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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.

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).

Accurate estimation of the State of Charge (SOC), maximum capacity (Qmax) and internal resistance (R0) are essential for efficient battery monitoring, which is an important part of the battery management system. SOC provides information regarding the instantaneous status of the battery system, while Qmax is a key indicator of the long-term State of Health (SOH) of the cell, which represents the abilities of a battery to store energy and retain charge over extended periods. In addition, the internal resistance is also required to predict the peak available power. Traditional methods use complex models and look-up tables that have high computation requirements and are thus unsuitable for online applications. In this paper, we propose a simple method for simultaneous SOC, Qmax and internal resistance estimation based on a second-order equivalent circuit model (ECM).

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).

As connectivity and sensing technologies become more mature, automated vehicles can predict future driving situations and utilize this information to drive more energy-efficiently than human-driven vehicles. However, future information beyond the limited connectivity and sensing range is difficult to predict and utilize, limiting the energy-saving potential of energy-efficient driving. Thus, we combine a conventional speed optimization planner, developed in our previous work, and reinforcement learning to propose a real-time intelligent speed optimization planner for connected and automated vehicles. We briefly summarize the conventional speed optimization planner with limited information, based on closed-form energy-optimal solutions, and present its multiple parameters that determine reference speed trajectories.

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 fuel efficiency improvement of a prototype Driver-Assistive-Truck-Platooning (DATP) system was evaluated using Computational Fluid Dynamics (CFD). The DATP system uses a combination of radar and GPS, integrated active safety systems, and V2V communications to enable regulation of the longitudinal distance between pairs of trucks without acceleration input from the driver in the following truck(s). The V2V linking of active safety systems and synchronized braking promotes increased safety of close following trucks while improving their fuel economy. Vehicle configuration, speed, and separation distance are considered. The objectives of the CFD analysis are to optimize the target separation distance and to determine the overall drag reduction of the platoon. This reduction directly results in fuel economy gains for all cooperating vehicles.

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 Computational Fluid Dynamics (CFD) study was conducted on four-vehicle platoons, and the aerodynamic data is then coupled with a high-fidelity truck simulation software (TruckSim) to determine fuel efficiency. Previous studies typically have focused on identical two vehicle platoons, whereas this study accounted for more complex platoon configurations. Heavy duty vehicles (HDVs), both military and commercial, make up a significant percentage of fuel consumption. This study aimed to quantify fuel savings of a platoon consisting of dissimilar trucks and trailers, thus reducing vehicle operational cost. The vehicle platoon featured two M915 trucks and two Peterbilt 579 trucks with dissimilar trailer configurations. An unloaded flatbed trailer, a centered 20 ft shipping container, two 20 ft shipping containers, and a 53 ft box trailer configurations were utilized.

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.

This study analyzes the performance of the Energy Recovery System (ERS) of a Formula One car (F1) based on the qualification performance of 19 drivers for the first calendar race of the 2019 FIA Formula One World Championship®. In this study, the race circuit analysed was split into different sectors to examine the energy transfer between the Motor Generator Unit-Kinetic (MGU-K) and the Energy Storage (ES) systems. Positive Kinetic Energy (PKE) concept was used for estimating the energy deployment potential of the ERS along with numerical simulations for estimating the energy recovering potential. This investigation highlights the strategies used by different drivers and the effect of driver to driver variation on their ERS performance during qualification. The methodology demonstrated in this study is able to identify the correlation between the unique driving style of individual drivers and the ERS strategies used by the teams for maximizing the performance of their car.

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).