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Research in plug in vehicles (PHEV and BEV) has of course been ongoing for decades, however now that these vehicles are finally being produced for a mass market an intense focus over the last few years has been given to proper evaluation techniques and standard information to effectively convey efficiency information to potential consumers. The first challenge is the development of suitable test procedures. Thanks to many contributions from SAE members, these test procedures have been developed for PHEVs (SAE J1711 now available) and are under development for BEVs (SAE J1634 available later this year). A bigger challenge, however, is taking the outputs of these test results and dealing with the issue of off-board electrical energy consumption in the context of decades-long consumer understanding of MPG as the chief figure of merit for vehicle efficiency.

In support of the U.S Department of Energy's Vehicle Technologies Program, numerous vehicle technology combinations have been simulated using Autonomie. Argonne National Laboratory (Argonne) designed and wrote the Autonomie modeling software to serve as a single tool that could be used to meet the requirements of automotive engineering throughout the development process, from modeling to control, offering the ability to quickly compare the performance and fuel efficiency of numerous powertrain configurations. For this study, a multitude of vehicle technology combinations were simulated for many different vehicles classes and configurations, which included conventional, power split hybrid electric vehicle (HEV), power split plug-in hybrid electric vehicle (PHEV), extended-range EV (E-REV)-capability PHEV, series fuel cell, and battery electric vehicle.

Software products in the automotive industry are by nature widely distributed and costly to update (recall), so high reliability is clearly of utmost importance. Just as clearly, the increasing reliance on remote access to such systems, for diagnostic and other purposes, has made security an essential requirement, and traditional techniques for software development are proving to be inadequate in dealing with these issues. Correctness by Construction is a software design and development methodology that builds reliability and security into the system from the start. It can be used to demonstrate, with mathematical rigor, a program's correctness properties while reducing the time spent during testing and debugging. This paper will discuss the use of Correctness by Construction, and its accompanying SPARK language technology, to improve automotive systems' security and reliability. (The approach can also account for safely issues, although that is not the focus of this paper.)

Quantitative planar laser-induced fluorescence (PLIF) of gaseous acetone as a fuel-tracer has been used in an optically accessible engine, fueled by direct hydrogen injection. The purpose of this article is to assess the accuracy and precision of the measurement and the associated data reduction procedures. A detailed description of the acetone seeding system is given as well. The key features of the experiment are a high-pressure bubbler saturating the hydrogen fuel with acetone vapor, direct injection into an optical engine, excitation of acetone fluorescence with an Nd:YAG laser at 266 nm, and detection of the resulting fluorescence by an unintensified camera. Key steps in the quantification of the single-shot imaging data are an in-situ calibration and a correction for the effect of local temperature on the fluorescence measurement.

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.

The effects of spray targeting on mixing, combustion, and pollutant formation under a low-load, late-injection, low-temperature combustion (LTC) diesel operating condition are investigated by optical engine measurements and multi-dimensional modeling. Three common spray-targeting strategies are examined: conventional piston-bowl-wall targeting (152° included angle); narrow-angle floor targeting (124° included angle); and wide-angle piston-bowl-lip targeting (160° included angle). Planar laser-induced fluorescence diagnostics in a heavy-duty direct-injection optical diesel engine provide two-dimensional images of fuel-vapor, low-temperature ignition (H2CO), high-temperature ignition (OH) and soot-formation species (PAH) to characterize the LTC combustion process.

Fuel injection during negative valve overlap offers a promising method of controlling HCCI combustion, but sorting out the thermal and chemical effects of NVO fueling requires knowledge of temperatures throughout the cycle. Computing bulk temperatures throughout closed portions of the cycle is relatively straightforward using an equation of state, once a temperature at one crank angle is established. Unfortunately, computing charge temperatures at intake valve closing for NVO operation is complicated by a large, unknown fraction of residual gases at unknown temperature. To address the problem, we model blowdown and recompression during exhaust valve opening and closing events, allowing us to estimate in-cylinder charge temperatures based on exhaust-port measurements. This algorithm permits subsequent calculation of crank-angle-resolved bulk temperatures and residual gas fraction over a wide range of NVO operation.

Lignin by-products from biorefineries has the potential to provide a low-cost alternative to petroleum-based precursors to manufacture carbon fiber, which can be combined with a binding matrix to produce a structural material with much greater specific strength and specific stiffness than conventional materials such as steel and aluminum. The market for carbon fiber is universally projected to grow exponentially to fill the needs of clean energy technologies such as wind turbines and to improve the fuel economies in vehicles through lightweighting. In addition to cellulosic biofuel production, lignin-based carbon fiber production coupled with biorefineries may provide $2,400 to $3,600 added value dry Mg−1 of biomass for vehicle applications. Compared to producing ethanol alone, the addition of lignin-derived carbon fiber could increase biorefinery gross revenue by 30% to 300%.

Production vehicles are commonly characterized and compared using fuel consumption (FC) and electric energy consumption (EC) metrics. Chassis dynamometer testing is a tool used to establish these metrics, and to benchmark the effectiveness of a vehicle's powertrain under numerous testing conditions and environments. Whether the vehicle is undergoing EPA Five-Cycle Fuel Economy (FE), component lifecycle, thermal, or benchmark testing, it is important to identify the vehicle and testing based variations of energy consumption results from these tests to establish the accuracy of the test's results. Traditionally, the uncertainty in vehicle test results is communicated using the variation. With the increasing complexity of vehicle powertrain technology and operation, a fixed energy consumption variation may no longer be a correct assumption.

