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A fully instrumented Tesla Model 3 was used to collect thousands of hours of real-world automated driving data, encompassing both Autopilot and Full Self-Driving modes. This comprehensive dataset included vehicle operational parameters from the data busses, capturing details such as powertrain performance, energy consumption, and the control of advanced driver assistance systems (ADAS). Additionally, interactions with the surrounding traffic were recorded using a perception kit developed in-house equipped with LIDAR and a 360-degree camera system. We collected the data as part of a larger program to assess energy-efficient driving behavior of production connected and automated vehicles. One important aspect of characterizing the test vehicle is predicting its car-following behavior. Using both uncontrolled on-road tests and dedicated tests with a lead car performing set speed maneuvers, we tuned conventional adaptive cruise control (ACC) equations to fit the vehicle’s behavior.

The primary purpose of this study was to obtain gas-phase and particular matter (PM) emissions from newer Tier 4 final off-road construction equipment using a Portable Emissions Measurement System (PEMS). This information can be used to provide accurate estimates of emissions from off-road construction equipment under real-world scenarios. Emission measurements were made for 10 pieces of Tier 4 final construction equipment including 3 excavators, 3 wheel loaders, 2 crawler tractors and 2 backhoe/loaders. The duty cycles included a pre-defined combined sequence of a cold-start phase, trenching, backfilling, travelling, and idling. For all types of equipment, the highest emissions were seen for the cold start phase, which showed NOx emissions levels ranging from 3.4 to 6.3 g/bhp-hr, from 15.8 to 26.1 g/kg-fuel and from 107 to 249 g/hour, with an average exhaust temperature around 100°C.The next highest emissions were found for the travel mode.

We assessed the engine-out emissions of an ultra-low sulfur diesel (ULSD) and a neat hydrogenated vegetable oil (HVO) from a light-duty diesel truck equipped with common rail direct injection. The vehicle was tested at least twice on each fuel using the LA-92 drive cycle and at steady-state conditions at 30 mph and 50 mph at different loads. Results showed reductions in the engine-out total hydrocarbon (THC), carbon monoxide (CO), nitrogen oxide (NOx), and particulate emissions with HVO. The reductions in soot mass, solid particle number, and particulate matter (PM) mass emissions with HVO were due to the absence of aromatic and polyaromatic hydrocarbon compounds, as well as sulfur species, which are known precursors of soot formation. Volumetric fuel economy, calculated based on the carbon balance method, did not show statistically significant differences between the fuels.

The emissions of two ultralow NOx heavy-duty (HD) vehicles equipped with 0.02 g/bhp-hr low NOx natural gas (NG) engines were evaluated on a chassis dynamometer. This included a waste hauler and a city transit bus, each with a 0.02 g/bhp-hr NOx L9N near zero (NZ) natural gas engine. The vehicles were tested over a variety of different cycles, including the Urban Dynamometer Driving Schedule (UDDS), port drayage cycles, transit bus cycles, and a refuse truck cycle. For both vehicles, the NOx emissions results were below the 0.02 g/bhp-hr level for most cycles, with the exception of some cold start tests. For the waste hauler, NOx emissions averaged between 0.014 and 0.002 g/bhp-hr for the hot start tests, and from 0.043 to 0.014 g/bhp-hr for the cold start tests. This represented NOx emissions reductions from 97%-100% of compared with previous ISL G 8.9 engines.

As emerging automated vehicle technology is making advances in safety and reliability, engineers are also exploring improvements in energy efficiency with this new paradigm. Powertrain efficiency receives due attention, but also impactful is finding ways to reduce driving losses in coordinated-driving scenarios. Efforts focused on simulation to quantify road load improvements require a sufficient amount of background validation work to support them. This study uses a practical approach to directly quantify road load changes by testing the coordinated driving of two vehicles on a test track at various speeds (64, 88, 113 km/h) and vehicle time gaps (0.3 to 1.3 s). Axle torque sensors were used to directly measure the load required to maintain steady-state speeds while following a lead vehicle at various gap distances.

Reductions in vehicle drive losses are as important to improving fuel economy as increases in powertrain efficiencies. In order to measure vehicle fuel economy, chassis dynamometer testing relies on accurate road load determinations. Road load is currently determined (with some exceptions) using established test track coastdown testing procedures. Because new vehicle technologies and usage cases challenge the accuracy and applicability of these procedures, on-road experiments were conducted using axle torque sensors to address the suitability of the test procedures in determining vehicle road loads in specific cases. Whereas coastdown testing can use vehicle deceleration to determine load, steady-state testing can offer advantages in validating road load coefficients for vehicles with no mechanical neutral gear (such as plug-in hybrid and electric vehicles).

A quasi-dimensional model for a direct injection diesel engine was developed based on experiments at Sandia National Laboratory. The Sandia researchers obtained images describing diesel spray evolution, spray mixing, premixed combustion, mixing controlled combustion, soot formation, and NOx formation. Dec [1] combined all of the available images to develop a conceptual diesel combustion model to describe diesel combustion from the start of injection up to the quasi-steady form of the jet. The end of injection behavior was left undescribed in this conceptual model because no clear image was available due to the chaotic behavior of diesel combustion. A conceptual end-of-injection diesel combustion behavior model was developed to capture diesel combustion throughout its life span. The compression, expansion, and gas exchange stages are modeled via zero-dimensional single zone calculations.

