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In an effort to characterize the dynamics typical of school bus operation, National Renewable Energy Laboratory (NREL) researchers set out to gather in-use duty cycle data from school bus fleets operating across the country. Employing a combination of Isaac Instruments GPS/CAN data loggers in conjunction with existing onboard telemetric systems resulted in the capture of operating information for more than 200 individual vehicles in three geographically unique domestic locations. In total, over 1,500 individual operational route shifts from Washington, New York, and Colorado were collected. Upon completing the collection of in-use field data using either NREL-installed data acquisition devices or existing onboard telemetry systems, large-scale duty-cycle statistical analyses were performed to examine underlying vehicle dynamics trends within the data and to explore vehicle operation variations between fleet locations.

Current artificial intelligence techniques for end to end driving of autonomous vehicles typically rely on a single form of learning or training processes along with a corresponding dataset or simulation environment. Relatively speaking, success has been shown for a variety of learning modalities in which it can be shown that the machine can successfully “drive” a vehicle. However, the realm of real-world driving extends significantly beyond the realm of limited test environments for machine training. This creates an enormous gap in capability between these two realms. With their superior neural network structures and learning capabilities, humans can be easily trained within a short period of time to proceed from limited test environments to real world driving.

Development of new air revitalization system (ARS) technology can initially be performed in a subscale laboratory environment, but in order to advance the maturity level, the technology must be tested in an end-to-end integrated environment. The Air Revitalization Technology Evaluation Facility (ARTEF) at the NASA Johnson Space Center (JSC) serves as a ground test bed for evaluating emerging ARS technologies in an environment representative of spacecraft atmospheres. At the center of the ARTEF is a hypobaric chamber which serves as a sealed atmospheric chamber for closed loop testing. A Human Metabolic Simulator (HMS) was custom-built to simulate the consumption of oxygen, and production of carbon dioxide, moisture and heat by up to eight persons. A variety of gas analyzers and dew point sensors are used to monitor the chamber atmosphere and the process flow upstream and downstream of a test article. A robust vacuum system is needed to simulate the vacuum of space.

The capabilities of the JPL Electronic Nose have been expanded to include characteristics required for a Technology Demonstration schedule on the International Space Station (ISS) in 2008-2009 [1,2]. Concurrently, to accommodate specific needs on ISS, the processes, tools and analyses which influence all aspects of development of the device have also been expanded. The Third Generation ENose developed for this program uses two types of sensor substrates, newly developed inorganic and organic sensor materials, redesigned electronics, onboard near real-time data analysis and power and data interfaces specifically for ISS. This paper will discuss the Third Generation ENose with a focus on detection of mercury in the parts-per-billion range.

The importance of being able to determine the usage rate of life support subsystem consumables was recognized well before the first Apollo Extravehicular Activity (EVA). Since that time, metabolic activity levels have been evaluated and recorded for each EVA crew member. Throughout the history of the United States space program, EVA metabolic rates have been shown to be variable depending upon the mission scenario and the equipment used. Knowing this historic information is invaluable for current EVA planning activities, as well as for the design of future Extravehicular Mobility Unit (EMU) systems. This paper presents an overview of historic metabolic expenditures for Apollo, Skylab, and Shuttle missions, along with a discussion of the types of EVA crew member activities which lead to various metabolic rate levels, and a discussion on how this data is being used to develop advanced EMU systems.

We have elicited a reliable Raman spectral signature for glucose in rabbit aqueous humor across mammalian physiological ranges in a rabbit model stressed by recent myocardial infarction. The technique employs near infrared Raman laser excitation at 785 nm, multivariate analysis, non-linear artificial neural networks and an offset spectra subtraction strategy. Aqueous humor glucose levels ranged from 37 to 323 mg/dL. Data were obtained in 80 uL samples to anticipate the volume constraints imposed by the human and rabbit anterior chamber of the eye. Total sample collection time was 10 seconds with total power delivered to sample of 30 Mw. Spectra generated from the aqueous humor were compared qualitatively to artificial aqueous samples and an excitation offset technique was devised to counteract broadband background noise partially obscuring the glucose signature.

