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In order to reduce fuel consumption, companies have been looking at hybridizing vehicles. So far, two main hybridization options have been considered: electric and hydraulic hybrids. Because of light duty vehicle operating conditions and the high energy density of batteries, electric hybrids are being widely used for cars. However, companies are still evaluating both hybridization options for medium and heavy duty vehicles. Trucks generally demand very large regenerative power and frequent stop-and-go. In that situation, hydraulic systems could offer an advantage over electric drive systems because the hydraulic motor and accumulator can handle high power with small volume capacity. This study compares the fuel displacement of class 6 trucks using a hydraulic system compared to conventional and hybrid electric vehicles. The paper will describe the component technology and sizes of each powertrain as well as their overall vehicle level control strategies.

Vehicle operation during cold-start powertrain conditions can have a significant impact on drivability, fuel economy and tailpipe emissions in modern passenger vehicles. As efforts continue to maximize fuel economy in passenger vehicles, considerable engineering resources are being spent in order to reduce the consumption penalties incurred shortly after engine start and during powertrain warmup while maintaining suitably low levels of tailpipe emissions. Engine downsizing, advanced transmissions and hybrid-electric architecture can each have an appreciable effect on cold-start strategy and its impact on fuel economy. This work seeks to explore the cold-start strategy of several passenger vehicles with different powertrain architectures and to understand the resulting fuel economy impact relative to warm powertrain operation. To this end, four vehicles were chosen with different powertrain architectures.

Modeling plume interaction and collapse for direct-injection gasoline sprays is important because of its impact on fuel-air mixing and engine performance. Nevertheless, the aerodynamic interaction between plumes and the complicated two-phase coupling of the evaporating spray has shown to be notoriously difficult to predict. With the availability of high-speed (100 kHz) Particle Image Velocimetry (PIV) experimental data, we compare velocity field predictions between plumes to observe the full temporal evolution leading up to plume merging and complete spray collapse. The target “Spray G” operating conditions of the Engine Combustion Network (ECN) is the focus of the work, including parametric variations in ambient gas temperature. We apply both LES and RANS spray models in different CFD platforms, outlining features of the spray that are most critical to model in order to predict the correct aerodynamics and fuel-air mixing.

Hydrotreated vegetable oil (HVO) is a high-cetane number alternative fuel with the potential of drastic emissions reductions in high-pressure diesel engines. In this study the behavior of HVO sprays is investigated computationally and compared with conventional diesel fuel sprays. The simulations are performed with a modified version of the C++ open source code OpenFOAM using Reynolds-averaged conservation equations for mass, species, momentum and energy. The turbulence has been modeled with a modified version of the RNG k-ε model. In particular, the turbulence interaction between the droplets and the gas has been accounted for by introducing appropriate source terms in the turbulence model equations. The spray simulations reflect the setup of the constant-volume combustion cell from which the experimental data were obtained.

Striving for sustainable transportation solutions, hydrogen is often identified as a promising energy carrier and internal combustion engines are seen as a cost effective consumer of hydrogen to facilitate the development of a large-scale hydrogen infrastructure. Driven by efficiency and emissions targets defined by the U.S. Department of Energy, a research team at Argonne National Laboratory has worked on optimizing a spark-ignited direct injection engine for hydrogen. Using direct injection improves volumetric efficiency and provides the opportunity to properly stratify the fuel-air mixture in-cylinder. Collaborative 3D-CFD and experimental efforts have focused on optimizing the mixture stratification and have demonstrated the potential for high engine efficiency with low NOx emissions. Performance of the hydrogen engine is evaluated in this paper over a speed range from 1000 to 3000 RPM and a load range from 1.7 to 14.3 bar BMEP.

A Machine Learning-Genetic Algorithm (ML-GA) approach was developed to virtually discover optimum designs using training data generated from multi-dimensional simulations. Machine learning (ML) presents a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. In the present work, a total of over 2000 sector-mesh computational fluid dynamics (CFD) simulations of a heavy-duty engine were performed. These were run concurrently on a supercomputer to reduce overall turnaround time. The engine being optimized was run on a low-octane (RON70) gasoline fuel under partially premixed compression ignition (PPCI) mode. A total of nine input parameters were varied, and the CFD simulation cases were generated by randomly sampling points from this nine-dimensional input space. These input parameters included fuel injection strategy, injector design, and various in-cylinder flow and thermodynamic conditions at intake valve closure (IVC).

