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A program has been completed to evaluate ultra-low sulfur diesel fuels and passive diesel particulate filters (DPFs) in truck and bus fleets operating in southern California. The fuels, ECD and ECD-1, are produced by ARCO (a BP Company) and have less than 15 ppm sulfur content. Vehicles were retrofitted with two types of catalyzed DPFs, and operated on ultra-low sulfur diesel fuel for over one year. Exhaust emissions, fuel economy and operating cost data were collected for the test vehicles, and compared with baseline control vehicles. Regulated emissions are presented from two rounds of tests. The first round emissions tests were conducted shortly after the vehicles were retrofitted with the DPFs. The second round emissions tests were conducted following approximately one year of operation. Several of the vehicles retrofitted with DPFs accumulated well over 100,000 miles of operation between test rounds.

The potential peaking of world conventional oil production and the possible imperative to reduce carbon emissions will put great pressure on vehicle manufacturers to produce more efficient vehicles, on vehicle buyers to seek them out in the marketplace, and on energy suppliers to develop new fuels and delivery systems. Four cases for stabilizing or reducing light vehicle fuel use, oil use, and/or carbon emissions over the next 50 years are presented. Case 1 - Improve mpg so that the fuel use in 2020 is stabilized for the next 30 years. Case 2 - Improve mpg so that by 2030 the fuel use is reduced to the 2000 level and is reduced further in subsequent years. Case 3 - Case 1 plus 50% ethanol use and 50% low-carbon fuel cell vehicles by 2050. Case 4 - Case 2 plus 50% ethanol use and 50% low-carbon fuel cell vehicles by 2050. The mpg targets for new cars and light trucks require that significant advances be made in developing cost-effective and very efficient vehicle technologies.

Presently, a main mobility sector objective is to reduce its impact on the global greenhouse gas emissions. While there are many techniques being explored, a promising approach to improve fuel economy is to reduce the required energy by using slipstream effects. This study analyzes the demanded engine power and mechanical energy used by heavy-duty trucks during platooning and non-platooning operation to determine the aerodynamic benefits of the slipstream. A series of platooning tests utilizing class 8 semi-trucks platooning via Cooperative Adaptive Cruise Control (CACC) are performed. Comparing the demanded engine power and mechanical energy used reveals the benefits of platooning on the aerodynamic drag while disregarding any potential negative side effects on the engine. However, energy savings were lower than expected in some cases.

Several configurations of truck tractor sleeper cabs were tested and modeled to investigate the potential to reduce heating and cooling loads. Two trucks were tested outdoors and a third was used as a control. Data from the testing were used to validate a computational fluid dynamics (CFD) model and this model was used to predict reductions in cooling loads during daytime rest periods. The test configurations included the application of standard-equipped sleeper privacy curtain and window shades, an optional insulated or arctic sleeper curtain, and insulated window coverings. The standard curtain reduced sleeper area heating load by 21% in one test truck, while the arctic curtain decreased it by 26%. Insulated window coverings reduced the heating load by 16% in the other test truck and lowered daytime solar temperature gain by 8°C. The lowered temperature resulted in a predicted 34% reduction in cooling load from the model.

In this paper, researchers at the National Renewable Energy Laboratory present the results of simulation studies to evaluate potential fuel savings as a result of improvements to vehicle rolling resistance, coefficient of drag, and vehicle weight as well as hybridization for four powertrains for medium-duty parcel delivery vehicles. The vehicles will be modeled and simulated over 1,290 real-world driving trips to determine the fuel savings potential based on improvements to each technology and to identify best use cases for each platform. The results of impacts of new technologies on fuel saving will be presented, and the most favorable driving routes on which to adopt them will be explored.

