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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.

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).

It is widely understood that cold ambient temperatures negatively impact vehicle system efficiency. This is due to a combination of factors: increased friction (engine oil, transmission, and driveline viscous effects), cold start enrichment, heat transfer, and air density variations. Although the science of quantifying steady-state vehicle component efficiency is mature, transient component efficiencies over dynamic ambient real-world conditions is less understood and quantified. This work characterizes wheel assembly efficiencies of a conventional and electric vehicle over a wide range of ambient conditions. For this work, the wheel assembly is defined as the tire side axle spline, spline housing, bearings, brakes, and tires. Dynamometer testing over hot and cold ambient temperatures was conducted with a conventional and electric vehicle instrumented to determine the output energy losses of the wheel assembly in proportion to the input energy of the half-shafts.

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.

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.

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.

Testing vehicles for emissions and fuel economy has traditionally been conducted with a single-axle chassis dynamometer. The 2006 SAE All Wheel Drive Symposium cited four wheel drive (4WD) and all wheel drive (AWD) sales as climbing from 20% toward 30% of a motor vehicle market share. With an increasing number of four wheel-drive vehicles being introduced to the market place, certification testing for emissions and fuel economy has been changed to allow both two wheel drive and four wheel drive testing [1]. As manufacturers plan to test these vehicles in this mode, test methods need to be developed to allow for these changes. This paper focuses on the tie down methods available for 4WD testing to determine possible effects of test methodologies on a traditional 4WD Vehicle and a hybrid vehicle.

As one of the U.S Department of Energy's (DOE's) vehicle systems benchmarking partners, Argonne National Laboratory (Argonne) has tested many plug-in hybrid electric vehicle (PHEV) conversions and purpose-built prototype vehicles. The procedures for testing follow draft SAE J1711 and California Air Resources Board (CARB) test concepts and calculation methods. This paper explains the testing procedures and calculates important parameters. It describes some parameters, such as cycle charge-depleting range, actual charge-depleting range, electric range fraction, equivalent all-electric range, and utility factor-weighted fuel economy.

Several plug-in hybrid electric vehicles (PHEVs) have recently been made available by conversion companies for laboratory testing. The viability of the technology must be evaluated by dynamometer benchmark testing, but because the technology is so new, existing and new test methods must first be investigated. Converted Gen 2 Toyota Prius vehicles from Hymotion, EnergyCS, and Hybrids Plus were tested at Argonne's dynamometer facility according to general testing concepts. These vehicles all share basic attributes - all are blended type PHEVs, all use Lithium battery technology, and all deplete charge in a similar fashion (although at different rates). In a time span of one year, lessons learned from one vehicle were carried over into the next test vehicle. A minimum test method was formulated that is well suited for all these vehicles. The method was validated with two vehicles of varying charge-depleting range.

Procedures are in place for testing emissions and fuel economy for virtually every type of light-duty vehicle with a single-axle chassis dynamometer, which is why nearly all emissions test facilities use single-axle dynamometers. However, hybrid electric vehicles (HEVs) employ regenerative braking. Thus, the braking split between the driven and non-driven axles may interact with the calculation of overall efficiency of the vehicle. This paper investigates the regenerative braking systems of a few production HEVs and provides an analysis of their differences in single-axle (2WD) and double-axle (4WD) dynamometer drive modes. The fuel economy results from 2WD and 4WD operation are shown for varied cycles for the 2000 Honda Insight, 2001 Toyota Prius, and the 2004 Toyota Prius. The paper shows that there is no evidence that a bias in testing an HEV exists because of the difference in operating the same hybrid vehicle in the 2WD and 4WD modes.

Vehicle emissions and fuel economy testing applications rely on accurate sensors to track power flow and measure component efficiencies. A non-intrusive in-vehicle torque sensor has been implemented in a hybrid powertrain to directly measure engine torque. Previously used off-the-shelf torque sensors required additional mechanical space, and so chassis modifications were needed to accommodate the sensor, which potentially limited the vehicle to only dynamometer testing. The challenges in implementing this type of sensor in automotive environments are described in detail, as are sensor capabilities and test results.

