Ethanol Basics
9/11/2018
Ethanol is a widely used, domestically produced renewable fuel made from corn and other plant materials. Ethanol can be blended with gasoline in different amounts. In fact, more than 98% of gasoline sold in the United States contains ethanol to oxygenate the fuel and help to reduce air pollution. Using ethanol in fuel also helps the nation increase the use of domestic alternative fuels, thereby potentially reducing reliance on imported oil. Gasoline and gasoline blendstocks can also use ethanol as an octane enhancer to increase vehicle performance.
A Life-Cycle Analysis of the Greenhouse Gas Emissions from Corn-Based Ethanol
9/5/2018
This analysis, conducted by ICF for the U.S. Department of Agriculture. This report has analyzed the current GHG profile of U.S. corn ethanol and two projected emissions profiles for 2022. The starting point is the GHG life-cycle analysis (LCA) done by the U.S. Environmental Protection Agency (EPA) in 2010 for U.S. corn ethanol as part of its Regulatory Impact Analysis (RIA) for the Revised Renewable Fuel Standard (RFS2). In the RIA, EPA projected that in 2022, the life cycle emissions associated with ethanol would be 21 percent lower than those of an energy equivalent quantity of gasoline.
The Zero Emission Vehicle Regulation
8/24/2018
This fact sheet provides an overview of California’s zero-emission vehicle (ZEV) regulation, which is designed to achieve the state’s long-term emission reduction goals by requiring manufacturers to offer for sale specific numbers of the very cleanest cars available. The ZEV regulation has been adopted by other states.
Co-Optimization of Fuels & Engines: Properties of Co-Optima Core Research Gasolines
8/23/2018
The Co-Optima Core gasolines are a set of fuels developed to have similar autoignition properties but a wide range of composition. The primary compositional components varied were isoparaffins, cycloparaffins, olefins, aromatics, and ethanol. These fuels are being used extensively across the DOE national laboratories in fuel property and engine combustion research.They provide a consistent baseline across research platforms. This document is a compilation of chemical analysis and property data on these fuels, including basic properties such as density, octane numbers, heating value, Reid vapor pressure, and distillation. Elemental composition is reported, as well as detailed hydrocarbon analysis and functional group analysis by NMR spectroscopy. Enthalpy of vaporization at 20 deg C as well as true vapor pressure over the temperature range -20 deg to 120 deg C are also reported.
Authors: Fouts, L.; Fioroni, G.M.; Christensen, E.; Ratcliff, M.; McCormick, R.L.; Zigler, B.T.; Sluder, S.; Szybist, J.P.; Dec, J.E.; Miles, P.C.; Ciatti, S.; Bays, J.T.; Pitz, W.; Mehl, M.
Economy and Emissions Impacts from Solazyme Fuel in UPS Delivery Vehicles
8/10/2018
To improve understanding of the potential fuel economy and emissions impacts from switching a fleet of vehicles from conventional petroleum diesel to synthetic renewable diesel, the National Renewable Energy Laboratory (NREL) conducted fuel economy and emissions analyses at NREL's Renewable Fuels and Lubricants (ReFUEL) Laboratory. Representative test cycles were developed based on real-world data from six package delivery vehicles and six class 8 day-cab tractors operated by UPS in the Dallas, Texas, area. A three-week in-field data collection period yielded 170 days of real-world vehicle operations data that NREL used to select representative standard drive cycles for testing. Fuel economy and emissions tests at the ReFUEL Laboratory showed that, in general, when switching from conventional diesel to renewable diesel observed changes in tailpipe carbon dioxide (CO2), fuel consumption, and fuel economy are primarily driven by changes in fuel properties such as the hydrogen-to-carbon ratio, density, and lower heating value (LHV). The vehicles tested with the renewable diesel showed a consistent 4.2% reduction in tailpipe CO2 emissions, but a 3.5%-4.8% reduction in fuel economy compared with local pump diesel. This is consistent with the 4.2% lower volumetric LHV of the sourced renewable diesel compared to the pump diesel. The UPS package car tested on renewable diesel also demonstrated a 4.1% oxides of nitrogen (NOx) reduction. NOx emissions from the UPS selective-catalyst-reduction-equipped tractor were an order of magnitude lower than the package car but showed relatively higher variability in results from cycle to cycle.
Authors: Kelly, K.; Ragatz, A.
Electrification Futures Study: Scenarios of Electric Technology Adoption and Power Consumption for the United States
8/8/2018
This report is the second publication in a series of Electrification Futures Study publications. The report presents scenarios of electric end-use technology adoption and resulting electricity consumption in the United States. The scenarios reflect a wide range of electricity demand growth through 2050 that result from various electric technology adoption and efficiency projections in the transportation, residential and commercial buildings, and industrial sectors.
Authors: Mai, T.; Jadun, P.; Logan, J.; McMillan, C.; Muratori, M.; Steinberg, D.; Vimmerstedt, L.; Jones, R.; Haley, B.; Nelson, B.
Model Year 2018: Alternative Fuel and Advanced Technology Vehicles
8/7/2018
The fact sheet details the model, vehicle type, emission class, transmission type/speeds, engine size, and fuel economy of a variety of flexible fuel vehicles, hybrid electric vehicles, all-electric, and extended range electric vehicles, as well as CNG and propane vehicles.
