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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.

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.

The Modular Automotive Technology Testbed (MATT) is a flexible platform built to test different technology components in a vehicle environment. This testbed is composed of physical component modules, such as the engine and the transmission, and emulated components, such as the energy storage system and the traction motor. The instrumentation on the tool enables the energy balance for individual components on drive cycles. Using MATT, a single set of hardware can operate as a conventional vehicle, a hybrid vehicle and a plug-in hybrid vehicle, enabling direct comparison of petroleum displacement for the different modes. The engine provides measured fuel economy and emissions. The losses of components which vary with temperature are also measured.

There are many challenges in implementing grid aware plug-in electric vehicle (PEV) charging systems with local load control. New opportunities for innovative load control were created as a result of changes to the 2014 National Electric Code (NEC) about automatic load control definitions for EV charging infrastructure. Stakeholders in optimized dispatch of EV charging assets include the end users (EV drivers), site owner/operators, facility managers and utilities. NEC definition changes allow for ‘over subscription’ of more potential EV charging station load than can be continuously supported if the total load at any time is within the supply system safety limit. Local load control can be implemented via compact submeter(s) with locally hosted control algorithms using direct communication to the managed electric vehicle supply equipment (EVSE).

Road vehicles can expend a significant amount of energy in undesirable vertical motions that are induced by road bumps, and much of that is dissipated in conventional shock absorbers as they dampen the vertical motions. Presented in this paper are some of the results of a study aimed at determining the effectiveness of efficiently transforming that energy into electrical power by using optimally designed regenerative electromagnetic shock absorbers. In turn, the electrical power can be used to recharge batteries or other efficient energy storage devices (e.g., flywheels) rather than be dissipated. The results of the study are encouraging - they suggest that a significant amount of the vertical motion energy can be recovered and stored.

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 presents a simple fan model that is based on the actuator disk approximation, and the blade element and vortex theory of a propeller. A set of equations are derived that require as input the rotational speed of the fan, geometric fan data, and the lift and drag coefficients of the blades. These equations are solved iteratively to obtain the body forces generated by the fan in the axial and circumferential directions. These forces are used as momentum sources in a CFD code to simulate the effect of the fan in an underhood thermal management simulation. To validate this fan model, a fan experiment was simulated. The model was incorporated into the CFD code STAR-CD and predictions were generated for axial and circumferential air velocities at different radial positions and at different planes downstream of the fan. The agreement between experimental measurements and predictions is good.

As a result of increasingly stringent regulations and higher customer expectations, auto manufacturers have been considering numerous technology options to improve vehicle fuel economy. Transmissions have been shown to be one of the most cost-effective technologies for improving fuel economy. Over the past couple of years, transmissions have significantly evolved and impacted both performance and fuel efficiency. This study validates the shifting control of advanced automatic transmission technologies in vehicle systems by using Argonne National Laboratory's model-based vehicle simulation tool, Autonomie. Different midsize vehicles, including several with automatic transmission (6-speeds, 7-speeds, and 8-speeds), were tested at Argonne's Advanced Powertrain Research Facility (APRF). For the vehicles, a novel process was used to import test data.

The study of spray-wall interaction is of great importance to understand the dynamics that occur during fuel impingement onto the chamber wall or piston surfaces in internal combustion engines. It is found that the maximum spreading length of an impinged droplet can provide a quantitative estimation of heat transfer and energy transformation for spray-wall interaction. Furthermore, it influences the air-fuel mixing and hydrocarbon and particle emissions at combusting conditions. In this paper, an analytical model of a single diesel droplet impinging on the wall with different inclined angles (α) is developed in terms of βm (dimensionless maximum spreading length, the ratio of maximum spreading length to initial droplet diameter) to understand the detailed impinging dynamic process.

Recent advances in x-ray spray diagnostics at Argonne National Laboratory's Advanced Photon Source have made absorption measurements of individual spray events possible. A focused x-ray beam (5×6 μm) enables collection of data along a single line of sight in the flow field and these measurements have allowed the calculation of quantitative, shot-to-shot statistics for the projected mass of fuel sprays. Raster scanning though the spray generates a two-dimensional field of data, which is a path integrated representation of a three-dimensional flow. In a previous work, we investigated the shot-to-shot variation over 32 events by visualizing the ensemble standard deviations throughout a two dimensional mapping of the spray. In the current work, provide further analysis of the time to steady-state and steady-state spatial location of the fluctuating field via the transverse integrated fluctuations (TIF).

A state-of-the-art spray modeling methodology, recently applied to RANS simulations, is presented for LES calculations. Key features of the methodology, such as Adaptive Mesh Refinement (AMR), advanced liquid-gas momentum coupling, and improved distribution of the liquid phase, are described. The ability of this approach to use cell sizes much smaller than the nozzle diameter is demonstrated. Grid convergence of key parameters is verified for non-evaporating and evaporating spray cases using cell sizes down to 1/32 mm. It is shown that for global quantities such as spray penetration, comparing a single LES simulation to experimental data is reasonable, however for local quantities the average of many simulated injections is necessary. Grid settings are recommended that optimize the accuracy/runtime tradeoff for LES-based spray simulations.

