes possible policy solutions. It analyzes main barriers including awareness, upfront cost, and range anxiety, as well as existing policy solutions, and a detailed case study examining policy differences in high adopting versus low adopting states. Awareness and eduction surrounding electric vehicles and their capabilities, financial incentives and market mechanisms for reducing costs, and charging infrastructure and efficiency improvements are examined. Conclusions were formed through interviews with various experts as a method of data collection. It was found that many existing state and local level policies could be scaled to a national level to facilitate rapid reductions in transportation emissions through electrification of the transportation sector.

With the ever-pressing threat of a climate crisis, the EU has decided to become the first climate-neutral continent by 2050. This in turn will require the road transportation sector to make a transition from fossil dependent to fossil-free vehicles. Sweden has the objective to become net positive in GHG emissions by 2045. To be on track to reach this goal, the GHG emissions of the domestic transport sector must be reduced by 70% by 2030 compared to 2017’s levels. Electric vehicles (EVs) are leading the way in the transition to fossil-free vehicles. To further springboard the diffusion of EVs, the development of a fully functional EV charging network is required. In order to assist the transition to electric vehicles, this report aims to analyse the development of the public fast charging infrastructure in Norrland and Svealand from now to 2030. Additionally, identify geographical areas where an expansion of the public EV fast charging network is needed to cover the future demand of electrified passenger cars. However, there are two major hurdles in building a fast charging network with full coverage. The first is the high monthly costs of providing fast charging which needs a certain utilisation rate to cover the expenses. The second hurdle is the difficulty to receive a grid connection, in certain areas, at the required power output to be able to provide EV fast charging. Therefore, a semi-mobile battery solution used for EV charging is analysed through a business model perspective. The semi-mobile battery solution requires a lower grid connection hence it could be possible to implement public EV fast charging at a lower monthly cost and to develop the public EV fast charging network in otherwise technical difficult areas. A mixed-method approach including both quantitative and qualitative elements was utilised. Primarily, a study of 10 interviews with respondents from a range of different fields connected to EV charging and batteries was performed in combination with a literature review and document analysis. In addition, existing traffic flow data and data of fast-charging infrastructure, were converged via ArcGIS Pro to illustrate the coverage of the fast charging network. Furthermore, projections of the development of the EV fleet were used in order to forecast the flow of EVs in Norrland and Svealand by 2030. Based on these forecasts the future demand of public EV fast charging was analysed. Resulting in a map showing areas of interest, where there will arise a need to expand the charging infrastructure. These areas are Umeå to Piteå, Lycksele with proximity, Bollnäs to Ljusdal and Leksand to Älvdalen. Additionally, the exiting public fast charging infrastructure was identified to require expansion of existing charging stations due to the increased traffic flow of EVs by 2030. The upgrade of existing stations was further assessed to be required to meet both a permanent and seasonal demand, hence making semi-mobile battery supported charging an attractive solution. Furthermore, the design of a semi-mobile battery supporting public EV fast charging was identified to be influenced by situational aspects and that the location-specific conditions were vital in determining profitability for a specific case. For example, the power output in the EV chargers should be adapted to the specifications of the geographical location and the customer segment identified. The energy storage capacity of the battery should also be designed based on the conditions of the location. A connection to the electricity grid exceeding 0.1 MW was also important since it enables the semi-mobile battery to provide additional services to the electricity grid and hence increase revenue streams. Furthermore, FCR-D Up was determined to be the most suitable complementary service to integrate into the system. One major challenge for the semi-mobile battery, based on a business model perspective, is the high costs for semi-mobile batteries and EV fast charging station hardware. However, these costs are projected to continue to decrease and consequently, improve the opportunities for semi-mobile lithium-ion batteries.

