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Hydrogen is widely considered a promising fuel for future transportation applications for both, internal combustion engines and fuel cells. Due to their advanced stage of development and immediate availability hydrogen combustion engines could act as a bridging technology towards a wide-spread hydrogen infrastructure. Although fuel cell vehicles are expected to surpass hydrogen combustion engine vehicles in terms of efficiency, the difference in efficiency might not be as significant as widely anticipated [1]. Hydrogen combustion engines have been shown capable of achieving efficiencies of up to 45 % [2]. One of the remaining challenges is the reduction of nitric oxide emissions while achieving peak engine efficiencies. This paper summarizes research work performed on a single-cylinder hydrogen direct injection engine at Argonne National Laboratory.

Diesel spray experimentation at controlled high-temperature and high-pressure conditions is intended to provide a more fundamental understanding of diesel combustion than can be achieved in engine experiments. This level of understanding is needed to develop the high-fidelity multi-scale CFD models that will be used to optimize future engine designs. Several spray chamber facilities capable of high-temperature, high-pressure conditions typical of engine combustion have been developed, but because of the uniqueness of each facility, there are uncertainties about their operation. For this paper, we describe results from comparative studies using constant-volume vessels at Sandia National Laboratories and IFP.

For a series plug-in hybrid electric vehicle (PHEV), it is critical that batteries be sized to maximize vehicle performance variables, such as fuel efficiency, gasoline savings, and zero emission capability. The wide range of design choices and the cost of prototype vehicles calls for a development process to quickly and systematically determine the design characteristics of the battery pack, including its size, and vehicle-level control parameters that maximize the net present value (NPV) of a vehicle during the planning stage. Argonne National Laboratory has developed Autonomie, a modeling and simulation framework. With support from The MathWorks, Argonne has integrated an optimization algorithm and parallel computing tools to enable the aforementioned development process. This paper presents a study that utilized the development process, where the NPV is the present value of all the future expenses and savings associated with the vehicle.

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.

This paper includes analysis results for the control strategy of the Peugeot 3008 Hybrid4, a diesel-electric hybrid vehicle, under different thermal conditions. The analysis was based on testing results obtained under the different thermal conditions in the Advanced Powertrain Research Facility (APRF) at Argonne National Laboratory (ANL). The objectives were to determine the principal concepts of the control strategy for the vehicle at a supervisory level, and to understand the overall system behavior based on the concepts. Control principles for complex systems are generally designed to maximize the performance, and it is a serious challenge to determine these principles without detailed information about the systems. By analyzing the test results obtained in various driving conditions with the Peugeot 3008 Hybrid4, we tried to figure out the supervisory control strategy.

Response surface methodology (RSM) techniques were applied to develop a predictive model of electric vehicle (EV) energy consumption over the Environmental Protection Agency's (EPA) standardized drive cycles. The model is based on measurements from a synthetic composite drive cycle. The synthetic drive cycle is a minimized statistical composite of the standardized urban (UDDS), highway (HWFET), and US06 cycles. The composite synthetic drive cycle is 20 minutes in length thereby reducing testing time of the three standard EPA cycles by over 55%. Vehicle speed and acceleration were used as model inputs for a third order least squared regression model predicting vehicle battery power output as a function of the drive cycle. The approach reduced three cycles and 46 minutes of drive time to a single test of 20 minutes.

Manufacturers have been considering various technology options to improve vehicle fuel economy. Some of the most promising technologies are related to vehicle electrification. To evaluate the benefits of vehicle electrification to support the 2017-2025 CAFE regulations, a study was conducted to simulate many of the most common electric drive powertrains currently available on the market: 12V Micro Hybrid Vehicle (start/stop systems), Belt-integrated starter generator (BISG), Crank-integrated starter generator (CISG), Full Hybrid Electric Vehicle (HEV), PHEV with 20-mile all-electric range (AER) (PHEV20), PHEV with 40-mile AER (PHEV40), Fuel-cell HEV and Battery Electric vehicle with 100-mile AER (EV100). Different vehicle classes were also analyzed in the study process: Compact, Midsize, Small SUV, Midsize SUV and Pickup. This paper will show the fuel displacement benefit of each powertrain across vehicle classes.

This paper first summarizes a new theoretical description that quantifies the effects of real-fluid thermodynamics on liquid fuel injection processes as a function of pressure at typical engine operating conditions. It then focuses on the implications this has on modeling such flows with emphasis on application of the Large Eddy Simulation (LES) technique. The theory explains and quantifies the major differences that occur in the jet dynamics compared to that described by classical spray theory in a manner consistent with experimental observations. In particular, the classical view of spray atomization as an appropriate model at some engine operating conditions is questionable. Instead, non-ideal real-fluid behavior must be taken into account using a multicomponent formulation that applies to hydrocarbon mixtures at high-pressure supercritical conditions.