A predictive model for estimating the fuel saving of “top tier” engine, axle and transmission lubricants (compared to “mainstream” lubricants), in a heavy duty truck, operating on a realistic driving cycle, is described. Simulations have been performed for different truck weights (10, 20 and 40 tonnes) and it was found that the model predicts percentage fuel economy benefits that are of a similar magnitude to those measured in well controlled field trials1. The model predicts the percentage fuel saving from the engine oil should decrease as the vehicle load increases (which is in agreement with field trial results). The percentage fuel saving from the axle and gearbox oils initially decreases with load and then stays more or less constant. This behaviour is due to the detailed way in which axle and gearbox efficiency varies with speed/load and lubricant type.

It is expected that the world's energy demand will double by 2050, which requires energy-efficient technologies to be readily available. With the increasing number of vehicles on our roads the demand for energy is increasing rapidly, and with this there is an associated increase in CO₂ emissions. Through the careful use of optimized lubricants it is possible to significantly reduce vehicle fuel consumption and hence CO₂. This paper evaluates the effects on fuel economy of high quality, low viscosity heavy-duty diesel engine type lubricants against mainstream type products for all elements of the vehicle driveline. Testing was performed on Shell's driveline test facility for the evaluation of fuel consumption effects due to engine, gearbox and axle oils and the variation with engine operating conditions.

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.

A study was performed in the spring of 2001 to chemically characterize exhaust emissions from trucks and buses fueled by various test fuels and operated with and without diesel particle filters. This study was part of a multi-year technology validation program designed to evaluate the emissions impact of ultra-low sulfur diesel fuels and passive diesel particle filters (DPF) in several different heavy-duty vehicle fleets operating in Southern California. The overall study of exhaust chemical composition included organic compounds, inorganic ions, individual elements, and particulate matter in various size-cuts. Detailed descriptions of the overall technology validation program and chemical speciation methodology have been provided in previous SAE publications (2002-01-0432 and 2002-01-0433).

Annual fuel use for long-haul truck rest period idling is estimated at 667 million gallons in the United States. The U.S. Department of Energy's National Renewable Energy Laboratory's CoolCab project aims to reduce heating, ventilating, and air conditioning (HVAC) loads and resulting fuel use from rest period idling by working closely with industry to design efficient long-haul truck climate control systems while maintaining occupant comfort. Enhancing the thermal performance of cab/sleepers will enable smaller, lighter, and more cost-effective idle reduction solutions. In order for candidate idle reduction technologies to be implemented at the original equipment manufacturer and fleet level, their effectiveness must be quantified. To address this need, a number of promising candidate technologies were evaluated through experimentation and modeling to determine their effectiveness in reducing rest period HVAC loads.

In previous SAE papers, the initial development and testing of a vacuum-insulated catalytic converter was presented. This paper provides an update of the converter development and an analysis of potential off-cycle emissions savings. Hot vibration, cool-down, and 1975 Federal Test Procedure (FTP-75) emissions test results are provided to demonstrate the effectiveness of design improvements in greatly increasing durability while retaining performance. Using standard drive cycles and “real-world” driving statistics with a vehicle simulator (ADVISOR©), catalyst temperature and vehicle exhaust emissions of a sport utility vehicle (SUV) were predicted for 16 days of driving (107 trips, 770 total miles). Compared to the baseline vehicle with a conventional catalytic converter, the SUV with a vacuum-insulated converter produced 66% less non-methane hydrocarbon (NMHC), 65% less carbon monoxide (CO), and 60% less oxides of nitrogen (NOx).

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.

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.

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.

In January 2004, two Mars Exploration Rovers (MER) landed on the surface of Mars to begin their mission as robotic geologists. A year prior to these historic landings, both rovers and the spacecraft that delivered them to Mars, were completing a series of environmental tests in facilities at the Jet Propulsion Laboratory. This paper describes the test program undertaken to validate the thermal design and verify the workmanship integrity of both rovers and the spacecraft. The spacecraft, which contained the rover within the aeroshell, were tested in a 7.5 m diameter thermal vacuum chamber. Thermal balance was performed for the near earth (hot case) condition and for the near Mars (cold case) condition. A solar simulator was used to provide the solar boundary condition on the solar array. IR lamps were used to simulate the solar heat load on the aeroshell for the off-sun attitudes experienced by the spacecraft during its cruise to Mars.

