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The fuel consumption and emissions of diesel engines is strongly influenced by the injection rate pattern, which influences the in-cylinder mixing and combustion process. Knowing the exact injection rate is mandatory for an optimal diesel combustion development. The short injection time of no more than some milliseconds prevents a direct flow rate measurement. However, the injection rate is deduced from the pressure change caused by injecting into a fuel reservoir or pipe. In an ideal case, the pressure increase in a fuel pipe correlates with the flow rate. Unfortunately, real measurement devices show measurement inaccuracies and errors, caused by non-ideal geometrical shapes as well as variable fuel temperature and fuel properties along the measurement pipe. To analyze the thermal effect onto the measurement results, an available rate measurement device is extended with a flexible heating system as well as multiple pressure and temperature sensors.

The High Voltage Power Distribution Unit (PDU) is constructed of magnesium in support of Fuel Cell Electric Vehicle (FCEV) weight reduction efforts. The PDU distributes and controls a nominal 75 kilowatts of power generated by the Fuel Cell, the primary source of High Voltage power, to all the vehicle loads and accessories. The constraints imposed on the design of the PDU resulted in a component highly susceptible to general and galvanic corrosion. Corrosion abatement was the focus of the PDU redesign. This paper describes the redesign efforts undertaken by Ford personnel to improve the part robustness and corrosion resistance.

The performance of three way and NOx catalysts was evaluated on vehicles utilizing non-feedback fuel control and electronic feedback fuel control. The vehicles accumulated 80,450 km (50,000 miles) using fuels representing the extremes in hydrogen-carbon ratio available for commercial use. Feedback carburetion compared to non-feedback carburetion improved highway fuel economy by about 0.4 km/l (1 mpg) and reduced deterioration of NOx with mileage accumulation. NOx emissions were higher with the low H/C fuel in the three way catalyst system; feedback reduced the fuel effect on NOx in these cars by improving conversion efficiency with the low H/C fuel. Feedback had no measureable effect on HC and CO catalyst efficiency. Hydrocarbon emissions were lower with the low H/C fuel in all cars. Unleaded gasoline octane improver, MMT, at 0.015g Mn/l (0.06 g/gal) increased tailpipe hydrocarbon emissions by 0.05 g/km (0.08 g/mile).

Ford Motor Company has recently implemented a Hardware-In-the-Loop (HIL) testing system for a new, highly complex, hybrid electric vehicle (HEV) Electronic Control Unit (ECU). The implementation of this HIL system has been quick and effective, since it is based on proven Commercial-Off-The-Shelf (COTS) automation tools for real-time that allow for a very flexible and intuitive design process. An overview of the HIL system implementation process and the derived development benefits will be shown in this paper. The initial concept for the use of this HIL system was a complete closed-loop vehicle simulation environment for Vehicle System Controller testing, but the paper will show that this concept has evolved to allow for the use of the HIL system for many facets of the design process.

The use of virtual prototyping early in the design stage of a product has gained popularity due to reduced cost and time to market. The state of the art in vehicle simulation has reached a level where full vehicles are analyzed through simulation but major difficulties continue to be present in interfacing the vehicle model with accurate powertrain models and in developing adequate formulations for the contact between tire and terrain (specifically, scenarios such as tire sliding on ice and rolling on sand or other very deformable surfaces). The proposed work focuses on developing a ground vehicle simulation capability by combining several third party packages for vehicle simulation, tire simulation, and powertrain simulation. The long-term goal of this project consists in promoting the Digital Car idea through the development of a reliable and robust simulation capability that will enhance the understanding and control of off-road vehicle performance.

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.

Two oxygenated fuels were evaluated on a single-cylinder diesel engine and compared to three hydrocarbon diesel fuels. The oxygenated fuels included canola biodiesel (canola methyl esters, CME) and CME blended with dibutyl succinate (DBS), both of which are or have the potential to be bio-derived. DBS was added to improve the cold flow properties, but also reduced the cetane number and net heating value of the resulting blend. A 60-40 blend of the two (60% vol CME and 40% vol DBS) provided desirable cold flow benefits while staying above the U.S. minimum cetane number requirement. Contrary to prior vehicle test results and numerous literature reports, single-cylinder engine testing of both CME and the 60-40 blend showed no statistically discernable change in NOx emissions relative to diesel fuel, but only when constant intake oxygen was maintained.

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.

Combustion in modern spark-ignition (SI) engines is increasingly knock-limited with the wide adoption of downsizing and turbocharging technologies. Fuel autoignition conditions are different in these engines compared to the standard Research Octane Number (RON) and Motor Octane Numbers (MON) tests. The Octane Index, OI = RON - K(RON-MON), has been proposed as a means to characterize the actual fuel anti-knock performance in modern engines. The K-factor, by definition equal to 0 and 1 for the RON and MON tests respectively, is intended to characterize the deviation of modern engine operation from these standard octane tests. Accurate knowledge of K is of central importance to the OI model; however, a single method for determining K has not been well accepted in the literature.