This paper implements a coupled approach to integrate the internal nozzle flow and the ensuing fuel spray using a Volume-of-Fluid (VOF) method in the CONVERGE CFD software. A VOF method was used to model the internal nozzle two-phase flow with a cavitation description closed by the homogeneous relaxation model of Bilicki and Kestin [1]. An Eulerian single velocity field approach by Vallet et al. [2] was implemented for near-nozzle spray modeling. This Eulerian approach considers the liquid and gas phases as a complex mixture with a highly variable density to describe near nozzle dense sprays. The mean density is obtained from the Favreaveraged liquid mass fraction. The liquid mass fraction is transported with a model for the turbulent liquid diffusion flux into the gas.

We investigate the mixing, penetration, and ignition characteristics of high-pressure n-dodecane sprays having a split injection schedule (0.5/0.5 dwell/0.5 ms) in a pre-burn combustion vessel at ambient temperatures of 750 K, 800 K and 900 K. High-speed imaging techniques provide a time-resolved measure of vapor penetration and the timing and progression of the first- and second-stage ignition events. Simultaneous single-shot planar laser-induced fluorescence (PLIF) imaging identifies the timing and location where formaldehyde (CH2O) is produced from first-stage ignition and consumed following second-stage ignition. At the 900-K condition, the second injection penetrates into high-temperature combustion products remaining in the near-nozzle region from the first injection. Consequently, the ignition delay for the second injection is shorter than that of the first injection (by a factor of two) and the second injection ignites at a more upstream location near the liquid length.

Well-mixed lean SI engine operation can provide improvements of the fuel economy relative to that of traditional well-mixed stoichiometric SI operation. This work examines the use of two methods for improving the stability of lean operation, namely multi-pulse transient plasma ignition and intake air preheating. These two methods are compared to standard SI operation using a conventional high-energy inductive ignition system without intake air preheating. E85 is the fuel chosen for this study. The multi-pulse transient plasma ignition system utilizes custom electronics to generate 10 kHz bursts of 10 ultra-short (12ns), high-amplitude pulses (200 A). These pulses were applied to a custom spark plug with a semi-open ignition cavity. High-speed imaging reveals that ignition in this cavity generates a turbulent jet-like early flame spread that speeds up the transition from ignition to the main combustion event.

For electrified vehicles, understanding the impact of temperature on vehicle control and performances becomes more important than before because the vehicle might consume more energy than conventional vehicles due to lack of the engine waste heat. Argonne has tested many advanced vehicles and analyzed the vehicle level control based on the test data. As part of its ongoing effort, Toyota Prius Plug-in Hybrid was tested in thermal environmental chamber, and the vehicle level control and performances are analyzed by observing the test results. The analysis results show that the control of the Plug-in Hybrid Electric Vehicle (PHEV) is similar with Prius Hybrid Electric Vehicle (HEV) when the vehicle is under a charge sustaining mode, and the vehicle tries to consume the electric energy first under a charge depleting mode.

Plug-in Hybrid Electric Vehicles (PHEVs) have demonstrated the potential to provide significant reduction in fuel use across a wide range of dynamometer test driving cycles. Companies and research organizations are involved in numerous research activities related to PHEVs. One of the current unknowns is the impact of driving behavior and standard test procedure on the true benefits of PHEVs from a worldwide perspective. To address this issue, five different PHEV powertrain configurations (input split, parallel, series, series-output split and series-parallel), implemented on vehicles with different all-electric ranges (AERs), were analyzed on three different standard cycles (i.e., Urban Dynamometer Driving Schedule, Highway Fuel Economy Test, and New European Driving Cycle). Component sizes, manufacturing cost, and fuel consumption were analyzed for a midsize car in model year 2020 through the use of vehicle system simulations.

It is widely understood that cold ambient temperatures negatively impact vehicle system efficiency. This is due to a combination of factors: increased friction (engine oil, transmission, and driveline viscous effects), cold start enrichment, heat transfer, and air density variations. Although the science of quantifying steady-state vehicle component efficiency is mature, transient component efficiencies over dynamic ambient real-world conditions is less understood and quantified. This work characterizes wheel assembly efficiencies of a conventional and electric vehicle over a wide range of ambient conditions. For this work, the wheel assembly is defined as the tire side axle spline, spline housing, bearings, brakes, and tires. Dynamometer testing over hot and cold ambient temperatures was conducted with a conventional and electric vehicle instrumented to determine the output energy losses of the wheel assembly in proportion to the input energy of the half-shafts.

