The promotion of Electric Vehicles (EV) has become a key measure of the governments to reduce greenhouse gas emissions. However, range anxiety is a big barrier for drivers to choose EVs over traditional vehicles. Installing more charging stations in appropriate locations can relieve EV drivers’ range anxiety. To help decide the location and number of public charging stations, we propose two optimization models for two different charging modes - fast and slow charging, which aim at minimizing the total cost while satisfying certain spatial coverage goals. Instead of using discrete points we employ network and polygons to represent charging demands. Importantly, we resolve the partial coverage problem (PCP) by segmenting the geometric objects into smaller ones using Geographic Information System (GIS) functions. We compare the geometric segmentation method (GS) and the complementary partial coverage method (CP) developed by Murray (2005) to solve the PCP. After applying the models to Greater Toronto and Hamilton Area (GTHA) and to Downtown Toronto, we show that that the proposed models are practical and effective in determining the locations and number of required charging stations. Moreover, comparison of the two methods shows that GS can fully eliminate PCP and provide much more accurate result than CP. / Thesis / Master of Science (MSc)

This thesis studies the logistical problem of serving customer demands by electric vehicles, a currently important problem given the growing emphasis on sustainability in industry. In optimization, this is called the electric vehicle routing problem (E-VRP), and it is generally to computationally demanding to solve with a single comprehensive model. The method of this thesis is therefore a heuristic approach, in which the routing and the charging of the vehicles are done separately. The routing disregards the energy considerations, and the scheduling assigns routes and charging to a fleet of electric vehicles. When considering the charging separately, it can be model more realistically than what is usually done in E-VRP. A bibliographical review of both E-VRP and of electrical scheduling modeling is conducted, with a focus on which aspects are normally prioritized. Then two different models for the routing, and four different models for the scheduling are described. This routing-scheduling framework is subsequently tested on both artificial trial instances and a real-world problem. Promising results are observed in terms of runtime, with the ability to obtain good solutions on large instances within seconds. The real-world scenario was also solved quickly. Additionally, the results suggest a versatility of the routing-scheduling framework across various E-VRP variations.

Significant advances have been made in the research and development of electric vehicles (EV’s). Along with the major challenge of energy storage, being also addressed is the efficient design of system energy transfer and consumption. This has had the effect of fundamentally changing perspectives across the mobility and transportation sector. Applied predominantly to road-going vehicles, the industrial context of non-road Electric Vehicles (nrEV’s) and specifically the use of manned electric forklift trucks integrated within the production related materials handling system has, to-date, received far less attention. The overarching aim of this research is to examine the impact and potential for the use of contactless occasional recharging of nrEV’s integrated within a manufacturing line, recognising the need to balance the (sometimes competing) demands of delivering sustainable production while exercising environmental responsibility. Meeting the objectives of this research resulted in the development of a location allocation model for electric charging station determination based on a fundamental understanding of the nature and quality of process inherent key performance indicators (KPI’s) as well as comprehensive process and energy monitoring while considering both Lean and Green Management perspectives. The integration of the generated knowledge and information into a generally valid simulation tool for occasional charging system implementation allows to more thoroughly investigate the impact from occasional charging to overall efficiency and sustainability to be realised. An investigation into relevant literature identified the need for specifically generated energy consumption data and confirmed the need for an energy optimisation model specific to the area of production related materials handling. Empirical data collected from repeated standardised materials handling operations within a selected production related materials handling environment resulted in the development of the Standard Energy Consumption Activity tool (SECA). Further work within this pilot study confirmed the tool as capable of generating reliable and valid data and confirmed the SECA tool as a generally applicable benchmark for energy consumption determination in material handling based on fractional process functions. Integrating this approach into a comprehensive process analysis and charging infrastructure optimisation resulted in the development of an Excel-based simulation model. The (Occasional Charging Station Location Model) OCSLM is based upon Maximal Covering Location Modelling and an endogenous covering distance definition in order to simulate process related potentials and optimal charging system implementation allocations, the target being to increase vehicles usable battery energy. A comprehensive case study based upon six individual and one combined data set confirmed the general and wider applicability of the OCSLM model while the application of the model provides a set of novel results. The application demonstrated a theoretical increase in usable battery energy of between 40% and 60% and within the same case study the impact of technology implementation identified that a reduction in battery and system cost of between 5% and 45% can be realised. However, the use of contactless power transfer resulted in an increase in CO2 emissions of up to 6.89% revealing a negative impact to overall ecology from the use of this energy transfer system. Depending on the availability of fast connecting, contact based energy transmission systems, the approach and results of OCSLM have shown to be directly applicable to contact based systems with resulting CO2 emissions decreasing by 0.94% at an energy transfer efficiency of 96%. Further novelty, of benefit to both academic and industry practice, was realised through the framework and information of the research with the provision of SECA as a process function-based and generally applicable energy consumption standard, OCSLM as a Maximal Covering Location Modell with a focus on occasional charging based on an endogenous covering distance and integrating detailed energy and process monitoring into electric charging station allocation, and the methodology for the application of this approach for fast connecting contactless and contact charging models and cases.

