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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.

The potential peaking of world conventional oil production and the possible imperative to reduce carbon emissions will put great pressure on vehicle manufacturers to produce more efficient vehicles, on vehicle buyers to seek them out in the marketplace, and on energy suppliers to develop new fuels and delivery systems. Four cases for stabilizing or reducing light vehicle fuel use, oil use, and/or carbon emissions over the next 50 years are presented. Case 1 - Improve mpg so that the fuel use in 2020 is stabilized for the next 30 years. Case 2 - Improve mpg so that by 2030 the fuel use is reduced to the 2000 level and is reduced further in subsequent years. Case 3 - Case 1 plus 50% ethanol use and 50% low-carbon fuel cell vehicles by 2050. Case 4 - Case 2 plus 50% ethanol use and 50% low-carbon fuel cell vehicles by 2050. The mpg targets for new cars and light trucks require that significant advances be made in developing cost-effective and very efficient vehicle technologies.

Fuel cell vehicles are currently undergoing extensive research and development because of their potential for high efficiency and low emissions. A complete well-to-wheels evaluation is helpful when considering the introduction of advanced vehicles that could use a new fuel, such as hydrogen. Several modeling tools developed by Argonne National Laboratory were used to evaluate the impact of several new vehicle configurations. A transient vehicle simulation software code, PSAT (Powertrain System Analysis Toolkit), was used with a transient fuel cell model derived from GCTool (General Computational Toolkit); and GREET (Greenhouse gases, Regulated Emissions and Energy use in Transportation) was employed in estimating well-to-tank performances. This paper compares the well-to-wheels impacts of several advanced SUVs, including conventional, parallel and series hybrid-electric and fuel cell vehicles.

This paper introduces a new systematic workflow for the rapid evaluation of energy-efficient automated driving controls in real vehicles in controlled laboratory conditions. This vehicle-in-the-loop (VIL) workflow, largely standardized and automated, is reusable and customizable, saves time and minimizes costly dynamometer time. In the first case study run with the VIL workflow, an automated car driven by an energy-efficient driving control previously developed at Argonne used up to 22 % less energy than a conventional control. In a VIL experiment, the real vehicle, positioned on a chassis dynamometer, has a digital twin that drives in a virtual world that replicates real-life situations, such as approaching a traffic signal or following other vehicles.

Prediction of vehicle velocity is important since it can realize improvements in the fuel economy/energy efficiency, drivability, and safety. Velocity prediction has been addressed in many publications. Several references considered deterministic and stochastic approaches such as Markov chain, autoregressive models, and artificial neural networks. There are numerous new sensor and signal technologies like vehicle-to-vehicle and vehicle-to-infrastructure communication that can be used to obtain inclusive datasets. Using these inclusive datasets of sensors in deep neural networks, high accuracy velocity predictions can be achieved. This research builds upon previous findings that Long Short-Term Memory (LSTM) deep neural networks provide low error velocity prediction. We developed an LSTM deep neural network that uses different groups of datasets collected in Fort Collins, Colorado.

The environmental implications of converting vehicles powered by Internal Combustion Engines (ICE) to battery powered and hybrid battery/ICE powered are evaluated for the case of Chile, one of the worldwide leaders in the production of lithium (Li) required for manufacturing of Li-ion batteries. The economic and environmental metrics were evaluated by techno-economic analysis (TEA) and Life Cycle Assessment (LCA) tools - SuperPro Designer and Gabi®/GREET® models. The system boundary includes both the renewable and nonrenewable energy sources available in Chile and well-to-pump energy consumptions and GHG emissions due to Li mining and Li-ion battery manufacturing. All the major input data required for TEA and LCA were generated using Autonomie vehicle modeling software. This study compares economic and environmental indicators of three vehicle models for the case of Chile including compact, mid-size, and a light duty truck.

Maintaining and diagnosing vehicle systems often involves a technician connecting a service computer to the vehicle diagnostic port through a vehicle diagnostics adapter (VDA). This creates a connection from the service software to the vehicle network through a protocol adapter. Often, the protocols for the personal computer (PC) hosted diagnostic programs use USB, and the diagnostic port provides access to the controller area network (CAN). However, the PC can also communicate to the VDA via WiFi or Bluetooth. There may be scenarios where these wireless interfaces are not appropriate, such as maintaining military vehicles. As such, a method to defeature the wireless capabilities of a typical vehicle diagnostic adapter is demonstrated without access to the source code or modifying the hardware. The process of understanding the vehicle diagnostic adapter system, its hardware components, the firmware for the main processor and subsystems, and the update mechanism is explored.