Spark-ignition (SI) engine efficiency is typically limited by fuel auto-ignition resistance, which is described in practice by the Research Octane Number (RON) and the Motor Octane Number (MON). The goal of this work is to assess whether fuel properties (i.e. RON, MON, and heat of vaporization) are sufficient to describe the antiknock behavior of varying gasoline formulations in modern engines. To this end, the auto-ignition resistance of three compositionally dissimilar gasoline-like fuels with identical RON values and varying or non-varying MON values were evaluated in a modern, prototype, 12:1 compression ratio, high-swirl (by nature of intake valve deactivation), directly injected spark ignition (DISI) engine at 1400 RPM. The three gasolines are an alkylate blend (RON=98, MON=97), a blend with high aromatic content (RON=98, MON=88), and a blend of 30% ethanol by volume with a gasoline BOB (RON=98, MON=87; see Table 2 for details).

In this work, a turbulent combustion model is developed for large eddy simulation (LES) using a novel flamelet tabulation technique based on the framework of the multi-flamelet representative interactive flamelet (RIF) model. The overall aim is to develop a detailed model with elaborate chemistry mechanisms, LES turbulence models and highly resolved grids leveraging the computational cost advantage of a tabulated model. A novel technique of implementing unsteady flamelet libraries by using the residence time instead of the progress variables is proposed. In this study, LES of n-dodecane spray flame is performed using the tabulated turbulent combustion model along with a dynamic structure subgrid model. A high-resolution mesh is employed with a cell size of 62.5 microns in the entire spray and combustion regions. This model is then validated against igniting n-dodecane sprays under diesel engine conditions.

Simulations of the US light duty vehicle stock help policy makers, investors, and auto manufacturers make informed decisions to influence the future of the stock and its associated green house gas emissions. Such simulations require an underlying framework that captures the key elements of consumer purchasing decisions, which can be uncertain. This uncertainty in a simulation’s logic is usually convolved with uncertainty in the underlying assumptions about the futures of energy prices and technology innovation and availability. By comparing simulated alternative energy vehicle (AEV) sales to historical sales data, one can assess the simulation’s ability to capture the dynamics of consumer choice, independent of many of those underlying uncertainties, thereby determining the factors that most strongly impact sales.

The importance of radiative heat transfer on the combustion and soot formation characteristics under nominal ECN Spray A conditions has been studied numerically. The liquid n-dodecane fuel is injected with 1500 bar fuel pressure into the constant volume chamber at different ambient conditions. Radiation from both gas-phase as well as soot particles has been included and assumed as gray. Three different solvers for the radiative transfer equation have been employed: the discrete ordinate method, the spherical-harmonics method and the optically thin assumption. The radiation models have been coupled with the transported probability density function method for turbulent reactive flows and soot, where unresolved turbulent fluctuations in temperature and composition are included and therefore capturing turbulence-chemistry-soot-radiation interactions. Results show that the gas-phase (mostly CO2 ad H2O species) has a higher contribution to the net radiation heat transfer compared to soot.

Vehicle lightweighting has been a focus of the automotive industry, as car manufacturers seek to comply with corporate average fuel economy (CAFE) and greenhouse gas (GHG) emissions standards for model year (MY) 2017-2025 vehicles. However, when developing a lightweight vehicle design, the automotive industry typically targets maximum vehicle weight reduction at minimal cost increase. In this paper, we consider the environmental impacts of the lightweighting technology options. The materials used for vehicle lightweighting include high-strength steel (HSS), aluminum, magnesium and carbon fiber reinforced plastic (CFRP). Except for HSS, the production of these light materials is more GHG-intensive (on a kg-to-kg basis) compared with the conventional automotive materials they substitute. Lightweighting with these materials, therefore, may partially offset the GHG emission reductions achieved through improved fuel economy.

Automated driving functionalities delivered through Advanced Driver Assistance System (ADAS) have been adopted more and more frequently in consumer vehicles. The development and implementation of such functionalities pose new challenges in safety and functional testing and the associated validations, due primarily to their high demands on facility and infrastructure. This paper presents a rather unique Vehicle-in-the-Loop (VIL) test setup and methodology compared those previously reported, by combining the advantages of the hardware-in-the-loop (HIL) and traditional chassis dynamometer test cell in place of on-road testing, with a multi-agent real-time simulator for the rest of test environment.