Using drones for cellular coverage enhancement is a recent technology that has shown a great potential in various practical scenarios. However, one of the main challenges that limits the performance of drone-enabled wireless networks is the limited flight time. In particular, due to the limited on-board battery size, the drone needs to frequently interrupt its operation and fly back to a charging station to recharge/replace its battery. In addition, the charging station might be responsible to recharge multiple drones. Given that the charging station has limited capacity, it can only serve a finite number of drones simultaneously. Hence, in order to accurately capture the influence of the battery limitation on the performance, it is required to analyze the dynamics of the time spent by the drones at the charging stations. In this thesis, we first use tools from queuing theory and stochastic geometry to study the influence of each of the charging stations limited capacity and spatial density on the performance of a drone-enabled wireless network. We then extend our work to rural areas where users are greatly impacted by low income, high cost of backhaul connectivity, and limited resources. Considering the limitation of the electricity supply scarcity in some rural regions, we investigate the possibility and performance enhancement of the deployment of renewable energy (RE) charging stations. We outline three practical scenarios, and use simulation results to demonstrate that RE charging stations can be a possible solution to address the limited on-board battery of UAVs in rural areas, specially when they can harvest and store enough energy.

The goal of this master thesis is execution of a strategy analysis. The subject of this analysis is electro-mobility i.e. products and services related to electric vehicles in the Czech Republic. The theoretical part of the paper is focused on approaches used for analyses of both external factors and forces affecting particular industry. The former is mainly focused on technology, regulations and incentives. The latter analysis is based on five forces supplemented by the role of complements. Actual status of the technology an analysed product is assessed by SWOT analyses. In the conclusion, final recommendations based on overall findings are stated.

The purpose of this project is to create a map showcasing the distribution of charging stations in the surrounding areas of Uppsala city in Uppsala Municipality. Additionally, the project examines the expected development of electric car utilization, identifies essential requirements, and highlights significant actors driving this development. Through an analysis of factors, such as demographic data, geographical data, car fleet data, car density and travel patterns, the project seeks to gain insight into the present and future usage patterns of electric cars. By understanding these factors, the research contributes to a better understanding of the dynamics surrounding the adoption of electric cars outside cities. Moreover, this project provides information on the optimal placement of charging infrastructure to facilitate the transition towards electric mobility. Calculations and assumptions are made to calculate the total number of electric cars in Uppsala city's surrounding areas in 2040. Based on this number, the total installed public power capacity needed is calculated in accordance with an EU regulation called AFIR. The total capacity is distributed on five different power capacities to match the need at each location. To achieve this, key actors have been examined. In addition, development and key actors in Norway are analyzed to examine similarities and to give a brief view into the future.  The results indicate that there will be a significant increase of electric cars in Uppsala Municipality. Therefore, expanding the charging infrastructure is shown to be essential. Due to the expected high usage of home charging outside cities, public charging is believed to be mostly necessary for tourists and passersby. Nevertheless, there are uncertainties that could influence the development of electric cars, which in turn would have an impact on the results. Finally, there follows a discussion both on the results and factors which could have affected the model.

With the debate on global warming and climate change, renewable energy resources, such as solar energy, are being considered. If solar energy is to make a major utility contribution, it will need to be more ubiquitous in today’s society. The research described hereafter analyzes the use of Solar in Public Spaces (SPS). SPS is defined as solar energy used in the public domain to power electronics away from the electric grid. This research specifically examines the viability of integrating solar panels into existing surfaces to charge portable electronics. Viability is evaluated using three criteria: (1) user interaction, (2) technical feasibility, and (3) cost analysis. User interaction is primarily focused on usage trends, user preferences, and user concerns. Technical feasibility includes shading effects, weather effects, and solar panel/battery sizing. Cost analysis is considered using energy savings, portability savings, and motivations.The research objective is answered through eleven research questions. All research questions are answered using surveys together with data from six different charging devices placed around Brigham Young University (BYU) campus. Surveys are used to add validity and support conclusions drawn from charging device data. A model is also developed to estimate solar panel and battery sizing needed to account for differences in geographical locations, incident solar power, weather, temperature, daylight hours, shading, and usage. All research questions are answered and demonstrate that solar panels integrated into existing surfaces is a viable solution for charging portable electronics in public spaces under the circumstances discussed herein

