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Fuel cell vehicles are currently undergoing extensive research and development because of their potential for high efficiency and low emissions. A complete well-to-wheels evaluation is helpful when considering the introduction of advanced vehicles that could use a new fuel, such as hydrogen. Several modeling tools developed by Argonne National Laboratory were used to evaluate the impact of several new vehicle configurations. A transient vehicle simulation software code, PSAT (Powertrain System Analysis Toolkit), was used with a transient fuel cell model derived from GCTool (General Computational Toolkit); and GREET (Greenhouse gases, Regulated Emissions and Energy use in Transportation) was employed in estimating well-to-tank performances. This paper compares the well-to-wheels impacts of several advanced SUVs, including conventional, parallel and series hybrid-electric and fuel cell vehicles.

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 National Highway Traffic Safety Administration (NHTSA) plays a crucial role in guiding the formulation of Corporate Average Fuel Economy (CAFE) standards, and at the forefront of this regulatory process stands Argonne National Laboratory (Argonne). Argonne, a U.S. Department of Energy (DOE) research institution, has developed Autonomie—an advanced and comprehensive full-vehicle simulation tool that has solidified its status as an industry standard for evaluating vehicle performance, energy consumption, and the effectiveness of various technologies. Under the purview of an Inter-Agency Agreement (IAA), the DOE Argonne Site Office (ASO) and Argonne have assumed the responsibility of conducting full-vehicle simulations to support NHTSA's CAFE rulemaking initiatives. This paper introduces an innovative approach that hinges on a large-scale simulation process, encompassing standard regulatory driving cycles tailored to various vehicle classes and spanning diverse timeframes.

Connectivity and automation provide the potential to use information about the environment and future driving to minimize energy consumption. Aerodynamic drag can also be reduced by close-gap platooning using information from vehicle-to-vehicle communications. In order to achieve these goals, the designers of control strategies need to simulate a wide range of driving situations in which vehicles interact with other vehicles and the infrastructure in a closed-loop fashion. RoadRunner is a new model-based system engineering platform based on Autonomie software, which can collectively provide the necessary tools to predict energy consumption for various driving decisions and scenarios such as car-following, free-flow, or eco-approach driving, and thereby can help in developing control algorithms.

Other than in Japan, medium and heavy duty vehicles (MHDVs) are not regulated despite accounting for a significant portion of the fuel consumed (about 26% in the US in 2008). Government agencies worldwide are currently evaluating options to address that issue. Due to the large number of vehicle applications, some of them being “one of a kind”, vehicle modelling and simulation offers an attractive solution to medium and heavy duty regulations. This paper discusses the advantages and challenges of vehicle simulation to support regulations.

Over the past couple of years, Argonne National Laboratory has tested, analyzed, and validated automobile models for the light duty vehicle class, including several types of powertrains including conventional, hybrid electric, plug-in hybrid electric and battery electric vehicles. Argonne’s previous works focused on the light duty vehicle models, but no work has been done on medium and heavy-duty vehicles. This study focuses on the validation of shifting control in advanced automatic transmission technologies for medium duty vehicles by using Argonne’s model-based high-fidelity, forward-looking, vehicle simulation tool, Autonomie. Different medium duty vehicles, from Argonne’s own fleet, including the Ram 2500, Ford F-250 and Ford F-350, were tested with the equipment for OBD (on-board diagnostics) signal data record. For the medium duty vehicles, a workflow process was used to import test data.

This paper introduces control strategy analysis and performance degradation for the 2010 Toyota Prius under different thermal conditions. The goal was to understand, in as much detail as possible, the impact of thermal conditions on component and vehicle performances by analyzing a number of test data obtained under different thermal conditions in the Advanced Powertrain Research Facility (APRF) at Argonne National Laboratory. A previous study analyzed the control behavior and performance under a normal ambient temperature; thus the first step in this study was to focus on the impact when the ambient temperature is cold or hot. Based on the analyzed results, thermal component models were developed in which the vehicle controller in the simulation was designed to mimic the control behavior when temperatures of the components are cold or hot. Further, the performance degradation of the components was applied to the mathematical models based on analysis of the test data.

Argonne National Laboratory (ANL), working with the Partnership for a New Generation of Vehicles (PNGV), maintains hybrid vehicle simulation software, the PNGV System Analysis Toolkit (PSAT). PSAT, originally proprietary, has been used by both DOE and the “Big Three” as a modeling tool. The number of PSAT users has increased recently because 15 universities participating in the 2001 FutureTruck competition were given the software for their use. PSAT allows companies to look at new types of vehicles (hybrids) and choose the best configuration according to customer expectations within a minimum of time. PSAT, a forward-looking model, allows the user to simulate a large number of different configurations (conventional, series, parallel, and power split). PSAT is well suited for development of control strategies; by using accurate dynamics component models as its code, PSAT can be implemented directly and tested at the bench scale or in a vehicle.

The Lunar-Mars Life Support Test Project (LMLSTP) Phase III test examined the use of biological and physicochemical life support technologies for the recovery of potable water from waste water, the regeneration of breathable air, and the maintenance of a shirt-sleeve environment for a crew of four persons for 91 days. This represents the longest duration ground-test of life support systems with humans performed in the United States. This paper will describe the test from the inside viewpoint, concentrating on three major areas: maintenance and repair of life support elements, the scientific projects performed primarily in support of the International Space Station, and numerous activities in the areas of public affairs and education outreach.

