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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.

A generalized re-normalization group (RNG) turbulence model based on the local "dimensionality" of the flow field is proposed. In this modeling approach the model coefficients C₁, C₂, and C₃ are all constructed as functions of flow strain rate. In order to further validate the proposed turbulence model, the generalized RNG closure model was applied to model the backward facing step flow (a classic test case for turbulence models). The results indicated that the modeling of C₂ in the generalized RNG closure model is reasonable, and furthermore, the predictions of the generalized RNG model were in better agreement with experimental data than the standard RNG turbulence model. As a second step, the performance of the generalized RNG closure was investigated for a complex engine flow.

Diesel engine combustion is simulated using Large Eddy Simulation (LES) with a multi-mode combustion (MMC) model. The MMC model is based on the combination of chemical kinetics, chemical equilibrium, and quasi-steady flamelet calculations in different local combustion regimes. The local combustion regime is identified by two combustion indices based on the local temperature and the extent of mixture homogeneity. The LES turbulence model uses the dynamic structure model (DSM) for sub-grid stresses. A new spray model in the LES context is used, and the Reynolds-averaged Navier-Stokes (RANS) based wall model is retained with the LES derived scales. These models are incorporated in the KIVA3V-ERC-Release 2 code for engine combustion simulations. A wide range of diesel engine operating conditions were chosen to validate the combustion model.

An Advanced Automotive Manikin (ADAM), is used to evaluate liquid cooling garments (LCG) for advanced space suits for extravehicular applications and launch and entry suits. The manikin is controlled by a finite-element physiological model of the human thermoregulatory system. ADAM's thermal response to a baseline LCG was measured.The local effectiveness of the LCG was determined. These new thermal comfort tools permit detailed, repeatable measurements and evaluation of LCGs. Results can extend to other personal protective clothing including HAZMAT suits, nuclear/biological/ chemical protective suits, fire protection suits, etc.

People who wear protective uniforms that inhibit evaporation of sweat can experience reduced productivity and even health risks when their bodies cannot cool themselves. This paper describes a new sweating manikin and a numerical model of the human thermoregulatory system that evaluates the thermal response of an individual to transient, non-uniform thermal environments. The physiological model of the human thermoregulatory system controls a thermal manikin, resulting in surface temperature distributions representative of the human body. For example, surface temperatures of the extremities are cooler than those of the torso and head. The manikin contains batteries, a water reservoir, and wireless communications and controls that enable it to operate as long as 2 hours without external connections. The manikin has 120 separately controlled heating and sweating zones that result in high resolution for surface temperature, heat flux, and sweating control.

One way to increase the load range in an HCCI engine is to increase boost pressure. In this modeling study, we investigate the effect of increased boost pressure on the fuel chemistry in an HCCI engine. Computed results of HCCI combustion are compared to experimental results in a HCCI engine. We examine the influence of boost pressure using a number of different detailed chemical kinetic models - representing both pure compounds (methylcyclohexane, cyclohexane, iso-octane and n-heptane) and multi-component models (primary reference fuel model and gasoline surrogate fuel model). We examine how the model predictions are altered by increased fueling, as well as reaction rate variation, and the inclusion of residuals in our calculations. In this study, we probe the low temperature chemistry (LTC) region and examine the chemistry responsible for the low-temperature heat release (LTHR) for wide ranges of intake boost pressure.

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.

This paper introduces and develops the concept of “Type X” and “Type Y” errors in model validation and calibration, and their implications on extrapolative prediction. Type X error is non-detection of model bias because it is effectively hidden by the uncertainty in the experiments. Possible deleterious effects of Type X error can be avoided by mapping uncertainty into the model until it envelopes the potential model bias, but this likely assigns a larger uncertainty than is needed to account for the actual bias (Type Y error). A philosophy of Best Estimate + Uncertainty modeling and prediction is probably best supported by taking the conservative choice of guarding against Type X error while accepting the downside of incurring Type Y error. An associated methodology involving data- and model- conditioning is presented and tested on a simple but rich test problem.

Laser excitation wavelengths for two-line planar laser-induced fluorescence (PLIF) of 3-pentanone have been optimized for simultaneous imaging of temperature and composition under engine-relevant conditions. Validation of the diagnostic was performed in a motored optical IC engine seeded homogeneously with 3-pentanone. PLIF measurements of the uniform mixture during the compression stroke were used to measure the average temperature and to access the random uncertainty in the measurements. To determine the accuracy of the temperature measurements, experimental average temperatures were compared to values computed assuming isentropic compression and to the output of a tuned 1-D engine simulation. The comparison indicated that the absolute accuracy of the temperature measurements is better than ±5%. Probability density functions (PDFs) calculated from the single-shot images were used to estimate the precision of the measurements.

