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Technical Paper

The Accuracy and Correction of Fuel Consumption from Controller Area Network Broadcast

2017-10-13
2017-01-7005
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.
Journal Article

Potentials for Platooning in U.S. Highway Freight Transport

2017-03-28
2017-01-0086
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.
Journal Article

Overcoming the Range Limitation of Medium-Duty Battery Electric Vehicles through the use of Hydrogen Fuel-Cells

2013-09-24
2013-01-2471
Battery electric vehicles possess great potential for decreasing lifecycle costs in medium-duty applications, a market segment currently dominated by internal combustion technology. Characterized by frequent repetition of similar routes and daily return to a central depot, medium-duty vocations are well positioned to leverage the low operating costs of battery electric vehicles. Unfortunately, the range limitation of commercially available battery electric vehicles acts as a barrier to widespread adoption. This paper describes the National Renewable Energy Laboratory's collaboration with the U.S. Department of Energy and industry partners to analyze the use of small hydrogen fuel-cell stacks to extend the range of battery electric vehicles as a means of improving utility, and presumably, increasing market adoption.
Technical Paper

Modeling Heavy/Medium-Duty Fuel Consumption Based on Drive Cycle Properties

2015-09-29
2015-01-2812
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.
Technical Paper

FASTSim: A Model to Estimate Vehicle Efficiency, Cost and Performance

2015-04-14
2015-01-0973
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.
Technical Paper

Drive Cycle Analysis, Measurement of Emissions and Fuel Consumption of a PHEV School Bus

2011-04-12
2011-01-0863
Plug-in hybrid electric vehicle (PHEV) technology may reduce fuel consumption and tailpipe emissions in many medium- and heavy-duty vehicle vocations, including school buses. The true magnitude of these reductions is best assessed by comparative testing over relevant drive cycles. The National Renewable Energy Laboratory (NREL) collected and analyzed real-world school bus drive cycle data, and selected similar standard drive cycles for testing on a chassis dynamometer. NREL tested a first-generation PHEV school bus equipped with a 6.4 L engine and an Enova PHEV drive system comprising a 25-kW/80 kW (continuous/peak) motor and a 370-volt lithium ion battery pack. For a baseline comparison, a Bluebird 7.2 L conventional school bus was also tested. Both vehicles were tested over three different drive cycles to capture a range of driving activity.
Technical Paper

Corroborative Evaluation of the Real-world Energy Saving Potentials of InfoRich Eco-Autonomous Driving System

2020-04-14
2020-01-0588
There has been an increasing interest in exploring the potentials of reducing energy consumption of future connected and automated vehicles (CAVs). People have extensively studied various eco-driving implementation that leverages preview information provided by on-board sensors and connectivity, as well as the control authority enabled by automation. To quantitatively evaluate the benefits of eco-driving in a real-world setting is a challenging task. The regulatory standard driving cycles that are being used for exhaust emissions and fuel economy measurements are not truly representative of real-world driving. To adequately take into account the real-world or “off-cycle” driving behavior, this paper presents four collaborative evaluation methods: large-scale simulation, in-depth simulation, vehicle-in-the-loop test, and vehicle road test. These four approaches, each focuses on certain aspects, evaluate the real-world fuel economy benefits with different ranges and resolutions.
Technical Paper

Contribution of Road Grade to the Energy Use of Modern Automobiles Across Large Datasets of Real-World Drive Cycles

2014-04-01
2014-01-1789
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.
Journal Article

Analyzing Vehicle Fuel Saving Opportunities through Intelligent Driver Feedback

2012-04-16
2012-01-0494
While it is well known that “MPG will vary” based on how one drives, little independent research exists on the aggregate fuel savings potential of improving driver efficiency and on the best ways to motivate driver behavior changes. This paper finds that reasonable driving style changes could deliver significant national petroleum savings, but that current feedback approaches may be insufficient to convince many people to adopt efficient driving habits. To quantify the outer bound fuel savings for drive cycle modification, the project examines completely eliminating stop-and-go driving plus unnecessary idling, and adjusting acceleration rates and cruising speeds to ideal levels. Even without changing the vehicle powertrain, such extreme adjustments result in dramatic fuel savings of over 30%, but would in reality only be achievable through automated control of vehicles and traffic flow.
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