With the introduction of more fuel cell electric vehicles (FCEVs) on U.S. roadways, especially in California, the need for available hydrogen refueling stations is growing. While funding from the California Energy Commission is helping to solve this problem, solutions need to be developed and implemented to help reduce the time to commission a hydrogen station. The current practice of hydrogen station acceptance can take months because each vehicle manufacturer conducts their own testing and evaluation. This process is not practical or sufficient to support the timely development of a hydrogen fueling station network. To address this issue, as part of the Hydrogen Fueling Infrastructure Research and Station Technology (H2FIRST) Project Sandia National Laboratories and the National Renewable Energy Laboratory along with a team of stakeholders and contractor Powertech Labs has developed the Hydrogen Station Equipment Performance (HyStEP) Device.

Global Positioning System (GPS) data acquisition devices have proven useful tools for gathering real-world driving data and statistics. The data collected by these devices provide valuable information in studying driving habits and conditions. When used jointly with vehicle simulation software, the data are invaluable in analyzing vehicle fuel use and performance, aiding in the design of more advanced and efficient vehicle technologies. However, when employing GPS data acquisition systems to capture vehicle drive-cycle information, a number of errors often appear in the captured raw data samples. Common sources of error in GPS data include sudden signal loss, extraneous or outlying data points, speed drifting, and signal white noise, all of which combine to limit the quality of field data for use in downstream applications.

In support of the National Aeronautics and Space Administration(NASA), a laboratory has been established at the Jet Propulsion Laboratory (JPL) to evaluate the characteristics of chemical sensors which are candidates for use in a controlled chemical processing life support system. Such a facility is required for characterizing those sensors under development as well as those commercially available but whose functional properties are typically based upon operating in industrial environments that will not be completely synonomous with space operations. Space environments, such as an orbiting station or lunar base, will generally have different sensor requirements than terrestrial applications with respect to size, multifunctionality, sensitivity, reliability, temperature, ruggedness, mass, consumables, life, and power requirements. Both commercially available and developmental moisture sensors have been evaluated.

Long-duration missions in space will require regenerative air revitalization processes. Human testing of these regenerative processes is necessary to provide focus to the system development process and to provide realistic metabolic and hygiene inputs. To this end, the Lyndon B. Johnson Space Center (JSC), under the sponsorship of NASA Headquarters Office of Life and Microgravity Sciences and Applications, is implementing an Early Human Testing (EHT) Project. As part of this project, an integrated physicochemical Air Revitalization System (ARS) is being developed and tested in JSC's Life Support Systems Integration Facility (LSSIF). The components of the ARS include a Four-Bed Molecular Sieve (4BMS) Subsystem for carbon dioxide (CO2) removal, a Sabatier CO2 Reduction Subsystem (CRS), and a Solid Polymer Electrolyte (SPE)™ Oxygen Generation Subsystem (OGS). A Trace Contaminant Control Subsystem (TCCS) will be incorporated at a later date.

The Mars Science Laboratory will be the next Martian mobility system that is scheduled to launch in the fall of 2011. The ChemCam Instrument is a part of the MSL science payload suite. It is innovative for planetary exploration in using a technique referred to as laser breakdown spectroscopy to determine the chemical composition of samples from distances of up to about 9 meters away. ChemCam is led by a team at the Los Alamos National Laboratory and the Centre d'Etude Spatiale des Rayonnements in Toulouse, France. The portion of ChemCam that is located inside the Rover, the ChemCam Body Unit contains the imaging charged-coupled device (CCD) detectors. Late in the design cycle, the ChemCam team explored alternate thermal design architectures to provide CCD operational overlap with the Rover's remote sensing instruments. This operational synergy is necessary to enable planning for subsequent laser firings and geological context.

