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This research project compares laboratory-measured fuel economy of a medium-duty diesel powered hydraulic hybrid vehicle drivetrain to both a conventional diesel drivetrain and a conventional gasoline drivetrain in a typical commercial parcel delivery application. Vehicles in this study included a model year 2012 Freightliner P10HH hybrid compared to a 2012 conventional gasoline P100 and a 2012 conventional diesel parcel delivery van of similar specifications. Drive cycle analysis of 484 days of hybrid parcel delivery van commercial operation from multiple vehicles was used to select three standard laboratory drive cycles as well as to create a custom representative cycle. These four cycles encompass and bracket the range of real world in-use data observed in Baltimore United Parcel Service operations.

The operation of air conditioning (A/C) systems is a significant contributor to the total amount of fuel used by light-and heavy-duty vehicles. Therefore, continued improvement of the efficiency of these mobile A/C systems is important. Numerical simulation has been used to reduce the system development time and to improve the electronic controls, but numerical models that include highly detailed physics run slower than desired for carrying out vehicle-focused drive cycle-based system optimization. Therefore, faster models are needed even if some accuracy is sacrificed. In this study, a validated model with highly detailed physics, the “Fully-Detailed” model, and two models with different levels of simplification, the “Quasi-Transient” and the “Mapped-Component” models, are compared. The Quasi-Transient model applies some simplifications compared to the Fully-Detailed model to allow faster model execution speeds.

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.

When operated, the cabin climate control system is the largest auxiliary load on a vehicle. This load has significant impact on fuel economy for conventional and hybrid vehicles, and it drastically reduces the driving range of all-electric vehicles (EVs). Heating is even more detrimental to EV range than cooling because no engine waste heat is available. Reducing the thermal loads on the vehicle climate control system will extend driving range and increase the market penetration of EVs. Researchers at the National Renewable Energy Laboratory have evaluated strategies for vehicle climate control load reduction with special attention toward grid-connected electric vehicles. Outdoor vehicle thermal testing and computational modeling were used to assess potential strategies for improved thermal management and to evaluate the effectiveness of thermal load reduction technologies. A human physiology model was also used to evaluate the impact on occupant thermal comfort.

In an effort to better understand the operational requirements of port drayage vehicles and their potential for adoption of advanced technologies, National Renewable Energy Laboratory (NREL) researchers collected over 36,000 miles of in-use duty cycle data from 30 Class 8 drayage trucks operating at the Port of Long Beach and Port of Los Angeles in Southern California. These data include 1-Hz global positioning system location and SAE J1939 high-speed controller area network information. Researchers processed the data through NREL’s Drive-Cycle Rapid Investigation, Visualization, and Evaluation tool to examine vehicle kinematic and dynamic patterns across the spectrum of operations. Using the k-medoids clustering method, a repeatable and quantitative process for multi-mode drive cycle segmentation, the analysis led to the creation of multiple drive cycles representing four distinct modes of operation that can be used independently or in combination.

The latent heat-of-vaporization (HoV) of blends of biofuel and hydrocarbon components into gasolines has recently experienced expanded interest because of the potential for increased HoV to increase fuel knock resistance in direct-injection (DI) engines. Several studies have been conducted, with some studies identifying an additional anti-knock benefit from HoV and others failing to arrive at the same conclusion. Consideration of these studies holistically shows that they can be grouped according to the level of fuel octane sensitivity variation within their fuel matrices. When comparing fuels of different octane sensitivity significant additional anti-knock benefits associated with HoV are sometimes observed. Studies that fix the octane sensitivity find that HoV does not produce additional anti-knock benefit. New studies were performed at ORNL and NREL to further investigate the relationship between HoV and octane sensitivity.

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.

The combined anticipated trends of vehicle sharing (ride-hailing), automated control, and powertrain electrification are poised to disrupt the current paradigm of predominately owner-driven gasoline vehicles with low levels of utilization. Shared, automated, electric vehicle (SAEV) fleets offer the potential for lower cost and emissions and have garnered significant interest among the research community. While promising, unmanaged operation of these fleets may lead to unintended negative consequences. One potentially unintended consequence is a high quantity of SAEVs charging during peak demand hours on the electric grid, potentially increasing the required generation capacity. This research explores the flexibility associated with charging loads demanded by SAEV fleets in response to servicing personal mobility travel demands. Travel demand is synthesized in four major United States metropolitan areas: Detroit, MI; Austin, TX; Washington, DC; and Miami, FL.

