1438238 (MacKenzie). The lack of empirically based models of charging behavior is increasingly acknowledged as a limitation of existing Plug-in Electric Vehicle (PEV) research. This project will expand knowledge of charging behavior by quantifying the effects of multiple factors on drivers' choices over (1) whether to use a PEV or an alternate vehicle for a particular travel day, and (2) whether or not to charge their PEV at various points throughout their day. An important innovation lies in modeling the interdependence of these choices. Data will be analyzed using dynamic discrete choice models, which have seen considerable development in economics but relatively little use in transportation. The investigators will test whether the dynamic choice modeling framework can better predict PEV owners' decisions about vehicle use and charging than can the static choice modeling approaches more commonly used in transportation engineering. Additionally, the work will provide insights into the monetary value that PEV drivers place on reducing gasoline consumption and greenhouse gas emissions, and how this value compares to the price of gasoline. Plug-in electric vehicles contribute to improved national security and enhanced economic competitiveness by reducing spending on oil imports, insulating the economy from oil price shocks, and reducing the country's strategic interests in geopolitically unstable regions. They enhance the wellbeing of individuals in society by reducing the impacts of transportation on local and global environmental quality. This research will facilitate a shift to a more environmentally and economically sustainable transportation system by (1) supporting the design of vehicles and charging infrastructure networks that better match consumers' actual behavior, and (2) allowing policymakers to design cost-effective incentives for PEVs and charging infrastructure, based on more accurate assessments of their potential benefits. This research will enrich STEM education by highlighting the importance of multidisciplinary competence for the modern engineer. The PI plans to incorporate products from this research into assignments and materials for a new course he is offering on Transportation Energy & Sustainability. The investigators have professional networks spanning the public, private, and academic sectors, and are committed to transferring to practitioners the knowledge created through this research. They anticipate benefits for automobile manufacturers and charging network operators, more effective policymaking, and strengthened relationships among these groups to result from this work.

A stated preference choice experiment will be administered via a customized, web-based interface to a sample of PEV drivers. The survey will collect key background information on demographics and vehicle ownership, then present respondents with a set of tailored choice situations. Respondents will be presented with a planned travel day that includes one or more segments of driving (of specified distances) interspersed with potential charging opportunities (characterized by the length of the stop and the cost, power, and typical availability of charging equipment). They will be asked whether they would use their PEV or an alternate vehicle for the specified travel day. Those who indicate that they would use their PEV will be taken through a simulation of the travel day, in which they will be asked whether or not they would charge their vehicle at each opportunity. The survey data will be analyzed using a dynamic discrete choice modeling approach. In this framework, choices made in earlier periods affect the potential payoffs of subsequent choices, and the earlier choices are assumed to be made with this foresight in mind. The PI hypothesizes that this modeling approach can better represent the decision processes of PEV drivers, who consider the likelihood of finding appropriate charging opportunities later in the day when deciding whether to drive or charge a PEV earlier in the day. A key product of this work will be a model of the probability of a PEV driver choosing to use and charge a PEV, conditional on (potentially uncertain) information about travel plans and subsequent charging opportunities.

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University of Washington
United States
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