Federal fleets are a frequent subject of legislative and executive efforts to lead a national transition to alternative fuels and advanced vehicle technologies. Section 701 of the Energy Policy Act of 2005 requires that all dual-fueled alternative fuel vehicles in the federal fleet be operated on alternative fuel 100% of the time when they have access to it. However, in Fiscal Year (FY) 2012, drivers of federal flex fuel vehicles (FFV) leased through the General Services Administration refueled with E85 24% of the time when it was available--falling well short of the mandate. The U.S. Department of Energy's National Renewable Energy Laboratory completed a 2-year Laboratory Directed Research and Development project to identify the factors that influence the refueling behavior of federal FFV drivers. The project began with two primary hypotheses. First, information scarcity increases the tendency to miss opportunities to purchase E85. Second, even with perfect information, there are limits to how far drivers will go out of their way to purchase E85. This paper discusses the results of the project, which included a June 2012 survey of federal fleet drivers and an empirical analysis of actual refueling behavior from FY 2009 to 2012. This research will aid in the design and implementation of intervention programs aimed at increasing alternative fuel use and reducing petroleum consumption.

Vehicles powered by electricity and alternative-fuels are becoming a more popular form of transportation since they have less of an environmental impact than standard gasoline vehicles. Unfortunately, their success is currently inhibited by the sparseness of locations where the vehicles can refuel as well as the fact that many of the vehicles have a range that is less than those powered by gasoline. These factors together create a "range anxiety" in drivers, which causes the drivers to worry about the utility of alternative-fuel and electric vehicles and makes them less likely to purchase these vehicles. For the new vehicle technologies to thrive it is critical that range anxiety is minimized and performance is increased as much as possible through proper routing and scheduling. In the case of long distance trips taken by individual vehicles, the routes must be chosen such that the vehicles take the shortest routes while not running out of fuel on the trip. When many vehicles are to be routed during the day, if the refueling stations have limited capacity then care must be taken to avoid having too many vehicles arrive at the stations at any time. If the vehicles that will need to be routed in the future are unknown then this problem is stochastic. For fleets of vehicles serving scheduled operations, switching to alternative-fuels requires ensuring the schedules do not cause the vehicles to run out of fuel. This is especially problematic since the locations where the vehicles may refuel are limited due to the technology being new. This dissertation covers three related optimization problems: routing a single electric or alternative-fuel vehicle on a long distance trip, routing many electric vehicles in a network where the stations have limited capacity and the arrivals into the system are stochastic, and scheduling fleets of electric or alternative-fuel vehicles with limited locations to refuel. Different algorithms are proposed to solve each of the three problems, of which some are exact and some are heuristic. The algorithms are tested on both random data and data relating to the State of Arizona.

Within the next 3-7 years the US light duty fleet and fuel supply will encounter what is commonly referred to as the "blend wall". This phenomenon describes the situation when more ethanol production has been mandated than can be blended legally in the existing gasoline fuel supply. While there are currently measures under review to extend fuel certification to from 10% to 15% ethanol blends, this will not be enough to reach the existing Renewable Fuel Standard targets that grow over the next decade to 36 billion gallons of biofuel. This research focuses on a quantitative assessment of how to effectively use policies to match the deployment of ethanol with capable vehicles to use ethanol, and the infrastructure to the fuel. A model of the light duty vehicle fleet has been used find the number of vehicles required to meet ethanol fuel usage targets. The key variables explored in this work are (i) the volumetric target for total biofuels (ii) the legal blend limit of ethanol in gasoline, (iii) fleet vehicle sales penetration and (iv) a metric for the relative utilization of ethanol and gasoline for flex fuel vehicles. Each of these factors can be varied independently to understand the existing relationship between each in the context of the US light-duty vehicle fleet. Ultimately, coordinated polices focusing on each of these key factors can ease the transformation of the automotive fuel industry away from petroleum dominated supplies.

