Rising concerns related to the effects of traffic congestion have led to the search for alternative transportation solutions. Advances in battery technology have resulted in an increase of electric vehicles (EVs), which serve to reduce the impact of many of the negative consequences of congestion, including pollution and the cost of wasted fuel. Furthermore, the energy-efficiency and quiet operation of electric motors have made feasible concepts such as Urban Air Mobility (UAM), in which electric aircraft transport passengers in dense urban areas prone to severe traffic slowdowns. Electrified transportation may be the solution needed to combat urban gridlock, but many logistical questions related to the design and operation of the resultant transportation networks remain to be answered. This research begins by examining the near-term effects of EV charging networks. Stationary plug-in methods have been the traditional approach to recharge electric ground vehicles; however, dynamic charging technologies that can charge vehicles while they are in motion have recently been introduced that have the potential to eliminate the inconvenience of long charging wait times and the high cost of large batteries. Using an agent-based model verified with traffic data, different network designs incorporating these dynamic chargers are evaluated based on the predicted benefit to EV drivers. A genetic optimization is designed to optimally locate the chargers. Heavily-used highways are found to be much more effective than arterial roads as locations for these chargers, even when installation cost is taken into consideration. This work also explores the potential long-term effects of electrified transportation on urban congestion by examining the implementation of a UAM system. Interdependencies between potential electric air vehicle ranges and speeds are explored in conjunction with desired network structure and size in three different regions of the United States. A method is developed to take all these considerations into account, thus allowing for the creation of a network optimized for UAM operations when vehicle or topological constraints are present. Because the optimization problem is NP-hard, five heuristic algorithms are developed to find potential solutions with acceptable computation times, and are found to be within 10% of the optimal value for the test cases explored. The results from this exploration are used in a second agent-based transportation model that analyzes operational parameters associated with UAM networks, such as service strategy and dispatch frequency, in addition to the considerations associated with network design. General trends between the effectiveness of UAM networks and the various factors explored are identified and presented.

Software testing is becoming more prominent within the automotive industry due to more complex systems, and functions are implemented in the vehicles. The vehicles in the future will have the functionality to manage different levels of automation, which also means that vehicles driven by humans will have more supportive functionality to increase safety and avoid accidents. These functionalities result in a massive growth in the number of test scenarios to indicate that the vehicles are safe, and this makes it impossible to continue performing the tests in the same way as it has been done until today. The new conditions require that the test scenarios and Test Cases both be generated and executed automatically. In this thesis, an investigation and evaluation are performed to analyze the Automatic Test Case Generation methods available for inputs from Natural Language Requirements in an automotive industrial context at NEVS AB. This study aims to evaluate the NAT2TEST strategy by replacing the manual method and obtain a similar or better result. A comparative analysis is performed between the manual and automated approaches for various levels of requirements. The results show that utilizing this strategy in an industrial scenario can improve efficiency if the requirements to be tested are for well-documented lower-level requirements.

In power distribution system planning, it is essential to understand the impacts that electric vehicles (EVs), and the non-linear, time-variant loading profiles associated with their charging units, may have on power distribution networks. This research presents a design methodology for the creation of both analytical and behavioral models for EV charging units within a VHDL-AMS simulation environment. Voltage and current data collected from Electric Avenue, located on the Portland State University campus, were used to create harmonic profiles of the EV charging units at the site. From these profiles, generalized models for both single-phase (Level 2) and three-phase (Level 3) EV chargers were created. Further, these models were validated within a larger system context utilizing the IEEE 13-bus distribution test feeder system. Results from the model's validation are presented for various charger and power system configurations. Finally, an online tool that was created for use by distribution system designers is presented. This tool can aid designers in assessing the impacts that EV chargers have on electrical assets, and assist with the appropriate selection of transformers, conductor ampacities, and protection equipment & settings.