The particulate matter (PM) produced from a diesel engine-simulating combustor was characterized in its morphology, microstructure, and fractal geometry by using a unique thermophoretic sampling and Transmission Electron Microscopy (TEM) system. These results revealed that diesel PM produced from the laboratory-scale burner showed similar morphological characteristics to the particulates produced from diesel engines. The flame air/fuel ratio and the particulate temperature history have significant influences on both particle size and fractal geometry. The primary particle sizes were measured to be 14.7 nm and 14.8 nm under stoichiometric and fuel-rich flame conditions, respectively. These primary particle sizes are smaller than those produced from diesel engines. The radii of gyration for the aggregate particles were 83.8 nm and 47.5 nm under these two flame conditions.

The Toyota Prius Prime is a new generation of Toyota Prius plug-in hybrid electric vehicle, the electric drive range of which is 25 miles. This version is improved from the previous version by the addition of a one-way clutch between the engine and the planetary gear-set, which enables the generator to add electric propulsive force. The vehicle was analyzed, developed and validated based on test data from Argonne National Laboratory’s Advanced Powertrain Research Facility, where chassis dynamometer set temperature can be controlled in a thermal chamber. First, we analyzed and developed components such as engine, battery, motors, wheels and chassis, including thermal aspects based on test data. By developing models considering thermal aspects, it is possible to simulate the vehicle driving not only in normal temperatures but also in hot, cold, or warmed-up conditions.

Today’s electric vehicle (EV) owners charge their vehicles mostly at home and seldom use public direct current fast charger (DCFCs), reducing the need for a large deployment of DCFCs for private EV owners. However, due to the emerging interest among transportation network companies to operate EVs in their fleet, there is great potential for DCFCs to be highly utilized and become economically feasible in the future. This paper describes a heuristic algorithm to emulate operation of EVs within a hypothetical transportation network company fleet using a large global positioning system data set from Columbus, Ohio. DCFC requirements supporting operation of EVs are estimated using the Electric Vehicle Infrastructure Projection tool. Operation and installation costs were estimated using real-world data to assess the economic feasibility of the recommended fast charging stations.

The recovery of braking energy through regenerative braking is a key enabler for the improved efficiency of Hybrid Electric Vehicles, Plug-in Hybrid Electric, and Battery Electric Vehicles (HEV, PHEV, BEV). However, this energy is often treated in a simplified fashion, frequently using an overall regeneration efficiency term, ξrg [1], which is then applied to the total available braking energy of a given drive-cycle. In addition to the ability to recapture braking energy typically lost during vehicle deceleration, hybrid and plug-in hybrid vehicles also allow for reduced or zero engine fueling during vehicle decelerations. While regenerative braking is often discussed as an enabler for improved fuel economy, reduced fueling is also an important component of a hybrid vehicle's ability to improve overall fuel economy.

The 2001 FutureTruck competition involved 15 universities from across North America that were invited to apply a wide range of advanced technologies to improve energy efficiency and reduce greenhouse gas impact while producing near-zero regulated exhaust emissions in a 2000 Chevrolet Suburban. The modified vehicles designated as FutureTrucks demonstrated improvements in greenhouse gas emissions, tailpipe emissions, and over-the-road fuel economy compared with the stock vehicle on which they were based. The technologies represented in the vehicles included ICE-engines and fuel cell hybrid electric vehicle propulsion systems, a range of conventional and alternative fuels, advanced exhaust emissions controls, and light weighting technologies.

In June of 2002, 15 universities participated in the third year of FutureTruck, an advanced vehicle competition sponsored by the U.S. Department of Energy and Ford Motor Company. Using advanced technologies, teams strived to improve vehicle energy efficiency by at least 25%, reduce tailpipe emissions to ULEV levels, and lower greenhouse gas impact of a 2002 Ford Explorer. The competition vehicles were tested for dynamic performance and emissions and were judged in static events to evaluate the design and features of the vehicle. The dynamic events include braking, acceleration, handling, and fuel economy, while the dynamometer testing provided data for both the emissions event and the greenhouse gas event. The vehicles were scored for their performance in each event relative to each other; those scores were summed to determine the winner of the competition. The competition structure included different available fuels and encouraged the use of hybrid electric drivetrains.

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.

The Battery Performance and Cost Model (BatPaC), developed by Argonne National Laboratory, is a versatile tool designed for lithium-ion battery (LIB) pack engineering. It accommodates user-defined specifications, generating detailed bill-of-materials calculations and insights into cell dimensions and pack characteristics. Pre-loaded with default data sets, BatPaC aids in estimating production costs for battery packs produced at scale (5 to 50 GWh annually). Acknowledging inherent uncertainties in parameters, the tool remains accessible and valuable for designers and engineers. BatPaC plays a crucial role in National Highway Transportation Traffic Safety Administration (NHTSA) regulatory assessments, providing estimated battery pack manufacturing costs and weight metrics for electric vehicles. Integrated with Argonne's Autonomie simulations, BatPaC streamlines large-scale processes, replacing traditional models with lookup tables.

Developed by the U.S. Environmental Protection Agency (EPA), the Multi-scale mOtor Vehicle Emission Simulator (MOVES) is used to estimate inventories and projections through 2050 at the county or national level for energy consumption, nitrous oxide (N2O), and methane (CH4) from highway vehicles. To simulate a large number of vehicles and fleets on numerous driving cycles, EPA developed a binning technique characterizing the energy rate for varying Vehicle Specific Power (VSP) under predefined vehicle speed ranges. The methodology is based upon the assumption that the vehicle behaves the same way for a predefined vehicle speed and power demand. While this has been validated for conventional vehicles, it has not been for advanced vehicle powertrains, including hybrid electric vehicles (HEVs) where the engine can be ON or OFF depending upon the battery State-of-Charge (SOC).