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A computational fluid dynamics (CFD) guided combustion system optimization was conducted for a heavy-duty compression-ignition engine with a gasoline-like fuel that has an anti-knock index (AKI) of 58. The primary goal was to design an optimized combustion system utilizing the high volatility and low sooting tendency of the fuel for improved fuel efficiency with minimal hardware modifications to the engine. The CFD model predictions were first validated against experimental results generated using the stock engine hardware. A comprehensive design of experiments (DoE) study was performed at different operating conditions on a world-leading supercomputer, MIRA at Argonne National Laboratory, to accelerate the development of an optimized fuel-efficiency focused design while maintaining the engine-out NOx and soot emissions levels of the baseline production engine.

Achieving robust ignitability for compression ignition of diesel engines at cold conditions is traditionally challenging due to insufficient fuel vaporization, heavy wall impingement, and thick wall films. Gasoline compression ignition (GCI) has shown the potential to offer an enhanced NOx-particulate matter tradeoff with diesel-like fuel efficiency, but it is unknown how the volatility and reactivity of the fuel will affect ignition under very cold conditions. Therefore, it is important to investigate the impact of fuel physical and chemical properties on ignition under pressures and temperatures relevant to practical engine operating conditions during cold weather. In this paper, 0-D and 3-D computational fluid dynamics (CFD) simulations of GCI combustion at cold conditions were performed.

The purpose of this research was to predict the amount of wear on exhaust valve seats in durability testing of gasoline engines. Through the rig wear test, a prediction formula was constructed with multiple factors as variables. In the rig test, the wear rate was measured in some cases where a number of factors of valve seat wear were within a certain range. Through these tests, sensitivity for each factor was determined from the measured wear data, and then a prediction formula for calculating the amount of wear was constructed with high sensitivity factors. Combining the wear amount calculation formula with the operation mode of the actual engine, the wear amount in that mode can be calculated. The calculated wear amount showed a high correlation with the wear amount measured in bench tests and the wear amount measured in vehicle tests.

Cyclic variability in internal combustion engines (ICEs) arises from multiple concurrent sources, many of which remain to be fully understood and controlled. This variability can, in turn, affect the behavior of the engine resulting in undesirable deviations from the expected operating conditions and performance. Shot-to-shot variation during the fuel injection process is strongly suspected of being a source of cyclic variability. This study focuses on the shot-to-shot variability of injector needle motion and its influence on the internal nozzle flow behavior using diesel fuel. High-speed x-ray imaging techniques have been used to extract high-resolution injector geometry images of the sac, orifices, and needle tip that allowed the true dynamics of the needle motion to emerge. These measurements showed high repeatability in the needle lift profile across multiple injection events, while the needle radial displacement was characterized by a much higher degree of randomness.

The process of spray atomization has typically been understood in terms of the Rayleigh-Taylor instability theory. However, this mechanism has failed to fully explain much of the measured data. For this reason a number of new atomization mechanisms have been proposed. The present study intends to gain an understanding of the spray dynamics and breakup processes in the near-nozzle region of heavy-duty diesel injector sprays. As this region is optically dense, synchrotron x-rays were used to gain new insights. This spray study was performed using a prototype common-rail injection system, by injecting a blend of diesel fuel and cerium-containing organometalic compound into a chamber filled with nitrogen at 1 atm. The x-rays were able to probe the dense region of the spray as close as 0.2 mm from the nozzle. These x-ray images showed two interesting features. The first was a breakup of the high density region about 22 μs After the Start Of Injection (ASOI).

In June of 2002, 15 universities participated in the third year of FutureTruck, an advanced vehicle competition sponsored by the U.S. Department of Energy and Ford Motor Company. Using advanced technologies, teams strived to improve vehicle energy efficiency by at least 25%, reduce tailpipe emissions to ULEV levels, and lower greenhouse gas impact of a 2002 Ford Explorer. The competition vehicles were tested for dynamic performance and emissions and were judged in static events to evaluate the design and features of the vehicle. The dynamic events include braking, acceleration, handling, and fuel economy, while the dynamometer testing provided data for both the emissions event and the greenhouse gas event. The vehicles were scored for their performance in each event relative to each other; those scores were summed to determine the winner of the competition. The competition structure included different available fuels and encouraged the use of hybrid electric drivetrains.

