Electric Vehicles represent a small niche market today, but is predicted to grow rapidly over the next years. In order to prepare for this upcoming trend it is the networks of Electric Vehicle Charging Stations (EVCS) must expand, leading to an increasing demand for EVCSs. The EVCS market is thus becoming increasingly more popular to companies, and therefore this study’s purpose is to investigate this market and its competitive situation. The method used in this study includes a brief market analysis and a competitor analysis. The market analysis includes identification of the EVCS markets together assessing the future of the markets, and identification of EVCS market drivers and restraints. The competitor analysis includes competitor identification, classification and analysis. The top ten competitors are analyzed by the use of document content analysis, the analysis involves understanding the competitors’ target customers, how they do business and how their marketing material is structured. The three most promising EVCS markets, both currently and in the future, are the Asia Pacific, Europe and the North America markets. Most of the top competitors are active within these three markets. Regional developments, and market drivers and restraints of these markets have been identified. The opportunities in the EVCS markets are many as they are relatively unexploited markets without any actual market leaders, and also that all markets are predicted to grow at a very high rate over the coming decade in parallel with the projected mass adoption if Electric Vehicles (EVs). / Idag utgör elfordon endast en liten nischmarknad i transportmarknaden, men denna förväntas växa snabbt under de närmaste åren. För att kunna hantera marknadsetableringen av elfordon måste elfordonsladdningsinfrastrukturen byggas ut, vilket leder till en ökad efterfrågan på elfordonsladdningsstationer. Elfordonsladdningsmarknaden förespås således bli allt mer intressant för företag. Detta examensarbete genomförs på grund av detta växande intresse, då studiens syfte är att undersöka elfordonsladdstationsmarknaden och dess konkurrenssituation. Metoden som används i denna studie inbegriper en kort marknadsanalys och en konkurrensanalys. Marknadsanalysen innehåller identifiering av elfordonsladdningsmarknaderna, vad som driver och hindrar marknaderna, och en bedömning av hur framtiden ser ut för marknaderna. I konkurrensanalysen ingår identifiering, klassificering och analys av de olika konkurrenterna. De tio mest konkurrenskraftiga konkurrenterna analyseras med hjälp av dokumentinnehållsanalys, syftet med analysen är att förstå konkurrenternas målgrupper, hur de gör affärer och hur deras marknadsföringsmaterial är strukturerad. De tre mest lovande elfordonsladdningsmarknaderna, både nu och i framtiden, är marknaderna i Asien och Stillahavsområdet, Europa och Nordamerika. De flesta av de analyserade konkurrenterna är verksamma inom dessa tre marknader. Den regionala utvecklingen, och vad som driver och begränsar marknaderna har identifierats för de tre mest lovande marknaderna. Eftersom dessa marknader är relativt oexploaterade i samband med att de förväntas växa med väldigt hög takt det kommande decenniet parallellt med massanvändningen av elfordon är möjligheterna många för de företag som inriktar sig mot elbilsladdning.

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.

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.

The worldwide fast growth of the transportation sector contributes to a large andgrowing share of global greenhouse gas (GHG) emissions. The Swedish TransportAdministration report indicates that emissions from domestic transport increasedin 2018. Having an idea that the workplace parking lots have the potential toincrease the share of renewable energy production in Sweden, an investigation forthe solar-powered electric vehicle (EV) charging station is conducted. This studyaims to clarify the knowledge on what the potentials are, financial assessment, andassessment of the photovoltaic (PV) self-consumption of EV charging in theworkplace charging station. Without knowledge about the highlighted parameters,investors may hesitate to invest in a PV project such as a solar-powered EV chargingstation system. To achieve the objective of this thesis, appropriate tools and/orsoftware are used. PV*SOL software tool is used for simulation and analysis ofenergy system efficiency with EV charging station integrated for different PVsystems deployed in the same location of Sweden. This software tool allows thedesign and calculations of the PV system and EV charging station integrated.Currently registered cars in Sweden indicate that EVs are dominating and will keepthe pulse in the future. This domination will enforce more need for electricity, callfor renewable energy use, and promising significant GHG emissions reduction –sustainable environment. The study has proven that there is no immense insolationin Sweden, thus, the power converter can be undersized up to 30 % with respect tothe PV array to reduced energy loss. A feasible solar-powered EV charging stationrequires several factors such as initial investment (EV charging station, PV module,inverter, transport and installation, operation, and maintenance, etc.), andelectricity trading rate. The study of five PV system cases showed that the increasein size significantly increases the self-sufficiency ratio while self-consumption ratiodecreases. By increasing the PV array, both levelized cost of electricity and paybackperiod were considerably decreased as was intended. However, the more PV arrayincreased in size the more initial investment is required. Study on GHG emissionsof the solar-powered EV charging station as well as the deployment of local energystorage and EV smart charging are recommended as future works.

