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A prototype 2007 ISL Cummins diesel engine equipped with a diesel oxidation catalyst (DOC), diesel particle filter (DPF), variable geometry turbocharger (VGT), and cooled exhaust gas recirculation (EGR) was tested at Southwest Research Institute (SwRI) under a high-load accelerated durability cycle for 1000 hours with B20 soy-based biodiesel blends and ultra-low sulfur diesel (ULSD) fuel to determine the impact of B20 on engine durability, performance, emissions, and fuel consumption. At the completion of the 1000-hour test, a thorough engine teardown evaluation of the overhead, power transfer, cylinder, cooling, lube, air handling, gaskets, aftertreatment, and fuel system parts was performed. The engine operated successfully with no biodiesel-related failures. Results indicate that engine performance was essentially the same when tested at 125 and 1000 hours of accumulated durability operation.

Until recently, the application of the detailed chemistry approach as a predictive tool for engine modeling has been sort of a “taboo” for different reasons, mainly because of an exaggerated rigor to the chemistry/turbulence interaction modeling. In terms of this ideology, if the interaction cannot be simulated properly, the detailed chemistry approach makes no sense. The novelty of the proposed methodology is the coupling of a generalized partially stirred reactor, PaSR, model with the high efficiency numerics to treat detailed oxidation kinetics of hydrocarbon fuels. In terms of this approach, chemical processes are assumed to proceed in two successive steps: the reaction follows after the micro-mixing is completed on a sub-grid scale.

An investigation of the possibility to extend the 3-dimensional modeling capabilities from conventional diesel to the HCCI combustion mode simulation was carried out. Experimental data was taken from a single cylinder engine operating with early injections for the HCCI and a split-injection (early pilot+main) for the high speed Diesel engine operation. To properly phase the HCCI mode in the experiments, high amounts of cooled EGR and a decreased compression ratio were used. In numerical simulation performed using KIVA3-V code, modified to incorporate the Detailed Chemistry Approach the same conditions were reproduced. Special attention is paid on the analysis of the events leading up to the auto-ignition, which was reasonably well predicted.

48V mild hybrid powertrains are promising technologies for cost-effective compliance with future CO2 emissions standards. Current 48V powertrains with integrated belt starter generators (P0) with downsized engines achieve CO2 emissions of 95 g/km in the NEDC. However, to reach 75 g/km, it may be necessary to combine new 48V powertrain architectures with alternative fuels. Therefore, this paper compares CO2 emissions from different 48V powertrain architectures (P0, P1, P2, P3) with different electric power levels under various driving cycles (NEDC, WLTC, and RTS95). A numerical model of a compact class passenger car with a 48V powertrain was created and experimental fuel consumption maps for engines running on different fuels (gasoline, Diesel, E85, CNG) were used to simulate its CO2 emissions. The simulation results were analysed to determine why specific powertrain combinations were more efficient under certain driving conditions.

The current work investigates a method in 1D modeling of cooling systems including discretized cooling package with non-uniform boundary conditions. In a stacked cooling package the heat transfer through each heat exchanger depends on the mass flows and temperature fields. These are a result of complex three-dimensional phenomena, which take place in the under-hood and are highly non-uniform. A typical approach in 1D simulations is to assume these to be uniform, which reduces the authenticity of the simulation and calls for additional calibrations, normally done with input from test measurements. The presented work employs 3D CFD simulations of complete vehicle in STAR-CCM+ to perform a comprehensive study of mass-flow and thermal distribution over the inlet of the cooling package of a Volvo FM commercial vehicle in several steady-state operating points.

The emerging need of building an efficient Electric Vehicle (EV) charging infrastructure requires the investigation of all aspects of Vehicle-Grid Integration (VGI), including the impact of EV charging on the grid, optimal EV charging control at scale, and communication interoperability. This paper presents a cloud-based simulation and testing platform for the development and Hardware-in-the-Loop (HIL) testing of VGI technologies. Although the HIL testing of a single charging station has been widely performed, the HIL testing of spatially distributed EV charging stations and communication interoperability is limited. To fill this gap, the presented platform is developed that consists of multiple subsystems: a real-time power system simulator (OPAL-RT), ISO 15118 EV Charge Scheduler System (EVCSS), and a Smart Energy Plaza (SEP) with various types of charging stations, solar panels, and energy storage systems.

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.

Combustion with knock is an abnormal phenomenon which constrains the engine performance, thermal efficiency and longevity. The advance timing of the ignition system requires it to be updated with respect to fuel octane number variation. The production series engines are calibrated by the manufacturer to run with a special fuel octane number. In the experiment, the engine was operated at different speeds, loads, spark advance timings and consumed commercial gasoline with research octane numbers (RON) 95, 97 and 100. A 1-dimensional validated engine combustion model was run in the GT-Power software to simulate the engine conditions required to define the knock envelope at the same engine operation conditions as experiment. The knock intensity investigation due to spark advance sweep shows that combustion with noise was started after a specific advance ignition timing and the audible knock occur by increasing the advance timing.

Heavy alcohols can be mixed with fossil diesel to produce blended fuels that can be used in diesel engines. Alcohols can be obtained from fossil resources, but can also be produced more sustainably from renewable raw materials. The use of such biofuels can help to reduce greenhouse gas (GHG) emissions from the transport sector. This study examines four alcohol/diesel blends each containing one heavy alcohol: n-butanol, iso-butanol, 2-ethyl hexanol and n-octanol. All of the blends where prepared to function as drop-in fuels in existing engines with factory settings. To compensate for the alcohols′ low cetane numbers (CN), a third component with high CN was added to each blend, namely hydrotreated vegetable oil (HVO). The composition of each mixture was selected to give an overall CN equal to that of fossil diesel fuel.