Indiana University-Purdue University Indianapolis (IUPUI) / With the rapid growth of technology, scientists are trying to find ways to make the world a more efficient and eco-friendly place. The research and development of electric vehicles suddenly boomed since natural resource are becoming very scarce. The significance of an electric vehicle goes beyond using free energy, it is environ- mental friendly. The objective of this thesis is to understand what Vehicle to Grid Communication (V2G) for an electric vehicle is, and to implement a model of this highly efficient system into a Hybrid Petri Net. This thesis proposes a Hybrid Petri net modeling of Vehicle to Grid (V2G) Communication topology. Initially, discrete, continuous, and hybrid Petri net's are defined, familiarized, and exemplified. Secondly, the Vehicle and Grid side of the V2G communication system is introduced in detail. The modeling of individual Petri nets, as well as their combination is discussed thoroughly. Thirdly, in order to prove these systems, simulation and programming is used to validate the theoretical studies. A Matlab embedded simulation program known as SimHPN is used to simulate specific scenario's in the system, which uses Depth-first Search (DFS) Algorithm. In addition to SimHPN simulation program, Matlab program is made to output four levels of the reachability tree as well as specifying duplicate and terminate nodes. This code incorporates a technique known as Breadth-first Search (BFS) Algorithm.

Indiana University-Purdue University Indianapolis (IUPUI) / With the exponential rise in manufacturing and usage of Li-ion batteries (LIBs) in the last decade, a huge quantity of spent LIBs is getting scrapped every year. Along with the efforts to making more capable and safer batteries over the last three decades, there is an immediate need for recycling these scrapped batteries. Most of these batteries typically use lithium manganese oxide (LMO), lithium cobalt oxide (LCO), lithium iron phosphate (LFP), and lithium nickel manganese cobalt oxide (NMC) cathode chemistries, and developing a technique towards regenerating these cathodes can ensure huge economic and environmental benefits for the present and future. This research focuses on a set of direct regeneration techniques with the goal of regenerating used cathode materials to be reused in LIBs. Used Apple iPad2 batteries with LCO chemistry and Nissan LEAF batteries with a combination of LMO-NMC chemistry are selected for this research. The scope of research can be divided into two parts as liberation/separation of cathode material and regeneration of liberated cathode. The liberation/separation process is carried out with the aid of ultrasonication and organic solvents with the objective being keeping the morphology and chemical composition intact for a better quality of the material. The regeneration process uses a hydrothermal technique with variations of parameters. 1:1 and 1:5 molar ratios between cathode material and a lithium lithiation agent are chosen to understand the effects of the molar ratio on cathode regeneration. In addition, the effects of processing solution (water vs. a solvent) are examined by replacing water with TEG. The effects of heat treatment on cathode regeneration are also investigated by observing phase changes of materials at different temperatures.

Diplomová práce se zabývá dopadem nabíjení elektrických vozidel a bateriových úložišť na schopnost distribučních sítí nízkého napětí absorbovat fotovoltaické systémy. Převážně venkovské, příměstské a převážně městské regiony s různými stupni penetrace nekontrolovaně i kontrolovaně nabíjených elektromobilů jsou analyzovány Monte Carlo simulacemi. Hostingová kapacita je také analyzována, jestliže jsou elektrická vozidla jak nahrazena, tak doplněna domácími bateriovými úložišti. Práce je zakončena krátkou analýzou využitelnosti BESS.

