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The impacts of fuel cell system power response capability on optimal hybrid and neat fuel cell vehicle configurations have been explored. Vehicle system optimization was performed with the goal of maximizing fuel economy over a drive cycle. Optimal hybrid vehicle design scenarios were derived for fuel cell systems with 10 to 90% power transient response times of 0, 2, 5, 10, 20, and 40 seconds. Optimal neat fuel cell vehicles where generated for responses times of 0, 2, 5, and 7 seconds. DIRECT, a derivative-free optimization algorithm, was used in conjunction with ADVISOR, a vehicle systems analysis tool, to systematically change both powertrain component sizes and the vehicle energy management strategy parameters to provide optimal vehicle system configurations for the range of response capabilities.

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.

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.

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.

This paper takes a realistic approach to develop a techno-economic analysis for fixed-route autonomous shuttles. To develop a model for analysis, the current state of technology was used to approximate three timelines for achieving SAE level 5 capabilities: progressive, realistic, and conservative. Within these timelines, there are four different increments for advancements in the technology laid out as follows: SAE Level 0 - human driver, SAE Level 4 - in-vehicle safety operator, SAE Level 4 - remote safety operator, and SAE Level 5 - no safety operator. These increments in the changes of the technology were chosen based on the trends in the industry. Various shuttle models were used based on different rider quantities and drive-train requirements (electric vs gas) in this analysis. This allows for further understanding of how these deployment plans will vary the cost for shuttles operating in high, mid, and low ridership demand environments.

Fatigue damage sensing and monitoring of any structure is a prerequisite for reliable and effective structural health diagnosis. The designed sensor has alternate slots and strips with different strain magnification factor with respect to the nominal strain at its location. The strips experience the strains which closely resemble the actual strain distribution in the critical area of the component. One of the major advantages of this sensor is that it can be placed at any convenient location, still experiencing the same fatigue damage as a critical location. It can be used on various structures from ground civilian and military vehicles to steel bridges. This can predict the remaining useful life of a component or the number of miles (for any automobile) left for the component before it needed replacement. This paper mainly describes the design aspects of this sensor following analytical and finite element analysis (FEA) approaches.

Pressure vessels are being widely employed worldwide as a means to carry, store or receive fluids. The pressure differential is dangerous and many fatal accidents have occurred in the history of their development and operation. Therefore, it is imperative to understand the behavioral effect of cylindrical pressure vessel with torispherical endclosure subjected to an internal pressure. In this paper, two dimensional static stress analyses are performed using the finite element method for different vessel thicknesses in order to understand the stresses and deflections in the vessel walls due to internal pressure. From the analysis, it is observed that the stress variation over the section of the geometry and thickness of the vessel play an important role in withstanding the applied internal pressure.

Reliable assessment of occupant thermal comfort can be difficult to obtain within automotive environments, especially under transient and asymmetric heating and cooling scenarios. Evaluation of HVAC system performance in terms of comfort commonly requires human subject testing, which may involve multiple repetitions, as well as multiple test subjects. Instrumentation (typically comprised of an array of temperature sensors) is usually only sparsely applied across the human body, significantly reducing the spatial resolution of available test data. Further, since comfort is highly subjective in nature, a single test protocol can yield a wide variation in results which can only be overcome by increasing the number of test replications and subjects. In light of these difficulties, various types of manikins are finding use in automotive testing scenarios.

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.

We describe a study to identify potential biofuels that enable advanced spark ignition (SI) engine efficiency strategies to be pursued more aggressively. A list of potential biomass-derived blendstocks was developed. An online database of properties and characteristics of these bioblendstocks was created and populated. Fuel properties were determined by measurement, model prediction, or literature review. Screening criteria were developed to determine if a bioblendstock met the requirements for advanced SI engines. Criteria included melting point (or cloud point) < -10°C and boiling point (or T90) <165°C. Compounds insoluble or poorly soluble in hydrocarbon were eliminated from consideration, as were those known to cause corrosion (carboxylic acids or high acid number mixtures) and those with hazard classification as known or suspected carcinogens or reproductive toxins.

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.

This paper describes a system-level view of a fully automated transit system comprising a fleet of automated vehicles (AVs) in driverless operation, each with an SAE level 4 Automated Driving System, along with its related safety infrastructure and other system equipment. This AV system-level control is compared to the automatic train control system used in automated guideway transit technology, particularly that of communications-based train control (CBTC). Drawing from the safety principles, analysis methods, and risk assessments of CBTC systems, comparable functional subsystem definitions are proposed for AV fleets in driverless operation. With the prospect of multiple AV fleets operating within a single automated mobility district, the criticality of protecting roadway junctions requires an approach like that of automated fixed-guideway transit systems, in which a guideway switch zone “interlocking” at each junction location deconflicts railway traffic, affirming safe passage.

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.

Modern vehicles use automated driving assistance systems (ADAS) products to automate certain aspects of driving, which improves operational safety. In the U.S. in 2020, 38,824 fatalities occurred due to automotive accidents, and typically about 25% of these are associated with inclement weather. ADAS features have been shown to reduce potential collisions by up to 21%, thus reducing overall accidents. But ADAS typically utilize camera sensors that rely on lane visibility and the absence of obstructions in order to function, rendering them ineffective in inclement weather. To address this research gap, we propose a new technique to estimate snow coverage so that existing and new ADAS features can be used during inclement weather. In this study, we use a single camera sensor and historical weather data to estimate snow coverage on the road. Camera data was collected over 6 miles of arterial roadways in Kalamazoo, MI.

Spark-ignition engine fuel standards have been put in place to ensure acceptable hot and cold weather driveability (HWD and CWD). Vehicle manufacturers and fuel suppliers have developed systems that meet our driveability requirements so effectively that drivers overwhelmingly find that their vehicles reliably start up and operate smoothly and consistently throughout the year. For HWD, fuels that are too volatile perform more poorly than those that are less volatile. Vapor lock is the apparent cause of poor HWD, but there is conflicting evidence in the literature as to where in the fuel system it occurs. Most studies have found a correlation between degraded driveability and higher dry vapor pressure equivalent or lower TV/L = 20, and less consistently with a minimum T50. For CWD, fuels with inadequate volatility can cause difficulty in starting and rough operation during engine warmup.

National concerns over energy consumption and emissions from the transportation sector have prompted regulatory agencies to implement aggressive fuel economy targets for light-duty vehicles through the U.S. National Highway Traffic Safety Administration/Environmental Protection Agency (EPA) Corporate Average Fuel Economy (CAFE) program. Automotive manufacturers have responded by bringing competitive technologies to market that maximize efficiency while meeting or exceeding consumer performance and comfort expectations. In a collaborative effort among Toyota Motor Corporation, Argonne National Laboratory (ANL), and the National Renewable Energy Laboratory (NREL), the real-world savings of one such technology is evaluated. A commercially available Toyota Highlander equipped with two-phase cold storage technology was tested at ANL’s chassis dynamometer testing facility.

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.