Transients in the rate of injection (ROI) with respect to time are ever-present in direct-injection engines, even with common-rail fueling. The shape of the injection ramp-up and ramp-down affects spray penetration and mixing, particularly with multiple-injection schedules currently in practice. Ultimately, the accuracy of CFD model predictions used to optimize the combustion process depends upon the accuracy of the ROI utilized as fuel input boundary conditions. But experimental difficulties in the measurement of ROI, as well as real-world affects that change the ROI from the bench to the engine, add uncertainty that may be mistaken for weaknesses in spray modeling instead of errors in boundary conditions. In this work we use detailed, time-resolved measurements of penetration at the Spray A conditions of the Engine Combustion Network to rigorously guide the necessary ROI shape required to match penetration in jet models that allow variable rate of injection.

An accurate prediction of internal nozzle flow in fuel injector offers the potential to improve predictions of spray computational fluid dynamics (CFD) in an engine, providing a coupled internal-external calculation or by defining better rate of injection (ROI) profile and spray angle information for Lagrangian parcel computations. Previous research has addressed experiments and computations in transparent nozzles, but less is known about realistic multi-hole diesel injectors compared to single axial-hole fuel injectors. In this study, the transient injector opening and closing is characterized using a transparent multi-hole diesel injector, and compared to that of a single axial hole nozzle (ECN Spray D shape). A real-size five-hole acrylic transparent nozzle was mounted in a high-pressure, constant-flow chamber. Internal nozzle phenomena such as cavitation and gas exchange were visualized by high-speed long-distance microscopy.

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.

This work investigates the impact of injector temperature on the characteristics of high-pressure n-dodecane sprays under conditions relevant to heavy-duty diesel engines. Sprays are injected from a pair of single-hole diesel injectors belonging to the family of “Spray C” and “Spray D” Engine Combustion Network (ECN) injectors. Low and high injector temperature conditions are achieved by activating or deactivating a cooling jacket. We quantify spray spreading angle and penetration using high-speed shadowgraphy and long-distance microscopy imaging. We evaluate differences in fuel/air mixture formation at key timings through one-dimensional modeling. Injections from a cooled injector penetrate faster than those from a higher temperature injector, especially for an injector already prone to cavitation (Spray C).

A key to the success of the national hydrogen and fuel cell codes and standards developments efforts to date was the creation and implementation of national templates through which the U.S. Department of Energy (DOE), the National Renewable Energy Laboratory (NREL), and the major standards development organizations (SDOs) and model code organizations coordinate the preparation of critical standards and codes for hydrogen and fuel cell technologies and applications and maintain a coordinated national agenda for hydrogen and fuel cell codes and standards

This paper summarizes the several of the studies in the Environmental Science Program being sponsored by DOE's Office of Heavy Vehicle Technologies (OHVT) through the National Renewable Energy Laboratory (NREL). The goal of the Environmental Science Program is to understand atmospheric impacts and potential health effects that may be caused by the use of petroleum-based fuels and alternative transportation fuels from mobile sources. The Program is regulatory-driven, and focuses on ozone, airborne particles, visibility and regional haze, air toxics, and health effects of air pollutants. Each project in the Program is designed to address policy-relevant objectives. Current projects in the Environmental Science Program have four areas of focus: improving technology for emissions measurements; vehicle emissions measurements; emission inventory development/improvement; ambient impacts, including health effects.

This paper summarizes current work in the Environmental Science & Health Effects (ES&HE) Program being sponsored by DOE's Office of Heavy Vehicle Technologies (OHVT) through the National Renewable Energy Laboratory (NREL). The program is regulatory-driven, and focuses on ozone, airborne particles, visibility and regional haze, air toxics, and health effects of air pollutants. The goal of the ES&HE Program is to understand atmospheric impacts and potential health effects that may be caused by the use of petroleum-based and alternative transportation fuels. Each project in the program is designed to address policy-relevant objectives. Studies in the ES&HE Program have four areas of focus: improving technology for emissions measurements; vehicle emissions measurements, emission inventory development/improvement; and ambient impacts, including health effects.

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.

Prior HCCI optical engine experiments utilizing laser-induced fluorescence (LIF) measurements of stratified fuel-air mixtures have demonstrated the utility of probability density function (PDF) statistics for correlating mixture preparation with combustion. However, PDF statistics neglect all spatial details of in-cylinder fuel distribution. The current computational paper examines the effects of spatial fuel distribution on combustion using a novel combination of a 3-D CFD model with a 1-D linear-eddy model of turbulent mixing. In the simulations, the spatial coarseness of initial fuel distribution prior to the start of heat release is varied while keeping PDF statistics constant. Several cases are run, and as the initial mixture is made coarser, combustion phasing monotonically advances due to high local equivalence ratios that persist longer. The effect of turbulent mixing is more complex.