The Rotating Linear Engine (RLE) derives improved fuel efficiency and decreased maintenance costs via a unique lubrication design, which decreases piston assembly friction and the associated wear for heavy-duty natural gas and diesel engines. The piston ring friction exhibited on current engines accounts for 1% of total US energy consumption. The RLE is expected to reduce this friction by 50-70%, an expectation supported by hot motoring and tear-down tests on the UT single cylinder RLE prototype. Current engines have stationary liners where the oil film thins near the ends of the stroke, resulting in metal-to-metal contact. This metal-to-metal contact is the major source of both engine friction and wear, especially at high load. The RLE maintains an oil film between the piston rings and liner throughout the piston stroke due to liner rotation. This assumption has also been confirmed by recent testing of the single cylinder RLE prototype.

It is a very challenging problem to reliably ignite extremely lean mixtures, especially for the low speed, high load conditions of large-bore natural gas engines. If these engines are to be use for the distributed power generation market, it will require operation with higher boost pressures and even leaner mixtures. Both place greater demands on the ignition system. The railplug is a very promising ignition system for lean burn natural gas engines with its high-energy deposition and high velocity plasma arc. It requires care to properly design railplugs for this new application, however. For these engines, in-cylinder pressure and mixture temperature are very high at the time of ignition due to the high boost pressure. Hot spots may exist on the electrodes of the ignitor, causing pre-ignition problems. A heat transfer model is proposed in this paper to aid the railplug design. The electrode temperature was measured in an operating natural gas engine.

An On-Board Distillation System (OBDS) was developed to extract, from gasoline, a highly volatile crank fuel that allows the reduction of startup fuel enrichment and significant spark retard during cold starts and warm-up. This OBDS was installed on a 2001 Lincoln Navigator to explore the emissions reductions possible on a large vehicle with a large-displacement engine. The fuel and spark calibration of the PCM were modified to exploit the benefits of the OBDS startup fuel. Three series of tests were performed: (1) measurement of the OBDS fuel composition and distillation curve per ASTM D86, (2) measurement of real-time cold start (20 °C) tailpipe hydrocarbon emissions for the first 20 seconds of engine operation, and (3) FTP drive cycles at 20 °C with engine-out and tailpipe emissions of gas-phase species measured each second. Baseline tests were performed using stock PCM calibrations and certification gasoline.

An optical access engine was used to image the liquid film evaporation off the piston of a simulated direct injected gasoline engine. A directional injector probe was used to inject liquid fuel (gasoline, i-octane and n-pentane) directly onto the piston of an engine primarily fueled on propane. The engine was run at idle conditions (750 RPM and closed throttle) and at the Ford World Wide Mapping Point (1500 RPM and 262 kPa BMEP). Mie scattering images show the liquid exiting the injector probe as a stream and directly impacting the piston top. Schlieren imaging was used to show the fuel vaporizing off the piston top late in the expansion stroke and during the exhaust stroke. Previous emissions tests showed that the presence of liquid fuel on in-cylinder surfaces increases engine-out hydrocarbon emissions.

The Prius is a major achievement by Toyota: it is the first mass-produced HEV with the first available HEV-optimized engine. Argonne National Laboratory's Advanced Powertrain Test Facility has been testing the Prius for model validation and technology performance and assessment. A significant part of the Prius test program is focused on testing and mapping the engine. A short-length torque sensor was installed in the powertrain in-situ. The torque sensor data allow insight into vehicle operational strategy, engine utilization, engine efficiency, and specific emissions. This paper describes the design and process necessary to install a torque sensor in a vehicle and shows the high-fidelity data measured during chassis dynamometer testing. The engine was found to have a maximum thermodynamic efficiency of 36.4%. Emissions and catalyst efficiency maps were also produced.

The effects of fuel properties on the emissions of a production vehicle with a gasoline direct injection engine operating over the Federal Test Procedure (FTP) cycle were investigated. The vehicle used was a 1998 Toyota Corona passenger car with a direct injection spark ignition (DISI) engine. Engine-out and tailpipe FTP emissions for six fuels and a California Phase 2 RFG reference fuel are presented. Four of the test fuels were blended from refinery components to meet specified distillation profiles. The remaining test fuels were iso-octane and toluene, an iso-alkane and an aromatic with essentially the same boiling point (at atmospheric pressure) that is near the T50 point for the blended fuels. Statistically significant effects, at the 95% confidence level, of the fuels on tailpipe emissions were found. Correlations were sought between the properties of the five blends and the Emissions Indices for engine-out hydrocarbons and NOx and for tailpipe particulates.