Search Results

This paper quantifies the potential of electric propulsion systems to reduce petroleum use and greenhouse gas (GHG) emissions in the 2030 U.S. light-duty vehicle fleet. The propulsion systems under consideration include gasoline hybrid-electric vehicles (HEVs), plug-in hybrid vehicles (PHEVs), fuel-cell hybrid vehicles (FCVs), and battery-electric vehicles (BEVs). The performance and cost of key enabling technologies were extrapolated over a 25-30 year time horizon. These results were integrated with software simulations to model vehicle performance and tank-to-wheel energy consumption. Well-to-wheel energy and GHG emissions of future vehicle technologies were estimated by integrating the vehicle technology evaluation with assessments of different fuel pathways. The results show that, if vehicle size and performance remain constant at present-day levels, these electric propulsion systems can reduce or eliminate the transport sector's reliance on petroleum.

Rare earths are a group of elements whose availability has been of concern due to monopolistic supply conditions and environmentally unsustainable mining practices. To evaluate the risks of rare earths availability to automakers, a first step is to determine raw material content and value in vehicles. This task is challenging because rare earth elements are used in small quantities, in a large number of components, and by suppliers far upstream in the supply chain. For this work, data on rare earth content reported by vehicle parts suppliers was assessed to estimate the rare earth usage of a typical conventional gasoline engine midsize sedan and a full hybrid sedan. Parts were selected from a large set of reported parts to build a hypothetical typical mid-size sedan. Estimates of rare earth content for vehicles with alternative powertrain and battery technologies were made based on the available parts' data.

Gasoline-electric hybrid vehicle technology has been gaining widespread acceptance and has the potential to reduce emissions through reduced fuel consumption. In this study, particulate matter number and mass emission rates, organic and elemental carbon compositions, and number-based size distributions were measured from four gasoline-electric hybrid vehicles (2005 Ford Escape Hybrid, 2004 Toyota Prius, 2003 Honda Civic Hybrid, and 2000 Honda Insight). In addition, one small conventional gasoline vehicle (2002 SmartCar) was tested. The vehicles were driven over five driving cycles and at steady-state speeds of 40 and 80 km/h. Each test was performed at 20°C and at -18°C. Testing took place at the Environmental Science & Technology Centre of Environment Canada using conventional chassis dynamometer procedures. Average distance based emission rates are given for each vehicle under each test condition.

Fuel consumption and gaseous emission data (CO, NOx, THC, and CO2) are reported for four commercially available gasoline-electric hybrid vehicles and one conventional gasoline vehicle tested on a chassis dynamometer over five transient driving cycles (LA4, LA92, HWFET, NYCC, US06), and two steady state modes (40 and 80 km/h), at two ambient temperatures (20 °C, and -18 °C). All vehicles exhibited higher fuel consumption during transient cycles compared to steady-state modes. Cold ambient temperature had a more detrimental effect on fuel consumption rates of the hybrid vehicles compared to those of the conventional gasoline vehicle.

This paper assesses the potential improvement of automotive powertrain technologies 25 years into the future. The powertrain types assessed include naturally-aspirated gasoline engines, turbocharged gasoline engines, diesel engines, gasoline-electric hybrids, and various advanced transmissions. Advancements in aerodynamics, vehicle weight reduction and tire rolling friction are also taken into account. The objective of the comparison is the potential of anticipated improvements in these powertrain technologies for reducing petroleum consumption and greenhouse gas emissions at the same level of performance as current vehicles in the U.S.A. The fuel consumption and performance of future vehicles was estimated using a combination of scaling laws and detailed vehicle simulations. The results indicate that there is significant potential for reduction of fuel consumption for all the powertrains examined.

The dependence of gasoline-electric hybrid vehicle energies on driving conditions and ambient temperature is presented for different drive cycles (2xLA4, 2xLA92, 2xUS06, HWFET and 2xNYCC) and temperatures (20°C and -18°C). The tests were carried out at the Emissions Research and Measurement Division of Environment Canada. Hybrid battery pack current was measured at a frequency of 10 Hz. Regenerative braking energy, charging energy from the engine and battery discharge energy were estimated by using modal speed. The magnitudes of battery energies were found to be directly related to drive cycle properties. Battery discharge energy was very strongly correlated to emission factors of CO₂, while energy recovered by regenerative braking and charging energy from the engine had low to very strong correlations to CO₂ emission factors. CO, NOx and HC had low linear correlations to battery discharge energy.

