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The goal of the automated mobility platforms (AMPs) initiative is to raise the bar of service regarding equity and sustainability for public mobility systems that are crucial to large facilities, and doing so using electrified, energy efficient technology. Using airports as an example, the rapid growth in air travel demand has led to facility expansions and congested terminals, which directly impacts equity (e.g., increased challenges for Passengers with Reduced Mobility [PRMs]) and sustainability—both of which are important metrics often overlooked during the engineering design process.

Fuel cell hybrid vehicles (FCHVs) use an energy management strategy to partition the power supplied by the fuel cell and energy storage system (ESS). This paper presents an adaptive energy management strategy, created in the ADVISOR™ software, for a series FCHV. The strategy uses a local or “real-time” optimization approach, which aims to reduce total energy consumption at each instantaneous time interval by dynamically adjusting the amount of power supplied by the fuel cell and ESS. Compared with a static control strategy, the adaptive strategy improved the simulated FCHV's fuel economy by 1.4%-8.5%, depending on the drive cycle.

Fleet managers need a tool to assist them in assessing their need to comply with EPAct and to provide them with the ability to obtain information that will allow them to make alternative fuel vehicle purchasing decisions. This paper will describe the Web-based tool that will inform a fleet manager, based on their geographic location, the type of fleet they own or operate, and the number and types of vehicles in their fleet, whether or not they need to meet the requirements of EPAct, and, if so, the percentage of new vehicle purchases needed to comply with the law. The tool provides detailed specifications on available OEM alternative fuel vehicles, including the purchase cost of the vehicles, fuel and fuel system characteristics, and incentives and rebates surrounding the purchase of each vehicle. The full set of federal, state, and local incentives is made available through the tool, as well as detailed access to refueling site and dealership locations.

The National Renewable Energy Laboratory (NREL) validated conventional diesel and diesel-hybrid, medium-duty parcel delivery vehicle models to evaluate petroleum reductions and cost implications of hybrid and plug-in hybrid diesel variants. The hybrid and plug-in hybrid variants are run on a field data-derived design matrix to analyze the effect of drive cycle, distance, engine downsizing, battery replacements, and battery energy on fuel consumption and lifetime cost. For an array of diesel fuel costs, the battery cost per kilowatt-hour at which the hybridized configuration becomes cost-effective is calculated. The results build on a previous analysis that found the fuel savings from medium-duty, plug-in hybrids more than offset vehicle incremental price for future battery and fuel cost projections; however, they seldom did so under present day cost assumptions in the absence of purchase incentives.

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.

In the United States, intercity long-haul trucks idle approximately 1,800 hrs per year primarily for sleeper cab hotel loads, consuming 838 million gallons of diesel fuel [1]. The U.S. Department of Energy's National Renewable Energy Laboratory (NREL) is working on solutions to this challenge through the CoolCab project. The objective of the CoolCab project is to work closely with industry to design efficient thermal management systems for long-haul trucks that keep the cab comfortable with minimized engine idling. Truck engine idling is primarily done to heat or cool the cab/sleeper, keep the fuel warm in cold weather, and keep the engine warm for cold temperature startup. Reducing the thermal load on the cab/sleeper will decrease air conditioning system requirements, improve efficiency, and help reduce fuel use. To help assess and improve idle reduction solutions, the CoolCalc software tool was developed.

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.

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.

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.

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.

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.

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.

Current vehicle climate control systems are dramatically overpowered because they are designed to condition the cabin air mass in a specified period of time. A more effective and energy efficient objective is to directly achieve thermal comfort of the passengers. NREL is developing numerical and experimental tools to predict human thermal comfort in non-uniform transient thermal environments. These tools include a finite element model of human thermal physiology, a psychological model that predicts both local and global thermal comfort, and a high spatial resolution sweating thermal manikin for testing in actual vehicles.

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.

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.

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.

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.

Current vehicle climate control systems are dramatically overpowered because they are designed to condition the cabin air mass in a specified period of time. A more effective and energy efficient objective is to directly achieve thermal comfort of the passengers. NREL is developing numerical and experimental tools to predict human thermal comfort in non-uniform transient thermal environments. These tools include a finite element model of human thermal physiology, a psychological model that predicts both local and global thermal comfort, and a high spatial resolution sweating thermal manikin for testing in actual vehicles.

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.

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.