We assessed gaseous and particulate matter (PM) emissions from a current technology stoichiometric natural gas waste hauler equipped with a 2011 model year 8.9L Cummins Westport ISL-G engine with cooled exhaust gas recirculation (EGR) and three-way catalyst (TWC). Testing was performed on five fuels with varying Wobbe and methane numbers over the William H. Martin Refuse Truck Cycle. The results showed lower nitrogen oxide (NOx) emissions for the low methane fuels (i.e., natural gas fuels with a relatively low methane content) for the transport and curbside cycles. Total hydrocarbon (THC) and methane (CH4) emissions did not show any consistent fuel trends. Non-methane hydrocarbon (NMHC) emissions showed a trend of higher emissions for the fuels containing higher levels of NMHCs. Carbon monoxide (CO) emissions showed a trend of higher emissions for the low methane fuels.

Emissions, fuel economy, and performance are determined over a light and a heavy driving cycle designed to represent the vehicles in-use driving patterns. The vehicles are 2010 class 8 Freightliner tractor trucks equipped with Cummins engines with Selective Catalytic Reduction and Diesel Particulate Filter emission control systems. The hybrid has lower carbon dioxide emissions, better fuel economy, and nitrogen oxide emissions statistically the same as the conventional. The CO emissions are well below the standards for both vehicles, but they are higher from the hybrid. The higher CO emissions for the hybrid are primarily related to the cooling of the Diesel Oxidation Catalyst (DOC) during the standard 20 minute key-off soak between repeats of the driving cycles. With a 1 minute key-off soak the CO emissions from the hybrid are negative.

Two modern light-duty passenger vehicles were selected for chassis dynamometer testing to evaluate differences in performance end efficiency resulting from CNG and gasoline combustion in a vehicle-based context. The vehicles were chosen to be as similar as possible apart from fuel type, sharing similar test weights and identical driveline configurations. Both vehicles were tested over several chassis dynamometer driving cycles, where it was found that the CNG vehicle exhibited 3-9% lower fuel economy than the gasoline-fueled subject. Performance tests were also conducted, where the CNG vehicle's lower tractive effort capability and longer acceleration times were consistent with the lower rated torque and power of its engine as compared to the gasoline model. The vehicles were also tested using quasi-steady-state chassis dynamometer techniques, wherein a series of engine operating points were studied.

The first commercially available Plug-In Hybrid Electric Vehicle (PHEV), the General Motors (GM) Volt, was introduced into the market in December 2010. The Volt's powertrain architecture provides four modes of operation, including two that are unique and maximize the Volt's efficiency and performance. The electric transaxle has been specially designed to enable patented operating modes both to improve the electric driving range when operating as a battery electric vehicle and to reduce fuel consumption when extending the range by operating with an internal combustion engine (ICE). However, details on the vehicle control strategy are not widely available because the supervisory control algorithm is proprietary. Since it is not possible to analyze the control without vehicle test data obtained from a well-designed Design-of-Experiment (DoE), a highly instrumented GM Volt, including thermal sensors, was tested at Argonne National Laboratory's Advanced Powertrain Research Facility (APRF).

As new advanced-technology vehicles are becoming more mainstream, analysts are studying their potential impact on petroleum use, carbon emissions, and smog emissions. Determining the potential impacts of widespread adoption requires testing and careful analysis. PHEVs possess unique operational characteristics that require evaluation in terms of actual in-use driving habits. SAE J2841, “Utility Factor Definitions for Plug-In Hybrid Electric Vehicles Using 2001 U.S. DOT National Household Travel Survey Data,” published by SAE in 2009 with a revision in 2010, is a guide to using DOT's National Household Travel Survey (NHTS) data to estimate the relative split between driving in charge-depleting (CD) mode and charge-sustaining (CS) mode for a particular PHEV with a given CD range. Without this method, direct comparisons of the merits of various vehicle designs (e.g., efficiency and battery size) cannot be made among PHEVs, or between PHEVs and other technologies.

This paper determines the impact of ambient temperature on energy consumption of a variety of vehicles in the laboratory. Several conventional vehicles, several hybrid electric vehicles, a plug-in hybrid electric vehicle and a battery electric vehicle were tested for fuel and energy consumption under test cell conditions of 20°F, 72°F and 95°F with 850 W/m₂ of emulated radiant solar energy on the UDDS, HWFET and US06 drive cycles. At 20°F, the energy consumption increase compared to 72°F ranges from 2% to 100%. The largest increases in energy consumption occur during a cold start, when the powertrain losses are highest, but once the powertrains reach their operating temperatures, the energy consumption increases are decreased. At 95°F, the energy consumption increase ranges from 2% to 70%, and these increases are due to the extra energy required to run the air-conditioning system to maintain 72°F cabin temperatures.