<p dir="ltr">The rising demand for EVs, motivated by their environmental benefits, is generating increased need for EV charging infrastructure. Also, it has been recognized that the adequacy of such infrastructure helps promote EV use. Therefore, to facilitate EV adoption, governments seek guidance on continued investments in EV charging infrastructure development. The high cost of these investments motivates governments to seek optimal decisions on EV-related investments including EV charging infrastructure, and such decisions include locations, capacities, and deployment scheduling of such infrastructure. Additionally, uncertainties in travel demand prediction and EV driving range constraints need to be considered in EV infrastructure investment planning. To help address these questions, this thesis developed a framework to establish optimal schedules and locations for new charging stations and for decommissioning gasoline refueling stations for any given network over a long-term planning horizon, considering uncertainties in travel demand forecasts and EV driving-range heterogeneity. To address the uncertainties, the proposed framework is formulated as a robust mathematical model that minimizes the worst-case total system travel cost and the total penalty for unused charging station capacity. This study uses an adaption of the cutting-plane method to solve the proposed model. In the numerical analyses, the performance of the robust framework and its deterministic counterpart are compared. The results show that the optimal robust plan outperforms the deterministic plan by yielding savings in the costs of travel and electricity charging. The thesis also investigates the effects of investment budget levels of robust planning. The numerical results throw light on the relationships between higher investment levels and electric charging station deployment levels and consequently, the savings in travel costs and impacts on unused charging capacity. The outcomes of this thesis can help road agencies and related private sector entities enhance preparations towards infrastructure investments to support electric charging stations in an efficient manner.</p>

This thesis proposes a planning framework for Autonomous Electric Vehicle (AEV) charging. The framework is intended to help transportation decision-makers determine Electric Vehicle (EV) charging facility locations and capacities for the mixed fleet of Autonomous Vehicle (AV) and Human-driven Vehicle (HDV). The bi-level nature of the framework captures the decision-making processes of the transportation agency decision-makers and travelers, thereby providing solid theoretical and practical foundations for the EV charging network design. At the upper level, the decision-makers seek to determine the locations and operating capacities of the EV charging facilities, in a manner that minimizes total travel time and construction costs subject to budgetary limitations. In addition, the transportation decision-makers provide AV-exclusive lanes to encourage AV users to reduce travel time, particularly at wireless-charging lanes, as well as other reasons, including safety. At the lower level, the travelers seek to minimize their travel time by selecting their preferred vehicle type (AV vs. HDV) and route. In measuring the users delay costs, the thesis considered network user equilibrium because the framework is designed for urban networks where travelers route choice affects their travel time. The bi-level model is solved using the Non-Dominated Sorting Genetic Algorithm (NSGA-II) algorithm.

No description available.

Adoption of electric vehicles has been restrained by the availability of charging stations and consumer fear of being stranded with a depleted battery, far from the nearest charger. In many areas of the world, charging stations are now widely available and the transition from vehicles with internal combustion engines is accelerating, though still in a fairly early stage. For electric vehicle drivers in those areas, anxiety that they will not be able to find a charger (“range anxiety”) is subsiding. However, differences in charging speed and pricing between stations and different outlets at the same station can be large. Total trip duration can vary significantly based on the charging outlet selected. Prior research has developed methods for helping all drivers find the fastest route and for electric vehicle drivers to ensure that they are able to complete their trip. Additional research has explored other complexities of route selection for electric vehicles such as how to select optimal stations for charging based on the total trip duration, including driving and charging time. Pricing for recharging electric vehicles at public chargers is more complex and diverse than for gas filling stations due to the differences in charging rates and the relatively low competition. This research investigates those differences. Using design science research methodology, a method is presented for determining which charging stops result in the lowest possible charging cost for a given route. The method is demonstrated through experiment with random routes within Sweden. The experimental results show that the average cost savings as compared to the duration-optimal route is 15% and 139 SEK per additional hour of trip time. One possible direction for future work is to improve the performance of the algorithm for use in real-time consumer route planning applications.