For this work, a methodology of modeling and predicting fuel consumption in a hybrid vehicle as a function of the engine operating temperature has been developed for cold ambient operation (-7°C, 266°K). This methodology requires two steps: 1) development of a temperature dependent engine brake specific fuel consumption (BSFC) map, and, 2) a data-fitting technique for predicting engine temperature to be used as an input to the temperature dependent BSFC maps. For the first step, response surface methodology (RSM) techniques were applied to generate brake specific fuel consumption (BSFC) maps as a function of the engine thermal state. For the second step, data fitting techniques were also used to fit a simplified lumped capacitance heat transfer model using several experimental datasets. Utilizing these techniques, an analysis of fuel consumption as a function of thermal state across a broad range of engine operating conditions is presented.

The transportation sector in the United States is a major contributor to global energy consumption and carbon dioxide emission. To assess the future potentials of different technologies in addressing these two issues, we used a family of simulation programs to predict fuel consumption for passenger cars in 2020. The selected technology combinations that have good market potential and could be in mass production include: advanced gasoline and diesel internal combustion engine vehicles with automatically-shifting clutched transmissions, gasoline, diesel, and compressed natural gas hybrid electric vehicles with continuously variable transmissions, direct hydrogen, gasoline and methanol reformer fuel cell hybrid electric vehicles with direct ratio drive, and battery electric vehicle with direct ratio drive.

A 1.9L turbocharged direct-injection engine representing a model year 1998-2003 Volkswagen vehicle, equipped with the OEM diesel oxidation catalyst (DOC) and exhaust gas recirculation (EGR), was tested on an eddy-current engine dynamometer with a critical flow venturi-constant volume sampling system (CFV-CVS). The engine was operated over three steady-state modes: 1600 rev/min at 54 Nm; 1800 rev/min at 81 Nm; and 2000 rev/min at 98 Nm. Commercially available ultra-low sulfur diesel fuel (≺15 ppm S) was splash-blended with fatty acid methyl ester biodiesels derived from three different feedstocks: canola, soy, and tallow/waste fry oil. Test blend levels included: 0%, 2%, 5%, 20%, 50%, and 100% biodiesel for each feedstock.

As more stringent vehicle emission standards are introduced worldwide, there is an increased need to provide a thorough assessment of the environmental impact of alternative fuels. With the advent of CNG as a viable transportation fuel, the development of advanced computer controlled fuel delivery systems is imperative in order to ensure acceptable emission performance. At present, the majority of light and medium duty engines operating on natural gas are primarily gasoline automotive engines which have been retrofitted to allow for the use of CNG. The Mobile Sources Emissions Division of Environment Canada and the Canadian Gas Association have conducted a joint test program in order to develop a database of exhaust emissions from vehicles typically converted for operation on either gasoline or natural gas at various operating temperatures.

The MIT-based Mars Gravity Biosatellite payload engineering team has been engaged in designing and prototyping sensor and control systems for deployment within the rodent housing zone of the satellite, including novel video processing and atmospheric management tools. The video module will be a fully autonomous real-time analysis system that takes raw video footage of the specimen mice as input and distills those parameters which are of primary physiological importance from a scientific research perspective. Such signals include activity level, average velocity and rearing behavior, all of which will serve as indicators of animal health and vestibular function within the artificial gravity environment. Unlike raw video, these parameters require minimal storage space and can be readily transmitted to earth over a radio link of very low bandwidth.

Measurements were made of exhaust histories of the following species: unburned hydrocarbons (HC), carbon monoxide, carbon dioxide, oxygen, and nitric oxide (NO). The measurements show that the exhaust flow can be divided into two distinct phases: a leading gas low in HC and high in NO followed by a trailing gas high in HC and low in NO. Calculations of time resolved equivalence ratio throughout the exhaust process show no evidence of a stratified combustion. The exhaust mass flow rate is time resolved by forcing the flow to be locally quasi-steady at an orifice placed in the exhaust pipe. The results with the quasi-steady assumption are shown to be consistent with the measurements. Predictions are made of time resolved mass flow rate which compare favorably to the experimental data base. The composition and flow histories provide sufficient information to calculate the time resolved flow rates of the individual species measured.

Accurately predicting real-world vehicle energy consumption is essential for optimizing vehicle designs, enhancing energy efficiency, and developing effective energy management strategies. This paper presents a data-driven approach that utilizes machine learning techniques and a comprehensive dataset of vehicle parameters and environmental factors to create precise energy consumption prediction models. The methodology involves recording real-world vehicle data using data loggers to extract information from the CAN bus systems for ICE and hybrid electric, as well as hydrogen and battery fuel cell vehicles. Data cleaning and cycle-based analysis are employed to process the dataset for accurate energy consumption prediction. This includes cycle detection and analysis using methods from statistics and signal processing, and then pattern recognition based on these metrics.