Search Results

The use of virtual prototyping early in the design stage of a product has gained popularity due to reduced cost and time to market. The state of the art in vehicle simulation has reached a level where full vehicles are analyzed through simulation but major difficulties continue to be present in interfacing the vehicle model with accurate powertrain models and in developing adequate formulations for the contact between tire and terrain (specifically, scenarios such as tire sliding on ice and rolling on sand or other very deformable surfaces). The proposed work focuses on developing a ground vehicle simulation capability by combining several third party packages for vehicle simulation, tire simulation, and powertrain simulation. The long-term goal of this project consists in promoting the Digital Car idea through the development of a reliable and robust simulation capability that will enhance the understanding and control of off-road vehicle performance.

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.

For several years there has been a great deal of effort made in researching ways to run a compression ignition engine with simultaneously high efficiency and low emissions. Recently much of this focus has been dedicated to using gasoline-like fuels that are more volatile and less reactive than conventional diesel fuel to allow the combustion to be more premixed. One of the key challenges to using fuels with such properties in a compression ignition engine is stable engine operation at low loads. This paper provides an analysis of how stable gasoline compression ignition (GCI) engine operation was achieved down to idle speed and load on a multi-cylinder compression ignition engine using only 87 anti-knock index (AKI) gasoline. The variables explored to extend stable engine operation to idle included: uncooled exhaust gas recirculation (EGR), injection timing, injection pressure, and injector nozzle geometry.

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 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.

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).

This paper analyzes fuel economy benefits of direct hydrogen and gasoline reformer fuel cell vehicles, with special focus on cold-start impacts on these fuel cell based vehicles. Comparing several existing influential studies reveals that the most probable estimates from these studies differ greatly on the implied benefits of both types of fuel cell vehicles at the tank-to-wheel level (vehicle-powertrain efficiency and/or specific power), leading to great uncertainties in estimating well-to-wheel fuel energy and/or greenhouse gas (GHG) emission reduction potentials. This paper first addresses methodological issues to influence the outcome of these analyses. With one exception, we find that these studies consistently ignore cold-start and warm-up issues, which play important roles in determining both energy penalties and start-up time of fuel cell vehicles. To better understand cold-start and warm-up behavior, this paper examines approaches and results based on two available U.S.

The basic idea behind large-eddy simulation (LES) is to accurately resolve the large energy-containing scales and to use subgrid-scale (SGS) models for the smaller scales. The accuracy of LES can be significantly impacted by the numerical discretization schemes and the choice of the SGS model. This work investigates the accuracy of low-order LES codes in the simulation of a turbulent round jet which is representative of fuel jets in engines. The turbulent jet studied is isothermal with a Reynolds number of 6800. It is simulated using Converge, which is second-order accurate in space and first-order in time, and FLEDS, developed at Purdue University, which is sixth-order accurate in space and fourth-order in time. The high-order code requires the resolution of acoustic time-scales and hence is approximately 10 times more expensive than the low-order code.

Hydrogen is widely considered a promising fuel for future transportation applications for both, internal combustion engines and fuel cells. Due to their advanced stage of development and immediate availability hydrogen combustion engines could act as a bridging technology towards a wide-spread hydrogen infrastructure. Although fuel cell vehicles are expected to surpass hydrogen combustion engine vehicles in terms of efficiency, the difference in efficiency might not be as significant as widely anticipated [1]. Hydrogen combustion engines have been shown capable of achieving efficiencies of up to 45 % [2]. One of the remaining challenges is the reduction of nitric oxide emissions while achieving peak engine efficiencies. This paper summarizes research work performed on a single-cylinder hydrogen direct injection engine at Argonne National Laboratory.