With concerns over increasing worldwide demand for gasoline and greenhouse gases, many automotive companies are increasing their product lineup of vehicles to include flex-fuel vehicles that are capable of operating on fuel blends ranging from 100% gasoline up to a blend of 15% gasoline/85% ethanol (E85). For the purpose of this paper, data was obtained that will enable an evaluation relating to the effect the use of E85 fuel has on exhaust system surface temperatures compared to that of regular unleaded gasoline while the vehicle undergoes a typical drive cycle. Three vehicles from three different automotive manufacturers were tested. The surface of the exhaust systems was instrumented with thermocouples at specific locations to monitor temperatures from the manifold to the catalytic converter outlet. The exhaust system surface temperatures were recorded during an operation cycle that included steady vehicle speed operation; cold start and idle and wide open throttle conditions.

In a recent study, quantitative measurements were presented of in-cylinder spatial distributions of mixture equivalence ratio in a single-cylinder light-duty optical diesel engine, operated with a non-reactive mixture at conditions similar to an early injection low-temperature combustion mode. In the experiments a planar laser-induced fluorescence (PLIF) methodology was used to obtain local mixture equivalence ratio values based on a diesel fuel surrogate (75% n-heptane, 25% iso-octane), with a small fraction of toluene as fluorescing tracer (0.5% by mass). Significant changes in the mixture's structure and composition at the walls were observed due to increased charge motion at high swirl and injection pressure levels. This suggested a non-negligible impact on wall heat transfer and, ultimately, on efficiency and engine-out emissions.

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).

Designing a durability test for an automatic transmission that appropriately reflects customer usage during the lifetime of the vehicle is a formidable task; while the transmission and its components must survive severe usage, overdesigning components leads to unnecessary weight, increased fuel consumption and increased emissions. Damage to transmission components is a function of many parameters including customer driving habits and vehicle and transmission characteristics such as weight, powertrain calibration, and gear ratios. Additionally, in some cases durability tests are required to verify only a subset of the total parameter space, for example, verifying only component modifications. Lastly, the ideal durability test is designed to impose the worst case loading conditions for the maximum number of internal components, be as short as practicable to reduce testing time, with minimal variability between tests in order to optimize test equipment and personnel resources.

A method of predicting brake specific fuel consumption characteristics from limited specifications of engine design has been investigated. For spark ignition engines operating on homogeneous mixtures, indicated specific fuel consumption based on gross indicated power is related to compression ratio and spark timing relative to optimum values. The influence of burn rate is approximately accounted for by the differences in spark timings required to correctly phase combustion. Data from engines of contemporary design shows that indicated specific fuel consumption can be defined as a generic function of relative spark timing, mixture air/fuel ratio and exhaust gas recirculation rate. The additional information required to generate brake specific performance maps is cylinder volumetric efficiency, rubbing friction, auxiliary loads, and exhaust back pressure characteristics.

To assess the fuel consumption of vehicles, three sets of input data are required; drive cycles, vehicle parameters, and environmental conditions. As the first part of a series of studies on real-world fuel consumption, this study focuses on the drive cycles. In principle, drive cycles should represent real-world usage. Some of them aim at a specific usage such as a city driving condition or an aggressive driving style. However, the definition of city or aggressive driving is very subjective and difficult to quantitatively correlate with the real-world usage. This study proposes a methodology to quantify the speed and dynamics of drive cycles, or vehicle speed traces in general, against the real-world usage. After reviewing parameter sets found in other studies, relative cubic speed (RCS) and positive kinetic energy (PKE) are selected to represent the speed and dynamics through energy flow balance at the wheels.

Subjective steering feel tuning and objective verification tests are conducted on vehicle prototypes that are a subset of the total number of buildable combinations of body style, drivetrain and tires. Limited development time, high prototype vehicle cost, and hence limited number of available prototypes are factors that affect the ability to tune and verify all the possible configurations. A new model-based process and a toolset have been developed to enhance the existing steering development process such that steering tuning efficiency and performance robustness can be improved. The innovative method utilizes the existing vehicle dynamics simulation and/or physical test data in conjunction with steering system control models, and provides users with simple interfaces which can be used by either CAE or development engineers to perform virtual tuning of the vehicle steering feel to meet performance targets.

With an emerging need for gasoline particulate filters (GPFs) to lower particle emissions from gasoline direct injection (GDI) engines, studies are being conducted to optimize GPF designs in order to balance filtration efficiency, backpressure penalty, filter size, cost and other factors. Metal fiber filters could offer additional designs to the GPF portfolio, which is currently dominated by ceramic wall-flow filters. However, knowledge on their performance as GPFs is still limited. In this study, modeling on backpressure and filtration efficiency of fibrous media was carried out to determine the basic design criteria (filtration area, filter thickness and size) for different target efficiencies and backpressures at given gas flow conditions. Filter media with different fiber sizes (8 - 17 μm) and porosities (80% - 95%) were evaluated using modeling to determine the influence of fiber size and porosity.

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 oil distribution system of an automotive light duty engine typically has an oil pump mechanically driven through the front-endancillaries-drive or directly off the crankshaft. Delivery pressure is regulated by a relief valve to provide an oil gallery pressure of typically 3 to 4 bar absolute at fully-warm engine running conditions. Electrification of the oil pump drive is one way to decouple pump delivery from engine speed, but this does not alter the flow distribution between parts of the engine requiring lubrication. Here, the behaviour and benefits of a system with an electrically driven, fixed displacement pump and a distributor providing control over flow to crankshaft main bearings and big end bearings is examined. The aim has been to demonstrate that by controlling flow to these bearings, without changing flow to other parts of the engine, significant reductions in engine friction can be achieved.