The inevitable choice for the automotive industry to suppress greenhouse gas emissions is zero-emission vehicles such as battery electric vehicles. Some of the main barriers regarding the adoption of electric vehicles are range anxiety, and lack of charging infrastructure, which can be addressed by ultra-fast chargers or charging stations. The conventional ultra-fast chargers are low-voltage (LV) connected through line-frequency transformers, which pose disadvantages such as low efficiency, high cost, and large footprints. The medium-voltage (MV) connected charging station is proposed by the researchers to overcome the challenges regarding the conventional chargers by eliminating the line-frequency transformer and direct connection to the medium voltage. The most challenging part of the medium-voltage ultra-fast chargers is the AC/DC stage connection to the medium voltage. Different medium-voltage multilevel converters have been proposed to facilitate the direct connection to the medium-voltage grid. However, disadvantages such as a high number of components and control complexity weaken the strength of medium-voltage connected stations. The main focus of this thesis is on novel advanced medium-voltage multilevel topologies and control techniques for medium-voltage connected ultra-fast EV charging applications. First, a novel controller based on SPWM is proposed to control the flying capacitor voltages of a four-level T-type Nested Neutral Point Clamped (NNPC) topology. Second, a new five-level T-type NNPC topology is proposed that has a minimum number of components in comparison to other existing five-level topologies. To extend the voltage and power rating, a novel seven-level topology is proposed that has the lowest number of components in comparison to other existing topologies. Moreover, three different controllers are developed to control the voltages of the seven-level topology based on Model Predictive Control, where the challenges regarding significant computational burden and weighting factor elimination are addressed. Finally, an MV-connected ultra-fast charging station architecture is proposed, where the proposed seven-level topology is considered as the AC/DC stage. Comparison of the proposed topology to the LV-connected stations shows that the efficiency, cost, and power quality of the charging stations can be improved significantly. / Thesis / Doctor of Philosophy (PhD)

The aim of this master thesis is to develop a suggested methodology for how to use Mavenoid infrastructure to improve customer support of DEFA EV chargers. Mavenoid is a company that helps other companies automate customer support, especially troubleshooting. This is done with Mavenoid models, interactive selfhelp tools that guide end users without technical knowledge through the troubleshooting process. Mavenoid models provide value both by deflecting cases (the end user solves the problem on their own using the model) and triaging cases (collect relevant information about the problem before escalating the case to a human support agent) The main methodology to develop a suggested methodology was learning by doing, using the suggested methodology to actually implement Mavenoid models available to end users on DEFA’s home page. This was complemented with a literature review, interviews and data analysis from model usage. The suggested methodology is to iteratively follow the steps of deciding which models to build, make priorities within these models, build the models, analyze their performance and continuously improve the models. To decide models, carefully evaluate DEFA’s support situation to decide where Mavenoid models would have the greatest impact. Force yourself to make quantitative assumptions to estimate a payback time for each possible model. For each model, carefully prioritize what to include and where the focus should be using estimates of frequency, value and time to model. Build the models to maximize deflection and triage and minimize abandoned sessions. Collect and analyze data from model usage and use this information to improve the models. To prioritize between possible improvements, force yourself to make quantitative assumptions of value and time to model and rank improvements by payback time. Limit the improvements you make either by time available or desired payback time. The potential business opportunity between Mavenoid and its customers is more attractive the more support cases the customer has and the larger fraction of end users that use Mavenoid. The business opportunity varies greatly with assumptions that are very difficult to estimate accurately at the early stages of a Mavenoid implementation. This indicates that Mavenoid models should be implemented step by step and assumptions updated when more data is available. Implementing Mavenoid models can be both positive and negative from a sustainable development perspective. They could encourage people to repair products instead of replacing them, scale renewable energy technology faster and remove boring and repetitive tasks from support staff. On the other hand, they might not be appreciated by all end users, could lead to increased electricity consumption and potential unemployment for support staff. Being about a largely unresearched topic, the results in this thesis are relatively subjective. This suggested methodology was used and proved to work to implement Mavenoid models for DEFA EV charging stations but it should be seen as one possible methodology, not the confirmed best methodology. / Syftet med detta examensarbete är att utveckla en metodologi för hur Mavenoids teknologi kan användas till att förbättra kundsupporten för DEFAs elbilsladdare. Mavenoid är ett företag som hjälper andra företag att automatisera kundsupport, särskilt felsökning. Detta görs med Mavenoidmodeller, interaktiva självhjälpsverktyg som guidar slutanvändare utan teknisk kunskap genom felsökningsprocessen. Mavenoidmodeller ger värde både genom att slutanvändaren löser problemet på egen hand genom att använda modellen (deflection) och genom att samla relevant information om problemet innan ärendet eskaleras till teknisk support (triage). Den huvudsakliga metoden för att utveckla metodologin var att lära genom att göra, faktiskt implementera Mavenoidmodeller och göra de tillgängliga för slutanvändare på DEFA: s hemsida. Detta kompletterades med en litteraturöversikt, intervjuer och dataanalys av hur modellerna användes. Den föreslagna metodologin är att iterativt följa stegen besluta vilka modeller som ska byggas, prioritera inom dessa modeller, bygga modellerna, analysera data från dem och kontinuerligt förbättra modellerna. För att bestämma modeller, utvärdera DEFAs supportsituation noggrant för att bestämma var Mavenoid-modellerna skulle ha störst inverkan. Tvinga dig själv att göra kvantitativa antaganden för att uppskatta en återbetalningstid för varje möjlig modell. För varje modell ska du noggrant prioritera vad du ska inkludera och var fokus ska vara genom att använda uppskattningar av frekvens, värde och tid att modellera. Bygg modellerna för att maximera deflection och triage och minimera övergivna sessioner. Samla och analysera data från modellerna och använd denna information för att förbättra modellerna. För att prioritera mellan möjliga förbättringar, tvinga dig själv att göra kvantitativa antaganden om värde och tid att modellera och rangordna förbättringar efter återbetalningstid. Begränsa de förbättringar du gör antingen utifrån tillgänglig tid eller önskad återbetalningstid. Den potentiella affärsmöjligheten mellan Mavenoid och dess kunder är mer attraktiv ju fler supportärenden kunden har och ju större andel slutanvändare som använder Mavenoid. Affärsmöjligheten varierar kraftigt med antaganden som är mycket svåra att uppskatta i början av ett projekt att implementera Mavenoidmodeller. Detta indikerar att Mavenoidmodeller bör implementeras steg för steg och antaganden uppdateras när mer data finns tillgängligt. Implementering av Mavenoid-modeller kan vara både positivt och negativt sett till hållbar utveckling. De kan uppmuntra människor att reparera produkter istället för att byta ut dem, skala upp förnybar energiteknologi snabbare och ta bort tråkiga och repetitiva uppgifter från teknisk support. Å andra sidan kanske de inte uppskattas av alla slutanvändare, kan leda till ökad elförbrukning och potentiell arbetslöshet för de som jobbar inom teknisk support. Eftersom examensarbetet handlar om ett relativt outforskat ämne är resultaten relativt subjektiva. Denna föreslagna metodologi användes och visade sig fungera för att implementera Mavenoidmodeller för DEFAs elbilsladdare men den bör ses som en möjlig metodologi, inte den bekräftat bästa metodologin.

