We present a parametric analysis of factors that can influence advanced fuel and technology deployments in U.S. Class 7-8 trucks through 2050. The analysis focuses on the competition between traditional diesel trucks, natural gas vehicles (NGVs), and ultra-efficient powertrains. Underlying the study is a vehicle choice and stock model of the U.S. heavy-duty vehicle market. The model is segmented by vehicle class, body type, powertrain, fleet size, and operational type. We find that conventional diesel trucks will dominate the market through 2050, but NGVs could have significant market penetration depending on key technological and economic uncertainties. Compressed natural gas trucks conducting urban trips in fleets that can support private infrastructure are economically viable now and will continue to gain market share. Ultra-efficient diesel trucks, exemplified by the U.S. Department of Energy's SuperTruck program, are the preferred alternative in the long haul segment, but could compete with liquefied natural gas (LNG) trucks if the fuel price differential between LNG and diesel increases. However, the greatest impact in reducing petroleum consumption and pollutant emissions is had by investing in efficiency technologies that benefit all powertrains, especially the conventional diesels that comprise the majority of the stock, instead of incentivizing specific alternatives.
This report describes work performed for an Early Career Research and Development project. This project developed a heavy-duty vehicle (HDV) sector model to assess the factors influencing alternative fuel and efficiency technology adoption. This model builds on a Sandia light duty vehicle sector model and provides a platform for assessing potential impacts of technological advancements developed at the Combustion Research Facility. Alternative fuel and technology adoption modeling is typically developed around a small set of scenarios. This HDV sector model segments the HDV sector and parameterizes input values, such as fuel prices, efficiencies, and vehicle costs. This parameterization enables sensitivity and trade space analyses to identify the inputs that are most associated with outputs of interest, such as diesel consumption and greenhouse gas emissions. Thus this analysis tool enables identification of the most significant HDV sector drivers that can be used to support energy security and climate change goals.
In the coming decades, vehicle and fuel options and their supporting infrastructure must undergo significant transformations to achieve aggressive national targets for reducing petroleum consumption and lowering greenhouse gas (GHG) emissions. Vehicle electrification, advanced biofuels, natural gas, and hydrogen fuel cells are among the promising technology options that are being explored as future alternatives. A number of recent U.S. studies have examined how a mix of technology and policy options can contribute to the aggressive goals of 50- 80% reduction in petroleum consumption and 80% reduction in GHG emissions by 2050. These include reports issued by the National Petroleum Council, National Academies, and U.S. Department of Energy. While these studies all generally point to the need for a portfolio of technologies for the transportation sector, they do not draw the same set of conclusions for the portfolio mix. Moreover, they were commissioned for a variety of reasons, applied different modelling and analytical approaches in their assessments, and used a variety of assumptions in reaching their findings and recommendations. Using four recent major U. S. scenario analyses, this paper will illustrate several factors that can influence the interpretation of their results. Consideration of the underlying technology and policy assumptions, analytical approaches, and presentation of results can enable a more robust comparison across projections for the vehicle and fuel mix.
This report presents a system dynamics based model of the supply-demand interactions between the US light-duty vehicle (LDV) fleet, its fuels, and the corresponding primary energy sources through the year 2050. An important capability of our model is the ability to conduct parametric analyses. Others have relied upon scenario-based analysis, where one discrete set of values is assigned to the input variables and used to generate one possible realization of the future. While these scenarios can be illustrative of dominant trends and tradeoffs under certain circumstances, changes in input values or assumptions can have a significant impact on results, especially when output metrics are associated with projections far into the future. This type of uncertainty can be addressed by using a parametric study to examine a range of values for the input variables, offering a richer source of data to an analyst.The parametric analysis featured here focuses on a trade space exploration, with emphasis on factors that influence the adoption rates of electric vehicles (EVs), the reduction of GHG emissions, and the reduction of petroleum consumption within the US LDV fleet. The underlying model emphasizes competition between 13 different types of powertrains, including conventional internal combustion engine (ICE) vehicles, flex-fuel vehicles (FFVs), conventional hybrids(HEVs), plug-in hybrids (PHEVs), and battery electric vehicles(BEVs).We find that many factors contribute to the adoption rates of EVs. These include the pace of technological development for the electric powertrain, battery performance, as well as the efficiency improvements in conventional vehicles. Policy initiatives can also have a dramatic impact on the degree of EV adoption. The consumer effective payback period, in particular, can significantly increase the market penetration rates if extended towards the vehicle lifetime.Widespread EV adoption can have noticeable impact on petroleum consumption and greenhouse gas(GHG) emission by the LDV fleet. However, EVs alone cannot drive compliance with the most aggressive GHG emission reduction targets, even as the current electricity source mix shifts away from coal and towards natural gas. Since ICEs will comprise the majority of the LDV fleet for up to forty years, conventional vehicle efficiency improvements have the greatest potential for reductions in LDV GHG emissions over this time.These findings seem robust even if global oil prices rise to two to three times current projections. Thus,investment in improving the internal combustion engine might be the cheapest, lowest risk avenue towards meeting ambitious GHG emission and petroleum consumption reduction targets out to 2050.3 Acknowledgment The authors would like to thank Dr. Andrew Lutz, Dr. Benjamin Wu, Prof. Joan Ogden and Dr. Christopher Yang for their suggestions over the course of this project. This work was funded by the Laboratory Directed Research and Development program at Sandia National Laboratories.
Contaminant sensor placement is often cast as an optimization problem to minimize objectives such as the probability of failed detection or public health impact. In the case of an actual incident, the sensor network data will also be utilized for event characterization to estimate source location, size and hazard areas. We present a sensor placement methodology to optimize for event characterization performance, and also compare the results to traditional placement objectives.