Plug-in hybrid (PHEV) technology offers the ability to achieve zero tailpipe emissions coupled with convenient refueling. Fleet adoption of PHEVs, often motivated by organizational and regulatory sustainability targets, may not always align with optimal use cases. In a car rental application, barriers to improving fuel economy over a conventional hybrid include: diminished benefits of additional battery capacity on long-distance trips, sparse electric charging infrastructure at the fleet location, lack of renter understanding of electric charging options, and a principle-agent problem where the driver accrues fewer benefits than costs for actions that improve fuel economy, like charging and eco-driving. This study uses high-resolution driving data collected from twelve Ford Fusion Energi sedans owned by University of California, Davis (UC Davis), where the vehicles are rented out for university-related activities. The data is analyzed to understand the degree to which the electric battery is taken advantage of by fleet management and end users to reduce fuel costs and emissions. Specifically, characteristics of trips assigned to those vehicles, driver behavior, locations of charging events and missed charging opportunities, state of charge (SOC) at the start of rentals, and segments of trips typically covered by electric driving range are examined. Finally, machine learning techniques, including a decision tree analysis, are used to understand predictors of fuel economy for this fleet. Conversations with fleet management, including presentation of analysis results, are used to generate policy recommendations. We find that due to the typical length of trips, a conventional hybrid would achieve roughly equivalent fuel economy in the UC Davis motor pool, although the plug-in option offers an opportunity to expand exposure of drivers to electrification. Steps for improvement include expanding on-site fleet charging infrastructure, educating users on charging options prior to rental, and consideration of trip destination in vehicle selection for rental.