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Development and demonstration of a class 6 range-extended electric vehicle for commercial pickup and delivery operation

National Renewable Energy Laboratory-Matthew A. Jeffers, Eric Miller, Kenneth Kelly
Cummins Inc.-John Kresse, Ke Li, Jesse Dalton
  • Technical Paper
  • 2020-01-0848
To be published on 2020-04-14 by SAE International in United States
Range-extended hybrids are an attractive option for medium- and heavy-duty (M/HD) commercial vehicle fleets because they offer the efficiency of an electrified powertrain and accessories with the range of a conventional diesel powertrain. The vehicle essentially operates as if it was purely electric for most trips, while ensuring that all commercial routes can be completed in any weather conditions or geographic terrain. Fuel use and point-source emissions can be significantly reduced, and in some cases eliminated, as many shorter routes can be fully electrified with this architecture. Under a U.S. Department of Energy award for M/HD Vehicle Powertrain Electrification, Cummins has developed a plug-in hybrid electric (PHEV) class 6 truck with a range-extending engine designed for pickup and delivery application. The National Renewable Energy Laboratory (NREL) assisted by developing a representative work day drive cycle for class 6 operation and adapting it to enable track testing. A novel, automated driving system was developed and utilized by Southwest Research Institute (SwRI) to improve the repeatability of vehicle track testing used to quantify vehicle energy consumption. Cummins…
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Leveraging real-world driving data sets for design and impact evaluation of energy efficient control strategies.

National Renewable Energy Laboratory-Michael O'Keefe, Jeffrey Gonder
General Motors LLC-Bharatkumar Hegde, Steven E. Muldoon
  • Technical Paper
  • 2020-01-0585
To be published on 2020-04-14 by SAE International in United States
Modeling and simulation are crucial in the development of advanced energy efficient control strategies. Utilizing real-world driving data as the underlying basis for control design and simulation lends veracity to projected real-world energy savings. Standardized drive cycles are limited in their utility for evaluating advanced driving strategies that utilize connectivity and on-vehicle sensing, primarily because they are non-causal and are typically intended for evaluating emission and fuel economy under controlled conditions. Real-world driving data, because of its scale, is a useful representation of various road types, driving styles, and driving environments. The scale of real-world data also presents challenges in effectively using it in simulations. A fast and efficient simulation methodology is necessary to handle the large number of simulations performed for design analysis and impact evaluation of control strategies. In this study, we present two methods of leveraging real-world data in both design optimization of energy efficient control strategies and in evaluating the real-world impact of those control strategies upon large-scale deployment. Through these methodologies, strategies with highest impact on energy savings were selected…
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Heterogeneous Machine Learning on High Performance Computing for End to End Driving of Autonomous Vehicles

National Renewable Energy Laboratory-Xiangyu Zhang, Peter Graf
Oak Ridge National Laboratory-Robert Patton, Shang Gao, Spencer Paulissen, Nicholas Haas, Brian Jewell
  • Technical Paper
  • 2020-01-0739
To be published on 2020-04-14 by SAE International in United States
Current artificial intelligence techniques for end to end driving of autonomous vehicles typically rely on a single form of learning or training processes along with a corresponding dataset or simulation environment. Relatively speaking, success has been shown for a variety of learning modalities in which it can be shown that the machine can successfully “drive” a vehicle. However, the realm of real-world driving extends significantly beyond the realm of limited test environments for machine training. This creates an enormous gap in capability between these two realms. With their superior neural network structures and learning capabilities, humans can be easily trained within a short period of time to proceed from limited test environments to real world driving. For machines though, this gap is guarded by at least two challenges: 1) machine learning techniques remain brittle and unable to generalize to a wide range of scenarios, and 2) effective training data that enhances generalization and generates the desired driving behavior. Further, each challenge can be computationally intensive on its own thereby exasperating the gap. Moreover, is has…
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RouteE: A Vehicle Energy Consumption Prediction Engine