The U.S. Environmental Protection Agency instrumented and tested a line-haul Class 8 tractor trailer on a 4-wheel-drive heavy-duty chassis dynamometer. A vehicle model was then developed in the Powertrain Systems Analysis Toolkit (PSAT), Argonne National Laboratory's vehicle simulation tool, using the truck technical specifications and the recorded data, which included the Portable Emissions Measurement System (PEMS) and Controller Area Network (CAN) signals. In this paper, we describe the test scenarios and the analysis performed on the data. We then present the vehicle model and assumptions. Finally, we compare the test and simulation data, including fuel consumption and component signals, as well as the main challenges specific to heavy-duty vehicle testing and simulation.

Hybrid electric vehicles (HEVs) combine two sources of energy and offer a wide variety of component and drivetrain configurations. However, optimizing the blending of these two energy sources is complex. Argonne National Laboratory (ANL) working with the Partnership for a New Generation of Vehicles (PNGV), maintains hybrid vehicle simulation software, the PNGV System Analysis Toolkit (PSAT). PSAT allows users to choose the best configuration and to optimize the control strategy in simulations. The importance of component models and the complexity involved in setting up optimized control laws require validation of the models and control strategies developed in PSAT. In this paper, we first describe our capability to validate each component model with an actual component test, using test stand facilities. Once each component model has been validated, ANL can perform tests on a whole HEV by using a chassis dynamometer.

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).

Studies have shown that obtaining and utilizing information about the future state of vehicles can improve vehicle fuel economy (FE). However, there has been a lack of research into whether near-term technologies can be utilized to improve FE and the impact of real-world prediction error on potential FE improvements. In this study, a speed prediction method utilizing simulated vehicle-to-vehicle (V2V) communication with real-world driving data and a drive cycle database was developed to understand if incorporating near-term technologies could be utilized in a predictive energy management strategy to improve vehicle FE. This speed prediction method informs a predictive powertrain controller to determine the optimal engine operation for various prediction durations. The optimal engine operation is input into a validated high-fidelity fuel economy model of a Toyota Prius.

Autonomous truck and trailer configurations face challenges when operating in reverse due to the lack of sensing on the trailer. It is anticipated that sensor packages will be installed on existing trailers to extend autonomous operations while operating in reverse in uncontrolled environments, like a customer's loading dock. Power Line Communication (PLC) between the trailer and the tractor cannot support high bandwidth and low latency communication. This paper explores the impact of using Ethernet or a wireless medium for commercial trailer-tractor communication on the lifecycle and operation of trailer electronic control units (ECUs) from a Systems Engineering perspective to address system requirements, integration, and security. Additionally, content-based and host-based networking approaches for in-vehicle communication, such as Named Data Networking (NDN) and IP-based networking are compared.

Propagation-based and phase-enhanced x-ray imaging was developed as a unique metrology technique to visualize the internal structure of high-pressure fuel injection nozzles. We have visualized the microstructures inside 200-μm fuel injection nozzles in a 3-mm-thick steel housing using this novel technique. Furthermore, this new x-ray-based metrology technique has been used to directly study the highly transient needle motion in the nozzles in situ and in real-time, which is virtually impossible by any other means. The needle motion has been shown to have the most direct effect on the fuel jet structure and spray formation immediately outside of the nozzle. In addition, the spray cone-angle has been perfectly correlated with the numerically simulated fuel flow inside the nozzle due to the transient nature of the needle during the injection.

The successful commercialization of Electric Vehicles (EVs) and Hybrid Electric Vehicles (HEVs) can provide significant benefits by reducing the United States' growing dependence on petroleum fuels for transportation; decreasing polluting and greenhouse gas emissions; and facilitating a long-term transition to sustainable renewable energy sources. Recognizing these benefits, the U.S. Department of Energy (DOE) supports an active program of long-range R&D to develop electric vehicle (EV) and hybrid electric vehicle (HEV) technologies and to accelerate their commercialization. The DOE Office of Advanced Automotive Technologies (OAAT) supports several innovative R&D programs, conducted in partnership with DOE's national laboratories, industry, other government agencies, universities, and small businesses. The Office has two key R&D cooperative agreements with the U.S. Advanced Battery Consortium (USABC) to develop high-energy batteries for EVs and high-power batteries for HEVs.

