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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.

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.

Thermal control systems for space application plant growth chambers offer unique challenges. The ability to control temperature and humidity independently gives greater flexibility for optimizing plant growth. Desired temperature and relative humidity range vary widely from 15°C to 35°C and 65% to 85% respectively. On top of all of these variables, the thermal control system must also be conservative in power and mass. These requirements to develop and test a robust thermal control system for space applications led to the design and development of the Environmental System Test Stand (ESTS) at NASA Johnson Space Center (JSC). The ESTS was designed to be a size constrained, environmental control system test stand with the flexibility to allow for a variety of thermal and lighting technologies. To give greater understanding to the environmental control system, the development of the ESTS included both mathematical models and the physical test stand.

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.

Research committed by the Langley Research Center through 1995 resulting in the HZETRN code provides the current basis for shield design methods according to NASA STD-3000 (2005). With this new prominence, the database, basic numerical procedures, and algorithms are being re-examined with new methods of verification and validation being implemented to capture a well defined algorithm for engineering design processes to be used in this early development phase of the Bush initiative. This process provides the methodology to transform the 1995 HZETRN research code into the 2005 HZETRN engineering code to be available for these early design processes. In this paper, we will review the basic derivations including new corrections to the codes to insure improved numerical stability and provide benchmarks for code verification.

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.

The NASA Johnson Space Center has plans to integrate a Solid Amine Water Desorbed (SAWD II) carbon dioxide removal subsystem into the Variable Pressure Growth Chamber (VPGC). The SAWD II subsystem will be used to remove any excess carbon dioxide (CO2) input into the VPGC which is not assimilated by the plants growing in the chamber. An analysis of the integrated VPGC-SAWD II system was performed using a mathematical model of the system implemented in the Computer-Aided System Engineering and Analysis (CASE/A) package. The analysis consisted of an evaluation of the SAWD II subsystem configuration within the VPGC, the planned operations for the subsystem, and the overall performance of the subsystem and other VPGC subsystems. Based on the model runs, recommendations were made concerning the SAWD II subsystem configuration and operations, and the chambers' automatic CO2 injection control subsystem.

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.

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.

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.

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.

The projections of increased Extravehicular Activity (EVA) operations for the Space Station Freedom (SSF) resulted in the development of advanced space suit technologies to increase EVA efficiency. To eliminate the overhead of denitrogenation, candidate higher-operating pressure suit technologies were developed. The AX-5 all metallic, multi-bearing technologies were developed at the Ames Research Center, and the Mk. III fabric and metallic technologies were developed at the Johnson Space Center. Following initial technology development, extensive tests and analyses were performed to evaluate all aspects of candidate technology performance. The current Space Shuttle space suit technologies were used as a baseline for evaluating those of the AX-5 and Mk. III. Tests included manned evaluations in the Weightless Environment Training Facility and KC-135 zero-gravity aircraft.

A system performance study on a portable life support system being developed for use in the Weightless Environment Training Facility (WETF) and the Neutral Buoyancy Laboratory (NBL) has been completed. The Neutral Buoyancy Portable Life Support System (NBPLSS) will provide life support to suited astronauts training for extravehicular activity (EVA) under water without the use of umbilicals. The basic configuration is characterized by the use of medium pressure (200 - 300 psi) cryogen (liquid nitrogen/oxygen mixture) which provides cooling within the Extravehicular Mobility Unit (EMU), the momentum which enables flow in the vent loop, and oxygen for breathing. NBPLSS performance was analyzed by using a modified Metabolic Man program to compare competing configurations. Maximum sustainable steady state metabolic rates and transient performance based on a typical WETF metabolic rate profile were determined and compared.

Electric vehicles (EVs) need highly optimized thermal management systems to improve range. Climate control can reduce vehicle efficiency and range by more than 50%. Due to the relative shortage of waste heat, heating the passenger cabin in EVs is difficult. Cabin cooling can take a high portion of the energy available in the battery. Compared to internal combustion engine-driven vehicles, different heating methods and more efficient cooling methods are needed, which can make EV thermal management systems more complex. More complex systems typically allow various alternative modes of operation that can be selected based on driving and ambient conditions. A good system simulation tool can greatly reduce the time and expense for developing these complex systems. A simulation model should also be able to efficiently co-simulate with vehicle simulation programs, and should be applicable for evaluating various control algorithms.

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.

With support from the U.S. Department of Energy's Vehicle Technologies Office, the National Renewable Energy Laboratory developed BLAST-V-the Battery Lifetime Analysis and Simulation Tool for Vehicles. The addition of high-resolution spatial-temporal travel histories enables BLAST-V to investigate user-defined infrastructure rollouts of publically accessible charging infrastructure, as well as quantify impacts on vehicle and station owners in terms of improved vehicle utility and station throughput. This paper presents simulation outputs from BLAST-V that quantify the utility improvements of multiple distinct rollouts of publically available Level 2 electric vehicle supply equipment (EVSE) in the Seattle, Washington, metropolitan area. Publically available data on existing Level 2 EVSE are also used as an input to BLAST-V. The resulting vehicle utility is compared to a number of mock rollout scenarios.

The quantitative analysis of the food system for long-term space missions is a crucial factor for the comparison of different food plans and for the evaluation of the food system as part of the overall mission. Such analysis should include important factors such as nutrition, palatability, diet cycle length, and psychological issues related to food. This paper will give the details of a mathematical model that was developed during the first author's participation as a Summer Faculty Fellow at Johnson Space Center. The model includes nutrition, palatability, diet cycle length, and psychological issues as important components. The model is compatible with the Equivalent System Mass (ESM) metric previously developed as the Advance Life Support (ALS) Research and Technology Metric.

While standardized laboratory-scale wear tests are available to predict the lubricity of liquid fuels under ambient conditions, the reality is that many injection systems operate at elevated temperatures where fuel vaporization is too excessive to perform the measure satisfactorily. Alternative fuels such as Dimethyl Ether (DME) are gaseous at ambient conditions and must be pressurized to form a lubricating liquid. The present paper describes an apparatus purposely designed to evaluate fuel lubricity in a pressurized environment at temperatures of up to 30°C. The remaining test parameters are identical to those of the widely standardized High Frequency Reciprocating Rig (HFRR), which allows use of previously developed correlations to full-scale injection equipment. Results obtained using the High Pressure High Frequency Reciprocating Rig (HPHFRR) indicate that DME, as well as other volatile fuels evaluated, have very poor lubricity.

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 typically intended for evaluating emissions 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.