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This paper evaluates the impact of energy management strategy on the cost benefits of a plug-in hybrid electric vehicle (PHEV) by taking into account the impact of PHEV energy management on battery life and petroleum displacement over the life of the vehicle. Using Battery in the Loop (BIL), a real battery is subjected to transient power demands by a virtual vehicle. The vehicle energy management strategy is varied, resulting in different battery utilization scenarios. Battery life, which varies with battery utilization, is estimated for the different energy management scenarios. The same representative drive cycle is used over the different energy management strategies to isolate the impact of energy management on battery utilization. PHEV gasoline savings, in comparison to a charge sustaining hybrid, are calculated for each of the energy management strategies, for a fixed distance of 40 miles.

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.

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.

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.

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.

In this study, the combustion system of a light-duty compression ignition engine running on a market gasoline fuel with Research Octane Number (RON) of 91 was optimized using computational fluid dynamics (CFD) and Machine Learning (ML). This work was focused on optimizing the piston bowl geometry at two compression ratios (CR) (17 and 18:1) and this exercise was carried out at full-load conditions (20 bar indicated mean effective pressure, IMEP). First, a limited manual piston design optimization was performed for CR 17:1, where a couple of pistons were designed and tested. Thereafter, a CFD design of experiments (DoE) optimization was performed where CAESES, a commercial software tool, was used to automatically perturb key bowl design parameters and CONVERGE software was utilized to perform the CFD simulations. At each compression ratio, 128 piston bowl designs were evaluated.

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.

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.

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 4th Workshop of the Engine Combustion Network (ECN) was held September 5-6, 2015 in Kyoto, Japan. This manuscript presents a summary of the progress in experiments and modeling among ECN contributors leading to a better understanding of soot formation under the ECN “Spray A” configuration and some parametric variants. Relevant published and unpublished work from prior ECN workshops is reviewed. Experiments measuring soot particle size and morphology, soot volume fraction (fv), and transient soot mass have been conducted at various international institutions providing target data for improvements to computational models. Multiple modeling contributions using both the Reynolds Averaged Navier-Stokes (RANS) Equations approach and the Large-Eddy Simulation (LES) approach have been submitted. Among these, various chemical mechanisms, soot models, and turbulence-chemistry interaction (TCI) methodologies have been considered.

This work involves modeling internal and near-nozzle flows of a gasoline direct injection (GDI) nozzle. The Engine Combustion Network (ECN) Spray G condition has been considered for these simulations using the nominal geometry of the Spray G injector. First, best practices for numerical simulation of the two-phase flow evolution inside and the near-nozzle regions of the Spray G injector are presented for the peak needle lift. The mass flow rate prediction for peak needle lift was in reasonable agreement with experimental data available in the ECN database. Liquid plume targeting angle and liquid penetration estimates showed promising agreement with experimental observations. The capability to assess the influence of different thermodynamic conditions on the two-phase flow nature was established by predicting non-flashing and flashing phenomena.

The peak efficiency of modern spark ignited engines varies from 36% to 40% depending on the exact technology utilized. Most engines can achieve this peak efficiency for a limited operating region. Multi-speed transmissions allow the engine to operate closer to its most efficient operating regions for more significant portions of operation. In the case of hybrid powertrains, electric machines help in improving engine efficiency by adjusting operating speed and load. Engine shutdown during idle events and low loads is another avenue for improving the overall efficiency. The choice of the ideal powertrain and component sizes depends on the engine characteristics, drive cycles and vehicle technical requirements. This study examines what type of powertrains will be suitable for more efficient engines that are likely to be available in the near future. Some of the new technologies achieve higher efficiency with a trade off on power or by accepting a more restrictive operating region.

The most common and lethal weapons against military vehicles are the improvised explosive devices (IEDs). In an explosion, critical cabin’s penetrations and high accelerations can cause serious injuries and death of military personnel. This investigation uses single and multi-fidelity Bayesian optimization (BO) to design sandwich composite armors for blast mitigation. BO is an efficient methodology to solve optimization problems that involve black-box functions. The black-box function of this work is the finite element (FE) simulation of the armor subjected to blast. The main two components of BO are the surrogate model of the black-box function and the acquisition function that guides the optimization. In this investigation, the surrogate models are Gaussian Process (GP) regression models and the acquisition function is the multi-objective expected improvement (MEI) function. Information from low and high fidelity FE models is used to train the GP surrogates.