Over the past couple of years, Argonne National Laboratory has tested, analyzed, and validated automobile models for the light duty vehicle class, including several types of powertrains including conventional, hybrid electric, plug-in hybrid electric and battery electric vehicles. Argonne’s previous works focused on the light duty vehicle models, but no work has been done on medium and heavy-duty vehicles. This study focuses on the validation of shifting control in advanced automatic transmission technologies for medium duty vehicles by using Argonne’s model-based high-fidelity, forward-looking, vehicle simulation tool, Autonomie. Different medium duty vehicles, from Argonne’s own fleet, including the Ram 2500, Ford F-250 and Ford F-350, were tested with the equipment for OBD (on-board diagnostics) signal data record. For the medium duty vehicles, a workflow process was used to import test data.

Hydrogen is considered one of the most promising future energy carriers. There are several challenges that must be overcome in order to establishing a “hydrogen economy”, including the development of a practical, efficient, and cost-effective power conversion device. Using hydrogen as a fuel for internal combustion engines is a huge step toward developing a large-scale hydrogen infrastructure. This paper summarizes the testing of a hydrogen powered pick-up truck on a chassis dynamometer. The vehicle is powered by a port-injected 8-cylinder engine with an integrated supercharger and intercooler. The 4-wheel drive chassis dynamometer is equipped with a hydrogen delivery, metering and safety system as well as hydrogen specific instrumentation. This instrumentation includes numerous sensors, includes a wide-band lambda sensor and an exhaust gas hydrogen analyzer. This analyzer quantifies the amount of unburned hydrogen in the exhaust indicating the completeness of the combustion.

Hybrid electric vehicles (HEVs) have seen tremendous improvements in performance, fuel economy and cost over the last two decades. As battery and motor prices decrease, HEVs are likely to be even more attractive to consumers. This study considers how HEVs can improve and whether advancements in engines and other components will play a large role in the HEV segment. Past studies have relied on a rule-based component sizing approach for hybrids to meet certain performance criteria. By going beyond this approach, we can explore the design space by varying engine power and electric drivetrain power. This can provide more insights into the fuel-saving potential of HEVs, and the trade-offs required on performance or cost characteristics to achieve those savings. In this study, we examine the fuel-saving potential of three main hybrid powertrain architectures (parallel, series, and power-split) with varying degrees of hybridization (DOH) and using various engine technologies.

Cyber assurance of heavy trucks is a major concern with new designs as well as with supporting legacy systems. Many cyber security experts and analysts are used to working with traditional information technology (IT) networks and are familiar with a set of technologies that may not be directly useful in the commercial vehicle sector. To help connect security researchers to heavy trucks, a remotely accessible testbed has been prototyped for experimentation with security methodologies and techniques to evaluate and improve on existing technologies, as well as developing domain-specific technologies. The testbed relies on embedded Linux-based node controllers that can simulate the sensor inputs to various heavy vehicle electronic control units (ECUs). The node controller also monitors and affects the flow of network information between the ECUs and the vehicle communications backbone.

Recent advances in fuel-cell technology and low-emission, direct-injection spark-ignition and diesel engines for vehicles could significantly change the transportation vehicle power plant landscape in the next decade or so. This paper is a scoping study that compares total fuel cycle options for providing power to personal transport vehicles. The key question asked is, “How much of the energy from the fuel feedstock is available for motive power?” Emissions of selected criteria pollutants and greenhouse gases are qualitatively discussed. This analysis illustrates the differences among options; it is not intended to be exhaustive. Cases considered are hydrogen fuel from methane and from iso-octane in generic proton-exchange membrane (PEM) fuel-cell vehicles, methane and iso-octane in spark-ignition (SI) engine vehicles, and diesel fuel (from methane or petroleum) in direct-injection (DI) diesel engine vehicles.

The introduction of Toyota's hybrid electric vehicle (HEV), the Prius, in Japan has generated considerable interest in HEV technology among U.S. automotive experts. In a follow-up survey to Argonne National Laboratory's two-stage Delphi Study on electric and hybrid electric vehicles (EVs and HEVs) during 1994-1996, Argonne researchers gathered the latest opinions of automotive experts on the future “top-selling” HEV attributes and costs. The experts predicted that HEVs would have a spark-ignition gasoline engine as a power plant in 2005 and a fuel cell power plant by 2020. The projected 2020 fuel shares were about equal for gasoline and hydrogen, with methanol a distant third. In 2020, HEVs are predicted to have series-drive, moderate battery-alone range and cost significantly more than conventional vehicles (CVs). The HEV is projected to cost 66% more than a $20,000 CV initially and 33% more by 2020.