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This dissertation studies a framework in support electric vehicle (EV) charging station expansion and management decisions. In the first part of the dissertation, we present mathematical model for designing and managing electric vehicle charging stations, considering both long-term planning decisions and short-term hourly operational decisions (e.g., number of batteries charged, discharged through Battery-to-Grid (B2G), stored, Vehicle-to-Grid (V2G), renewable, grid power usage) over a pre-specified planning horizon and under stochastic power demand. The model captures the non-linear load congestion effect that increases exponentially as the electricity consumed by plugged-in EVs approaches the capacity of the charging station and linearizes it. The study proposes a hybrid decomposition algorithm that utilizes a Sample Average Approximation and an enhanced Progressive Hedging algorithm (PHA) inside a Constraint Generation algorithmic framework to efficiently solve the proposed optimization model. A case study based on a road network of Washington, D.C. is presented to visualize and validate the modeling results. Computational experiments demonstrate the effectiveness of the proposed algorithm in solving the problem in a practical amount of time. Finding of the study include that incorporating the load congestion factor encourages the opening of large-sized charging stations, increases the number of stored batteries, and that higher congestion costs call for a decrease in the opening of new charging stations. The second part of the dissertation is dedicated to investigate the performance of a collaborative decision model to optimize electricity flow among commercial buildings, electric vehicle charging stations, and power grid under power demand uncertainty. A two-stage stochastic programming model is proposed to incorporate energy sharing and collaborative decisions among network entities with the aim of overall energy network cost minimization. We use San Francisco, California as a testing ground to visualize and validate the modeling results. Computational experiments draw managerial insights into how different key input parameters (e.g., grid power unavailability, power collaboration restriction) affect the overall energy network design and cost. Finally, a novel disruption prevention model is proposed for designing and managing EV charging stations with respect to both long-term planning and short-term operational decisions, over a pre-determined planning horizon and under a stochastic power demand. Long-term planning decisions determine the type, location, and time of established charging stations, while short-term operational decisions manage power resource utilization. A non-linear term is introduced into the model to prevent the evolution of excessive temperature on a power line under stochastic exogenous factors such as outside temperature and air velocity. Since the re- search problem is NP-hard, a Sample Average Approximation method enhanced with a Scenario Decomposition algorithm on the basis of Lagrangian Decomposition scheme is proposed to obtain a good-quality solution within a reasonable computational time. As a testing ground, the road network of Washington, D.C. is considered to visualize and validate the modeling results. The results of the analysis provide a number of managerial insights to help decision makers achieving a more reliable and cost-effective electricity supply network.