This is a master thesis project carried out during a 20-week period in the spring of 2020 and that corresponds to 30 credits. The project covered concept development of a contactor (switch for controlling high current). The client ABB Control Products in Västerås, Sweden, have noticed an emerging need within the megatrend electrification in line with a growing energy demand. This comprises a new 1-pole DC-contactor (direct current contactor) within the application of EV-charging (electric vehicle charging). The problem, that this project has been based on, was to create a theoretically functioning concept for a 1-pole DC-contactor based on the client's existing 2-pole DC-contactor. In addition, some other requirements for the concept (formulated as project objectives) have also composed the problem. The research question below has been formulated as a support for carrying out the project. “How can a 2-pole DC-contactor be redesigned into a 1-pole DC-contactor, applicable in EV-charging?” By answering the research question, the project sought to contribute with a value that describes the general benefit of the project by what the concept brings in relation to the growing energy demand. The project has been carried out by using several product development methods that have led to a result which is a theoretically functioning concept. The concept has been presented as a CAD-model, it consists of three main sections: the bottom, the middle and the top. The sections consist of different components that together constitutes the concept. The concept has been able to mimic existing product to such an extent that it can be perceived to fit into the same product family. The core of the concept is that it is estimated to be capable of conducting current at 3000 A and breaking it at 1500 V. By taking advantage of the concept, which in consultation with the client has been considered to consist of a good overall solution, the further development of the new contactor can proceed towards industrialization. This, in despite to the fact that not all project objectives have been fulfilled. In future work it is recommended to develop certain areas of the design in order to later proceed to, among other things, testing the strength and conductivity of a future prototype. The project has resulted in an economic value and a scientific value due to a pending patent of a solution which has helped to make the concept work. In addition, the developed concept has created an opportunity to be able to charge heavy vehicles and charge more vehicles with higher power and higher speed. Thus, the concept has contributed to the megatrend electrification. Finally, the value generated by the entirety of the project can be summarized to that the concept can contribute to a more sustainable future in line with a growing energy demand, where more people choose renewable sources using electric vehicles for transportation.