<p>It is widely recognized that a requisite aspect of addressing climate goals is to develop a more sustainable transportation sector. One initiative towards this is the federal administration’s stated goal that 50% of all new vehicle sales will be electric by the year 2030. However, it is a common consensus that this will not occur without significant changes in electric vehicle (EV) adoption trends. In order to meet this goal and significantly diminish transportation greenhouse gas emissions, it is critical to better understand EV adoption at scale. To do this, we must understand at the system level what the progression of adoption will look like and what factors influence that adoption.</p> <p>This problem requires a more granular analysis than has been previously performed. We analyze adoption at the ZIP code level in four US states (CA, CO, NY, WA) with historical data dating to 2011. To understand the progression of adoption, we consider two adoption models (the logistic model and the Bass model) to forecast future EV levels in ZIP codes. We find that the logistic is better for the data that is currently publicly available.</p> <p>We additionally find that EV forecasts must be decomposed into both battery electric vehicle (BEV) and plug-in hybrid electric vehicle (PHEV) forecasts. There is sufficient evidence that the adoption processes for these two types of EVs differ.</p> <p>Critically, we extend this analysis to consider the factors influencing adoption. Utilizing the adoption forecasts, we perform spatial regression analyses on the parameters that define the forecast shapes. We examine how multiple sociodemographic, land use, and charging measures correlate with the rate of EV adoption and the lateral shift of early EV adoption.</p> <p>Crucially, we find that multiple measures of charging infrastructure availability correspond with increased adoption; of these, a variation on the distance to fast-charging stations is the most consistent metric across final models. We additionally find that land use type is indeed relevant to adoption. Finally, we are able to corroborate at a granular spatial level numerous sociodemographic variables from the literature.</p> <p>Ultimately, this research can provide valuable insights into adoption trends at a local level and what factors may be best leveraged to promote adoption.</p>

The presented dissertation deals with the problem of deploying the charging infrastructure for electric vehicles in the Czech Republic. The core of the thesis is a mathematical optimization model, which is implemented in the language of MATLAB computing software. The model consists of several sub-units representing separate models of studied sub-problems. The individual chapters of the work describe successively these sub models. The sub models are: demand model of the charging service, model of charging supply, charging simulator model, optimization model and its resolving optimization method. The optimization model is accelerated by parallelization on the graphics card. The optimization method is designed as a case-specific implementation of genetic algorithms on a population of tree-structured individuals. The final chapter deals with an economic aspect of the problem under consideration, the implications of the findings and the role that the optimization model plays in the context under consideration. The main benefit of the work lies in the formulation of the problem as a mathematical model, the accompanying analyses and the provided justifications. Any user with updated data can then use this work along with the attached scripts to find answers to questions about the relationship between electromobility and the charging infrastructure.

Die Arbeit beschäftigt sich mit der Untersuchung von Einflussfaktoren auf eine Ladeinfrastruktur von Elektrofahrzeugen in Peking. Neben den politischen, technischen und wirtschaftlichen Faktoren, liegt der Fokus auf einer Analyse von Nutzerdaten und einer räumlichen Auswertung der Wohn- und Parkplatzsituationen in Peking in Bezug auf die Errichtung einer Ladeinfrastruktur für Elektrofahrzeuge. Der Hauptteil der Arbeit analysiert die städtebauliche Struktur Pekings und kombiniert diese Erkenntnisse mit der Analyse von realen Fahrprofilen von Elektrofahrzeugen. Auf Grund der geringen Anzahl von Parkplätzen sowohl im öffentlichen Raum als auch in den geschlossenen Wohnsiedlungen (Compounds), gestaltet sich die Installation von Ladesäulen, an denen EVs mehrere Stunden laden müssen, als schwierig. Dies betrifft sowohl poly-zentrische als auch mono-zentrische Stadtbezirke im Zentrum. Die Analyse der Fahrprofile basiert auf einem zweiwöchigen Zeitraum mit 60 E-Fahrzeugen, die von Nutzern in Peking gefahren wurden. Ladevorgänge fanden circa 2-3 mal die Woche statt, fanden auf privat Grundstücken statt und dauerten meistens über 10 Stunden. Auf Grund der aufgenommenen GPS-Daten konnte der genaue Aufenthaltsort der Park- und Ladevorgänge untersucht werden. Dabei zeigte sich, dass Fahrzeugführer immer nachts an denselben Orten parkten und luden. Mit den gewonnen Daten konnte auch festgestellt werden, dass home-charging, also das Laden daheim, für die aller meisten Fahrten gänzlich ausreicht. Ergänzt werden kann es durch destination-charging, dem Laden am Zielort, oder on-the-go-charging, dem Laden auf dem Weg zum Zielort. Dabei sind die beiden letzteren Optionen in Peking im halb-öffentlichen Raum anzuordnen, weil reines, öffentliches Laden an Straßenrändern oder Parkplätzen auf Grund von mangelndem Platz nicht möglich sind. / The dissertation deals with the analysis of factors of influence on an implementation of a charging infrastructure for electric vehicles in Beijing. Next to the political, technical and economic factors, the focus is on an analysis of user data and on a spatial analysis of residential housing and parking situations in Beijing. All analyses were done in relation to the implementation of a charging infrastructure for electric vehicles. The main part of the work analyzes the urban structure of Beijing and combines its findings with the analysis of driving profiles of electric vehicles. Due to the small number of parking spaces in public spaces as well as in closed housing estates, the installation of charging stations, becomes a challenge. This concerns both poly-centric and mono-centric city districts in the center of the city. This applies especially to compounds from the 1960s to 1980s, because they were built without adequate parking lots in those days. These properties cannot accommodate the current number of vehicles by far, resulting in the fact that public and semi-public space for parking has to be used. An analysis of the driving profiles is based on a two-week period with 60 electric vehicles driven by real users in Beijing. The investigated vehicles could be charged with alternating current, which prolongs the charging time compared with direct current charging considerably. It was found that most of the parking events took place with relatively full batteries and the distances could be overcome by a single battery charge. Due to the recorded GPS data, the exact location of the parking and charging events could be examined. It was found that EV drivers always parked and charged at night at the same locations. Furthermore it was discovered that home-charging is sufficient for most tracks of the user. This charging type can be extended by destination-charging, or on-the-go-charging.