Background: Climate changes and global warming call for behaviour changes from mankind and for new business models to introduce sustainable innovations. Financial analysis plays an important role in guiding the choice of these business models. However, assumptions and uncertainties pose challenges to the use of financial analysis in business modelling. Purpose: The purpose of this study is to develop a proactive systematic approach of financial analysis in business modelling. Accounting for the important role of assumptions and uncertainty factors, the approach should guide the choices of capital structure, revenue model, and strategic partnerships in the business model. Methodology: The developed approach combines the use of different methods to assess different business models for a public fast-charging stations for electric heavy-duty vehicles in Sweden. The used techniques are DCF analysis, What-If analysis, Tornado diagram, Monte-Carlo simulation, and multiple linear regression analysis. Results and analysis: Applied to the case of a public fast-charging station for electric heavy-duty vehicles, the approach leads to the identification of potential viable business models. Under the condition of using financial leverage through debt, additional revenue sources such as per-charge event user fee and advertising as well as partnership with the public sector in the form of grants, it is possible to achieve a viable business model. Conclusions: A systematic proactive approach of the use of financial analysis in business modelling was successfully developed and applied to the case of fast-charging stations for electric heavy-duty vehicles. The identified viable business models rely on financial leverage through debt, additional revenue sources and partnership with the public sector in the form of grants. Recommendations for future research: Simulations with more input parameters as well as combinations with observational studies of existing business models can be further investigated.

The projected and current adoption rates of electric vehicles are increasing. Since electric vehicles require that they be recharged continually over time, the energy needs to support them is immense and growing. Given existing infrastructure is insufficient to supply the projected energy needs, models are necessary to help decision makers plan for how to best expand the power grid to meet this need. A successful power grid expansion is one that enables charging stations to service the electric vehicle community. Thus, plans for power expansion need to be coordinated between the power grid and charging station investors. The infrastructure for the charging stations has to also be resilient and reliable to absorb this increase in load. Charging stations therefore should be included in the plans for post power disruption planning. In this work, two two-stage stochastic programming models are developed that can be used to determine a power grid expansion plan that sup- ports the energy needs, or load, from an uncertain set of electric vehicles geographically dispersed over a region. Another three-stage stochastic programming model is presented, where the decisions are made first to select which charging stations to install and expand uninterruptible power supply units and renewable energy sources. Then, when the disrup- tion occurs in the second-stage, repairs in power system and charging stations take place ahead of the arrival of panicked population to prepare for the expected surge in power de- mand. Finally, as demand is unveiled, managerial and operational decisions at the charging stations are made in the third-stage. To solve the mathematical models, we utilize hybrid approaches which mainly make use of Sample Average Approximation and Progressive Hedging algorithm. To validate the proposed model and gain key insights, we perform computational experiments using realistic data representing the Washington, DC area. Our computational results indicate the robustness of the proposed algorithm while providing a number of managerial insights to the decision makers.