Stringent emission standards are driving the development of diesel-fuel injection concepts to mitigate in-cylinder formation of particulates. While research has demonstrated significant reduction in particulate formation using micro-orifice technology, implementation requires development of industrial processes to fabricate micro-orifices with diameters as low as 50 μm and with large length-to-diameter ratios. This paper reviews the different processes being pursued to fabricate micro-orifices and the advanced techniques applied to characterize the performance of micro-orifices. The latter include the use of phase-contrast x-ray imaging of electroless nickel-plated micro-orifices and laser imaging of fuel sprays at elevated pressures. The experimental results demonstrate an industrially viable process to create small uniform orifices that improve spray formation for fuel injection.

The first commercially available Plug-In Hybrid Electric Vehicle (PHEV), the General Motors (GM) Volt, was introduced into the market in December 2010. The Volt's powertrain architecture provides four modes of operation, including two that are unique and maximize the Volt's efficiency and performance. The electric transaxle has been specially designed to enable patented operating modes both to improve the electric driving range when operating as a battery electric vehicle and to reduce fuel consumption when extending the range by operating with an internal combustion engine (ICE). However, details on the vehicle control strategy are not widely available because the supervisory control algorithm is proprietary. Since it is not possible to analyze the control without vehicle test data obtained from a well-designed Design-of-Experiment (DoE), a highly instrumented GM Volt, including thermal sensors, was tested at Argonne National Laboratory's Advanced Powertrain Research Facility (APRF).

Conventional hydraulic steering systems keep improving performance and driving comfort by introducing advanced features via mechanical design. The ever increasing mechanical complexity requires the advanced modeling and simulation technology to mitigate the risks in the early stage of the development process. In this paper, we focus on advanced modeling tools environment with an example of a load sensing hydraulic steering system. The complete system architecture is presented. Analytical equations are developed for a priority valve and a steering control unit as the foundation of modeling. The full version of hydraulic steering system model is developed in Dymola platform. In order to capture interaction between steering and vehicle, the co-simulation platform between the hydraulic steering system and vehicle dynamics is established by integrating Dymola, Carsim and Simulink.

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.

In order to improve understanding of the primary atomization process for diesel-like sprays, a collaborative experimental and computational study was focused on the near-nozzle spray structure for the Engine Combustion Network (ECN) Spray D single-hole injector. These results were presented at the 5th Workshop of the ECN in Detroit, Michigan. Application of x-ray diagnostics to the Spray D standard cold condition enabled quantification of distributions of mass, phase interfacial area, and droplet size in the near-nozzle region from 0.1 to 14 mm from the nozzle exit. Using these data, several modeling frameworks, from Lagrangian-Eulerian to Eulerian-Eulerian and from Reynolds-Averaged Navier-Stokes (RANS) to Direct Numerical Simulation (DNS), were assessed in their ability to capture and explain experimentally observed spray details. Due to its computational efficiency, the Lagrangian-Eulerian approach was able to provide spray predictions across a broad range of conditions.

Recent advancements in x-ray radiography diagnostics for direct injection sprays at Argonne's Advanced Photon Source have allowed absorption measurements of individual spray events, in addition to ensemble-averaged measurements. These measurements offer insight into the shot-to-shot variation of these sprays in the near-nozzle, spray formation region. Three single hole diesel injectors are studied across various injection and ambient pressures, spanning 14 different conditions. We calculated two dimensional maps of the standard deviation in line of sight mass distribution between individual spray events. These illuminated the spatial and temporal extent of variability between spray events. Regions of large fluctuations were observed to move downstream during the initial spray period and reached a steady state location after this initial transient.

Cavitation plays an important role in fuel injection systems. It alters the nozzle's internal flow structure and discharge coefficient, and also contributes to injector wear. Quantitatively measuring and mapping the cavitation vapor distribution in a fuel injector is difficult, as cavitation occurs on very short time and length scales. Optical measurements of transparent model nozzles can indicate the morphology of large-scale cavitation, but are generally limited by the substantial amount of scattering that occurs between vapor and liquid phases. These limitations can be overcome with x-ray diagnostics, as x-rays refract, scatter and absorb much more weakly from phase interfaces. Here, we present an overview of some recent developments in quantitative x-ray diagnostics for cavitating flows. Measurements were conducted at the Advanced Photon Source at Argonne National Laboratory, using a submerged plastic test nozzle.