Abstract

On-road electric vehicle recharging infrastructure is essential in the transformation of electric vehicles into a practical transportation option. This study focuses upon assessing the need for recharging infrastructure for long distance travel for a large market share of electric vehicles, finding the optimal infrastructure deployment, and understanding the economic, social and environmental costs and benefits associated with the optimal infrastructure deployment. The analysis considers quick-charging and battery-switching as plausible recharging technologies. Results show: (i) the promotion of electric vehicles is beneficial when considering economic costs and benefits for operators and users, tax redistribution, and environmental externalities, even with a relatively modest market share; (ii) the number of required recharging stations for satisfaction of the travel demand is at the magnitude of 1–2% of the current gasoline infrastructure, under the assumption of wide availability of off-road recharging at home and the workplace; (iii) the optimal deployment of the recharging stations is along the main national highways outside of urban conurbations, under the assumption of wide availability of home recharging; (iv) the battery-switching technology is far more attractive to the consumer than the quick-charging technology for long-distance travel requiring more than one recharging visit. Document type: Part of book or chapter of book

Abstract

Under the necessities of reducing emissions and air pollution, and also for increasing fuel economy, automotive companies have been developing electric and plug-in hybrid electric vehicles. Since these vehicles are parked when the batteries are being charged, it is possible to use the traction power converter as on-board charger, also allowing to reduce weight, volume and costs of components in the vehicle. In this context, this paper presents a model predictive control algorithm for an on-board fast battery charging that uses the traction power converter of an electric vehicle. Simulation results and system implementation are depicted, and finally, are presented some experimental results obtained with the proposed control system. (undefined) info:eu-repo/semantics/publishedVersion Document type: Part of book or chapter of book

Abstract

This paper presents a model predictive current control applied to a slow electric vehicle (EV) battery charger. Taking into account the similarities between the power converters inside the EV, it is possible to combine the battery charger and the motor driver in a single integrated converter, thus reducing the weight and volume of the proposed solution, and also contributing to reduce the final price of the EV. Due to the bidirectional power flow capability of the integrated power converter, when working as a slow EV battery charger it can operate in grid-to-vehicle (G2V) mode and in vehicle-to-grid (V2G) mode, contributing to make EVs an important assets in the future smart grids. The integrated power converter working as battery charger operates with sinusoidal current and unitary power factor, contributing to improve the power quality of the electrical grid. This paper provides simulation and experimental results that validate the model predictive control algorithm applied to the proposed integrated power converter working as slow EV battery charger. (undefined) info:eu-repo/semantics/publishedVersion Document type: Part of book or chapter of book

Abstract

The mass adoption of plug-in electric vehicles (PEVs) requires the deployment of public charging stations. Such facilities are expected to employ distributed generation and storage units to reduce the stress on the grid and boost sustainable transportation. While prior work has made considerable progress in deriving insights for understanding the adverse impacts of PEV chargings and how to alleviate them, a critical issue that affects the accuracy is the lack of real world PEV data. As the dynamics and pertinent design of such charging stations heavily depend on actual customer demand profile, in this paper we present and evaluate the data obtained from a $17$ node charging network equipped with Level $2$ chargers at a major North American University campus. The data is recorded for $166$ weeks starting from late $2011$. The result indicates that the majority of the customers use charging lots to extend their driving ranges. Also, the demand profile shows that there is a tremendous opportunity to employ solar generation to fuel the vehicles as there is a correlation between the peak customer demand and solar irradiation. Also, we provided a more detailed data analysis and show how to use this information in designing future sustainable charging facilities. Document type: Part of book or chapter of book

Abstract

Modern studies on green technologies for transportation sector are attaining attraction among the research communities from diverse parts. We learn the significance of plug-in hybrid electric vehicles which play a key role toward the policy option to reduce the environmental concern. There are major uncertainties in the diffusion of electric vehicles timing of market diffusion among consumers. However, there has been a considerable effort made towards the benefit of electric vehicles demand. Yet, the debates on consumer behavior towards the adoption of electric vehicles are less recognized. Researcher's highlighting the significance of plug-in hybrid electric vehicles from a combined perspective considering V2G technology which allows bi-directional flow of electricity. On the other hand, when electric vehicles are recharged from electricity produced from conventional technology power plants such as oil or coal-fired plants, they may produce equal or sometimes more greenhouse gas emissions than conventional gasoline vehicles. Document type: Part of book or chapter of book

