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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.

As connected and automated vehicle technologies emerge and proliferate, lower frequency vehicle trajectory data is becoming more widely available. In some cases, entire fleets are streaming position, speed, and telemetry at sample rates of less than 10 seconds. This presents opportunities to apply powertrain simulators such as the National Renewable Energy Laboratory’s Future Automotive Systems Technology Simulator to model how advanced powertrain technologies would perform in the real world. However, connected vehicle data tends to be available at lower temporal frequencies than the 1-10 Hz trajectories that have typically been used for powertrain simulation. Higher frequency data, typically used for simulation, is costly to collect and store and therefore is often limited in density and geography. This paper explores the suitability of lower frequency, high availability, connected vehicle data for detailed powertrain simulation.

The combined anticipated trends of vehicle sharing (ride-hailing), automated control, and powertrain electrification are poised to disrupt the current paradigm of predominately owner-driven gasoline vehicles with low levels of utilization. Shared, automated, electric vehicle (SAEV) fleets offer the potential for lower cost and emissions and have garnered significant interest among the research community. While promising, unmanaged operation of these fleets may lead to unintended negative consequences. One potentially unintended consequence is a high quantity of SAEVs charging during peak demand hours on the electric grid, potentially increasing the required generation capacity. This research explores the flexibility associated with charging loads demanded by SAEV fleets in response to servicing personal mobility travel demands. Travel demand is synthesized in four major United States metropolitan areas: Detroit, MI; Austin, TX; Washington, DC; and Miami, FL.

The goal of this project was to demonstrate a low emissions, high efficiency heavy-duty on-highway natural gas engine. The emissions targets for this project are to demonstrate US 2010 emissions standards on the 13-mode steady state test. To meet this goal, a chemically correct combustion (stoichiometric) natural gas engine with exhaust gas recirculation (EGR) and a three way catalyst (TWC) was developed. In addition, a Sturman Industries, Inc. camless Hydraulic Valve Actuation (HVA) system was used to improve efficiency. A Volvo 11 liter diesel engine was converted to operate as a stoichiometric natural gas engine. Operating a natural gas engine with stoichiometric combustion allows for the effective use of a TWC, which can simultaneously oxidize hydrocarbons and carbon monoxide and reduce NOx. High conversion efficiencies are possible through proper control of air-fuel ratio.

Due to its high efficiency and superior durability the diesel engine is again becoming a prime candidate for future light-duty vehicle applications within the United States. While in Europe the overall diesel share exceeds 40%, the current diesel share in the U.S. is 1%. Despite the current situation and the very stringent Tier 2 emission standards, efforts are being made to introduce the diesel engine back into the U.S. market. In order to succeed, these vehicles have to comply with emissions standards over a 120,000 miles distance while maintaining their excellent fuel economy. The availability of technologies such as high-pressure common-rail fuel systems, low sulfur diesel fuel, NOx adsorber catalysts (NAC), and diesel particle filters (DPFs) allow the development of powertrain systems that have the potential to comply with the light-duty Tier 2 emission requirements. In support of this, the U.S.

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.

The National Renewable Energy Laboratory (NREL) has conducted a series of chassis dynamometer and road tests on the 2000 model-year Honda Insight. This paper will focus on results from the testing, how the results have been applied to NREL's Advanced Vehicle Simulator (ADVISOR), and how test results compare to the model predictions and published data. The chassis dynamometer testing included the FTP-75 emissions certification test procedure, highway fuel economy test, US06 aggressive driving cycle conducted at 0°C, 20°C, and 40°C, and the SC03 test performed at 35°C with the air conditioning on and with the air conditioning off. Data collection included bag and continuously sampled emissions (for the chassis tests), engine and vehicle operating parameters, battery cell temperatures and voltages, motor and auxiliary currents, and cabin temperatures.

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.

It is widely understood that cold ambient temperatures increase vehicle fuel consumption due to heat transfer losses, increased friction (increased viscosity lubricants), and enrichment strategies (accelerated catalyst heating). However, relatively little effort has been dedicated to thoroughly quantifying these impacts across a large set of real world drive cycle data and ambient conditions. This work leverages experimental dynamometer vehicle data collected under various drive cycles and ambient conditions to develop a simplified modeling framework for quantifying thermal effects on vehicle energy consumption. These models are applied over a wide array of real-world usage profiles and typical meteorological data to develop estimates of in-use fuel economy. The paper concludes with a discussion of how this integrated testing/modeling approach may be applied to quantify real-world, off-cycle fuel economy benefits of various technologies.

Mixing controlled compression ignition, i.e., diesel engines are efficient and are likely to continue to be the primary means for movement of goods for many years. Low-net-carbon biofuels have the potential to significantly reduce the carbon footprint of diesel combustion and could have advantageous properties for combustion, such as high cetane number and reduced engine-out particle and NOx emissions. We developed a list of over 400 potential biomass-derived diesel blendstocks and populated a database with the properties and characteristics of these materials. Fuel properties were determined by measurement, model prediction, or literature review. Screening criteria were developed to determine if a blendstock met the basic requirements for handling in the diesel distribution system and use as a blend with conventional diesel. Criteria included cetane number ≥40, flashpoint ≥52°C, and boiling point or T90 ≤338°C.

The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum 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 are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a 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 data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts.