National Renewable Energy Laboratory-Jacob Holden, Nicholas Reinicke, Jeff Cappellucci
  • Technical Paper
  • 2020-01-0939
To be published on 2020-04-14 by SAE International in United States
The emergence of Connected and Automated Vehicles and Smart Cities technologies create the opportunity for new mobility mode and routing decision tools, among many others. In order to achieve maximal mobility and minimal energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy Prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data is unavailable. Applications include vehicle route selection, energy accounting/optimization in transportation simulation, and corridor energy analyses, among others. The software is an open-source Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own datasets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts. The pre-trained RouteE models are trained using NREL’s Future Automotive Systems Technology Simulator (FASTSim) paired with approximately 1 million miles of drive cycle data from the Transportation Secure Data Center (TSDC) resulting…
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Understanding Charging Flexibility of Shared Autonomous Electric Vehicle Fleets

National Renewable Energy Laboratory-Matthew Moniot, Yanbo Ge, Nicholas Reinicke, Alex Schroeder
  • Technical Paper
  • 2020-01-0941
To be published on 2020-04-14 by SAE International in United States
The combined anticipated trends of vehicle sharing, autonomous control, and powertrain electrification are poised to disrupt the current paradigm of predominately gasoline vehicles with low levels of utilization. Shared, autonomous, electric vehicle (SAEV) fleets, which encompass all three of these trends, have garnered significant interest among the research community due to the opportunity for low-cost mobility with congestion and emissions reductions. This paper explores the charging loads demanded by SAEV fleets in response to servicing personal light-duty vehicle travel demand in four major United States metropolitan areas: Detroit, Austin, Washington DC, and Miami. A coordinated charging model is introduced which minimizes fleet charging costs and corresponding plant emissions in response to different renewable energy penetration rates and shares of personal trip demand served (between 1% and 25%). The relationship between trip demand by time of day, electricity price by time of day, and SAEV fleet size versus overall charging flexibility is explored for each city. SAEV results are presented across various scenarios assuming fleetwide attempts to minimize charging costs while still constrained by offering adequate…
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Impact to Cooling Airflow from Truck Platooning

National Renewable Energy Laboratory-Chen Zhang, Michael P. Lammert
  • Technical Paper
  • 2020-01-1298
To be published on 2020-04-14 by SAE International in United States
We investigate tradeoffs between the airflow strategies related to engine cooling and the aerodynamic-enabled fuel savings created by platooning. By analyzing cooling air flow, operating temperatures and platoon aerodynamics, we recommend configurations (including gaps distances and offsets) that will balance these tradeoffs. Previously, we have collected wind and thermal data for numerous heavy duty truck platoon configurations (gaps ranging from 4 to 87 meters) and reported the significant fuel savings enabled by these configurations. The fuel consumption for all trucks in the platoon were measured using the SAE J1321 gravimetric procedure as well as calibrated J1939 instantaneous fuel rate while travelling at 65 mph and loaded to a gross weight of 65,000 lb. Using COBRA probes and thermocouples mounted 1 m ahead of each truck, anemometers at the grill and a grid of underhood thermocouples as well as J1939 reported engine temperatures, we analyze the impact to critical operating temperatures from different platoon configurations. We created a CFD model to expand understanding of the cooling impacts measured on the test track. Results show significant changes…
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Corroborative Evaluation of the Real-world Energy Saving Potentials of InfoRich Eco-Autonomous Driving (iREAD) System

National Renewable Energy Laboratory-Jeffrey Gonder
Carnegie Mellon University-Raj Rajkumar
  • Technical Paper
  • 2020-01-0588
To be published on 2020-04-14 by SAE International in United States
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. The large-scale simulation analyses real-world human driving data to generate statistical results of eco-driving benefits in various road network and to suggest representative routes for further evaluation. Based on the representative routes, in-depth simulation relies on high-fidelity models and looks into how different…
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Real-world Evaluation of National Energy Efficiency Potential of Cold Storage Evaporator Technology in the Context of Engine Start-Stop Systems