Machine learning algorithms are effective tools to reduce the number of engine dynamometer tests during internal combustion engine development and/or optimization. This paper provides a case study of using such a statistical algorithm to characterize the heat transfer from the combustion chamber to the environment during combustion and during the entire engine cycle. The data for building the machine learning model came from a single cylinder compression ignition engine (13.3 compression ratio) that was converted to natural-gas port fuel injection spark-ignition operation. Engine dynamometer tests investigated several spark timings, equivalence ratios, and engine speeds, which were also used as model inputs. While building the model it was found that adding the intake pressure as another model input improved model efficiency.

As a result of the Viking missions in 1970s, the presence of a strong oxidant in Martian soil was suggested. Here we present a testable, by near-term missions, hypothesis that iron(VI) contributes to that oxidizing pool. Ferrate(VI) salts were studied for their spectral and oxidative properties and biological activities. Ferrate(VI) has distinctive spectroscopic features making it available for detection by remote sensing reflectance spectra and contact measurements via Mössbauer spectroscopy. The relevant miniaturized instrumentation has been developed or is underway, while XANES spectroscopy is shown to be a method of choice for the returned samples. Ferrate(VI) is capable of splitting water to yield oxygen, and oxidizing organic carbon to CO2. Organic oxidation was strongly abated after pre-heating ferrate, similar to the observations with Mars soil samples.

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.

Under a collaborative interagency agreement between the U.S. Environmental Protection Agency and the U.S. Department of Energy (DOE), the National Renewable Energy Laboratory (NREL) performed a series of in-depth analyses to characterize on-road driving behavior including distributions of vehicle speed, idle time, accelerations and decelerations, and other driving metrics of medium- and heavy-duty vocational vehicles operating within the United States. As part of this effort, NREL researchers segmented U.S. medium- and heavy-duty vocational vehicle driving characteristics into three distinct operating groups or clusters using real-world drive cycle data collected at 1 Hz and stored in NREL’s Fleet DNA database. The Fleet DNA database contains millions of miles of historical drive cycle data captured from medium- and heavy-duty vehicles operating across the United States. The data encompass existing DOE activities as well as contributions from valued industry stakeholder participants.

This paper explores the feasibility of using in-cylinder pressure-based variables to predict gaseous exhaust emissions levels from a Navistar T444 direct injection diesel engine through the use of neural networks. The networks were trained using in-cylinder pressure derived variables generated at steady state conditions over a wide speed and load test matrix. The networks were then validated on previously “unseen” real-time data obtained from the Federal Test Procedure cycle through the use of a high speed digital signal processor data acquisition system. Once fully trained, the DSP-based system developed in this work allows the real-time prediction of NOX and CO2 emissions from this engine on a cycle-by-cycle basis without requiring emissions measurement.

NREL completed a temporal and geospatial analysis of telematics data to estimate the fraction of platoonable miles traveled by class 8 tractor trailers currently in operation. This paper discusses the value and limitations of very large but low time-resolution data sets, and the fuel consumption reduction opportunities from large scale adoption of platooning technology for class 8 highway vehicles in the US based on telematics data. The telematics data set consist of about 57,000 unique vehicles traveling over 210 million miles combined during a two-week period. 75% of the total fuel consumption result from vehicles operating in top gear, suggesting heavy highway utilization. The data is at a one-hour resolution, resulting in a significant fraction of data be uncategorizable, yet significant value can still be extracted from the remaining data. Multiple analysis methods to estimate platoonable miles are discussed.

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.

Heavy-duty commercial vehicles consume a significant amount of energy due to their large size and mass, directly leading to vehicle operators prioritizing energy efficiency to reduce operational costs and comply with environmental regulations. One tool that can be used for the evaluation of energy efficiency in heavy-duty vehicles is the evaluation of energy efficiency using vehicle modeling and simulation. Simulation provides a path for energy efficiency improvement by allowing rapid experimentation of different vehicle characteristics on fuel consumption without the need for costly physical prototyping. The research presented in this paper focuses on using real-world, sparsely sampled telematics data from a large fleet of heavy-duty vehicles to create high-fidelity models for simulation. Samples in the telematics dataset are collected sporadically, resulting in sparse data with an infrequent and irregular sampling rate.