This degree project is done in collaboration with Vinnergi AB at their officein Halmstad. The aim for this project is to deliver a proposal for a new charging station in Trelleborg with focus on technology implementation. With an increase in electrification in the transport sector comes the need to construct more charging stations in order to keep up with the increased demand for charging points for electric vehicles. This report will cover research for existing chargers at the same location in order to determine the current usage of those chargers, the amount of chargers that the nearby electrical grid can handle as well as a power quality study for the existing electrical grid. To reach the specified goals, simulations of the actual electrical grid were conducted along with several calculations and technology considerations that were partly based on the simulations. This research came up with a new proposal for a charging station with two charging posts for fast charging with a charging capability of between 175 kW and 350 kW. Cables, breakers and a grounding system for the chargerswere evaluated and chosen along with a fundamental budget for the whole project. This proposed charging station will extend the charging capabilities in the area and be a part of keeping up with the increased demand for fast andreliable charging in Trelleborg.

Owing to advancements in technology, substantial investments within the automotive industry, and the formulation of supportive state policies, the future landscape of the transportation sector is poised to witness a shift from traditional internal combustion engine vehicles (ICEVs) to electric vehicles (EVs). While EVs have made inroads in the market, they still face significant hurdles in the form of range anxiety and prolonged charging durations, inhibiting their widespread adoption. To tackle these challenges, a comprehensive approach to smart transportation electrification is proposed, emphasizing the pivotal roles of infrastructure development, particularly in the allocation of charging stations, and strategic operational decisions, including charging and platoon scheduling. This dissertation is structured around four essential components. The initial stage entails grasping the intricacies of charging demand, recognized as the foundational step before embarking on any transportation electrification initiative. Subsequently, the allocation of charging stations is addressed, with a specific focus on ride-sourcing vehicles, distinct from private EVs due to issues such as relocation time, waiting time, and dynamic pricing that affects spatiotemporal value of time (VOT) costs. This approach, which considers VOT costs, is essential in avoiding biased results in the planning of charging infrastructure for electrified ride-sourcing services. The third chapter centers on the optimization of charging and platoon scheduling, particularly within the context of long-haul freight vehicles. The objective here is to harness the flexibility of charging schedules to facilitate vehicle platooning, thereby reducing the demand for charging, and, consequently, energy consumption. This chapter involves the development of a mixed-integer programming model and explores various techniques, such as hyperparameter tuning and hybrid meta-heuristic methods, to optimize the model for large-scale applications. Lastly, the fourth chapter takes on the challenge of addressing uncertainty in scheduling problems. This is achieved by formulating a two-stage stochastic model and applying it within a hypothetical numerical example, providing a framework for optimizing charging station (CS) planning while accounting for uncertain operational parameters.

No description available.

Recharge är en laddstation för elbilar med lokalproducerad el och en mötesplats för lokala och regionala aktörer att samlas, nätverka och utöva sina småskaliga verksamheter på. Projektet grundar sig i en undersökning av motorvägens roll på landsbygden. I undersökningen kartlades även småskaliga verksamheter inom matproduktion i området. Vidare diskuterar projektet tre huvudsakliga frågeställningar. Vilken är bilens roll i framtiden och hur en kan arkitektur som tar bilen i fokus ihop med ett hållbarhetsperspektiv se ut. Landsbygden har inte samma förutsättningar som staden för att bli fossilfria, därför utgår projektet från tesen att den rurala framtiden kommer bygga med bilen i fokus. De ytterligare frågeställningarna undersöker om det går att skapa en plats som integrerar motorvägen med landsbygden samt vilket resultat det blir om man kontrasterar de stora aktörerna Burger King och Cirkle K som ligger på platsen och byter ut deras globala näringskedja mot en småskalig, lokal och hållbar istället. / Recharge is a car charging station powered by locally produced electricity and a meeting place for local and regional users to gather, network and carry out their small-scale activities. The project is based in a survey of the motorway and its role in the countryside. The survey also mapped small businesses of food production in the area. The project discusses three main issues. What role does the car have in the future and what will a car and sustainability centred architecture look like. The countryside does not have the same opportunities for a car free future as the city does, therefore the project assumes that the future of the rural is going to be built with the car in focus. The other issues examines whether it is possible to create a place that integrates the motorway with the countryside and what result do you get if you contrast the large businesses Burger King and Cirkle K and swap their global chain of production with a small scale, local and sustainable one.