Building a vehicle model with sufficient accuracy for fuel economy analysis is a time-consuming process, even with the modern-day simulation tools. Obtaining the right kind of data for modeling a vehicle can itself be challenging, given that while OEMs advertise the power and torque capability of their engines, the efficiency data for the components or the control algorithms are not usually made available for independent verification. The U.S. Department of Energy (DOE) funds the testing of vehicles at Argonne National Laboratory, and the test data are publicly available. Argonne is also the premier DOE laboratory for the modeling and simulation of vehicles. By combining the resources and expertise with available data, a process has been created to automatically develop a model for any conventional vehicle that is tested at Argonne. This paper explains the process of analyzing the publicly available test data and computing the parameters of various components from the analysis.

Süd-Chemie Inc. is producing and supplying an autothermal reforming (ATR) catalyst that was developed by Argonne National Laboratory (ANL) for reforming hydrocarbon fuels to generate H2 for automotive fuel cell systems. The catalyst is derived from solid oxide fuel cell technology, where a transition metal is supported on an oxide-ion-conducting substrate, such as ceria or zirconia, that is doped with an un-reducible oxide, such as gadolinium or samarium, to improve its oxide ion conductivity and to increase the number of surface oxygen ion vacancies. The catalyst has been shown to produce an H2-rich gas (reformate) from a wide variety of hydrocarbon fuels, including methane, natural gas, and commercial-grade gasolines and diesels with high selectivity. Platinum was the transition metal used in the first generation of the ANL catalyst.

Alternative electric motor geometry with potentially increased efficiency is being considered for hybrid electric vehicle applications. An axial flux motor with a dynamically adjustable air gap (i.e., mechanical field weakening) has been tested, analyzed, and modeled for use in a vehicle simulation tool at Argonne National Laboratory. The advantage of adjusting the flux is that the motor torque-speed characteristics can better match the vehicle load. The challenge in implementing an electric machine with these qualities is to develop a control strategy that takes advantage of the available efficiency improvements without using excessive energy to mechanically adjust the air gap and thus reduce the potential energy savings. Motor efficiency was mapped in terms of speed, torque, supply voltage, and rotor-to-stator air gap.

The U.S. Department of Energy (DOE) Office of Advanced Automotive Technologies (OAAT), in partnership with industry, is developing transportation technologies that will improve the energy efficiency of our transportation system. Most OAAT programs are focused exclusively on technology development. However, the twin goals of developing innovative technologies and transferring them to industry led OAAT to realize the growing need for people trained in non-traditional, emerging technologies. The Graduate Automotive Technology Education (GATE) program combines graduate-level education with technology development and transfer by training a new generation of automotive engineers in critical multi-disciplinary technologies, by fostering cooperative research in those technologies, and by transferring those technologies directly to industrial organizations.

Gasoline is a complex fuel. Many of the constituents of gasoline that are beneficial for the internal combustion engine (ICE) are expected to be challenging for on-board reformers in fuel-cell vehicles. To address these issues, the autothermal reforming of gasoline and individual components of gasoline has been investigated. The results indicate that aromatic components require higher temperatures and longer contact times to reform than paraffinic components. Napthenic components require higher temperatures to reform, but can be reformed at higher space velocities than paraffinic components. The effects of sulfur are dependent on the catalyst. These results suggest that further evolution of gasoline could reduce the demands on the reformer and provide a better fuel for a fuel-cell vehicle.

Because of their high efficiency and low emissions, fuel-cell vehicles are undergoing extensive research and development. When considering the introduction of advanced vehicles, a complete well-to-wheel evaluation must be performed to determine the potential impact of a technology on carbon dioxide and Green House Gases (GHGs) emissions. Several modeling tools developed by Argonne National Laboratory (ANL) were used to evaluate the impact of advanced powertrain configurations. The Powertrain System Analysis Toolkit (PSAT) transient vehicle simulation software was used with a variety of fuel cell system models derived from the General Computational Toolkit (GCtool) for pump-to-wheel (PTW) analysis, and GREET (Green house gases, Regulated Emissions and Energy use in Transportation) was used for well-to-pump (WTP) analysis. This paper compares advanced propulsion technologies on a well-to-wheel energy basis by using current technology for conventional, hybrid and fuel cell technologies.