This paper presents a unified modeling environment to simulate vehicle driving and powertrain operations within the context of the surrounding environment, including interactions between vehicles and between vehicles and the road. The goal of this framework is to facilitate the analysis of the energy impacts of vehicle connectivity and automation, as well as the development of eco-driving algorithms. Connectivity and automation indeed provide the potential to use information about the environment and future driving to minimize energy consumption. To achieve this goal, the designers of eco-driving control strategies need to simulate a wide range of driving situations, including the interactions with other vehicles and the infrastructure in a closed-loop fashion.

The Modular Automotive Technology Testbed (MATT) is a flexible platform built to test different technology components in a vehicle environment. This testbed is composed of physical component modules, such as the engine and the transmission, and emulated components, such as the energy storage system and the traction motor. The instrumentation on the tool enables the energy balance for individual components on drive cycles. Using MATT, a single set of hardware can operate as a conventional vehicle, a hybrid vehicle and a plug-in hybrid vehicle, enabling direct comparison of petroleum displacement for the different modes. The engine provides measured fuel economy and emissions. The losses of components which vary with temperature are also measured.

An experimental investigation of the ignition and combustion characteristics of two low cetane fuels in a spark assisted Diesel engine is described. A three cylinder Diesel engine was modified for single cylinder operation and fitted with a spark plug located in the periphery of the spray plume. Optical observations of ignition and combustion were obtained with high speed photography. Optical access was provided by a quartz piston crown and extended head arrangement. The low cetane fuels, a light end, low viscosity fuel and a heavy end, high viscosity fuel which were blended to bracket No. 2 Diesel fuel on the distillation curve, demonstrated extended operation in the modified Diesel engine. Qualitative and quantitative experimental observations of ignition delay, pressure rise, heat release, spray penetration and geometery were compared and evaluated against theoretical predictions.

Plug-in hybrid electric vehicle (PHEV) technologies have the potential for considerable petroleum consumption reductions, possibly at the expense of increased tailpipe emissions due to multiple “cold” start events and improper use of the engine for PHEV specific operation. PHEVs operate predominantly as electric vehicles (EVs) with intermittent assist from the engine during high power demands. As a consequence, the engine can be subjected to multiple cold start events. These cold start events may have a significant impact on the tailpipe emissions due to degraded catalyst performance and starting the engine under less than ideal conditions. On current hybrid electric vehicles (HEVs), the first cold start of the engine dictates whether or not the vehicle will pass federal emissions tests. PHEV operation compounds this problem due to infrequent, multiple engine cold starts.

Road vehicles can expend a significant amount of energy in undesirable vertical motions that are induced by road bumps, and much of that is dissipated in conventional shock absorbers as they dampen the vertical motions. Presented in this paper are some of the results of a study aimed at determining the effectiveness of efficiently transforming that energy into electrical power by using optimally designed regenerative electromagnetic shock absorbers. In turn, the electrical power can be used to recharge batteries or other efficient energy storage devices (e.g., flywheels) rather than be dissipated. The results of the study are encouraging - they suggest that a significant amount of the vertical motion energy can be recovered and stored.

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.

Experimental Brake Specific Fuel Consumption (BSFC) data are presented for two engines as a function of engine speed, load, outlet coolant temperature and inlet oil temperature. The engines used in the study were the Cummins VT-903 (turbocharged) and the Caterpillar 3208, both being direct-injection and four-cycle. The data were taken for the Cat 3208 engine using a fractional factorial statistical method which reduced the total test matrix from 256 to 64 data points. The experimental data are used in the development of BSFC regression equations as a function of load, speed, outlet coolant temperature and inlet oil temperatures. A mathematical parameter for expressing quantitatively the change of BSFC per 10°F change in coolant and oil temperature is presented. It was found that an increase in the coolant and/or oil temperatures had the effect of reducing BSFC in both engines.