We have come a long way in the pursuit of reducing our carbon footprint from our way of living, bycontinuously development of batteries and charging infrastructure for electric vehicles to decrease thedemand for fossil fuels, improving the overall energy efficiency and to increase awareness of the problemto the population. One of the industries, that during the last decades has undergone vast improvements,is the development of the airplane engines due to increased emission regulations, for the aviationindustry, and to reduce the costs of air travel. Despite tighter regulations, global impact from travellingby air is increasing due to the explosive increase in number of travels and travellers. In order to copewith the situation, it is of course necessary to further develop fuel and emission effective airplanes, butalso to study the whole chain of emission sources correlated to the air transport industry. So, whilewaiting for improved airplanes there are well known emission effective technologies that can beimplemented already today – implement electric vehicles as support vehicles at airports.Today, and throughout history, most of the focus of air travel has been on the airplane itself. This thesis,that was carried out at KTH Royal Institute of Technology during late spring and autumn 2018, didinstead study the support vehicles used in airports. In this thesis, a generic economic model wasdeveloped in order to estimate the costs involved when replacing traditionally vehicles to suggestedelectrically propelled alternatives. To test and support the development of an economic model, a casestudy has been carried out at Stockholm Arlanda Airport. This case study included a field study to thementioned airport, and in combination with interviews with former employees from one of the groundhandling companies that are currently active in the airport. Raw data was collected over the equipmentand vehicles currently in use. This data was used to describe the vehicles purpose, requirements and toensure that the alternative electric vehicles proposed would offer at least the same performance as thetraditional vehicles. The developed generic economic model was modulated with five stages thatrepresented a selection of input parameters. The collected data became a result in itself and was used asinput to three concurrent theses.The results from the five stages presents the costs during an investment period of between of one tofifteen years. One of the most significant result could be seen from Stage V. This stage showed that thecombined cost to replace all vehicles currently used, with either all new diesel vehicles or electricalternative vehicles, are lower for electric vehicles than for diesel vehicles. Another significant resultcould be seen from the investigation of Stage IV, Stage IV-B, were the model was modulated to representthe case of replacing a vehicle. The results showed that the Letter and Cargo procedures, that travel thefarthest and has the highest fuel consumption of the investigated vehicles, had negative costs throughthe whole investment period. This means that the expenses will always be lower when these vehicles arereplaced. The model was validated through a sensitivity analysis, performed on the discount rates,depreciation rates and as well as costs for battery replacement during the depreciation period. / Vi har kommit långt i vår strävan att minska vårt koldioxidavtryck genom vårat sätt att leva, genom attkontinuerligt utveckla batterier och laddningsinfrastruktur för elfordon med syftet att minskaefterfrågan på fossila bränslen, förbättra den totala energieffektiviteten och öka befolkningensmedvetenhet om problemet. En av de branscher, som under de senaste decennierna har genomgått storaförbättringar, är utvecklingen av flygplansmotorer och regler för flygplan. Men eftersom antaletflygresenärer fortsätter att öka årligen krävs ytterligare arbete för att förbättra den totala effektivitetenoch minska det negativa globala avtrycket från flygresor. Medan väntan på att morgondagens flygplanska utvecklas så finns det teknik som redan kan appliceras idag – elfordon.I dag, och genom historien, har det mesta av all fokus för flygresor varit på flygplanen själva. Dettaexamensarbete, som genomfördes på KTH, Kungliga Tekniska Högskolan, under den senare delen avvåren och hösten 2018, utredde istället de fordon som omger ett flygplan, under den tid flygplanet stårstilla på en flygplats. Under denna uppsats utvecklades en generisk ekonomiskmodell, med syftet attestimera kostnaderna att ersätta och använda elfordon istället för de nuvarande fordonen. För attutveckla den ekonomiska modellen genomfördes en fallstudie på Stockholm Arlanda Airport.Fallstudien innehöll en fältundersökning till den nämnda flygplatsen, och i kombination med intervjuermed tidigare anställda från ett av de marktjänstföretag som är verksamma på flygplatsen, insamladesrådata om vilken utrustning och fordon som vid tillfället användes. Denna data användes för att beskrivafordonens syfte, vilka krav som ställs på fordonen. Detta var för att säkerställa att det valda elfordonetkunde utföra uppgiften. Den utvecklade generiska ekonomiska modellen modulerades med fem steg,som representerade vilka indataparametrar som bör väljas. De insamlade uppgifterna blev ett resultat isig och användes som indata till tre samtidiga avhandlingar.Den generiska modellen resultat presenterade kostnaderna från de fem stegen under en varierandeinvesteringsperiod på ett till femton år. Ett av de viktigaste resultaten var från Steg V, som visade attden sammanlagda kostnaden för att ersätta alla de fordon som för närvarande används, med antingenalla nya dieselfordon eller elektriska alternativa fordon, är lägre för elfordon än för dieselfordon. Detandra betydande resultatet kommer från undersökningen av Steg IV, Steg IV-B. I Steg IV-B moduleradesmodellen i syfte att representera scenariot då ett fordon ska ersättas, där Letter och Cargo procedurernavar de som färdades längst och även har den högsta bränsleförbrukningen utav de undersökta fordonen,hade en negativ kostnad under hela investeringslängden. Detta betyder att kostnaderna alltid är lägre,om fordonet ersätts. Modellen har validerats med hjälp utav en känslighetsanalys som utfördes pådiskonteringsräntan, avskrivningssatsen och kostnader för ersättningsbatterier.

Self-balancing vehicles or those without any seat so called Personal Light Electric Vehicles (PLEV) are excluded from the scope of the Type Approval Regulation (EU) No. 168/2013 for two- or three-wheel vehicles and quadricycles (category L vehicles) thus they have to be regulated on national level since 2016. Furthermore, at that time a definition of micromobility devices was missing - and therefore different national categories with detached requirements were established. In 2019 SAE International published the J3 l 94TM for the classification of powered micromobility vehicles. A fundamental research project undertaken by the German Federal Highway Research Institute provided recommendations to integrate PLEV with respect to traffic safety into the existing road traffic. Subsequently, the legislative frame called Personal light electric vehicles regulation enforces the approval of PLEV and safe usage in Germany as well as administrative offences. Behavioural rules, technical and safety requirements characterize the PLEV regulation. In a next step, PLEVs market introduction with respect to traffic safety is evaluated in a current project with different aspects e.g. a market dissemination, user analysis and user behaviour, traffic surveillance and accident analysis. [from Introduction]