Electric vehicles can contribute to climate change mitigation if coupled with decarbonized electricity, but only if vehicle range matches travelers' needs. Evaluating electric vehicle range against a population's needs is challenging because detailed driving behavior must be taken into account. Here we develop a model to combine information from coarse-grained but expansive travel surveys with high-resolution GPS data to estimate the energy requirements of personal vehicle trips across the U.S. We find that the energy requirements of 87% of vehicle-days could be met by an existing, affordable electric vehicle. This percentage is markedly similar across diverse cities, even when per-capita gasoline consumption differs significantly. We also find that for the highest-energy days, other vehicle technologies are likely needed even as batteries improve and charging infrastructure expands. Car-sharing or other means to serve this small number of high-energy days could play an important role in the electrification and decarbonization of transportation.
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 typically 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. Here we introduce a model for estimating privately operated vehicle energy consumption, TripEnergy, that accurately reconstructs detailed driving behavior across the U.S. and simulates vehicle performance for different driving conditions. 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, we expect it to be useful for a variety of applications, including technology assessment, cost and energy savings from eco-driving, and the integration of electric vehicle technologies into the grid.
Mesoscopic transport models can efficiently simulate complex travel behavior and traffic patterns over large networks, but simulating energy consumption in these models is difficult with traditional methods. Since mesoscopic transport models rely on a simplified handling of traffic flow, they cannot provide the accurate second-by-second measurement of vehicle speeds and accelerations that are required for widely-used energy models. Here we present extensions to the TripEnergy model that fill in the gaps of low resolution trajectories with realistic contextual driving behavior. TripEnergy also includes a vehicle energy model capable of simulating the impact of traffic conditions on energy consumption and CO 2 emissions, with inputs in the form of widely-available calibration data, allowing it to simulate thousands of different real-world vehicle makes and models. This design allows TripEnergy to integrate with mesoscopic transport models and to be fast enough to run on a large network with minimal additional computation time. We expect it to benefit from and enable advances in transport simulation, including optimizing traffic network controls to minimize energy, evaluating the performance of different vehicle technologies under wide-scale adoption, and better understanding the energy and climate impacts of new infrastructure and policies.
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