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Fuel consumption (FC) has always been an important factor in vehicle cost. With the advent of electronically controlled engines, the controller area network (CAN) broadcasts information about engine and vehicle performance, including fuel use. However, the accuracy of the FC estimates is uncertain. In this study, the researchers first compared CAN-broadcasted FC against physically measured fuel use for three different types of trucks, which revealed the inaccuracies of CAN-broadcast fueling estimates. To match precise gravimetric fuel-scale measurements, polynomial models were developed to correct the CAN-broadcasted FC. Lastly, the robustness testing of the correction models was performed. The training cycles in this section included a variety of drive characteristics, such as high speed, acceleration, idling, and deceleration. The mean relative differences were reduced noticeably.

The National Renewable Energy Laboratory's (NREL's) Fleet Test and Evaluations team recently conducted chassis dynamometer tests of a class 8 conventional regional delivery truck over the Heavy Heavy-Duty Diesel Truck (HHDDT), West Virginia University City (WVU City), and Composite International Truck Local and Commuter Cycle (CILCC) drive cycles. A quantitative study analyzed the impacts of various factors on fuel consumption (FC) and fuel economy (FE) by modeling and simulating the truck using NREL's Future Automotive Systems Technology Simulator (FASTSim). Factors included vehicle weight and the coefficients of rolling resistance and aerodynamic drag. Simulation results from a single parametric study revealed that FC was approximately a linear function of the weight, coefficient of aerodynamic drag, and rolling resistance over various drive cycles.

This paper presents multiple methods for predicting heavy/medium-duty vehicle fuel consumption based on driving cycle information. A polynomial model, a black box artificial neural net model, a polynomial neural network model, and a multivariate adaptive regression splines (MARS) model were developed and verified using data collected from chassis testing performed on a parcel delivery diesel truck operating over the Heavy Heavy-Duty Diesel Truck (HHDDT), City Suburban Heavy Vehicle Cycle (CSHVC), New York Composite Cycle (NYCC), and hydraulic hybrid vehicle (HHV) drive cycles. Each model was trained using one of four drive cycles as a training cycle and the other three as testing cycles. By comparing the training and testing results, a representative training cycle was chosen and used to further tune each method.

The Future Automotive Systems Technology Simulator (FASTSim) is a high-level advanced vehicle powertrain systems analysis tool supported by the U.S. Department of Energy's Vehicle Technologies Office. FASTSim provides a quick and simple approach to compare powertrains and estimate the impact of technology improvements on light- and heavy-duty vehicle efficiency, performance, cost, and battery life. The input data for most light-duty vehicles can be automatically imported. Those inputs can be modified to represent variations of the vehicle or powertrain. The vehicle and its components are then simulated through speed-versus-time drive cycles. At each time step, FASTSim accounts for drag, acceleration, ascent, rolling resistance, each powertrain component's efficiency and power limits, and regenerative braking. Conventional vehicles, hybrid electric vehicles, plug-in hybrid electric vehicles, all-electric vehicles, compressed natural gas vehicles, and fuel cell vehicles are included.

Vehicles continuously report real-time fuel consumption estimates over their data bus, known as the controller area network (CAN). However, the accuracy of these fueling estimates is uncertain to researchers who collect these data from any given vehicle. To assess the accuracy of these estimates, CAN-reported fuel consumption data are compared against fuel measurements from precise instrumentation. The data analyzed consisted of eight medium/heavy-duty vehicles and two medium-duty engines. Varying discrepancies between CAN fueling rates and the more accurate measurements emerged but without a vehicular trend-for some vehicles the CAN under-reported fuel consumption and for others the CAN over-reported fuel consumption. Furthermore, a qualitative real-time analysis revealed that the operating conditions under which these fueling discrepancies arose varied among vehicles.