National Renewable Energy Laboratory-Jason Lustbader, Eric Wood, Michael O'keefe, Nicholas Reinicke, Jeff Mosbacher
Argonne National Laboratory-Forrest Jehlik, Alvaro Demingo
  • Technical Paper
  • 2020-01-1252
To be published on 2020-04-14 by SAE International in United States
National concerns over energy consumption and emissions from the transportation sector have prompted regulatory agencies to implement aggressive fuel economy targets for light-duty vehicles through the NHTSA/EPA corporate average fuel economy (CAFE) program. Automotive manufacturers have responded by bringing competitive technologies to market that maximize efficiency while meeting or exceeding consumer performance and comfort expectations. In a collaborative effort between Toyota Motor Corporation, Argonne National Laboratory (ANL), and the National Renewable Energy Laboratory (NREL), the real-world savings of one such technology is evaluated. A commercially available Toyota Highlander equipped with two-phase cold storage technology was tested at ANL’s chassis dynamometer testing facility. The cold storage technology maintains the thermal state of air-conditioning evaporators to enable longer and more frequent engine off operation in vehicles equipped with start-stop functionality. Test results were analyzed and provided to NREL where a novel simulation framework was developed and calibrated to test data. The vehicle model was then exercised over a large set of real-world drive cycle and ambient condition data to estimate national-level fuel economy benefits. Results indicate that…
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Impact of Lateral Alignment on the Energy Savings of a Truck Platoon

National Renewable Energy Laboratory-Michael P. Lammert
Auburn University-Patrick Smith, Mark Hoffman, David Bevly
  • Technical Paper
  • 2020-01-0594
To be published on 2020-04-14 by SAE International in United States
A truck platooning system was tested on two heavy-duty tractor-trailer trucks on a closed test track to investigate the sensitivity of manual lateral control and intentional lateral offsets over a range of inter-vehicle spacing. The fuel consumption for both trucks in the platoon was measured using the SAE J1321 gravimetric procedure while travelling at 65 mph and loaded to a gross weight of 65,000 lb and in addition a calibrated SAE J1939 instantaneous fuel rate was calculated to serve as proxies for additional analyses. The testing campaign demonstrated the effects of: inter-vehicle gaps, following vehicle lateral offsets, following vehicle longitudinal and lateral control impacts, NOx emission impacts of platooning and cooling air flow impacts of platooning. The new results are compared to past truck platooning studies to reinforce the value of the new information. The results showed that energy savings generally increased in a non-linear fashion as the gap was reduced. The impacts of different following-truck lateral offsets had a measurable impact and the value of lateral control evaluated. The fuel-consumption savings on the curves…
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Impact of Mixed Traffic on the Energy Savings of a Truck Platoon

National Renewable Energy Laboratory-Michael Lammert
Auburn University-Patrick Smith, Mark Hoffman, David Bevly
  • Technical Paper
  • 2020-01-0679
To be published on 2020-04-14 by SAE International in United States
A two-truck platooning system was tested on a closed test track in a variety of realistic traffic and transient operating scenarios - conditions that truck platoons are likely to face on real highways. The fuel consumption for both trucks in the platoon was measured using the SAE J1321 gravimetric procedure as well as calibrated J1939 instantaneous fuel rate, serving as proxies to evaluate the impact of aerodynamic drag-reduction under constant-speed conditions. These measurements demonstrate the effects of: cut-in and cut-out maneuvers by other vehicles, transient traffic, the use of mismatched platooned vehicles (van trailer mixed with flatbed trailer), platoon following another truck with adaptive cruise control (ACC) and the presence of a multiple-passenger-vehicle pattern ahead of and adjacent to the platoon. These scenarios are intended to address the possibility of “background aerodynamic platooning” impacting realized savings on public roads. Using calibrated J1939 fuel rate analysis, fuel savings for curved track sections vs straight track sections was also evaluated for these scenarios. The presence of passenger-vehicle traffic patterns had a measurable impact on platoon performance, but…