A multi-year technology validation program was completed in 2001 to evaluate ultra-low sulfur diesel fuels and passive diesel particle filters (DPF) in several different diesel fleets operating in Southern California. The fuels used throughout the validation program were diesel fuels with less than 15-ppm sulfur content. Trucks and buses were retrofitted with two types of passive DPFs. Two rounds of emissions testing were performed to determine if there was any degradation in the emissions reduction. The results demonstrated robust emissions performance for each of the DPF technologies over a one-year period. Detailed descriptions of the overall program and results have been described in previous SAE publications [2, 3, 4, 5]. In 2002, a third round of emission testing was performed by NREL on a small subset of vehicles in the Ralphs Grocery Truck fleet that demonstrated continued robust emissions performance after two years of operation and over 220,000 miles.

Annual fuel use for sleeper cab truck rest period idling is estimated at 667 million gallons in the United States, or 6.8% of long-haul truck fuel use. Truck idling during a rest period represents zero freight efficiency and is largely done to supply accessory power for climate conditioning of the cab. The National Renewable Energy Laboratory’s CoolCab project aims to reduce heating, ventilating, and air conditioning (HVAC) loads and resulting fuel use from rest period idling by working closely with industry to design efficient long-haul truck thermal management systems while maintaining occupant comfort. Enhancing the thermal performance of cab/sleepers will enable smaller, lighter, and more cost-effective idle reduction solutions. In addition, if the fuel savings provide a one- to three-year payback period, fleet owners will be economically motivated to incorporate them.

Among on-road motor vehicles, diesel-fueled heavy-duty trucks emit disproportionately high amounts of oxides of nitrogen (NOx) and particulate matter (PM). The trucking industry has taken an active interest in the use of engines powered by liquefied natural gas (LNG) to reduce NOx and PM emissions. However, major barriers exist to widespread use of LNG in trucking applications, including reduced performance and higher initial capital costs compared to diesel-fueled vehicles, as well as a limited fueling infrastructure. To help address these barriers, the California Energy Commission (Commission) joined with the South Coast Air Quality Management District (SCAQMD) and the U.S. Department of Energy's National Renewable Energy Laboratory (DOE/NREL) in cost sharing a program led by the West Coast Transportation Technology Group of Arthur D. Little, Inc. (ADLittle).

The objective of this project, which is supported by the U.S. Department of Energy (DOE) through the National Renewable Energy Laboratory (NREL), is to provide a comprehensive comparison of heavy-duty trucks operating on alternative fuels and diesel fuel. Data collection from up to eight sites is planned. Currently, the project has four sites: Raley's in Sacramento, CA (Kenworth, Cummins L10-300G, liquefied natural gas - LNG); Pima Gro Systems, Inc. in Fontana, CA (White/GMC, Caterpillar 3176B Dual-Fuel, compressed natural gas - CNG); Waste Management in Washington, PA (Mack, Mack E7G, LNG); and United Parcel Service in Hartford, CT (Freightliner Custom Chassis, Cummins B5.9G, CNG). This paper summarizes current data collection and evaluation results from this project.

An integrated adaptive cruise control (ACC) and cooperative ACC (CACC) was implemented and tested on three heavy-duty tractor-trailer trucks on a closed test track. The first truck was always in ACC mode, and the followers were in CACC mode using wireless vehicle-vehicle communication to augment their radar sensor data to enable safe and accurate vehicle following at short gaps. The fuel consumption for each truck in the CACC string was measured using the SAE J1321 procedure while travelling at 65 mph and loaded to a gross weight of 65,000 lb, demonstrating the effects of: inter-vehicle gaps (ranging from 3.0 s or 87 m to 0.14 s or 4 m, covering a much wider range than previously reported tests), cut-in and cut-out maneuvers by other vehicles, speed variations, the use of mismatched vehicles (standard trailers mixed with aerodynamic trailers with boat tails and side skirts), and the presence of a passenger vehicle ahead of the platoon.