The light-duty vehicle fleet is expected to undergo substantial technological changes over the next several decades. New powertrain designs, alternative fuels, advanced materials and significant changes to the vehicle body are being driven by increasingly stringent fuel economy and greenhouse gas emission standards. By the end of the next decade, cars and light-duty trucks will be more fuel efficient, weigh less, emit less air pollutants, have more safety features, and will be more expensive to purchase relative to current vehicles. Though the gasoline-powered spark ignition engine will continue to be the dominant powertrain configuration even through 2030, such vehicles will be equipped with advanced technologies, materials, electronics and controls, and aerodynamics. And by 2030, the deployment of alternative methods to propel and fuel vehicles and alternative modes of transportation, including autonomous vehicles, will be well underway. What are these new technologies - how will they work, and will some technologies be more effective than others? Written to inform The United States Department of Transportation's National Highway Traffic Safety Administration (NHTSA) and Environmental Protection Agency (EPA) Corporate Average Fuel Economy (CAFE) and greenhouse gas (GHG) emission standards, this new report from the National Research Council is a technical evaluation of costs, benefits, and implementation issues of fuel reduction technologies for next-generation light-duty vehicles. Cost, Effectiveness, and Deployment of Fuel Economy Technologies for Light-Duty Vehicles estimates the cost, potential efficiency improvements, and barriers to commercial deployment of technologies that might be employed from 2020 to 2030. This report describes these promising technologies and makes recommendations for their inclusion on the list of technologies applicable for the 2017-2025 CAFE standards.

For a century, almost all light-duty vehicles (LDVs) have been powered by internal combustion engines operating on petroleum fuels. Energy security concerns about petroleum imports and the effect of greenhouse gas (GHG) emissions on global climate are driving interest in alternatives. Transitions to Alternative Vehicles and Fuels assesses the potential for reducing petroleum consumption and GHG emissions by 80 percent across the U.S. LDV fleet by 2050, relative to 2005. This report examines the current capability and estimated future performance and costs for each vehicle type and non-petroleum-based fuel technology as options that could significantly contribute to these goals. By analyzing scenarios that combine various fuel and vehicle pathways, the report also identifies barriers to implementation of these technologies and suggests policies to achieve the desired reductions. Several scenarios are promising, but strong, and effective policies such as research and development, subsidies, energy taxes, or regulations will be necessary to overcome barriers, such as cost and consumer choice.

A large body of evidence suggests that drivers who receive real-time fuel economy information can increase their vehicle fuel economy by 5%, a process commonly known as ecodriving. However, few studies have directly addressed the human side of the feedback, that is, why drivers would (or would not) be motivated to change their behavior and how to design feedback devices to maximize the motivation to ecodrive. This dissertation approaches the question using a mixed qualitative and quantitative approach to explore driver responses and psychology as well as to quantify the process of behavior change. The first chapter discusses the use of mile-per-gallon fuel economy as a metric for driver feedback and finds that an alternative energy economy metric is superior for real-time feedback. The second chapter reviews behavioral theories and proposes a number of practical solutions for the ecodriving context. In the third chapter the theory of planned behavior is tested against driver responses to an existing feedback system available in the 2008 model Toyota Prius. The fourth chapter presents a novel feedback design based on behavioral theories and drivers' responses to the feedback. Finally, chapter five presents the quantitative results of a natural-driving study of fuel economy feedback. The dissertation findings suggest that behavior theories such as the Theory of Planned Behavior can provide important improvements to existing feedback designs. In addition, a careful analysis of vehicle energy flows indicates that the mile-per-gallon metric is deeply flawed as a real-time feedback metric, and should be replaced. Chapters 2 and 3 conclude that behavior theories have both a theoretical and highly practical role in feedback design, although the driving context requires just as much care in the application. Chapters 4 and 5 find that a theory-inspired interface provides drivers with engaging and motivating feedback, and that integrating personal goal into the feedback is the most motivating theory-based addition. Finally, the behavioral model results in chapter 5 suggest that driver goals not only influence in-vehicle energy use, but are themselves flexible constructs that can be directly influenced by energy feedback.