Recent years have seen a growing tendency that a large number of generators are connected to the electricity distribution networks, including renewables such as solar photovoltaics, wind turbines and biomass-fired power plants. Meanwhile, on the demand side, there are also some new types of electric loads being connected at increasing rates, with the most important of them being the electric vehicles (EVs). Uncertainties both from generation and consumption of electricity mentioned above are thereby being introduced, making the management of the system more challenging. With the proportion of electric vehicle ownership rapidly increasing, uncontrolled charging of large populations may bring about power system issues such as increased peak demand and voltage variations, while at the same time the cost of electricity generation, as well as the resulting Greenhouse Gases (GHG) emissions, will also rise. The work reported in this PhD Thesis aims to provide solutions to the three significant challenges related to EV integration, namely voltage regulation, generation cost minimisation and GHG emissions reduction. A novel, high-resolution, bottom-up probabilistic EV charging demand model was developed, that uses data from the UK Time Use Survey and the National Travel Survey to synthesise realistic EV charging time series based on user activity patterns. Coupled with manufacturers' data for representative EV models, the developed probabilistic model converts single user activity profiles into electrical demand, which can then be aggregated to simulate larger numbers at a neighbourhood, city or regional level. The EV charging demand model has been integrated into a domestic electrical demand model previously developed by researchers in our group at the University of Edinburgh. The integrated model is used to show how demand management can be used to assist voltage regulation in the distribution system. The node voltage sensitivity method is used to optimise the planning of EV charging based on the influence that every EV charger has on the network depending on their point of connection. The model and the charging strategy were tested on a realistic "highly urban" low voltage network and the results obtained show that voltage fluctuation due to the high percentage of EV ownership (and charging) can be significantly and maintained within the statutory range during a full 24-hour cycle of operation. The developed model is also used to assess the generation cost as well as the environmental impact, in terms of GHG emissions, as a result of EV charging, and an optimisation algorithm has been developed that in combination with domestic demand management, minimises the incurred costs and GHG emissions. The obtained results indicate that although the increased population of EVs in distribution networks will stress the system and have adverse economic and environmental effects, these may be minimised with careful off-line planning.

This thesis deals with design of microprocessor controlled battery charger with support of NiCd, NiMH and Li-Ion cells. The project focus is aimed within circuits design at algorithm of charge detection implemented into microprocessor. User interface is formed by PC application which communicates with charger via USB. PC connection allows recording of charging characteristics behaviour. The result of thesis is completely designed and created battery charger including microprocessor and PC application source codes.

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.

Inductive charging, a form of wireless charging, uses an electromagnetic field to transfer energy between two objects. This emerging technology offers an alternative solution to users having to physically plug in their electric vehicle (EV) to charge. Whilst manufacturers claim inductive charging technology is market ready, the efficiency of transfer of electrical energy is highly reliant on the accurate alignment of the coils involved. Therefore understanding the issue of parking misalignment and driver behaviour is an important human factors question, and the focus of this paper. Two studies were conducted, one a retrospective analysis of 100 pre-parked vehicles, the second a dynamic study where 10 participants parked an EV aiming to align with a charging pad with no bay markings as guidance. Results from both studies suggest that drivers are more accurate at parking laterally than in the longitudinal direction, with a mean lateral distance from the centre of the bay being 12.12 and 9.57 cm (retrospective and dynamic studies respectively) compared to longitudinally 23.73 and 73.48 cm. With current inductive charging systems having typical tolerances of approximately ±10 cm from their centre point, this study has shown that only 5% of vehicles in both studies would be aligned sufficiently accurately to allow efficient transfer of electrical energy through induction.

No description available.