Regulated and unregulated emissions (individual hydrocarbons, ethanol, aldehydes and ketones, polynuclear aromatic hydrocarbons (PAH), nitro-PAH, and soluble organic fraction of particulate matter) were characterized in engines utilizing duplicate ISO 8178-C1 eight-mode tests and FTP smoke tests. Certification No. 2 diesel (400 ppm sulfur) and three ethanol/diesel blends, containing 7.7 percent, 10 percent, and 15 percent ethanol, respectively, were used. The three, Tier II, off-road engines were 6.8-L, 8.1-L, and 12.5-L in displacement and each had differing fuel injection system designs. It was found that smoke and particulate matter emissions decreased with increasing ethanol content. Changes to the emissions of carbon monoxide and oxides of nitrogen varied with engine design, with some increases and some decreases. As expected, increasing ethanol concentration led to higher emissions of acetaldehyde (increases ranging from 27 to 139 percent).

Driver behavior models play a significant role in representing different driving styles and the associated relationships with traffic patterns and vehicle energy consumption in simulation studies. The models often serve as a proxy for baseline human driving when assessing energy-saving strategies that alter vehicle velocity. Such models are especially important in connectivity-enabled energy-saving strategy research because they can easily adapt to changing driving conditions like posted speed limits or change in traffic light state. While numerous driver models exist, parametric driver models provide the flexibility required to represent variability in real-world driving through different combinations of model parameters. These model parameters must be informed by a representative set of parameter values for the driver model to adequately represent a real-world driver.

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.

The disparate characteristics between conventional (CVs) and battery electric vehicles (BEVs) in terms of driving range, refill/recharge time, and availability of refuel/recharge infrastructure inherently limit the relative utility of BEVs when benchmarked against traditional driver travel patterns. However, given a high penetration of high-power public charging combined with driver tolerance for rerouting travel to facilitate charging on long-distance trips, the difference in utility between CVs and BEVs could be marginalized. We quantify the relationships between BEV utility, the deployment of fast chargers, and driver tolerance for rerouting travel and extending travel durations by simulating BEVs operated over real-world travel patterns using the National Renewable Energy Laboratory's Battery Lifetime Analysis and Simulation Tool for Vehicles (BLAST-V). With support from the U.S.

The National Renewable Energy Laboratory (NREL) has developed a suite of thermal comfort tools to help develop smaller and more efficient climate control systems in automobiles. The tools consist of a thermal comfort manikin, physiological model, and psychological model that are linked together to assess comfort in a transient non-homogeneous environment. The manikin, which consists of 120 individually controlled zones, mimics the human body by heating, sweating, and breathing. The physiological model is a 40,000-node numerical simulation of the human body. The model receives heat loss data from the manikin and predicts the human physiological response and skin temperatures. Based on human subject test data, the psychological model takes the temperatures of the human and predicts thermal sensation and comfort.

Adding oxygenates to diesel fuel has shown the potential for reducing particulate (PM) emissions in the exhaust. The objective of this study was to select the most promising oxygenate compounds as blending components in diesel fuel for advanced engine testing. A fuel matrix was designed to consider the effect of molecular structure and boiling point on the ability of oxygenates to reduce engine-out exhaust emissions from a modern diesel engine. Nine test fuels including a low-sulfur (∼1 ppm), low-aromatic hydrocracked base fuel and 8 oxygenate-base fuel blends were utilized. All oxygenated fuels were formulated to contain 7% wt. of oxygen. A DaimlerChrysler OM611 CIDI engine for light-duty vehicles was controlled with a SwRI Rapid Prototyping Electronic Control System. The base fuel was evaluated in four speed-load modes and oxygenated blends only in one mode. Each operating mode and fuel combination was run in triplicate.

This paper describes the results of Phase 1 of the Diesel Emission Control - Sulfur Effects (DECSE) Program. The objective of the program is to determine the impact of fuel sulfur levels on emissions control systems that could be used to lower emissions of nitrogen oxides (NOx) and particulate matter (PM) from vehicles with diesel engines. The DECSE program has now issued four interim reports for its first phase, with conclusions about the effect of diesel sulfur level on PM and total hydrocarbon (THC) emissions from the high-temperature lean-NOx catalyst, the increase of engine-out sulfate emissions with higher sulfur fuel levels, the effect of sulfur content on NOx adsorber conversion efficiencies, and the effect of fuel sulfur content on diesel oxidation catalysts, causing increased PM emissions above engine-out emissions under certain operating conditions.

Alternative compression ignition engine fuels are of interest both to reduce emissions and to reduce U.S. petroleum fuel demand. A Malaysian Fischer-Tropsch gas-to-liquid fuel was compared with California #2 diesel by characterizing emissions from over the road Class 8 tractors with Caterpillar 3176 engines, using a chassis dynamometer and full scale dilution tunnel. The 5-Mile route was employed as the test schedule, with a test weight of 42,000 lb. Levels of oxides of nitrogen (NOx) were reduced by an average of 12% and particulate matter (PM) by 25% for the Fischer-Tropsch fuel over the California diesel fuel. Another distillate fuel produced catalytically from Fischer-Tropsch products originally derived from natural gas by Mossgas was also compared with 49-state #2 diesel by characterizing emissions from Detroit Diesel 6V-92 powered transit buses, three of them equipped with catalytic converters and rebuilt engines, and three without.