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In recent studies, optimal control has shown promise as a strategy for enhancing the energy efficiency of connected autonomous vehicles. To maximize optimization performance, it is important to accurately predict constraints, especially separation from a vehicle in front. This paper proposes a novel prediction method for forecasting the trajectory of the nearest preceding car. The proposed predictor is designed to produce short to medium-length speed trajectories using a locally weighted polynomial regression algorithm. The polynomial coefficients are trained by using two types of information: (1) vehicle-to-vehicle (V2V) messages transmitted by multiple preceding vehicles and (2) vehicle-to-infrastructure (V2I) information broadcast by roadside equipment. The predictor’s performance was tested in a multi-vehicle traffic simulation platform, RoadRunner, previously developed by Argonne National Laboratory.

This paper introduces an eco-driving highway cruising algorithm based on optimal control theory that is applied to a conventionally-powered connected and automated vehicle. Thanks to connectivity to the cloud and/or to infrastructure, speed limit and slope along the future route can be known with accuracy. This can in turn be used to compute the control variable trajectory that will minimize energy consumption without significantly impacting travel time. Automated driving is necessary to the implementation of this concept, because the chosen control variables (e.g., torque and gear) impact vehicle speed. An optimal control problem is built up where quadratic models are used for the powertrain. The optimization is solved by applying Pontryagin’s minimum principle, which reduces the problem to the minimization of a cost function with parameters called co-states.

A fuel-cycle model-called the Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) model-has been developed at Argonne National Laboratory to evaluate well-to-wheels (WTW) energy and emission impacts of motor vehicle technologies fueled with various transportation fuels. The new GREET version has up-to-date information regarding energy use and emissions for fuel production activities and vehicle operations. In this study, a complete WTW evaluation targeting energy use, greenhouse gases (CO2, CH4, and N2O), and typical criteria air pollutants (VOC, NOX, and PM10) includes the following fuel options-gasoline, diesel, and hydrogen; and the following vehicle technologies-spark-ignition engines with or without hybrid configurations, compression-ignition engines with hybrid configurations, and hydrogen fuel cells with hybrid configurations.

This paper presents the results of Argonne National Laboratory's assessment of the fuel-cycle energy and emissions impacts of 13 combinations of fuels and propulsion systems that are potential candidates for light-duty vehicles with tripled fuel economy (3X vehicles). These vehicles are being developed by the Partnership for a New Generation of Vehicles (PNGV). Eleven fuels were considered: reformulated gasoline (RFG), reformulated diesel (RFD), methanol, ethanol, dimethyl ether, liquefied petroleum gas (LPG), compressed natural gas (CNG), liquefied natural gas (LNG), biodiesel, Fischer-Tropsch diesel and hydrogen. RFG, methanol, ethanol, LPG, CNG and LNG were assumed to be burned in spark-ignition, direct-injection (SIDI) engines. RFD, Fischer-Tropsch diesel, biodiesel and dimethyl ether were assumed to be burned in compression-ignition, direct-injection (CIDI) engines. Hydrogen, RFG and methanol were assumed to be used in fuel-cell vehicles.