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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.

Increased market penetration of electric drive vehicles (EDVs) requires overcoming a number of hurdles, including limited vehicle range and the elevated cost in comparison to conventional vehicles. Climate control loads have a significant impact on range, cutting it by over 50% in both cooling and heating conditions. To minimize the impact of climate control on EDV range, the National Renewable Energy Laboratory has partnered with Hyundai America and key industry partners to quantify the performance of thermal load reduction technologies on a Hyundai Sonata plug-in hybrid electric vehicle. Technologies that impact vehicle cabin heating in cold weather conditions and cabin cooling in warm weather conditions were evaluated. Tests included thermal transient and steady-state periods for all technologies, including the development of a new test methodology to evaluate the performance of occupant thermal conditioning.

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.

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 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.

The third long-term ATES cycle (LT3) was conducted between October 1989 and March 1990. Objectives of LT3 were to demonstrate that high-temperature ATES could supply a real heating load and to simplify the water chemistry modeling. For LT3 the Field Test Facility (FTF) was connected to a nearby campus building to demonstrate the FTF's ability to meet a real heating load. For LT3 the wells were modified so that only the most permeable portions of the Ironton-Galesville aquifer were used to simplify water chemistry comparisons and modeling. The campus steam plant was the source for heat stored during LT3. A total volume of 63.2 x 103 m3 of water was injected at a rate of 54.95 m3/hr into the storage well at a mean temperature of 104.7°C from October through December 1989. Tie-in to the Animal Sciences Veterinary Medicine (ASVM) building was not completed until late December.

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.

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.

Transportation vehicle and network system efficiency can be defined in two ways: 1) reduction of travel times across all the vehicles in the system, and 2) reduction in total energy consumed by all the vehicles in the system. The mechanisms to realize these efficiencies are treated as independent (i.e., vehicle and network domains) and, when combined, they have not been adequately studied to date. This research aims to integrate previously developed and published research on Predictive Optimal Energy Management Strategies (POEMS) and Intelligent Traffic Systems (ITS), to address the need for quantifying improvement in system efficiency resulting from simultaneous vehicle and network optimization. POEMS and ITS are partially independent methods which do not require each other to function but whose individual effectiveness may be affected by the presence of the other. In order to evaluate the system level efficiency improvements, the Mobility Energy Productivity (MEP) metric is used.

Cab climate conditioning is one of the primary reasons for operating the main engine in a long-haul truck during driver rest periods. In the United States, sleeper cab trucks use approximately 667 million gallons of fuel annually for rest period idling. The U.S. Department of Energy's National Renewable Energy Laboratory's (NREL) CoolCab Project works closely with industry to design efficient thermal management systems for long-haul trucks that minimize engine idling and fuel use while maintaining occupant comfort. Heat transfer to the vehicle interior from opaque exterior surfaces is one of the major heat pathways that contribute to air conditioning loads during long-haul truck daytime rest period idling. To quantify the impact of paint color and the opportunity for advanced paints, NREL collaborated with Volvo Group North America, PPG Industries, and Dometic Environmental Corporation.

A turbocharged lean burn natural gas engine was upgraded to operate on a blend of hydrogen and natural gas (HCNG). Tests were carried out to determine the most suitable H2/NG blend for H2 fractions between 20 and 32 vol%. A 20 vol% H2 content was found to provide the desired benefits when taking into consideration the engine and vehicle performance attributes. A full engine map was developed for the chosen mixture, and was verified over the steady-state AVL8 cycle. In general, the HCNG calibration included operation at higher air-fuel ratios and retarded spark timings. The results indicated that the NOx and NMHC emissions were reduced by 50% and 58% respectively, while the CO and CH4 emissions were slightly reduced. The HCNG engine torque, power and fuel consumption were maintained the same as for the natural gas fuel. The chassis dynamometer transient testing confirmed large NOx reduction of about 56% for HCNG operation.

Hybrid electric vehicles (HEV's) offer additional flexibility to enhance the fuel economy and emissions of vehicles. The Real-Time Control Strategy (RTCS) presented here optimizes efficiency and emissions of a parallel configuration HEV. In order to determine the ideal operating point of the vehicle's engine and motor, the control strategy considers all possible engine-motor torque pairs. For a given operating point, the strategy predicts the possible energy consumption and the emissions emitted by the vehicle. The strategy calculates the “replacement energy” that would restore the battery's state of charge (SOC) to its initial level. This replacement energy accounts for inefficiencies in the energy storage system conversion process. User- and standards-based weightings of time-averaged fuel economy and emissions performance determine an overall impact function. The strategy continuously selects the operating point that is the minimum of this cost function.

The energy used to air condition an automobile has a significant effect on vehicle fuel economy and tailpipe emissions. If a small reduction in energy use can be applied to many vehicles, the impact on national fuel consumption could be significant. The SCO3 is a new emissions test conducted with the air conditioner (A/C) operating that is part of the Supplemental Federal Test Procedure (SFTP). With the 100% phase-in of the SFTP in 2004 for passenger cars and light light-duty trucks, there is additional motivation to reduce the size of the A/C system. The U.S. Department of Energy's National Renewable Energy Laboratory (NREL) is investigating ways to reduce the amount of energy consumed for automobile climate control. If the peak soak temperature in an automobile can be reduced, the power consumed by the air conditioner may be decreased while passenger comfort is maintained or enhanced. Solar reflective glass is one way to reduce the peak soak temperature.