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.

Automotive aerodynamics measurements and simulations now routinely use a moving ground and rotating wheels (MVG&RW), which is more representative of on-road conditions than the fixed ground-fixed wheel (FG&FW) alternative. This can be understood as a combination of three elements: (a) moving ground (MVG), (b) rotating front wheels (RWF) and (c) rotating rear wheels (RWR). The interaction of these elements with the flow field has been explored to date by mainly experimental means. This paper presents a mainly computational (CFD) investigation of the effect of RWF and RWR, in combination with MVG, on the flow field around a saloon vehicle. The influence of MVG&RW is presented both in terms of a combined change from a FG&FW baseline and the incremental effects seen by the addition of each element separately. For this vehicle, noticeable decrease in both drag and rear lift is shown when adding MVG&RW, whereas front lift shows little change.

A Vehicle-Engine-Cooling (VEC) system computer simulation model was used to study the transient performance of control devices and their temperature settings on oil, coolant and cab temperatures. The truck used in the study was an International Harvester COF-9670 cab over chassis heavy-duty vehicle equipped with a standard cab heater, a Cummins NTC-350 diesel engine with a McCord radiator and standard cooling system components and aftercooler. Input data from several portions of a Columbus to Bloomington, Indiana route were used from the Vehicle Mission Simulation (VMS) program to determine engine and vehicle operating conditions for the VEC system computer simulation model. The control devices investigated were the standard thermostat, the Kysor fan-clutch and shutter system. The effect of shutterstat location on shutter performance along with thermostat, shutter and fan activation temperature settings were investigated for ambient temperatures of 32, 85 and 100°F.

The three-way-catalyst (TWC) is an essential part of the exhaust aftertreatment system in spark-ignited powertrains, converting nearly all toxic emissions to harmless gasses. The TWC’s conversion efficiency is significantly temperature-dependent, and cold-starts can be the dominating source of emissions for vehicles with frequent start/stops (e.g. hybrid vehicles). In this paper we develop a thermal TWC model and calibrate it with experimental data. Due to the few number of state variables the model is well suited for fast offline simulation as well as subsequent on-line control, for instance using non-linear state-feedback or explicit MPC. Using the model could allow an on-line controller to more optimally adjust the engine ignition timing, the power in an electric catalyst pre-heater, and/or the power split ratio in a hybrid vehicle when the catalyst is not completely hot.

A common fan model to use in automotive under hood simulations is the Multiple Reference Frame (MRF) model and within the industry, for this specific application, this model is well known to under predict performance. In this paper we have examined the possibilities of correcting this deficiency with a simple “speed correction”. This is done by testing and simulating a production fan in the Volvo Fan Test Rig for two operating speeds, 1200 rpm and 2400 rpm. Pressure rise, fan power and static efficiency are presented as functions of volumetric flow rate. The simulations verify that using the MRF model the common behavior of under predicting pressure rise and performance of the fan occur. In addition, this work shows that; although the MRF is not predicting fan performance correctly it constitutes a reliable fan modeling strategy.

A diesel engine model, designed for studying events during automated gear shifting in a heavy duty truck is presented. It will be used for developing and evaluating powertrain control strategies. The deceleration in engine speed to the new synchronous speed, during an upshift, is of special intereset. The straightforward approach is to cut fuel and wait for the engine to slow down due to friction and pumping losses. In many cases, this approach is too slow, and the engine compression brake needs to be activated. The engine model, assuming quasi-steady, bidirectional thermodynamic flow with constant specific heat capacities, is implemented using Modelica. A simple model of the hydraulic circuit that governs the activation of the compression brake mode is incorporated in the model. Problems related to the simulation of the engine brake systems are discussed. They are handled by empirical correction factors. Measurements from rapid engine speed decelerations are used for verification.

A single cylinder, naturally aspirated, four-stroke and camless (Otto) engine was operated in homogeneous charge compression ignition (HCCI) mode with commercial gasoline. The valve timing could be adjusted during engine operation, which made it possible to optimize the HCCI engine operation for different speed and load points in the part-load regime of a 5-cylinder 2.4 liter engine. Several tests were made with differing combinations of speed and load conditions, while varying the valve timing and the inlet manifold air pressure. Starting with conventional SI combustion, the negative valve overlap was increased until HCCI combustion was obtained. Then the influences of the equivalence ratio and the exhaust valve opening were investigated. With the engine operating on HCCI combustion, unthrottled and without preheating, the exhaust valve opening, the exhaust valve closing and the intake valve closing were optimized next.

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).

The compression ratio is an important engine design parameter. It determines to a large extend engine properties like the achievable efficiency, the heat losses from the combustion chamber and the exhaust losses. The same properties are affected by insulation of the combustion chamber. It is therefore especially important to know the compression ratio when doing experiments with thermal barrier coatings (TBC). In case of porous TBCs, the standard methods to measure the compression ratio can give wrong results. When measuring the compression ratio by volume, using a liquid, it is uncertain if the liquid fills the total porous volume of the coating. And for a thermodynamic compression ratio estimation, a model for the heat losses is needed, which is not available when doing experiments with insulation. The subject of this paper is the evaluation of an alternative method to assess the compression ratio.