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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.

The Hybrid Electric Vehicle Team of Virginia Tech participated in the three-year EcoCAR Advanced Vehicle Technology Competition organized by Argonne National Laboratory, and sponsored by General Motors and the U.S. Department of Energy. The team established goals for the design of a plug-in, range-extended hybrid electric vehicle that meets or exceeds the competition requirements for EcoCAR. The challenge involved designing a crossover SUV powertrain to reduce fuel consumption, petroleum energy use, regulated tailpipe emissions, and well-to-wheel greenhouse gas emissions. To interface with and control the hybrid powertrain, the team added a Hybrid Vehicle Supervisory Controller, which enacts a torque split control strategy. This paper builds on an earlier paper [1] that evaluated the petroleum energy use, criteria tailpipe emissions, and greenhouse gas emissions of the Virginia Tech EcoCAR vehicle and control strategy from the 2nd year of the competition.

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.)

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.

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.

Low temperature combustion through in-cylinder blending of fuels with different reactivity offers the potential to improve engine efficiency while yielding low engine-out NOx and soot emissions. A Navistar MaxxForce 13 heavy-duty compression ignition engine was modified to run with two separate fuel systems, aiming to utilize fuel reactivity to demonstrate a technical path towards high engine efficiency. The dual-fuel engine has a geometric compression ratio of 14 and uses sequential, multi-port-injection of a low reactivity fuel in combination with in-cylinder direct injection of diesel. Through control of in-cylinder charge reactivity and reactivity stratification, the engine combustion process can be tailored towards high efficiency and low engine-out emissions. Engine testing was conducted at 1200 rpm over a load sweep.

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.

Compared to conventional diesel combustion (CDC), isobaric combustion can achieve a similar or higher indicated efficiency, lower heat transfer losses, reduced nitrogen oxides (NOx) emissions; however, with a penalty of soot emissions. While the engine performance and exhaust emissions of isobaric combustion are well known, the overall flame development, in particular, the flow-field details within the flames are unclear. In this study, the performance analysis of CDC and two isobaric combustion cases was conducted, followed by high-speed imaging of Mie-scattering and soot luminosity in an optically accessible, single-cylinder heavy-duty diesel engine. From the soot luminosity imaging, qualitative flow-fields were obtained using flame image velocimetry (FIV). The peak motoring pressure (PMP) and peak cylinder pressure (PCP) of CDC are kept fixed at 50 and 70 bar, respectively.

Isobaric combustion has shown the potential of improving engine efficiency by lowering the heat transfer losses. Previous studies have achieved isobaric combustion through multiple injections from a single central injector, controlling injection timing and duration of the injection. In this study, we employed three injectors, i.e. one centrally mounted (C) on the cylinder head and two side-injectors (S), slant-mounted on cylinder head protruding their nozzle tip near piston-bowl to achieve the isobaric combustion. This study visualized the flame development of isobaric combustion, linking flow-field details to the observed trends in engine efficiency and soot emissions. The experiments were conducted in an optically accessible single-cylinder heavy-duty diesel engine using n-heptane as fuel. Isobaric combustion, with a 50 bar peak pressure, was achieved with three different injection strategies, i.e. (C+S), (S+C), and (S+S).

The high-pressure isobaric combustion has been proposed as the most suitable combustion mode for the double compre4ssion expansion engine (DCEE) concept. Previous experimental and simulation studies have demonstrated an improved efficiency compared to the conventional diesel combustion (CDC) engine. In the current study, isobaric combustion was achieved using a single injector with multiple injections. Since this concept involves complex phenomena such as spray to spray interactions, the computational models were extensively validated against the optical engine experiment data, to ensure high-fidelity simulations. The considered optical diagnostic techniques are Mie-scattering, fuel tracer planar laser-induced fluorescence (PLIF), and natural flame luminosity imaging. Overall, a good agreement between the numerical and experimental results was obtained.

National concerns over energy consumption and emissions from the transportation sector have prompted regulatory agencies to implement aggressive fuel economy targets for light-duty vehicles through the U.S. National Highway Traffic Safety Administration/Environmental Protection Agency (EPA) Corporate Average Fuel Economy (CAFE) program. Automotive manufacturers have responded by bringing competitive technologies to market that maximize efficiency while meeting or exceeding consumer performance and comfort expectations. In a collaborative effort among Toyota Motor Corporation, Argonne National Laboratory (ANL), and the National Renewable Energy Laboratory (NREL), the real-world savings of one such technology is evaluated. A commercially available Toyota Highlander equipped with two-phase cold storage technology was tested at ANL’s chassis dynamometer testing facility.

The regulatory requirements to reduce both greenhouse gases and exhaust gas pollutants from heavy duty engines are driving new perspectives on the interaction between fuels and engines. Fuels that reliefs the burden on engine manufacturers to reach these goals are of particular interest. A low carbon fuel with a higher volatility and heating value than diesel is one such fuel that reduces engine-out emissions and carbon footprint from the entire hydrocarbon lifecycle (well-to-wheel) and improves fuel efficiency, which is a main enabler for gasoline compression ignition (GCI) technology. The present study investigated the potential of GCI technology by evaluating the performance of a low carbon high efficiency, high reactivity gasoline fuel in Doosan’s 6L medium duty diesel engine.

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).

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.