Several studies, conducted from 1997 to 2001, have employed vehicle and powertrain simulation models to estimate fuel economy gains for a variety of fuel-cell powertrains. Many of those studies have attempted to control for the comparability of performance between conventional and fuel-cell vehicles (FCVs), but different sets of performance goals and simulation models have been used. This paper reviews the estimates of fuel economy gain (in mpg) vs. varying measures of performance change for a set of those studies. We examine some of the potential causes for the variability of these estimates - fuel used, powertrain hybridization, vehicle raw energy requirements (load), and variations in analysts' assumptions/estimates - when substituting several types of fuel-cell powertrains. Our study includes development of a database and detailed examination of the relationships among powertrain and vehicle characteristics and fuel economy gain estimates for the selected studies.

The control analysis and model validation of a 2014 BMW i3-Range Extender (REX) was conducted based on the test data in this study. The vehicle testing was performed on a chassis dynamometer set within a thermal chamber at the Advanced Powertrain Research Facility at Argonne National Laboratory. The BMW i3-REX is a series-type plug-in hybrid range extended vehicle which consists of a 0.65L in-line 2-cylinder range-extending engine with a 26.6kW generator, 125kW permanent magnet synchronous AC motor, and 18.8kWh lithium-ion battery. Both component and vehicle model including thermal aspects, were developed based on the test data. For example, the engine fuel consumption rate, battery resistance, or cabin HVAC energy consumption are affected by the temperature. Second, the vehicle-level control strategy was analyzed at normal temperature conditions (22°C ambient temperature). The analysis focuses on the engine on/off strategy, battery SOC balancing, and engine operating conditions.

Vehicles with electrified powertrains, such as hybrid electric vehicles (HEVs), plug-in HEV (PHEVs), and AEVs (all-electric vehicles using grid-supplied battery energy exclusively), are potentially marketable because of low operating costs, but each comes with a significant initial cost penalty in comparison to a conventional vehicle (CV) powered by an internal combustion engine. Accordingly, a high rate of utilization is necessary for cost effectiveness. This paper examines the projected future (2020) cost effectiveness of several alternative powertrains within a standard compact sedan glider: an AEV and a set of selected input-split and output-split HEV and PHEV powertrains with various battery power and energy storage capabilities. Vehicle performance and consumption rates of fuel and electricity were estimated using vehicle simulations, and vehicle prices were estimated using cost models.

As vehicle technology progresses to new levels of sophistication, so too, vehicle test methods must evolve. This is true for analytical testing in a laboratory and for on-road vehicle testing. Every year since 1993, the U.S. Department of Energy (DOE) and original equipment manufacturer (OEM) sponsors have organized a series of competitions featuring advanced hybrid electric vehicle (HEV) technology to develop and promote DOE goals in fuel savings and alternative fuel usage. The competition has evolved over many years and has included many alternative fuels feeding the prime mover (including hydrogen fuel cells). EcoCAR turned its focus to plug-in hybrid electric vehicles (PHEVs) and it was quickly realized that to keep using on-road testing methods to evaluate fuel and electricity consumption, a new method needed to be developed that would properly weight depleting operation with the sustaining operation, using the established Utility Factor (UF) method.

The detailed morphological properties of diesel particulate matter were analyzed along the exhaust system at various engine operating conditions (in a range of 1000 - 2500 rpm and 10 - 75 % loads of maximum torques). A 1.7-L turbocharged light-duty diesel engine was powered with California low-sulfur diesel fuel injected by a common-rail injection system, of which particulate emissions were controlled by an exhaust gas recirculation (EGR) system and two oxidation catalysts. A unique thermophoretic sampling system first developed for internal combustion engine research, a high-resolution transmission electron microscope (TEM), and a customized image processing/data acquisition system were key instruments that were used for the collection of particulate matter, subsequent imaging of particle morphology, and detailed analysis of particle dimensions and fractal geometry, respectively.