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Connectivity and automation are increasingly being developed for cars and trucks, aiming to provide better safety and better driving experience. As these technologies mature and reach higher adoption rates, they will also have an impact on the energy consumption: Connected and Automated Vehicles (CAVs) may drive more smoothly, stop less often, and move at faster speeds, thanks to overall improvements to traffic flows. These potential impacts are not well studied, and any existing studies tend to focus solely on conventional engine-powered cars, leaving aside electrified vehicles such as Hybrid Electric Vehicles (HEVs) and Battery Electric Vehicles (BEVs). This work intends to address this issue by analyzing the energy impact of various CAV scenarios on different types of electric vehicles using high-fidelity models. The vehicles-all midsize, one HEV, one BEV, and a conventional-are modeled in Autonomie, a high-fidelity, forward-looking vehicle simulation tool.

Rising fuel costs, increased regulations, and heightened customer sensitivity to energy efficiency has prompted the evaluation of numerous powertrain technology improvements to introduce into production. The actual impact of such technologies can differ broadly, depending on the technology or application. To evaluate the fuel consumption impact, various baseline vehicles have been created and simulated by using Argonne National Laboratory's vehicle modeling and simulation tool, the Powertrain Systems Analysis Toolkit (PSAT). This paper provides a quantitative evaluation of several technologies or combinations of technologies. First, we assess the impact of single technologies, including vehicle/chassis characteristics, such as weight, aerodynamics, or rolling resistance. Next, we consider advanced powertrain technologies, ranging from dieselization to transmissions with a higher gear number, and hybridization.

The U.S. Environmental Protection Agency instrumented and tested a line-haul Class 8 tractor trailer on a 4-wheel-drive heavy-duty chassis dynamometer. A vehicle model was then developed in the Powertrain Systems Analysis Toolkit (PSAT), Argonne National Laboratory's vehicle simulation tool, using the truck technical specifications and the recorded data, which included the Portable Emissions Measurement System (PEMS) and Controller Area Network (CAN) signals. In this paper, we describe the test scenarios and the analysis performed on the data. We then present the vehicle model and assumptions. Finally, we compare the test and simulation data, including fuel consumption and component signals, as well as the main challenges specific to heavy-duty vehicle testing and simulation.

Hybrid electric vehicles have demonstrated their ability to significantly reduce fuel consumption for several medium- and heavy-duty applications. In this paper we analyze the impact on fuel economy of the hybridization of a tractor-trailer. The study is done in PSAT (Powertrain System Analysis Toolkit), which is a modeling and simulation toolkit for light- and heavy-duty vehicles developed by Argonne National Laboratory. Two hybrid configurations are taken into account, each one of them associated with a level of hybridization. The mild-hybrid truck is based on a parallel configuration with the electric machine in a starter-alternator position; this allows start/stop engine operations, a mild level of torque assist, and a limited amount of regenerative braking. The full-hybrid truck is based on a series-parallel configuration with two electric machines: one in a starter-alternator position and another one between the clutch and the gearbox.

A multimode transmission combines several power-split modes and possibly several fixed gear modes, thanks to complex arrangements of planetary gearsets, clutches and electric motors. Coupled to a battery, it can be used in a highly flexible hybrid configuration, which is especially practical for larger cars. The Chevrolet Tahoe Hybrid is the first light-duty vehicle featuring such a system. This paper introduces the use of a high-level vehicle controller based on instantaneous optimization to select the most appropriate mode for minimizing fuel consumption under a broad range of vehicle operating conditions. The control uses partial optimization: the engine ON/OFF and the battery power demand regulating the battery state-of-charge are decided by a rule-based logic; the transmission mode as well as the operating points are chosen by an instantaneous optimization module that aims at minimizing the fuel consumption at each time step.