The development of electric vehicle (EV) greatly promotes building a green and sustainable society. The new technology also brings new challenges. With the penetration of electric vehicles, the charging demands are increasing, and how to efficiently coordinate EVs' charging activities is a major challenge and sparks numerous research efforts. In this dissertation, we investigate the EV charging scheduling problem under the public charging and home charging scenarios from different perspectives. First, we investigate the EV charging scheduling problem under a charging station scenario by jointly considering the revenue of the charging station and the service requirements of charging customers. We first propose an admission control algorithm to guarantee the non-flexible charging requirements of all admitted EVs being satisfied before their departure time. Then, a utility based charging scheduling algorithm is proposed to maximize the profit for the charging station. With the proposed charging scheduling algorithm, a win-win situation is achieved where the charging station enjoys a higher profit and the customer enjoys more cost savings. Second, we investigate the EV charging scheduling problem under a parking garage scenario, aiming to promote the total utility of the charging operator subject to the time-of-use pricing. By applying the analyzed battery charging characteristic, an adaptive utility oriented scheduling algorithm is proposed to achieve a high profit and low task declining probability for the charging operator. We also discuss a reservation mechanism for the charging operator to mitigate the performance degradation caused by charging information mismatching. Third, we investigate the EV charging scheduling problem of a park-and-charge system with the objective to minimize the EV battery degradation cost during the charging process while satisfying the battery charging characteristic. A vacant charging resource allocation algorithm and a dynamic power adjustment algorithm are proposed to achieve the least battery degradation cost and alleviate the peak power load, which is beneficial for both the customers and charging operator. Fourth, we investigate the EV charging scheduling problem under a residential community scenario. By jointly considering the charging energy and battery performance degradation during the charging process, we propose a utility maximization problem to optimize the gain of the community charging network. A utility maximized charging scheme is correspondingly proposed to achieve the utility optimality for the charging network. In summary, the research outcomes of the dissertation can contribute to the effective management of the EV charging activities to meet increasing charging demands. / Graduate

The purpose of this bachelor thesis is to investigate different outcomes of the usage of photovoltaic (PV) power for electric vehicle (EV) charging adjacent to workplaces. In the investigated case, EV charging stations are assumed to be connected to photovoltaic systems as well as the electricity grid. The model used to simulate different scenarios is based on a goal of achieving constant power exchange with the grid by adjusting EV charging to a solar irradiance forecast. The model is implemented in MATLAB. This enables multiple simulations for varying input parameters. Data on solar irradiance are used to simulate the expected PV power generation. Data on driving distances are used to simulate hourly electricity demands of the EVs at the charging stations. A sensitivity analysis, based on PV irradiance that deviates from the forecast, is carried out. The results show what power the grid needs to have installed capacity for if no PV power system is installed. Furthermore, appropriate PV power installation sizes are suggested. The suggestions depend on whether the aim is to achieve 100 percent self-consumption of PV generated power or full PV power coverage of charging demands. For different scenarios, PV power installations appropriate for reducing peak powers on the grid are suggested. The sensitivity analysis highlights deviations caused by interference in solar irradiance.

Power converters are used in many distributed energy resources (DER) applications. With proper controls, DER systems can reduce losses and achieve higher energy efficiency if various power sources and loads are integrated through DC bus. High voltage electric vehicle (EV) DC charging station is becoming popular in order to reduce charging time and improve energy efficiency. A DC EV charging station model involving photovoltaic, energy storage system (ESS), fuel cell and DC loads is studied in this work. A dynamic programming technique that considers various uncertainties involved in the system is adopted to obtain optimal dispatch of ESS and fuel cell system. The effects of different tariffs, demand response programs and contract capacities of demand in the power scheduling are investigated and the results are presented.

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.

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.

No description available.

Electric vehicles play a crucial role in the global transition towards sustainability, particularly highlighted in initiatives like the European Green Deal. With projections indicating a significant increase in electric vehicle adoption worldwide, including a notable surge in the EU and Sweden, the strain on existing electric infrastructure becomes a concern. Managed charging – the process of regulating the charging of electric vehicles in a coordinated manner – emergesas a promising strategy to mitigate this strain, optimizing charging schedules to alleviate peakloads, and reduce the need for extensive grid upgrades. However, naive peak shaving approaches may fall short in addressing systemic issues, prompting the need for smarter solutions based on predictive modelling. This thesis focuses on Dansmästaren, a parking garage designed for mass electric vehicle charging, located in Uppsala, Sweden. Through load shifting techniques, one approach being explored at Dansmästaren aims to avoid grid capacity constraints by strategically scheduling EV charging to off-peak hours. This is being done using smart charging, which utilizes predictive models to predict charging durations for the scheduling of EV charging. This thesis aims to aid such predictive models by constructing a new feature for these models totrain on, namely clusters. These clusters are created using time series clustering, a technique that groups time series to clusters by running a range of algorithms comparing the similarity of different time series to each other using a variety of distance measures. In this case, the study uses data collected during three months in the form of time series, split by charging sessions, to construct the clusters. The performance of these clusters are then tested using deep learning as a predictive model to evaluate whether or not, and to which degree, the construction of clusters helped the predictive model achieve better results. Different approaches and algorithms are tested and evaluated for the time series clustering with the intention of getting the best possible performance — here meaning the specific construction of clusters resulting in the best performance increase for overall predictions. Different approaches were also tested and evaluated for the deep learning model, although not to the same extent, since the time series clustering is the focus of this thesis. In the end, a predictive performance increase of up to 17% was achieved by the predictive model using the constructed clusters as an additional feature. This suggests that time series clustering can aid deep learning models better predict charging durations.