A heavy-duty vehicle (HDV) module of the Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREETTM) model has been developed at Argonne National Laboratory. The fuel-cycle GREET model has been published extensively and contains data on fuel-cycles and vehicle operation of light-duty vehicles. The addition of the HDV module to the GREET model allows for well-to-wheel (WTW) analyses of heavy-duty advanced technology and alternative fuel vehicles (AFVs), which has been lacking in the literature. WTW analyses of HDVs becomes increasingly important to understand the fuel consumption and greenhouse gas (GHG) emissions impacts of newly enacted and future HDV regulations from the Environmental Protection Agency and the Department of Transportation’s National Highway Traffic Safety Administration.

To restrain the environmental and energy problems caused by oil consumption and improve fuel economy of heavy-duty commercial vehicles, China started developing relevant standards from 2008. This paper introduces the background and development of China's national standard “Fuel consumption test methods for heavy-duty commercial vehicles”, and mainly describes the test method schemes, driving cycle and weighting factors for calculating average fuel consumption of various vehicle categories. The standard applies to heavy-duty vehicles with the maximum design gross mass greater than 3500 kg, including semi-trailer tractors, common trucks, dump trucks, city buses and common buses. The standard adopts the C-WTVC driving cycle which is adjusted on the basis of the World Transient Vehicle Cycle[1, 2] and specifies weighting factors of urban, rural and motorway segments for different vehicle categories.

A key strategy to improving the real-world fuel consumption and emissions of medium and heavy duty vehicles is the hybridization of these applications. Unlike the passenger vehicle market, medium and heavy duty applications are typically comprised of a range of components from a variety of manufacturers. The vocational market diversity and size places considerable demand on fuel efficiency and emission compliance. Medium and heavy duty applications have the ability to be successfully hybridized in ways that are not currently, or would not be practical within a passenger vehicle. This would also drive greater truck and bus vertical integration of the hybrid components. However, medium and heavy duty manufacturers have been prevented from certifying a full vehicle level platform due to the current engine only certification requirements.

This paper discusses the development of an integrated tool for the design, optimization, and real-time control of engines from a performance and emissions standpoint. Our objectives are threefold: (1) develop a tool that computes the engine performance and emissions on the order of a typical engine cycle (25-50 milliseconds); (2) enable the use of the tool for a wide variety of engine geometries, operating conditions, and fuels with minimal user changes; and (3) couple the engine module to an efficient optimization module to enable real-time control and optimization. The design tool consists of two coupled modules: an engine module and an optimization module.

With rising oil prices, the issue of energy economy in transportation is getting much attention. At the same time, new emissions standards for tractor-trailer vehicles introduce additional challenges for the manufacturers to achieve improvements in vehicle fuel economy. As part of the U.S. Department of Energy Office of FreedomCAR and Vehicle Technologies' Heavy Vehicle Aerodynamic Drag Consortium, Argonne National Laboratory is currently developing guidelines for the use of commercial computational fluid dynamics (CFD) software to facilitate energy efficiency improvements through improved aerodynamic design of tractor-trailer vehicles. The development of these guidelines requires the consideration of the sensitivity of the accuracy of the analysis to the various modeling choices available to the end user.

This paper describes a configuration and controller, designed using Autonomie,1 for dual-motor battery electric vehicle (BEV) heavy-duty trucks. Based on the literature and current market research, this model was designed with two electric motors, one on the front axle and the other on the rear axle. A rule-based control algorithm was designed for the new dual-motor BEV, based on the model, and the control parameters were optimized by using a genetic algorithm (GA). The model was simulated in diverse driving cycles and gradeability tests. The results show both a good following of the desired cycle and achievement of truck gradeability performance requirements. The simulation results were compared with those of a single-motor BEV and showed reduced energy consumption with the high-efficiency operation of the two motors.

In recent years, electrification of powertrains has been promoted to improve fuel efficiency and CO2 emissions. Along with electrification, it is possible to reduce engine usage frequency and improve the fuel efficiency in traveling. Especially in a hybrid electric vehicle (HEV), the state changes from motor assist mode to engine firing mode. As a result, stay time in eigenvalue of a powertrain is shortened, and vibration of the vehicle body at the engine start situation is able to be reduced as compared with conventional engine-driven vehicle. However, since the HEV is equipped with a high compression ratio engine for improving fuel economy, there is cause for concern that excitation force generated by the powertrain at the time of engine start increases. Also, the vehicle body vibration at engine start situations requires further consideration, because the operation frequency of engine decreases.

A simulation approach is defined that integrates a military mission assessment tool (One Semi-Automated Forces) with a commercial automotive control/energy consumption development tool (Autonomie). The objective is to enable vehicle energy utilization and fuel consumption impact assessments relative to US Army mission effectiveness and commercial drive cycles. The approach to this integration will be described, along with its potential to meet its objectives.

