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            "title": "Smart infrastructure for carbon foot print analysis of electric vehicles",
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            "abstractNote": "Electric powered vehicles use energy stored in some form of battery for the vehicle propulsion drive and to power auxiliary instruments such as air conditioning, stereo and in car equipments. Many research initiatives are currently underway to evaluate the potential and performance of electric cars and to identify any barriers to their uptake (being, technical, economic or social) as a greener alternative to the internal combustion engine traditionally powered by fossil fuels. This study provides an early insight into research undertaken by Newcastle University to investigate the performance of electric vehicles through on-road testing, user led trials and the analysis of the data collected from the vehicle. Newcastle University is currently involved in creating a smart ITS infrastructure to analyse the electric vehicle performance by monitoring the vehicles in terms of the power consumed, distance traveled, trip profile, auxiliary loads and driving styles to determine how the battery discharges and recharges under different conditions, By measuring the energy usage on any particular journey the equivalent carbon footprint for the journey can be estimated from the prevailing CO2 per KWh assuming the mean electricity generation profile in the UK. This paper will discuss the results from electric vehicle monitoring infrastructure by analysing the energy regeneration and energy usage, thereby calculating the impact to the environment.",
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            "abstractNote": "Under sponsorship of the Florida Department of Transportation, a study was conducted to measure the carbon footprint for Florida's public transit agencies. The state's 31 public transportation agencies were contacted to obtain data. With the Climate Registry's general reporting protocol (GRP), the research team calculated emissions on the basis of the provided data. The research team found that carbon dioxide emissions from revenue vehicles accounted for 82.5% of a typical agency's emissions (86.5% if weighted by footprint size). The balance of the footprint included trace emissions from vehicle operations; emissions from purchased electricity; emissions from operating nonrevenue vehicles; emissions from fuel used to heat, operate, and maintain facilities; and refrigerants lost from vehicle and building air-conditioning systems. Refrigerant losses accounted for about 5% of a typical agency's footprint. Data on losses were the most difficult to collect. The collected data were estimated to account for 75% to 80% of the total carbon footprint of Florida's transit agencies, and the data total footprint accounted for less than 1% of the state's surface transportation carbon footprint. This research also examined how Florida's transit agencies could help reduce the state's transportation carbon footprint by providing service that encourages people to ride public transportation instead of driving. The study also provides lessons for conducting similar inventories elsewhere, including data collection, constraints on agencies' abilities to provide data on a voluntary basis, some limitations of the GRP, and possibilities for simplifying the process in some circumstances.",
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            "abstractNote": "Mobility is one of the greatest contributors to the personal carbon footprint and to pollution and noise in urban areas. Still, these factors are not yet easily quantifiable in personal or urban scale, e.g. impact of each car trip or areas most exposed to CO 2 emissions. In this article, we propose an innovative solution for estimating fuel consumption and emissions leveraging the opportunities generated by the ubiquitous availability of mobile devices. We collect a large data set of GPS and fuel consumption data crowd-sourced by volunteer participants with an Android mobile application that logs the smartphone's embedded GPS data and gathers vehicle data using an external On-Board Diagnostics (OBD) device. This data is used to develop a model that estimates the instantaneous fuel consumption from the smartphone's GPS data alone, using the OBD data as ground truth. We use speed, acceleration and steepness as predictor variables to train polynomial models with and without cross-product terms. With the best general model (trained and tested on all participant vehicles), we obtain an average residual standard deviation of 1.58 l/100km for average consumption on 1min intervals. For individual models (trained and tested on each participant vehicle), we obtain an average residual standard deviation of 1.43 l/100km. The average fuel consumption for the used data set was 6.7 l/100km.",
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            "abstractNote": "This paper explores a new vision for urban and suburban transportation, termed Personalised Public Transportation, which builds upon recent trends in vehicle sharing, electric vehicles, mobile payments and cloud computing. The goal is to build on the best of the worlds of private and public transportation. Private transportation offers ownership, comfort and convenience, but is higher cost, and subject to externalities (traffic jams, pollution, etc.). Public transportation is efficient, cheaper and has lower energy/carbon footprint, but has a last-mile problem (access) and low spatio-temporal coverage in suburbia. We envisage a future model of leasing public transportation via a service similar to cell-phone services, where the user pays for convenience and sharing of a network. We describe the key design features inherent to this mobility model. The vehicular platform allows the entire fleet to be operated and managed via a cloud computing service in order to maximise convenience and minimise cost. An optimisation formulation to quantify the benefits of Personalised Public Transportation shows that it is a promising approach for transforming future generations of transportation into sustainable ecosystems.",
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            "abstractNote": "Significant cost reduction in recent years has made solar power an economically competitive power source in many regions today. In view of this, and the widespread introduction of electric vehicles (EVs) to the mass market, we study public EV charging stations with on-site solar generation that are backed up by conventional power from the grid. Since the carbon footprint of conventional power is higher than solar power, charging deadlines can critically affect the total solar energy available to charging stations and therefore the overall carbon footprint of the charging service. In this paper, we propose a method to quantify how much carbon footprint can be reduced as a function of the charging deadline by describing a performance-guaranteed fair power allocation algorithm in a public charging station. This enables us to study the three-way tradeoff between the charging deadline, the utility of EV owners, and the carbon footprint of EV charging. We find that our algorithm makes nearly optimal use of available green energy, while still guaranteeing that solar charging performs no worse than grid charging.",
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