Four metrics related to vehicle duty cycle are derived from the energy equation of vehicle motion. Three key application areas are introduced. The first is the ability to quantify the sameness between vehicle duty cycles and the ability to asses a duty cycle's suitability for hybrid vehicle usage. The second area of application allows for the estimation of fuel consumption for a given vehicle over a target duty cycle. The third area of application allows us to predict how non-propulsion fuel use will affect energy use. The paper ends with real-world examples involving actual heavy-duty hybrids.

A Rotary Separator/Accumulator (RSA) has been developed to function as a phase separator and accumulator in the Oxygen Generator Assembly (OGA) in the microgravity environment of the International Space Station. The RSA design utilizes a fixed housing with rotating disks to create a centrifugal force field to separate hydrogen gas from water. The volume within the assembly is utilized to act as an accumulator for the OGA. During the development of the RSA, design refinements were made to meet the changing system operating requirements. Two proof of concept (POC) units and a “flight-like” development unit were fabricated and tested as system requirements evolved. Testing of the first POC unit demonstrated that a combined rotary separator and accumulator was feasible and showed areas where improvements could be made. The second POC unit incorporated a fifty percent volume increase to accommodate changing system requirements and geometry changes to help reduce power consumption.

As demand for consumer electric vehicles (EVs) has drastically increased in recent years, manufacturers have been working to bring heavy-duty EVs to market to compete with Class 6-8 diesel-powered trucks. Many high-profile companies have committed to begin electrifying their fleet operations, but have yet to implement EVs at scale due to their limited range, long charging times, sparse charging infrastructure, and lack of data from in-use operation. Thus far, EVs have been disproportionately implemented by larger fleets with more resources. To aid fleet operators, it is imperative to develop tools to evaluate the electrification potential of heavy-duty fleets. However, commercially available tools, designed mostly for light-duty vehicles, are inadequate for making electrification recommendations tailored to a fleet of heavy-duty vehicles.

Range-extended hybrids are an attractive option for medium- and heavy-duty commercial vehicle fleets because they offer the efficiency of an electrified powertrain with the driving range of a conventional diesel powertrain. The vehicle essentially operates as if it was purely electric for most trips, while ensuring that all commercial routes can be completed in any weather conditions or geographic terrain. Fuel use and point-source emissions can be significantly reduced, and in some cases eliminated, as many shorter routes can be fully electrified with this architecture. Under a U.S. Department of Energy (DOE)-funded project for Medium- and Heavy-Duty Vehicle Powertrain Electrification, Cummins has developed a plug-in hybrid electric Class 6 truck with a range-extending engine designed for pickup and delivery application.

NASA's proposed lunar lander, Altair, will be exposed to vastly different external temperatures following launch till its final destination on the moon. In addition, the heat rejection is lowest at the lowest environmental temperatures (0.5 kW @ 4K) and highest at the highest environmental temperature (4.5 kW @ 215K). This places a severe demand on the radiator design to handle these extreme turn-down requirements. A radiator with digital turn-down capability is currently under study at JPL as a robust means to meet the heat rejection demands and provide freeze protection while minimizing mass and power consumption. Turndown is achieved by independent control of flow branches with isolating latch valves and a gear pump to evacuate the isolated branches. A bench-top test was conducted to characterize the digital radiator concept. Testing focused on the demonstration of proper valve sequencing to achieve turn-down and recharge of flow legs.

There has been an increasing interest in exploring the potential to reduce energy consumption of future connected and automated vehicles. People have extensively studied various eco-driving implementations that leverage preview information provided by on-board sensors and connectivity, as well as the control authority enabled by automation. Quantitative real-world evaluation of eco-driving benefits is a challenging task. The standard regulatory driving cycles used for measuring exhaust emissions and fuel economy are not truly representative of real-world driving, nor for capturing how connectivity and automation might influence driving trajectories. To adequately consider real-world driving behavior and potential “off-cycle” impacts, this paper presents four collaborative evaluation methods: large-scale simulation, in-depth simulation, vehicle-in-the-loop testing, and vehicle road testing.

Understanding the real-world power demand of modern automobiles is of critical importance to engineers using modeling and simulation in the design of increasingly efficient powertrains. Increased use of global positioning system (GPS) devices has made large-scale data collection of vehicle speed (and associated power demand) a reality. While the availability of real-world GPS data has improved the industry's understanding of in-use vehicle power demand, relatively little attention has been paid to the incremental power requirements imposed by road grade. This analysis quantifies the incremental efficiency impacts of real-world road grade by appending high-fidelity elevation profiles to GPS speed traces and performing a large simulation study. Employing a large, real-world dataset from the National Renewable Energy Laboratory's Transportation Secure Data Center, vehicle powertrain simulations are performed with and without road grade under five vehicle models.