Electric vehicles (EVs) offer an exciting opportunity in China both in terms of the potential to build a domestic manufacturing base and the potential to create a strong domestic market for the product. The Chinese nation stands to benefit from both supply-side and demand-side promotion due to the economic stimulus from EV manufacturing and export, the environmental benefits of reduced air pollution and reduced greenhouse gas emissions, and the energy security benefits of transitioning away from foreign oil dependence. The Chinese have several advantages when it comes to stimulating EV industry development and EV deployment, including: leadership in battery technology, great potential for cost competitiveness, an enormous and emerging number of new car buyers, and high level government support. Yet a number of challenges must be taken into account as well, including: shortfalls in overall automobile R&D spending, consumer concerns about Chinese cars’ safety and reliability, enhancing the appeal of the Chinese brand, and heavy national infrastructure demands. This paper will seek to examine the opportunities and challenges associated with EV deployment in China and identify industry actions and policy measures to facilitate the process.

Increasing debates over a gasoline independent future and the reduction of greenhouse gas (GHG) emissions has led to a surge in plug-in hybrid electric vehicles (PHEVs) being developed around the world. Due to the limited all-electric range of PHEVs, a daytime PHEV charging infrastructure will be required for most PHEVs’ daily usage. This dissertation, for the first time, presents a mixed integer mathematical programming model to solve the PHEV charging infrastructure planning (PCIP) problem. Our case study, based on the Oak Ridge National Laboratory (ORNL) campus, produced encouraging results, indicates the viability of the modeling approach and substantiates the importance of considering both employee convenience and appropriate grid connections in the PCIP problem. Unfortunately, the classical optimization methods do not scale up well to larger practical problems. In order to effectively and efficiently attack larger PCIP problems, we develop a new MASTS based TS algorithm, PCIP-TS to solve the PCIP. The results from computational experiments for the ORNL campus problem establish the dominant supremacy of the PCIP-TS method both in terms of solution quality and computational time. Additional experiments with simulated data representative of a problem that might be faced by a small city show that PCIP-TS outperforms CPLEX based optimization. Once the charging infrastructure is in place, the immediate problem is to judiciously manage this system on a daily basis. This thesis formally develops a mixed integer linear program to solve the daily the energy management problem (DEM) faced by an organization and presented results of a case study performed for ORNL campus. The results from our case study, based on the Oak Ridge National Laboratory (ORNL) campus, are encouraging and substantiate the importance of controlled PHEV fleet charging and realizing V2G capabilities as opposed to uncontrolled charging methods. Although optimal solutions are obtained, the solver requires practically unacceptable computational times for larger problems. Hence, we develop a new MASTS based TS algorithm, DEM-TS, for the DEM models. Results for ORNL campus data set prove the dominant computational efficiency of the DEM-TS. For the simulated extended sized problems that resemble the complexity of a problem faced by a small city, the results prove that DEM-T not only achieves optimality, but also produces sets of multiple alternate optimal solutions. These could be very helpful in practical settings when alternate solutions are necessary because some solutions may not be deployable due to unforeseen circumstances. / text