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In response to the stipulations of the Energy Policy and Conservation Act and the global momentum toward carbon mitigation, there has been a pronounced tightening of fuel economy standards for manufacturers. This stricter regulation is coupled with an accelerated transition to electric vehicles, catalyzed by advances in electrification technology and a decline in battery cost. Improvements in the fuel economy of medium- and heavy-duty vehicles through electrification are particularly noteworthy. Estimating the magnitude of fuel economy improvements that result from technological advances in these vehicles is key to effective policymaking. In this research, we generated vehicle models based on assumptions regarding advanced transportation component technologies and powertrains to estimate potential vehicle-level fuel savings. We also developed a systematic approach to evaluating a vehicle’s fuel economy by calibrating the size of the components to satisfy performance requirements.

As connectivity and sensing technologies become more mature, automated vehicles can predict future driving situations and utilize this information to drive more energy-efficiently than human-driven vehicles. However, future information beyond the limited connectivity and sensing range is difficult to predict and utilize, limiting the energy-saving potential of energy-efficient driving. Thus, we combine a conventional speed optimization planner, developed in our previous work, and reinforcement learning to propose a real-time intelligent speed optimization planner for connected and automated vehicles. We briefly summarize the conventional speed optimization planner with limited information, based on closed-form energy-optimal solutions, and present its multiple parameters that determine reference speed trajectories.

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 introduces control strategy analysis and performance degradation for the 2010 Toyota Prius under different thermal conditions. The goal was to understand, in as much detail as possible, the impact of thermal conditions on component and vehicle performances by analyzing a number of test data obtained under different thermal conditions in the Advanced Powertrain Research Facility (APRF) at Argonne National Laboratory. A previous study analyzed the control behavior and performance under a normal ambient temperature; thus the first step in this study was to focus on the impact when the ambient temperature is cold or hot. Based on the analyzed results, thermal component models were developed in which the vehicle controller in the simulation was designed to mimic the control behavior when temperatures of the components are cold or hot. Further, the performance degradation of the components was applied to the mathematical models based on analysis of the test data.

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).

The Prius - a power-split hybrid electric vehicle from Toyota - has become synonymous with the word “Hybrid.” As of October 2010, two million of these vehicles had been sold worldwide, including one million vehicles purchased in the United States. In 2004, the second generation of the vehicle, the Prius MY04, enhanced the performance of the components with advanced technologies, such as a new magnetic array in the rotors. However, the third generation of the vehicle, the Prius MY10, features a remarkable change of the configuration - an additional reduction gear has been added between the motor and the output of the transmission [1]. In addition, a change in the energy management strategy has been found by analyzing the results of a number of tests performed at Argonne National Laboratory's Advanced Powertrain Research Facility (ARRF).

In this paper we present the results of full-scale chassis dynamometer testing of two hybrid transit bus configurations, parallel and series and, in addition, quantify the impact of air conditioning. We also study the impact of using an electrically controlled cooling fan. The main trend that is noted, and perhaps expected, is that a significant fuel penalty is encountered during operation with air conditioning, ranging from 17-27% for the four buses considered. The testing shows that the series hybrid architecture is more efficient than the parallel hybrid in improving fuel economy during urban, low speed stop and go transit bus applications. In addition, smart cooling systems, such as the electrically controlled cooling fan can show a fuel economy benefit especially during high AC (or other increased engine load) conditions.

Over the past couple of years, numerous Hybrid Electric Vehicle (HEV) powertrain configurations have been introduced into the marketplace. Currently, the dominant architecture is the power-split configuration, notably the input splits from Toyota Motor Sales and Ford Motor Company. This paper compares two vehicle-level control strategies that have been developed to minimize fuel consumption while maintaining acceptable performance and drive quality. The first control is rules based and was developed on the basis of test data from the Toyota Prius as provided by Argonne National Laboratory's (Argonne's) Advanced Powertrain Research Facility. The second control is based on an instantaneous optimization developed to minimize the system losses at every sample time. This paper describes the algorithms of each control and compares vehicle fuel economy (FE) on several drive cycles.

An electronic particulate matter sensor (EPMS) developed at the University of Texas was used to characterize exhaust gases from a single-cylinder diesel engine and a light-duty diesel vehicle. Measurements were made during transient tip-in events with multiple sensor configurations in the single-cylinder engine. The sensor was operated in two modes: one with the electric field energized, and the other with no electric field present. In each mode, different characteristic signals were produced in response to a tip-in event, highlighting the two primary mechanisms of sensor operation. The sensor responded to both the natural charge of the particulate matter (PM) emitted from the engine, and was also found to create a signal by charging neutral particles. The characteristics of the two mechanisms of operation are discussed as well as their implications on the placement and operation of the sensor.

