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A 2-D computational model was developed to describe the flow and filtration processes, in a honeycomb structured ceramic diesel particulate trap. This model describes the steady state trap loading, as well as the transient behavior of the flow and filtration processes. The theoretical model includes the effect of a copper fuel additive on trap loading and transient operation. The convective terms were based on a 2-D analytical flow field solution derived from the conservation of mass and momentum equations. The filtration theory incorporated in the time dependent numerical code included the diffusion, inertia, and direct interception mechanisms. Based on a measured upstream particle size distribution, using the filtration theory, the downstream particle size distribution was calculated. The theoretical filtration efficiency, based on particle size distribution, agreed very well (within 1%) with experimental data for a number of different cases.

A 2-D CFD model was developed to describe the heat transfer, and reaction kinetics in a honeycomb structured ceramic diesel particulate trap. This model describes the steady state as well as the transient behavior of the flow and heat transfer during the trap regeneration processes. The trap temperature profile was determined by numerically solving the 2-D unsteady energy equation including the convective, heat conduction and viscous dissipation terms. The convective terms were based on a 2-D analytical flow field solution derived from the conservation of mass and momentum equations (Opris, 1997). The reaction kinetics were described using a discretized first order Arrhenius function. The 2-D term describing the reaction kinetics and particulate matter conservation of mass was added to the energy equation as a source term in order to represent the particulate matter oxidation. The filtration model describes the particulate matter accumulation in the trap.

Power split in Fuel Cell Hybrid Electric Vehicles (FCHEVs) has been controlled using different strategies ranging from rule-based to optimal control. Dynamic Programming (DP) and Model Predictive Control (MPC) are two common optimal control strategies used in optimization of the power split in FCHEVs with a trade-off between global optimality of the solution and online implementation of the controller. In this paper, both control strategies are developed and tested on a FC/battery vehicle model, and the results are compared in terms of total energy consumption. In addition, the effects of the MPC prediction horizon length on the controller performance are studied. Results show that by using the DP strategy, up to 12% less total energy consumption is achieved compared to MPC for a charge sustaining mode in the Urban Dynamometer Driving Schedule (UDDS) drive cycle.

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.

Damping measurements on vehicle subsystems are rarely straightforward due to the complexity of the dynamic interaction of system joints, trim, and geometry. Various experimental techniques can be used for damping estimation, such as frequency domain modal analysis curve-fitting methods, time domain decay-rate methods, and other methods based on energy and wave propagation. Each method has its own set of advantages and drawbacks. This paper describes an analytical and an experimental comparison between two, widely used loss factor estimation techniques frequently used in Statistical Energy Analysis (SEA). The single subsystem Power Injection Method (PIM) and the Impulse Response Decay Method (IRDM) were compared using analytical models of a variety of simulated simple spring-mass-damper systems. Frequency averaged loss factor values were estimated from both methods for comparison.

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.

This paper presents an overview of the connected controls and optimization system for vehicle dynamics and powertrain operation on a light-duty plug-in multi-mode hybrid electric vehicle developed as part of the DOE ARPA-E NEXTCAR program by Michigan Technological University in partnership with General Motors Co. The objective is to enable a 20% reduction in overall energy consumption and a 6% increase in electric vehicle range of a plug-in hybrid electric vehicle through the utilization of connected and automated vehicle technologies. Technologies developed to achieve this goal were developed in two categories, the vehicle control level and the powertrain control level. Tools at the vehicle control level include Eco Routing, Speed Harmonization, Eco Approach and Departure and in-situ vehicle parameter characterization.

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.

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.

At recent American Society for Testing and Materials (ASTM) Subcommittee D02.01 meetings, committee members and attendees from the petroleum industry have reiterated a longstanding desire to see precision improvements to the ASTM D613 Standard Test Method for Cetane Number of Diesel Fuel Oil. The existing ASTM D613 precision limits were calculated using ASTM National Exchange Group (NEG) monthly test data from the mid-1970s through the early 1990s. Over the past few decades, many detailed studies were performed to identify and better understand the shortcomings of the cetane method (both engine equipment and instrumentation). Many of these studies concluded that inconsistent combustion is the main contributing factor behind the lack of precision in the cetane number method, followed by shortcomings in the instrumentation used to measure ignition delay.

Over the past few decades, numerous studies have been performed to investigate how to improve the precision of the ASTM D613 Standard Test Method for Cetane Number of Diesel Fuel Oil. Many of these studies concluded that inconsistent combustion is the main contributing factor behind the lack of precision in the cetane number method, followed by shortcomings in the instrumentation used to measure ignition delay. This study is a continuation of recent work that investigated the benefits of installing a high-pressure common rail electronic fuel injection (EFI) system onto a CFR F5 cetane engine. The previous work presented baseline engine measurements that compared EFI against the original mechanical fuel injection system, along with computational fluid dynamics (CFD) simulations of the EFI injection and combustion processes. The previous work also indicated EFI makes it possible to improve the current ASTM D613 cetane test precision limits by at least a factor of two.

Diesel engine fuel consumption is mainly a function of engine component design and power requirements. However, fuel consumption can also be affected by the environment in which the engine operates. This paper considers two controlling parameters of the engine's thermal environment, oil temperature and coolant temperature. The effects of oil and coolant temperatures on Brake Specific Fuel Consumption (BSFC) are established for a turbocharged diesel engine. Data are also presented for a direct injection, naturally aspirated diesel engine. A matrix of test conditions was run on a Cummins VT-903 diesel engine to evaluate the effects of oil and coolant temperatures on BSFC for several loads and speeds. Loads and speeds were selected based on where a typical semi-tractor engine would operate over the road on a hills and curves route. Oil temperature was monitored and controlled between the oil cooler and the engine. Coolant temperature was monitored and controlled at the engine outlet.

To meet future NO, heavy-duty diesel emissions standards, exhaust gas recirculation (EGR) technology is likely to be used. To improve fuel economy and further lower emissions, the recirculated exhaust gas needs to be cooled, with the possibility that cooling of the exhaust gas may form sulfuric acid condensate in the EGR cooler. This corrosive condensate can cause EGR cooler failure and consequentially result in severe damage to the engine. Both a literature review and a preliminary experimental study were conducted. In this study, a manually controlled EGR system was installed on a 1995 Cummins Ml l-330E engine which was operated at EPA mode 9* (1800 rpm and 75% load). The Goksoyr-Ross method (1)** was used to measure the particle-phase sulfate and vapor-phase H2SO4 and SO2 at the inlet and outlet locations of the EGR cooler, obtaining H2SO4 and SO2 concentrations. About 0.5% of fuel sulfur in the EGR cooler was in the particle-phase.

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.

Turbocharging, in addition to increasing an engine's power output, can be effectively used to maintain exhaust emission levels while improving fuel economy. This paper presents the emission and performance results obtained from a turbocharged multicylinder spark ignition engine with thermal reactors and exhaust gas recirculation (EGR) operated at steady-state, part-load conditions for four engine speeds. When comparing a turbocharged engine to a larger displacement naturally aspirated engine of equal power output, the emissions expressed in grams per mile were relatively unchanged both with and without EGR. However, turbocharging provided an average of 20% improvement in fuel economy both with and without EGR. When comparing the turbocharged and nonturbocharged versions of the same engine without EGR at a given load and speed, turbocharging increased the hydrocarbon (HC) and carbon monoxide (CO) emissions and decreased oxides of nitrogen (NOx) emissions.