Several configurations of truck tractor sleeper cabs were tested and modeled to investigate the potential to reduce heating and cooling loads. Two trucks were tested outdoors and a third was used as a control. Data from the testing were used to validate a computational fluid dynamics (CFD) model and this model was used to predict reductions in cooling loads during daytime rest periods. The test configurations included the application of standard-equipped sleeper privacy curtain and window shades, an optional insulated or arctic sleeper curtain, and insulated window coverings. The standard curtain reduced sleeper area heating load by 21% in one test truck, while the arctic curtain decreased it by 26%. Insulated window coverings reduced the heating load by 16% in the other test truck and lowered daytime solar temperature gain by 8°C. The lowered temperature resulted in a predicted 34% reduction in cooling load from the model.

Accurate evaluation of vehicles' transient total power requirement helps achieving further improvements in vehicle fuel efficiency. When operated, the air-conditioning (A/C) system is the largest auxiliary load on a vehicle, therefore accurate evaluation of the load it places on the vehicle's engine and/or energy storage system is especially important. Vehicle simulation models, such as "Autonomie," have been used by OEMs to evaluate vehicles' energy performance. However, the load from the A/C system on the engine or on the energy storage system has not always been modeled in sufficient detail. A transient A/C simulation tool incorporated into vehicle simulation models would also provide a tool for developing more efficient A/C systems through a thorough consideration of the transient A/C system performance. The dynamic system simulation software MATLAB/Simulink® is frequently used by vehicle controls engineers to develop new and more efficient vehicle energy system controls.

The Energy Independence and Security Act of 2007 requires the U.S. to use 36 billion gallons of renewable fuel per year by 2022. Domestic ethanol production has increased steadily in recent years, growing from less than 5 billion gallons per year (bgpy) in 2006 to over 13 bgpy in 2010. While there is interest in developing non-oxygenated renewable fuels for use in conventional vehicles as well as interest in expanding flex-fuel vehicle (FFV) production for increased E85 use, there remains concern that EISA compliance will require further use of oxygenated biofuels in conventional vehicles. The Environmental Protection Agency (EPA) recently granted partial approval to a waiver allowing the use of E15 in 2001 and newer light-duty vehicles.

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.

In the last three years, three quality surveys on ethanol-blended fuels intended for use in flex-fuel vehicles have been published. Two of these surveys cover Flex-Fuel quality, and the third encompasses the quality of mid-level ethanol blends (MLEBs) from blender pumps. The purpose of these surveys was to report on the quality of the fuels and provide a snapshot in time of fuel quality. This study examines the larger picture portrayed by these surveys and looks for broader trends in fuel quality. The analysis found that compliance with vapor pressure specification limits for Flex Fuel improved from 40% to 66% in Class 1, from 31% to 43% in Class 2, and from 12% to 30% in Class 3 between 2008 and 2010. Failures on other critical properties, such as acidity, pHe, water, and inorganic chloride were less than 6% in these studies. The 2010 Flex Fuel samples readily met the ethanol content specification, with 88%, 92%, and 95% compliance for Classes 1, 2, and 3, respectively.

The objective of this work was to measure knock resistance metrics for ethanol-hydrocarbon blends with a primary focus on development of methods to measure the heat of vaporization (HOV). Blends of ethanol at 10 to 50 volume percent were prepared with three gasoline blendstocks and a natural gasoline. Performance properties and composition of the blendstocks and blends were measured, including research octane number (RON), motor octane number (MON), net heating value, density, distillation curve, and vapor pressure. RON increases upon blending ethanol but with diminishing returns above about 30 vol%. Above 30% to 40% ethanol the curves flatten and converge at a RON of about 103 to 105, even for the much lower RON NG blendstock. Octane sensitivity (S = RON - MON) also increases upon ethanol blending. Gasoline blendstocks with nearly identical S can show significantly different sensitivities when blended with ethanol.