This paper presents the latest developments in the design and performance of an electronic particulate matter (PM) sensor developed at The University of Texas at Austin (UT) and suitable, with further development, for applications in active engine control of PM emissions. The sensor detects the carbonaceous mass component of PM in the exhaust and has a time-resolution less than 20 (ms), allowing PM levels to be quantified for engine transients. Sample measurements made with the sensor in the exhaust of a single-cylinder light duty diesel engine are presented for both steady-state and transient operations: a steady-state correlation with gravimetric filter measurements is presented, and the sensor response to rapid increases in PM emission during engine transients is shown for several different tip-in (momentary increases in fuel delivery) conditions.

SunDiesel™ is an alternative bio-fuel derived from wood chips that has certain properties that are superior to those of conventional diesel (D2). In this investigation, 100% SunDiesel was tested in a Mercedes A-Class (model year 1999), 1.7L, turbocharged, direct-injection diesel engine (EURO II) equipped with a common-rail injection system. By using an endoscope system, Argonne researchers collected in-cylinder visualization data to compare the engine combustion characteristics of the SunDiesel with those of D2. Measurements were made at one engine speed and load condition (2,500 rpm, 50% load) and four start-of-injection (SOI) points, because of a limited source of SunDiesel fuel. Significant differences in soot concentration, as measured by two-color optical pyrometry, were observed. The optical and cylinder pressure data clearly show significant differences in combustion duration and ignition delay between the two fuels.

We have examined the influence of piston wetting on the size distribution and mass of particulate matter (PM) emissions in a SI engine using several different fuels. Piston wetting was isolated as a source of PM emissions by injecting known amounts of liquid fuel onto the piston top using an injector probe. The engine was run predominantly on propane with approximately 10% of the fuel injected as liquid onto the piston. The liquid fuels were chosen to examine the effects of fuel volatility and molecular structure on the PM emissions. A nephelometer was used to characterize the PM emissions. Mass measurements from the nephelometer were compared with gravimetric filter measurements, and particulate size measurements were compared with scanning electron microscope (SEM) photos of particulates captured on filters. The engine was run at 1500 rpm at the Ford world-wide mapping point with an overall equivalence ratio of 0.9.

A nephelometer system was developed to characterize engine particulate emissions from DISI engines. Results were correlated with images showing the location and history of particulates in the cylinder of an optical engine. The nephelometer's operation is based upon the dependence of scattered laser light on particulate size from a flow sampled from the exhaust of an engine. The nephelometer simultaneously measured the scattered light from angles of 20° to 160° from the forward scattering direction in 4° increments. The angular scattering measurements were then compared with calculations using a Mie scattering code to infer information regarding particulate size. Measurements of particulate mass were made based upon a correlation developed between the scattered light intensity and particulate mass samples trapped in a 0.2-micron filter. Measurements were made in a direct injection single-cylinder spark ignition research engine having a transparent quartz cylinder.

Two limited-production hybrid electric vehicles (HEVs) - a 1988 Japanese model Toyota Prius and a 2000 Honda Insight - were tested at Argonne National Laboratory to collect data from vehicle component and systems operation. The test data are used to analyze operation and efficiency and to help validate computer simulation models. Both HEVs have FTP fuel economy greater than 45 miles per gallon and also have attributes very similar to those of conventional gasoline vehicles, even though each HEV has a unique powertrain configuration and operation control strategy. The designs and characteristics of these vehicles are of interest because they represent production technology with all the compromises for production included. This paper will explore both designs, their control strategies, and under what conditions high fuel economy was achieved.

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.

A 1998 Toyota Corona passenger car with a direct injection spark ignition (DISI) engine was tested at constant engine speed (2000 rpm) over a range of loads. Engine-out and tailpipe emissions of gas phase species were measured each second. This allowed examination of the engine-out emissions for late and early injection. Seven fuels were used for these tests: five blended fuels and two pure hydrocarbon fuels. These seven fuels can be divided into groups for examination of the effects of volatility, MTBE, and structure (an aromatic versus an i-alkane). Correlations between the fuel properties and their effects on emissions are presented. Use of steady state tests rather than driving cycles to examine fuel effects on emissions eliminates the complications resulting from accelerations, decelerations, and changes of injection timing but care had to be taken to account for the periodic regenerations of the lean NOx trap/catalyst.

Hybrid Electric Vehicles (HEVs) can be designed and operated to satisfy many different operational missions. The three most common HEV types differ with respect to component sizing and operational capabilities. However, HEV technology offers design opportunities beyond these three types. This paper presents a detailed HEV categorization process that can be used to describe unique HEV prototype designs entered in college and university-level HEV design competitions. We explored possible energy management strategies associated with designs that control the utilization of the two on-board energy sources and use the competition vehicles to illustrate various configurations and designs that affect the vehicle's capabilities. Experimental data is used to help describe the details of the power control strategies which determine how the engine and electric motor of HEV designs work together to provide motive power to the wheels.