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A heavy-duty vehicle (HDV) module of the Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREETTM) model has been developed at Argonne National Laboratory. The fuel-cycle GREET model has been published extensively and contains data on fuel-cycles and vehicle operation of light-duty vehicles. The addition of the HDV module to the GREET model allows for well-to-wheel (WTW) analyses of heavy-duty advanced technology and alternative fuel vehicles (AFVs), which has been lacking in the literature. WTW analyses of HDVs becomes increasingly important to understand the fuel consumption and greenhouse gas (GHG) emissions impacts of newly enacted and future HDV regulations from the Environmental Protection Agency and the Department of Transportation’s National Highway Traffic Safety Administration.

This paper describes a configuration and controller, designed using Autonomie,1 for dual-motor battery electric vehicle (BEV) heavy-duty trucks. Based on the literature and current market research, this model was designed with two electric motors, one on the front axle and the other on the rear axle. A rule-based control algorithm was designed for the new dual-motor BEV, based on the model, and the control parameters were optimized by using a genetic algorithm (GA). The model was simulated in diverse driving cycles and gradeability tests. The results show both a good following of the desired cycle and achievement of truck gradeability performance requirements. The simulation results were compared with those of a single-motor BEV and showed reduced energy consumption with the high-efficiency operation of the two motors.

Connectivity and automation provide the potential to use information about the environment and future driving to minimize energy consumption. Aerodynamic drag can also be reduced by close-gap platooning using information from vehicle-to-vehicle communications. In order to achieve these goals, the designers of control strategies need to simulate a wide range of driving situations in which vehicles interact with other vehicles and the infrastructure in a closed-loop fashion. RoadRunner is a new model-based system engineering platform based on Autonomie software, which can collectively provide the necessary tools to predict energy consumption for various driving decisions and scenarios such as car-following, free-flow, or eco-approach driving, and thereby can help in developing control algorithms.

A simulation approach is defined that integrates a military mission assessment tool (One Semi-Automated Forces) with a commercial automotive control/energy consumption development tool (Autonomie). The objective is to enable vehicle energy utilization and fuel consumption impact assessments relative to US Army mission effectiveness and commercial drive cycles. The approach to this integration will be described, along with its potential to meet its objectives.

To properly compare and contrast the environmental performance of one vehicle technology against another, it is necessary to consider their production, operation, and end-of-life fates. Since 1995, Argonne’s GREET® life cycle analysis model (Greenhouse gases, Regulated Emissions, and Energy use in Technologies) has been annually updated to model and refine the latest developments in fuels and materials production, as well as vehicle operational and composition characteristics. Updated cradle-to-grave life cycle analysis results from the model’s latest release are described for a wide variety of fuel and powertrain options for U.S. light-duty and medium/heavy-duty vehicles. Light-duty vehicles include a passenger car, sports utility vehicle (SUV), and pick-up truck, while medium/heavy-duty vehicles include a Class 6 pickup-and-delivery truck, Class 8 day-cab (regional) truck, and Class 8 sleeper-cab (long-haul) truck.