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The objective of this research project was to compare B20 (20% biodiesel fuel) and ultra-low-sulfur (ULSD) diesel-fueled buses in terms of fuel economy, vehicle maintenance, engine performance, component wear, and lube oil performance. We examined 15 model year (MY) 2002 Gillig 40-foot transit buses equipped with MY 2002 Cummins ISM engines. The engines met 2004 U.S. emission standards and employed exhaust gas recirculation (EGR). For 18 months, eight of these buses operated exclusively on B20 and seven operated exclusively on ULSD. The B20 and ULSD study groups operated from different depots of the St. Louis (Missouri) Metro, with bus routes matched for duty cycle parity. The B20- and ULSD-fueled buses exhibited comparable fuel economy, reliability (as measured by miles between road calls), and total maintenance costs. Engine and fuel system maintenance costs were also the same for the two groups after correcting for the higher average mileage of the B20 group.

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.

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.

This study compared fuel economy and emissions between heavy-duty hybrid electric vehicles (HEVs) and equivalent conventional diesel vehicles. In-use field data were collected from daily fleet operations carried out at a FedEx facility in California on six HEV and six conventional 2010 Freightliner M2-106 straight box trucks. Field data collection primarily focused on route assessment and vehicle fuel consumption over a six-month period. Chassis dynamometer testing was also carried out on one conventional vehicle and one HEV to determine differences in fuel consumption and emissions. Route data from the field study was analyzed to determine the selection of dynamometer test cycles. From this analysis, the New York Composite (NYComp), Hybrid Truck Users Forum Class 6 (HTUF 6), and California Air Resource Board (CARB) Heavy Heavy-Duty Diesel Truck (HHDDT) drive cycles were chosen.

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.

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.

Electric drive vehicles (EDVs) have complex thermal management requirements not present in conventional vehicles. In addition to cabin conditioning, the energy storage system (ESS) and power electronics and electric motor (PEEM) subsystems also require thermal management. Many current-generation EDVs utilize separate cooling systems, adding both weight and volume, and lack abundant waste heat from an engine for cabin heating. Some use battery energy to heat the cabin via electrical resistance heating, which can result in vehicle range reductions of 50% under cold ambient conditions. These thermal challenges present an opportunity for integrated vehicle thermal management technologies that reduce weight and volume and increase cabin heating efficiency. Bench testing was conducted to evaluate a combined fluid loop technology that unifies the cabin air-conditioning and heating, ESS thermal management, and PEEM cooling into a single liquid coolant-based system.

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.

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.

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.

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.

Due to raising interest in diesel powered passenger cars in the U.S. in combination with a desire to reduce dependency on imported petroleum, there has been increased attention to the operation of diesel vehicles on fuels blended with biodiesel. One of several factors to be considered when operating a vehicle on biodiesel blends is understanding the impact and performance of the fuel on the emission control system. This paper documents the impact of the biodiesel blends on engine-out emissions as well as the overall system performance in terms of emission control system calibration and the overall system efficiency. The testing platform is a light-duty HSDI diesel engine with a Euro 4 base calibration in a 1700 kg sedan vehicle. It employs 2nd generation common-rail injection system with peak pressure of 1600 bar as well as cooled high-pressure EGR. The study includes 3 different fuels (U.S.

Raising interest in Diesel powered passenger cars in the United States in combination with the government mandated policy to reduce dependency of foreign oil, leads to the desire of operating Diesel vehicles with Biodiesel fuel blends. There is only limited information related to the impact of Biodiesel fuels on the performance of advanced emission control systems. In this project the implementation of a NOx storage and a SCR emission control system and the development for optimal performance are evaluated. The main focus remains on the discussion of the differences between the fuels which is done for the development as well as useful life aged components. From emission control standpoint only marginal effects could be observed as a result of the Biodiesel operation. The NOx storage catalyst results showed lower tailpipe emissions which were attributed to the lower exhaust temperature profile during the test cycle. The SCR catalyst tailpipe results were fuel neutral.

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.

Experiments were conducted with ultra low sulfur diesel (ULSD) and 20% biodiesel blends (B20) to compare lube oil dilution levels and lubricant properties for systems using late in-cylinder fuel injection for aftertreatment regeneration. Lube oil dilution was measured by gas chromatography (GC) following ASTM method D3524 to measure diesel content, by Fourier transform infrared (FTIR) spectrometry following a modified ASTM method D7371 to measure biodiesel content, and by a newly developed back-flush GC method that simultaneously measures both diesel and biodiesel. Heavy-duty (HD) engine testing was conducted on a 2008 6.7L Cummins ISB equipped with a diesel oxidation catalyst (DOC) and diesel particle filter (DPF). Stage one of engine testing consisted of 10 consecutive repeats of a forced DPF regeneration event. This continuous operation with late in-cylinder fuel injection served as a method to accelerate lube-oil dilution.

While it is well known that “MPG will vary” based on how one drives, little independent research exists on the aggregate fuel savings potential of improving driver efficiency and on the best ways to motivate driver behavior changes. This paper finds that reasonable driving style changes could deliver significant national petroleum savings, but that current feedback approaches may be insufficient to convince many people to adopt efficient driving habits. To quantify the outer bound fuel savings for drive cycle modification, the project examines completely eliminating stop-and-go driving plus unnecessary idling, and adjusting acceleration rates and cruising speeds to ideal levels. Even without changing the vehicle powertrain, such extreme adjustments result in dramatic fuel savings of over 30%, but would in reality only be achievable through automated control of vehicles and traffic flow.

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 objective of this research project was to compare the emissions of oxides of nitrogen (NOx) from transit buses on as many as five different fuels and three standard transit duty cycles to establish if there is a real-world biodiesel NOx increase for transit bus duty cycles and engine calibrations. Prior studies have shown that B20 can cause a small but significant increase in NOx emissions for some engines and duty cycles. Six buses spanning engine build years 1998 to 2011 were tested on the National Renewable Energy Laboratory's Renewable Fuels and Lubricants research laboratory's heavy-duty chassis dynamometer with certification diesel, certification B20 blend, low aromatic [California Air Resources Board (CARB)] diesel, low aromatic B20 blend, and B100 fuels over the Manhattan, Orange County and UDDS test cycles.