Urban air quality in California can have a large impact on the state's economy, natural and managed ecosystems, and human health and mortality. The use of alternative, low-carbon fuels is considered to be an effective measure to meet strict emissions regulations of particulate matter (PM) and oxides of nitrogen (NOx). Natural gas may be a potential alternative to conventional liquid fuels for use in automotive internal combustion engines, and can be used in fulfilling these requirements. The primary objective of this study is to evaluate the impact of varying natural gas composition on the exhaust emissions from a transit bus equipped with a 2003 Cummins C Gas Plus, lean-burn, spark-ignited natural gas engine and an oxidation catalyst while operating on the Central Business District (CBD) cycle on a chassis dynamometer.

The Rotating Liner Engine (RLE) is an engine design concept where the cylinder liner rotates in order to reduce piston assembly friction and liner/ring wear. The reduction is achieved by the elimination of the mixed and boundary lubrication regimes that occur near TDC. Prior engines for aircraft developed during WW2 with partly rotating liners (Sleeve Valve Engines or SVE) have exhibited reduction of bore wear by factor of 10 for high BMEP operation, which supports the elimination of mixed lubrication near the TDC area via liner rotation. Our prior research on rotating liner engines experimentally proved that the boundary/mixed components near TDC are indeed eliminated, and a high friction reduction was quantified compared to a baseline engine. The added friction required to rotate the liner is hydrodynamic via a modest sliding speed, and is thus much smaller than the mixed and boundary friction that is eliminated.

As vehicle technology progresses to new levels of sophistication, so too, vehicle test methods must evolve. This is true for analytical testing in a laboratory and for on-road vehicle testing. Every year since 1993, the U.S. Department of Energy (DOE) and original equipment manufacturer (OEM) sponsors have organized a series of competitions featuring advanced hybrid electric vehicle (HEV) technology to develop and promote DOE goals in fuel savings and alternative fuel usage. The competition has evolved over many years and has included many alternative fuels feeding the prime mover (including hydrogen fuel cells). EcoCAR turned its focus to plug-in hybrid electric vehicles (PHEVs) and it was quickly realized that to keep using on-road testing methods to evaluate fuel and electricity consumption, a new method needed to be developed that would properly weight depleting operation with the sustaining operation, using the established Utility Factor (UF) method.

Over the past several years, new recommended practices for testing plug-in vehicles have been developed by SAE standards committees. At first only proprietary or prototype vehicles were available to validate new procedures. However, with the recent availability of Chevy Volt and Nissan Leaf, these test procedures were put to the test in Argonne�s National Laboratory�s dynamometer test facility. Procedures for the Volt were according to the SAE J1711 procedures. The Leaf was tested according to procedures still under development in the SAE J1634 task force. Identified were aspects of the tests that were successful and areas where more development is needed. As described in SAE J2841, the Volt results were analyzed using a �utility factor� to estimate in-use expectations of electric-only miles.

Research in plug in vehicles (PHEV and BEV) has of course been ongoing for decades, however now that these vehicles are finally being produced for a mass market an intense focus over the last few years has been given to proper evaluation techniques and standard information to effectively convey efficiency information to potential consumers. The first challenge is the development of suitable test procedures. Thanks to many contributions from SAE members, these test procedures have been developed for PHEVs (SAE J1711 now available) and are under development for BEVs (SAE J1634 available later this year). A bigger challenge, however, is taking the outputs of these test results and dealing with the issue of off-board electrical energy consumption in the context of decades-long consumer understanding of MPG as the chief figure of merit for vehicle efficiency.

EmiSense Technologies, LLC (www.emisense.com) is commercializing its electronic particulate matter (PM) sensor that is based on technology developed at the University of Texas at Austin (UT). To demonstrate the capability of this sensor for real-time PM measurements and on board diagnostics (OBD) for failure detection of diesel particle filters (DPF), independent measurements were performed to characterize the engine PM emissions and to compare with the PM sensor response. Computational fluid dynamics (CFD) modeling was performed to characterize the hydrodynamics of the sensor's housing and to develop an improved PM sensor housing with reproducible hydrodynamics and an internal baffle to minimize orientation effects. PM sensors with the improved housing were evaluated in the truck exhaust of a heavy duty (HD) diesel engine tested on-road and on a chassis dynamometer at the University of California, Riverside (UCR) using their Mobile Emissions Laboratory (MEL).

The success of improved fuel economy is the proper integration of thermal management components which are appropriately performed to reduce friction and wasted energy. The thermal management systems of vehicle are able to balance the multiple needs such as heating, cooling, or appropriate operation within specified temperature ranges of propulsion systems. Since the propulsion systems of vehicle have changed from a single energy source based on conventional internal combustion engine to hybrid system including more electrical system such as full type of hybrid electric vehicle or plug-in hybrid electric vehicles, a new transition associated with vehicle thermal management arises. More efficient thermal management systems are required to improve the fuel economy in the hybrid electric vehicles because of the driving of electric traction motor and the increase of engine off time. The decrease of engine operation time may not sustain the proper temperature ranges of engine and gearbox.