Εlectric vehicles are replacing the internal combustion engine vehicles rapidly and they will dominate the market completely in the next years. The amount of energy and power needed to support this new technology is huge. This will increase the already high electricity demand of our societies. The electric vehicles can provide a solution by using them to transfer energy to any other vehicles or infrastructure in combination with electricity management. This can be achieved by controlling the electric vehicle chargers and by knowing the exact consumption of the other vehicles or infrastructures. In Dansmästaren, Uppsala, there is a parking garage with 30 Charge Amps Aura charging stations. The same type of charger has been used in order to examine if it is possible to extract and update data through programming, as well as its functions regarding Vehicle-to-everything (V2X). This thesis presents two Python scriptswhere the first is used to update different functions of the charger and the secondto get high resolution electricity data and the energy consumption of the charger.The collected data is stored in two MySQL database every 30 seconds for future use. The data that can be updated by the user immediately, from anywhere and at any time. Similarly, the data collection has shown that different charging patterns exist and they can be observed by using the data that are generated and saved in the databases.

Promoting the use of electric vehicles (EVs) has become an important measure to solve the environmental issue in China. In Beijing, the number of EVs has increased rapidly during recent years. In parallel, an extensive charging infrastructure has been deployed. However, most charging infrastructure operators find it difficult to make a profit by only providing charging services due to the lack of a sound business model. This thesis aims to investigate the current status of charging infrastructure for electric vehicles in urban Beijing and the business models of Beijing’s main charging infrastructure operators. Furthermore, based on the empirical findings, the weaknesses in the business models are identified. Beijing was chosen as case study in which the three main operators were studied in order to identify their business models in terms of value proposition, value creation and value capture. Questionnaire and interview as data collection methods were used to collect qualitative data. The study has shown that owing to the market demand and governmental promotion, the charging infrastructure industry retains its rapid development in Beijing. Moreover, the study indicates that the EV users’ most important demands on the charging services are: safety, convenience, speed, and stability during charging. The services need to be delivered at a reasonable price, and this is the development orientation for the charging operators. The business models of the three main charging infrastructure operators are almost identical, as all of them both manufacture and deploy charging piles as well as deliver charging services. They create and capture value by providing charging piles and service as well as various services based on mobile apps. Furthermore, through the investigation and analysis of their business models, five weaknesses in the business model have been identified: the slow pace of technology adoption, high initial investment requirements, few revenue streams, high cost for both internal personnel and external contractors, and insufficient information from App/mobile platform. / Att främja användningen av elbilar har blivit en viktig åtgärd för att lösa miljöproblemet i Kina. I Peking har antalet elbilar ökat snabbt de senaste åren. Parallellt har en utbyggnad av laddningsinfrastruktur skett. De flesta laddningsinfrastrukturoperatörer har dock svårt att göra vinst genom att endast tillhandahålla laddningstjänster på grund av bristen på en sund affärsmodell. Denna avhandling syftar till att undersöka den nuvarande situationen för laddningsinfrastrukturen för elbilar i Peking samt affärsmodellerna hos Pekings främsta laddningsinfrastrukturoperatörer. Enligt de empiriska resultaten identifieras svagheterna i affärsmodellerna. Peking valdes som fallstudie där de tre huvudoperatörerna studerades för att identifiera deras affärsmodeller i fråga om värderbjudande, värdeskapande och värdefångst. Frågeformulär och intervju som datainsamlingsmetoder användes för att samla in kvalitativa data. Studien har visat att, på grund av efterfrågan på marknaden och statens främjande behåller laddningsinfrastrukturbranschen sin snabba utveckling i Peking. Dessutom visar studien att elbilanvändarnas viktigaste krav på laddningstjänsterna är: säkerhet, bekvämlighet, hastighet och stabilitet under laddning. Tjänsterna måste levereras till ett rimligt pris, och detta är utvecklingsorienteringen för laddningsoperatörerna. Affärsmodellerna för de tre huvudoperatörerna är nästan identiska, eftersom alla tillverkar och distribuerar laddstolpar samt levererar laddningstjänster. De skapar värde genom att tillhandahålla laddningspolar och service samt olika tjänster baserade på mobilapp. Vidare, har fem svagheter identifierats genom undersökningen och analysen av affärsmodellerna: den långsamma teknikspridningen, höga initiala investeringskrav, få inkomstströmmar och höga kostnader för både intern personal och externa entreprenörer samt otillräcklig information från app / mobil plattform.