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.

A full understanding and characterization of the near-field of diesel sprays is daunting because the dense spray region inhibits most diagnostics. While x-ray diagnostics permit quantification of fuel mass along a line of sight, most laboratories necessarily use simple lighting to characterize the spray spreading angle, using it as an input for CFD modeling, for example. Questions arise as to what is meant by the “boundary” of the spray since liquid fuel concentration is not easily quantified in optical imaging. In this study we seek to establish a relationship between spray boundary obtained via optical diffused backlighting and the fuel concentration derived from tomographic reconstruction of x-ray radiography. Measurements are repeated in different facilities at the same specified operating conditions on the “Spray A” fuel injector of the Engine Combustion Network, which has a nozzle diameter of 90 μm.

Cavitation plays a significant role in high pressure diesel injectors. However, cavitation is difficult to measure under realistic conditions. X-ray phase contrast imaging has been used in the past to study the internal geometry of fuel injectors and the structure of diesel sprays. In this paper we extend the technique to make in-situ measurements of cavitation inside unmodified diesel injectors at pressures of up to 1200 bar through the steel nozzle wall. A cerium contrast agent was added to a diesel surrogate, and the changes in x-ray intensity caused by changes in the fluid density due to cavitation were measured. Without the need to modify the injector for optical access, realistic injection and ambient pressures can be obtained and the effects of realistic nozzle geometries can be investigated. A range of single and multi-hole injectors were studied, both sharp-edged and hydro-ground. Cavitation was observed to increase with higher rail pressures.

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.

Shock waves have been recently observed in high-pressure diesel sprays. In this paper, three-dimensional numerical simulations of supersonic diesel spray injection have been performed to investigate the underlying dynamics of the induced shock waves and their interactions with the spray. A Volume-of-Fluid based method in the CFD software (CONVERGE) is used to model this multiphase phenomena. An adaptive Mesh Refinement (AMR) scheme is employed to capture the front of the spray and the shock waves with high fidelity. Simulation results are compared to the available experimental observations to validate the numerical procedure. Parametric studies with different injection and ambient conditions are conducted to examine the effect of these factors on the generation of shock waves and their dynamics.

It is widely understood that cold ambient temperatures increase vehicle fuel consumption due to heat transfer losses, increased friction (increased viscosity lubricants), and enrichment strategies (accelerated catalyst heating). However, relatively little effort has been dedicated to thoroughly quantifying these impacts across a large set of real world drive cycle data and ambient conditions. This work leverages experimental dynamometer vehicle data collected under various drive cycles and ambient conditions to develop a simplified modeling framework for quantifying thermal effects on vehicle energy consumption. These models are applied over a wide array of real-world usage profiles and typical meteorological data to develop estimates of in-use fuel economy. The paper concludes with a discussion of how this integrated testing/modeling approach may be applied to quantify real-world, off-cycle fuel economy benefits of various technologies.

When utilizing large models containing numerous uncertain parameters, model calibration becomes a critical step in the analysis. Traditional methods of calibration involve adjusting uncertain parameters based on expert opinion or best estimates. While this traditional calibration may lead to better model predictions, it usually only yields better estimates for certain specific conditions. This drastically reduces the functionality of the model in question. Bayesian calibration is an alternative to traditional calibration methods which utilizes available information (simulation results and/or real world measured values) to iteratively refine uncertain parameters (either assumed or measured uncertainty) while considering not only parametric uncertainty, but also model, observational, and residual uncertainties at every step of the calibration process.

This work contributes to the understanding of physical mechanisms that control flashback, or more appropriately combustion recession, in diesel sprays. A large dataset, comprising many fuels, injection pressures, ambient temperatures, ambient oxygen concentrations, ambient densities, and nozzle diameters is used to explore experimental trends for the behavior of combustion recession. Then, a reduced-order model, capable of modeling non-reacting and reacting conditions, is used to help interpret the experimental trends. Finally, the reduced-order model is used to predict how a controlled ramp-down rate-of-injection can enhance the likelihood of combustion recession for conditions that would not normally exhibit combustion recession. In general, fuel, ambient conditions, and the end-of-injection transient determine the success or failure of combustion recession.

Negative valve overlap (NVO) is a viable control strategy that enables low-temperature gasoline combustion (LTGC) at low loads. Thermal effects of NVO fueling on main combustion are well understood, but fuel reforming chemistry during NVO has not been extensively studied. The objective of this work is to analyze the impact of global equivalence ratio and available oxidizer on NVO product concentrations. Experiments were performed in a LTGC single-cylinder engine under a sweep of NVO oxygen concentration and NVO fueling rates. Gas sampling at the start and end of the NVO period was performed via a custom dump-valve apparatus with detailed sample speciation by gas chromatography. Single-zone reactor models using detailed chemistry at relevant mixing and thermodynamic conditions were used in parallel to the experiments to evaluate expected yields of partially oxidized species under representative engine time scales.