Abstract

The work reported here is a result of the EPSRC Hydrogen and Fuel Cell Research Hub research programme (Grant ref. EP/J016454/1). A core theme of the UK Government's new Industrial Strategy is exploiting opportunities for domestic supply chain development. This extends to a special ‘Automotive Sector Deal’ that focuses on the shift to low emissions vehicles (LEVs). Here attention is on electric vehicle and battery production and innovation. In this paper, we argue that a more straightforward gain in terms of framing policy around potential economic benefits may be made through supply chain activity to support refuelling of battery/hydrogen vehicles. We set this in the context of LEV refuelling supply chains potentially replicating the strength of domestic upstream linkages observed in the UK electricity and/or gas industries. We use input-output multiplier analysis to deconstruct and assess the structure of these supply chains relative to that of more import-intensive petrol and diesel supply. A crucial multiplier result is that for every £1million of spending on electricity (or gas), 8 full-time equivalent jobs are supported throughout the UK. This compares to less than 3 in the case of petrol/diesel supply. Moreover, the importance of service industries becomes apparent, with 67% of indirect and induced supply chain employment to support electricity generation being located in services industries. The comparable figure for GDP is 42%. Publisher PDF Peer reviewed Document type: Book

Abstract

This thesis deals with the study of current charging infrastructure availability in highways, as well as proposing optimal allocations for new stations. First, a Machine Learning model is trained in order to estimate the actual range of an electric vehicle. This model will be constructed using heterogeneous data sources and variables that influence the total autonomy, such as speed, temperature, degradation or elevation, among others. Second, this model is used in combination with geospatial data regarding French highway and charging infrastructure locations, in order to propose a methodology for analyzing the availability level of charging stations in highways for electric vehicles. Finally, an optimization framework is implemented to decide the opening of several charging stations inside a highway, providing as possible locations rest and service areas already built, and considering current highway operational charging points.

Abstract

PFC del programa Erasmus EPS Treball desenvolupat dins el marc del programa 'European Project Semester'. Development of electric vehicle architectures requires complex analysis and innovative designs in order to produce a highly efficient mode of personal transportation acceptable to the target demographic. Using computer-aided modeling and simulation has been proven to decrease the development time of conventional vehicles while increasing overall success of the product design. Computer-aided automotive development also allows a fast response to the testing and inclusion of developing technologies in individual systems. Therefore, it follows to use this technique in the research and development of electric vehicles for consumer markets. This paper presents a system level model development and simulation for an electric vehicle using the Matlab-Simulink platform and its associated process. The current state of the art technologies for electric and plug-in hybrid electric vehicles are given to provide an introduction into the subject. Following, the project development is briefly described, detailing the specific goals for the project and the methods by which results were achieved. Next the paper discusses the analytical and simulation models for each key component as divided by the following systems: battery, charging, and traction. Model assembly and the development of a graphic user interface follows. Finally, the testing procedures for model validation, along with results, and future project works are provided.

Abstract

International audience; In the latest few years, lot of efforts have been done to pave the way to sustainable mobility, in order to solve pollution problems and fuel shortage. The use of electric vehicles (EV) is considered as one of the best ecologic and economic solution. However, autonomy barriers and limitations slow the progress and the deployment of this technology. In this paper, we propose an advanced electric vehicles' fleet management architecture. This architecture considers the most important factors that can affect the traveling mode of electric vehicles, in order to offer different services to fleet management companies for an efficient monitoring and management of their fleets. One of the most important service, considered in this paper, consists on providing economic itineraries planning for electric vehicles. Best routes, in term of electric power consumption, are computed based on the collected information about road topology (elevation variations, source, destination, etc.), weather conditions, vehicle characteristics, driver profile, traffic conditions and electric charging stations positions. In case of battery drop, new routes passing through the nearest available charging stations are recalculated and provided to the driver. To prove the concept, we implemented a lightweight traffic management server that provides this itinerary planning service to an EV fleet company. The server provides a web application to book an EV and plan the trip. All possible itineraries are displayed on a map with electric power consumption, traveled distance and duration details. Then, the server uploads the chosen itinerary into the vehicle's GPS or into the driver's smartphone.