© 2017, SAGE Publications Ltd. All rights reserved. Estimating personal vehicle energy consumption is important for nationwide climate policy, local and statewide environmental policy, and technology planning. Transportation energy use is complex, depending on vehicle performance and the driving behavior of individuals, as well as on travel patterns of cities and regions. Previous studies combine large samples of travel behavior with fixed estimates of per mile fuel economy or use detailed models of vehicles with limited samples of travel behavior. This paper presents a model for estimating privately operated vehicle energy consumption—TripEnergy—that accurately reconstructs detailed driving behavior across the United States and simulates vehicle performance for different driving conditions. The accuracy of this reconstruction was tested by using out-of-sample predictions, and the vehicle model was tested against microsimulation. TripEnergy consists of a demand model, linking GPS drive cycles to travel survey trips, and a vehicle model, efficiently simulating energy consumption across different types of driving. Because of its ability to link small-scale variation in vehicle technology and driver behavior with large-scale variation in travel patterns, it is expected to be useful for a variety of applications, including technology assessment, cost and energy savings from ecodriving, and the integration of electric vehicle technologies into the grid.