Le contexte écologique et économique actuel incite les autorités et le public à la réduction des émissions de CO2 et les dépendances vis-à-vis des hydrocarbures. Le transport représente 23 % des émissions de polluants dans le monde, et ce chiffre passe à 39 % pour la France. L’adoption de nouvelles solutions de transport est primordiale pour la réduction de ces émissions. L’électromobilité représente une alternative viable aux véhicules thermiques conventionnels. Si les véhicules électriques permettent une mobilité avec zéro émission, certaines de leurs caractéristiques empêchent leur développement. Les principaux freins à l’adoption de ce type de véhicules sont l’autonomie limitée, le faible déploiement des stations de recharge en milieu urbain (et extra urbain) ainsi que les temps de recharge importants. Aussi, afin de promouvoir l’usage de ce type de mobilité, il incombe de développer des outils visant à optimiser la consommation électrique tenant compte des caractéristiques liées à ce type de mobilité. C’est l’objectif de ce travail de thèse qui se focalise sur le développement d’outils permettant d’optimiser l’usage de véhicules électriques. Pour ce faire, trois grands axes sont définis : la modélisation des véhicules électriques, l’affectation des stations de recharge et le choix d’éco-itinéraires. La première partie de cette thèse s’intéresse à l’estimation de la consommation des véhicules électriques ainsi qu’à la présentation de la librairie de modèles dynamiques VEHLIB d’estimation de la consommation de ce type de véhicules. La seconde partie est consacrée à l’affectation optimale des stations de recharge. Une méthodologie de déploiement d’infrastructures de recharge est proposée pour la ville de Lyon avec prise en compte de la demande de mobilité issue des enquêtes ménages déplacements. La troisième partie de la thèse s’intéresse à la thématique du choix d’éco-itinéraire (green routing). Celle-ci aboutit à la proposition d’une méthodologie multi-objectif de recherche de stations de recharge afin de déterminer des itinéraires optimaux avec déviation vers ces stations lorsque l’état de charge de la batterie du véhicule ne permet pas de terminer le trajet. Pour finir, une expérimentation a été réalisée à l’aide d’un véhicule électrique équipé de capteurs de position et de consommation pour d’une part valider les méthodologies proposées et d’autre part analyser les facteurs exogènes qui influent sur la consommation des véhicules électriques. / The current ecological and economic context encourages the authorities and the public to reduce CO2 emissions and oil dependence. The transportation is responsible for 23% of pollutants emissions in the world, and this proportion increases up to 37% in France/ The adoption of new transport solutions is primordial to reduce these emissions. Electro mobility is a viable alternative to conventional vehicles. While electric vehicles offer mobility with zero emissions, some of their characteristicds impede their development. The main obtacle to the adoption of these vehicles is the limited autonomy, a sparse distribution of charging stations in urban areas as well as a significant charging time. Also, to promote the use of this type of mobility, it is primordial to develop tools that optimize the energy consumption and take in to account the characteristics associated with this type of mobility. To achieve this, three areas are difined: modeling of electric vehicles, optimized charging station deployment and eco routing. The first part of this theis focuses on the consumption estimation of the electric vehicles and the presentation of the dynamic model library VEHLIB. The second part is dedicated to optimal allocation of charging stations; A methodology for the deployment of electric vehicle charging infrastructures is proposed for the urban area o fthe city of Lyon, taking into account the mobility demand derived from the household travel surveys.The third part of the thesis deals with the eco-routing (green routing). A multi-objective methodology for eco routing with recharge en-route is proposed. The solutions take into account battery state does not permit to finish the trip.Finally, an experiment was carried out using an electric vehicle equipped with position and consumption sensors in order to validate the proposed methodologies and analyze exogenous factor that impact the electric vehicle consumption.

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.