This research project evaluates fuel consumption results of two Class 8 tractor-trailer combinations platooned together compared to their standalone fuel consumption. A series of ten modified SAE Type II J1321 fuel consumption track tests were performed to document fuel consumption of two platooned vehicles and a control vehicle at varying steady-state speeds, following distances, and gross vehicle weights (GVWs). The steady-state speeds ranged from 55 mph to 70 mph, the following distances ranged from a 20-ft following distance to a 75-ft following distance, and the GVWs were 65K lbs and 80K lbs. All tractors involved had U.S. Environmental Protection Agency (EPA) SmartWay-compliant aerodynamics packages installed, and the trailers were equipped with side skirts. Effects of vehicle speed, following distance, and GVW on fuel consumption were observed and analyzed. The platooning demonstration system used in this study consisted of radar systems, Dedicated Short-Range Communication (DSRC) vehicle-to-vehicle (V2V) communications, vehicle braking and torque control interface, cameras and driver displays. The lead tractor consistently demonstrated an improvement in average fuel consumption reduction as following distance decreased, with results showing 2.7% to 5.3% fuel savings at a GVW of 65k. The trailing vehicle achieved fuel consumption savings ranging from 2.8% to 9.7%; tests during which the engine cooling fan did not operate achieved savings of 8.4% to 9.7%. "Team" fuel savings, considering the platooned vehicles as one, ranged from 3.7% to 6.4%, with the best combined result being for 55 mph, 30-ft following distance, and 65k GVW.
Global Positioning System (GPS) data acquisition devices have proven useful tools for gathering real-world driving data and statistics. The data collected by these devices provide valuable information in studying driving habits and conditions. When used jointly with vehicle simulation software, the data are invaluable in analyzing vehicle fuel use and performance, aiding in the design of more advanced and efficient vehicle technologies. However, when employing GPS data acquisition systems to capture vehicle drive-cycle information, a number of errors often appear in the captured raw data samples. Common sources of error in GPS data include sudden signal loss, extraneous or outlying data points, speed drifting, and signal white noise, all of which combine to limit the quality of field data for use in downstream applications. Unaddressed, these errors significantly impact the reliability of source data and limit the effectiveness of traditional drive cycle analysis approaches and vehicle simulation software. Without reliable speed and time information, the validity of derived metrics for drive cycles, such as acceleration, power, and distance become questionable. This study explores some of the common sources of error present in collected raw GPS data and presents a detailed filtering process designed to correct for these issues. To illustrate the effectiveness of the proposed filtration process across the range of vehicle vocations, test data from both light-and medium/heavy-duty applications are examined. Graphical comparisons of raw and filtered cycles are presented, and statistical analyses performed to determine the effects of the proposed filtration process on raw data. Finally, the paper concludes with an evaluation of the overall benefits of data filtration on raw GPS data and presents potential areas for continued research.
Smart technologies enabling connection among vehicles and between vehicles and infrastructure as well as vehicle automation to assist human operators are receiving significant attention as a means for improving road transportation systems by reducing fuel consumption-and related emissions-while also providing additional benefits through improving overall traffic safety and efficiency. For truck applications, which are currently responsible for nearly three-quarters of the total U.S. freight energy use and greenhouse gas (GHG) emissions, platooning has been identified as an early feature for connected and automated vehicles (CAVs) that could provide significant fuel savings and improved traffic safety and efficiency without radical design or technology changes compared to existing vehicles. A statistical analysis was performed based on a large collection of real-world U.S. truck usage data to estimate the fraction of total miles that are technically suitable for platooning. In particular, our analysis focuses on estimating "platoonable" mileage based on overall highway vehicle use and prolonged high-velocity traveling, and established that about 65% of the total miles driven by combination trucks from this data sample could be driven in platoon formation, leading to a 4% reduction in total truck fuel consumption. This technical potential for "platoonable" miles in the United States provides an upper bound for scenario analysis considering fleet willingness and convenience to platoon as an estimate of overall benefits of early adoption of connected and automated vehicle technologies. A benefit analysis is proposed to assess the overall potential for energy savings and emissions mitigation by widespread implementation of highway platooning for trucks.
SummaryMedium and heavy duty (MD and HD respectively) vehicles are responsible for 26 percent of the total U.S.
This paper presents multiple methods for predicting heavy/mediumduty 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. HHDDT as the training cycle gave the best predictive results, because HHDDT contains a variety of drive characteristics, such as high speed, acceleration, idling, and deceleration. Among the four model approaches, MARS gave the best predictive performance, with an average percent error of −1.84% over the four chassis dynamometer drive cycles. To further evaluate the accuracy of the predictive models, the approaches were applied to real-world data. MARS outperformed the other three approaches, providing an average percent error of −2.2% over four real-world road segments. The MARS model performance was then compared to powertrain modeling results over HHDDT, CSHVC, NYCC, and HHV drive cycles using NREL's Future Automotive Systems Technology Simulator (FASTSim). The results indicated that the MARS method achieved comparable predictive performance with FASTSim.
Understanding the real-world power demand of modern automobiles is of critical importance to engineers using modeling and simulation in the design of increasingly efficient powertrains. Increased use of global positioning system (GPS) devices has made large-scale data collection of vehicle speed (and associated power demand) a reality. While the availability of real-world GPS data has improved the industry's understanding of in-use vehicle power demand, relatively little attention has been paid to the incremental power requirements imposed by road grade. This analysis quantifies the incremental efficiency impacts of real-world road grade by appending high-fidelity elevation profiles to GPS speed traces and performing a large simulation study. Employing a large, real-world dataset from the National Renewable Energy Laboratory's Transportation Secure Data Center, vehicle powertrain simulations are performed with and without road grade under five vehicle models. Aggregate results of this study suggest that road grade could be responsible for 1% to 3% of fuel use in lightduty automobiles.
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