The purpose of this research was to study the pressure drop profiles and regeneration temperature characteristics of Silicon Carbide (SiC) filters with and without a copper-based additive in the fuel, and also to compare their performance with two cordierite traps designated as EX-47 and EX-80. The collection of the particulate matter inside the trap imposes a backpressure on the engine which requires a periodic oxidation or regeneration of the particulate matter. The presence of copper additive in the fuel reduces the particulate ignition temperature from approximately 500 to 375°C. Two SiC systems were tested during this research. The first system consisted of one 14 L SiC trap, while the second system, the dual trap system (DTS), consisted of two 12 L SiC traps mounted in parallel. The test matrix included two types of regeneration tests, controlled and uncontrolled and three levels of Cu fuel additive (0, 30, and 60 ppm).

The goals of this research are to understand the regeneration process in ceramic (Cordierite) monolith traps using a copper fuel additive and to investigate the various conditions that lead to trap regeneration failure. The copper additive lowers the trap regeneration temperature from approximately 500 °C to 375 °C and decreases the time necessary for regeneration. Because of these characteristics, it is important to understand the effect of the additive on regeneration when excessive particulate matter accumulation occurs in the trap. The effects of particulate mass loading on regeneration temperatures and regeneration time were studied for both the controlled (engine operated at full load rated speed) and uncontrolled (trap regeneration initiated at full load rated speed after which the engine was cut to idle) conditions. The trap peak temperatures were higher for the uncontrolled than the controlled regeneration.

Spray behavior is regarded as one of main factors which influence engine performance, fuel consumption and emissions for diesel engine. In practice, spray characteristics from each orifice from a multi-hole nozzle are normally arranged symmetrically, while the hole-to-hole spray variation is unavoidable. This variation will cause spatial uneven distribution of spray and combustion degrade, which will be no longer inconsiderable in face of the more and more stringent emission rules. In this paper, two methods including spray macro-characteristics experiment and separated fuel mass measurement are employed to test the hole-to-hole spray variation of two six-hole symmetric VCO injectors of different brands, and experiments are operated under different conditions including different injection pressures, back pressures and injection durations.

The purpose of this study was to investigate the pressure drop and regeneration characteristics of a silicon carbide (SiC) wall-flow diesel particulate filter. The performance of a 25 μm mean pore size SiC dual trap system (DTS) consisting of two 12 liter traps connected in parallel in conjunction with a copper (Cu) fuel additive was evaluated. A comparison between the 25 μm DTS and a 15 μm DTS was performed, in order to show the effect of trap material mean pore size on trap loading and regeneration behavior. A 1988 Cummins LTA 10-300 diesel engine was used to evaluate the performance of the 15 and 25 μm DTS. A mathematical model was developed to better understand the thermal and catalytic oxidation of the particulate matter. For all the trap steady-state loading tests, the engine was run at EPA mode 11 for 10 hours. Raw exhaust samples were taken upstream and downstream of the trap system in order to determine the DTS filtration efficiency.

For several years there has been a great deal of effort made in researching ways to run a compression ignition engine with simultaneously high efficiency and low emissions. Recently much of this focus has been dedicated to using gasoline-like fuels that are more volatile and less reactive than conventional diesel fuel to allow the combustion to be more premixed. One of the key challenges to using fuels with such properties in a compression ignition engine is stable engine operation at low loads. This paper provides an analysis of how stable gasoline compression ignition (GCI) engine operation was achieved down to idle speed and load on a multi-cylinder compression ignition engine using only 87 anti-knock index (AKI) gasoline. The variables explored to extend stable engine operation to idle included: uncooled exhaust gas recirculation (EGR), injection timing, injection pressure, and injector nozzle geometry.

As a result of increasingly stringent regulations and higher customer expectations, auto manufacturers have been considering numerous technology options to improve vehicle fuel economy. Transmissions have been shown to be one of the most cost-effective technologies for improving fuel economy. Over the past couple of years, transmissions have significantly evolved and impacted both performance and fuel efficiency. This study validates the shifting control of advanced automatic transmission technologies in vehicle systems by using Argonne National Laboratory's model-based vehicle simulation tool, Autonomie. Different midsize vehicles, including several with automatic transmission (6-speeds, 7-speeds, and 8-speeds), were tested at Argonne's Advanced Powertrain Research Facility (APRF). For the vehicles, a novel process was used to import test data.

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.