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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.

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 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.

In comparison to the state of knowledge of standard internal combustion vehicles, there is relatively little known on how to best implement component sub-systems and best integrate these systems together to create a hybrid electric vehicle.

In hybrid electric vehicle (HEV) design and operation, no parameter plays a more important role than efficiency. Unlike conventional vehicles, HEVs are generally energy and power limited by the battery bank and auxiliary power unit. As a result, the overall system efficiency in the conversion of chemical or stored energy into kinetic energy of the vehicle is the key parameter that drives the overall system design. We have undertaken a sensitivity analysis of HEVs to understand in detail the various factors and their respective weights that affect the overall system efficiency. Our goal is to identify the parameters that most significantly influence vehicle efficiency. The results of this study may be used as a guide to focus work on the areas of most benefit for HEVs as well as aiding in sizing vehicle components for maximum efficiency.

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.