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.

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.

Argonne National Laboratory (Argonne) and Idaho National Laboratory (INL), working with the FreedomCAR and Fuels Partnership, lead activities in vehicle dynamometer and fleet testing as well as in modeling activities. By using Argonne’s Advanced Powertrain Research Facility (APRF), the General Motors (GM) Tahoe 2-mode was instrumented and tested in the 4-wheel-drive test facility. Measurements included both sensors and controller area network (CAN) messages. In this paper, we describe the vehicle instrumentation as well as the test results. On the basis of the analysis performed, we discuss the vehicle model developed in Argonne’s vehicle simulation tool, the Powertrain System Analysis Toolkit (PSAT), and its comparison with test data. Finally, on-road vehicle data, performed by INL, is discussed and compared with the dynamometer results.

Many of today's advanced simulation tools are suitable for modeling specific systems; however, they provide rather limited support for model building and management. Setting up a detailed vehicle simulation model requires more than writing down state equations and running them on a computer. In this paper, we describe how modern software techniques can be used to support modeling and design activities, with the objective of providing better system models more quickly by assembling these system models in a “plug-and-play” architecture. Instead of developing detailed models specifically for Argonne National Laboratory's Autonomie modeling tool, we have chosen to place emphasis on integrating and re-using the system models, regardless of the environment in which they were initially developed. By way of example, this paper describes a vehicle model composed of a detailed engine model from GT Power, a transmission from AMESim, and with vehicle dynamics from CarSim.

In this study, three batteries were designed and these designs were evaluated in a hybrid vehicle simulation program. The battery designs were based on laboratory tests of 18650 cells for which a Lumped Parameter Battery Model was employed to correlate the cell impedance data. The three battery designs were each tested on three driving cycles, the Federal Urban Driving Schedule, the Highway Fuel Economy Test, and a special cycle developed to test the full power of the vehicle. The results of these simulation tests showed that the battery impedances were low for much of the time because the discharging and charging currents are not maintained at high levels for long periods of time on these cycles. For these conditions, the rates of heat generation in the batteries that were calculated by the simulation programs were low and may not be a serious problem.

As part of NASA’s Aviation Safety and Security Program, a simulation study of a twin-jet transport aircraft crew training simulation was conducted to address fidelity for upset or loss-of-control flight conditions. Piloted simulation studies were conducted to compare the baseline crew training simulation model with an enhanced aerodynamic model that was developed for high-angle-of-attack conditions. These studies were conducted in a flaps-up configuration and covered the approach-to-stall, stall and post-stall flight regimes. Qualitative pilot comments and preliminary comparison with flight test data indicate that the enhanced model is a significant improvement over the baseline. Some of the significant unrepresentative characteristics that are predicted by the baseline crew training simulation for flight in the post-stall regime have been identified.

The Boeing Company is developing and implementing the tools for the 21st Century for product development with their Design Manufacturing and Producibility Simulation (DMAPS) program. DMAPS combines the best of people, hardware and software tools commercially available to develop product and process simulation applications. The DMAPS toolset enhances the process of preparing concept layouts, assembly layouts and build-to-packages. Comprised of an Integrated Product and Process Team (IPPT), DMAPS produces products faster and with higher quality. The result is a process that eliminates costly changes and rework, and provides all IPPT's the tools and training necessary to perform their tasks right the first time. Boeing applies DMAPS tools to a variety of existing and new programs to build more affordable products. Savings goals set forth by the program are shown in Figure 1.

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.

NASA has long recognized the advantages of providing improved information interfaces to EVA astronauts and has pursued this goal through a number of development programs over the past decade. None of these activities or parallel efforts in industry and academia has so far resulted in the development of an operational system to replace or augment the current extravehicular mobility unit (EMU) Display and Controls Module (DCM) display and cuff checklist. Recent advances in display, communications, and information processing technologies offer exciting new opportunities for EVA information interfaces that can better serve the needs of a variety of NASA missions. Hamilton Sundstrand Space Systems International (HSSSI) has been collaborating with Simon Fraser University and others on the NASA Haughton Mars Project and with researchers at the Massachusetts Institute of Technology (MIT), Boeing, and Symbol Technologies in investigating these possibilities.

A new type of solar-reflective glass that improves reflection of the near-infrared (NIR) portion of the solar spectrum has been developed. Also developed was a prototype solar-reflective paint that increases the NIR reflection of opaque vehicle surfaces while maintaining desired colors in the visible portion of the spectrum. Both of these technologies, as well as solar-powered parked car ventilation, were tested on a Cadillac STS as part of the Improved Mobile Air Conditioning Cooperative Research Program (I-MAC). Significant reductions in interior and vehicle skin temperatures were measured. The National Renewable Energy Laboratory (NREL) performed an analysis to determine the impact of reducing the thermal load on the vehicle. A simplified cabin thermal/fluid model was run to predict the potential reduction in A/C system capacity. The potential reduction in fuel use was calculated using a vehicle simulation tool developed by the U.S. Department of Energy (DOE).

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.