The NASA-wide CEV Smart Buyer Team (SBT) was assembled in January 2006 and was tasked with the development of a NASA in-house design for the CEV Crew Module (CM), Service Module (SM), and Launch Abort System (LAS). This effort drew upon over 250 engineers from all of the 10 NASA Centers. In 6 weeks, this in-house design was developed. The Thermal Systems Team was responsible for the definition of the active and passive design architecture. The SBT effort for Thermal Systems can be best characterized as a design architecting activity. Proof-of-concepts were assessed through system-level trade studies and analyses using simplified modeling. This nimble design approach permitted definition of a point design and assessing its design robustness in a timely fashion. This paper will describe the architecting process and present trade studies and proposed thermal designs

An EVA simulation with a medical contingency scenario was conducted in 2006 with the NASA Haughton-Mars and EVA Physiology System and Performance Projects, to develop medical contingency management and evacuation techniques for planetary surface exploration. A rescue/evacuation system to allow two rescuer astronauts to evacuate one incapacitated astronaut was evaluated. The rescue system was utilized effectively to extract an injured astronaut up a slope of15-25° and into a surface mobility rover for transport to a simulated habitat for advanced medical care. Further research is recommended to evaluate the effects of reduced gravity and to develop synergies with other surface systems for carrying out the contingency procedures.

This project investigates methods to capture an astronaut's exhaled carbon dioxide (CO2) before it becomes diluted with the high volumetric oxygen flow present within a space suit. Typical expired breath contains CO2 partial pressures (pCO2) in the range of 20-35 mm Hg (.0226-.046 atm). This research investigates methods to capture the concentrated CO2 gas stream prior to its dilution with the low pCO2 ventilation flow. Specifically this research is looking at potential designs for a collection cup for use inside the space suit helmet. The collection cup concept is not the same as a breathing mask typical of that worn by firefighters and pilots. It is well known that most members of the astronaut corps view a mask as a serious deficiency in any space suit helmet design. Instead, the collection cup is a non-contact device that will be designed using a detailed Computational Fluid Dynamic (CFD) analysis of the ventilation flow environment within the helmet.

In this work, the influences of ethanol and iso-butanol blended with gasoline on engine-out and post three-way catalyst (TWC) particle size distribution and number concentration were studied using a General Motors (GM) 2.0L turbocharged spark ignition direct injection (SIDI) engine. The engine was operated using the production engine control unit (ECU) with a dynamometer controlling the engine speed and the accelerator pedal position controlling the engine load. A TSI Fast Mobility Particle Sizer (FMPS) spectrometer was used to measure the particle size distribution in the range from 5.6 to 560 nm with a sampling rate of 1 Hz. U.S. federal certification gasoline (E0), two ethanol-blended fuels (E10 and E20), and 11.7% iso-butanol blended fuel (BU12) were tested. Measurements were conducted at 10 selected steady-state engine operation conditions. Bi-modal particle size distributions were observed for all operating conditions with peak values at particle sizes of 10 nm and 70 nm.

Hydrogen fuel cell vehicles (FCVs) appear to be a promising solution for the future of clean and efficient personal transportation. Issues of how to generate the hydrogen and then store it on-board to provide satisfactory driving range must still be resolved before they can compete with conventional vehicles. Alternatively, FCVs could obtain hydrogen from on-board reforming of gasoline or other fuels such as methanol or ethanol. On-board reformers convert fuel into a hydrogen-rich fuel stream through catalytic reactions in several stages. The high temperatures associated with fuel processing present an engineering challenge to warm up the reformer quickly and efficiently in a vehicle environment. Without a special warmup phase or vehicle hybridization, the reformer and fuel cell system must provide all power to move the vehicle, including ¼ power in 30 s, and ½ power in 3 min to satisfy the Federal Test Procedure (FTP) cycle demands.

Delphi and the National Renewable Energy Laboratory (NREL) collaborated to develop a simulation code to model the mechanical and electrical architectures of a series hybrid vehicle simultaneously. This co-simulation code is part of the larger ADVISOR® product created by NREL and diverse partners. Simulation of the macro power flow in a series hybrid vehicle requires both the mechanical drivetrain and the entire electrical architecture. It is desirable to solve the electrical network equations in an environment designed to comprehend such a network and solve the equations in terms of current and voltage. The electrical architecture for the series hybrid vehicle has been modeled in Saber™ to achieve these goals. This electrical architecture includes not only the high-voltage battery, generator, and traction motor, but also the normal low-voltage bus (14V) with loads common to all vehicles.