The objective of this study was to evaluate the fuel saving potential of various hybrid powertrain architectures for medium and heavy duty vehicles. The relative benefit of each powertrain was analyzed, and the observed fuel savings was explained in terms of operational efficiency gains, regenerative braking benefits from powertrain electrification and differences in vehicle curb weight. Vehicles designed for various purposes, namely urban delivery, utility, transit, refuse, drayage, regional and long haul were included in this work. Fuel consumption was measured in regulatory cycles and various real world representative cycles. A diesel-powered conventional powertrain variant was first developed for each case, based on vehicle technical specifications for each type of truck. Autonomie, a simulation tool developed by Argonne National Laboratory, was used for carrying out the vehicle modeling, sizing and fuel economy evaluation.

Dual-fuel combustion using port-injected gasoline with a direct diesel injection has been shown to achieve low-temperature combustion with moderate peak pressure rise rates, low engine-out soot and NOx emissions, and high indicated thermal efficiency. A key requirement for extending high-load operation is moderating the reactivity of the premixed charge prior to the diesel injection. Reducing compression ratio, in conjunction with a higher expansion ratio using alternative valve timings, decreases compressed charge reactivity while maintain a high expansion ratio for maximum work extraction. Experimental testing was conducted on a 13L multi-cylinder heavy-duty diesel engine modified to operate dual-fuel combustion with port gasoline injection to supplement the direct diesel injection. The engine employs intake variable valve actuation (VVA) for early (EIVC) or late (LIVC) intake valve closing to yield reduced effective compression ratio.

A computational fluid dynamics (CFD) guided combustion system optimization was conducted for a heavy-duty diesel engine running with a gasoline fuel that has a research octane number (RON) of 80. The goal was to optimize the gasoline compression ignition (GCI) combustion recipe (piston bowl geometry, injector spray pattern, in-cylinder swirl motion, and thermal boundary conditions) for improved fuel efficiency while maintaining engine-out NOx within a 1-1.5 g/kW-hr window. The numerical model was developed using the multi-dimensional CFD software CONVERGE. A two-stage design of experiments (DoE) approach was employed with the first stage focusing on the piston bowl shape optimization and the second addressing refinement of the combustion recipe. For optimizing the piston bowl geometry, a software tool, CAESES, was utilized to automatically perturb key bowl design parameters. This led to the generation of 256 combustion chamber designs evaluated at several engine operating conditions.

The US Department of Energy’s Co-Optimization of Engine and Fuels Initiative (Co-Optima) investigated how unique properties of bio-blendstocks considered within Co-Optima help address emissions challenges with mixing controlled compression ignition (i.e., conventional diesel combustion) and enable advanced compression ignition modes suitable for implementation in a diesel engine. Additionally, the potential synergies of these Co-Optima technologies in hybrid vehicle applications in the medium- and heavy-duty sector was also investigated. In this work, vehicles system were simulated using the Autonomie software tool for quantifying the benefits of Co-Optima engine technologies for medium-duty trucks. A Class 6 delivery truck with a 6.7 L diesel engine was used for simulations over representative real-world and certification drive cycles with four different powertrains to investigate fuel economy, criteria emissions, and performance.

Fan and fan-shroud design is critical for underhood air flow management. The objective of this work is to demonstrate a method to optimize fan-shroud shape in order to maximize cooling air mass flow rates through the heat exchangers using the Adjoint Solver in STAR-CCM+®. Such techniques using Computational Fluid Dynamics (CFD) analysis enable the automotive/transport industry to reduce the number of costly experiments that they perform. This work presents the use of CFD as a simulation tool to investigate and assess the various factors that can affect the vehicle thermal performance. In heavy-duty trucks, the cooling package includes heat exchangers, fan-shroud, and fan. In this work, the STAR-CCM+® solver was selected and a java macro built to run the primal flow and the Adjoint solutions sequentially in an automated fashion.

This paper presents a comprehensive analysis of emerging powertrain technologies for a wide spectrum of vehicles, ranging from light-duty passenger vehicles to medium and heavy-duty trucks. The study focuses on the anticipated evolution of these technologies over the coming decades, assessing their potential benefits and impact on sustainability. The analysis encompasses simulations across a wide range of vehicle classes, including compact, midsize, small SUVs, midsize SUVs, and pickups, as well as various truck types, such as class 4 step vans, class 6 box trucks, and class 8 regional and long-haul trucks. It evaluates key performance metrics, including fuel consumption, estimated purchase price, and total cost of ownership, for these vehicles equipped with advanced powertrain technologies such as mild hybrid, full hybrid, plug-in hybrid, battery electric, and fuel cell powertrains.