Workplace Charge Management with Aggregated Building Loads
8/1/2018
This paper was presented at the 2018 IEEE Transportation Electrification Conference and Expo (ITEC), 13-15 June 2018, Long Beach, California. It describes a workplace charge management system developed to control plug-in electric vehicle charging stations based on aggregated building loads. A system to collect information from drivers was also developed for better charge management performance since the present AC charging station standard does not provide battery state of charge information. First, simulations with uncontrolled charging data were conducted to investigate several scenarios and control methods, and then one method with the most power curtailment during peak load was selected for verification tests. This paper illustrates load reduction test results for 36 charging stations and real-time campus net load data.
Authors: Jun, M.; Meintz, A.
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This copyrighted publication can be viewed and purchased on the Institute of Electrical and Electronics Engineers's website.
Re-Additization of Commercial Biodiesel Blends During Long-Term Storage
8/1/2018
Commercial biodiesel blends were aged at 43 degrees C while monitoring stability. The oxidation stability -- or oxidation reserve expressed as Rancimat induction period (IP) -- gradually decreased from its initial value. At a predetermined IP threshold, an antioxidant was used to restore IP to the ASTM D7467 specification minimum of 6 h, referred to as re-additization. At lower IP values, the amount of antioxidant required increased significantly, and the effectiveness tended to be reduced. Once IP fell to essentially zero, the acid content increased to above the allowable limit and insoluble material was also detected. Storage life was increased relative to the as-received fuels as evidenced by longer time to produce acids. Experience in the field may vary based on storage conditions; however, these results indicate re-additization can significantly increase storage life of biodiesel blends when used with regular monitoring of IP and acid number. An assessment of the storage stability of the as-received fuels showed that the initial IP did not predict storage behavior, although fuels above the specification minimum remained stable for >12 weeks accelerated aging (1 year simulated).
Authors: Christensen, E.D.; Alleman, T.; McCormick, R.L.
Notes:
This copyrighted publication can be downloaded from the Elsevier ScienceDirect website.
Future Automotive Systems Technology Simulator (FASTSim) Validation Report
7/27/2018
The National Renewable Energy Laboratory's Future Automotive Systems Technology Simulator (FASTSim) captures the most important factors influencing vehicle power demands and performs large-scale fuel efficiency calculations very quickly. These features make FASTSim well suited to evaluate a representative distribution of real-world fuel efficiency over a large quantity of in-use driving profiles, which have become increasingly available in recent years owing to incorporation of global positioning system data collection into various travel surveys and studies. In addition, by being open source, computationally lightweight, freely available, and free from expensive third-party software requirements, analyses conducted using FASTSim may be easily replicated and critiqued in an open forum. This is highly desirable for situations in which technical experts seek to reach consensus over questions about what vehicle development plans or public interest strategies could maximize fuel savings and minimize adverse environmental impacts with an evolving vehicle fleet. While FASTSim continues to be refined and improved on an on-going basis, this report compiles available runs using versions of the tool from the past few years to provide illustrative comparison of the model results against measured data.
Authors: Gonder, J.; Brooker, A.; Wood, E.; Moniot, M.
The Role of Demand-Side Incentives and Charging Infrastructure on Plug-in Electric Vehicle Adoption: Analysis of US States. Paper No. 074032
7/13/2018
In the U.S., over 400 state and local incentives have been issued to increase the adoption of plug-in electric vehicles (PEVs) since 2008. This article quantifies the influence of key incentives and enabling factors like charging infrastructure and receptive demographics on PEV adoption. The study focuses on three central questions. First, do consumers respond to certain types of state level vehicle purchase incentives? Second, does the density of public charging infrastructure increase PEV purchases? Finally, does the impact of various factors differ for plug-in hybrid electric vehicles (PHEV), battery electric vehicles (BEV) and vehicle attributes within each category? Based on a regression of vehicle purchase data from 2008 to 2016, we found that tax incentives and charging infrastructure significantly influence per capita PEV purchases. Within tax incentives, rebates are generally more effective than tax credits. BEV purchases are more affected by tax incentives than PHEVs. The correlation of public charging and vehicle purchases increases with the battery-only driving range of a PHEV, while decreasing with increasing driving range of BEVs. Results indicate that early investments in charging infrastructure, particularly along highways; tax incentives targeting BEVs at the lower end of the price premium and PHEVs with higher battery only driving range, and better reflection of the environmental cost of owning gasoline vehicles are likely to increase PEV adoption in the U.S.
Authors: Narassimhan, E.; Johnson, C.
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This journal article (Environmental Research Letters, Volume 13, Number 7) is copyrighted by IOP Publishing and can be downloaded from the IOPScience website.
Empirical Analysis of Electric Vehicle Fast Charging Under Cold Temperatures
7/1/2018
This paper presents an empirical analysis of the effects of temperature on direct current fast charger (DCFC) charging rate and discusses the impact of such effects on wider adoptions of electric vehicles. The authors conducted statistical analysis on the effects of temperature and constructed an electric vehicle charging model that can show the dynamics of DCFC charging process under different temperatures. The results indicate that DCFC charging rate can deteriorate considerably in cold temperatures. These findings may be used as a reference to identify and assess the regions that may suffer from slow charging.
Authors: Motoaki, Y.; Yi, W.; Salisbury, S.
Notes: This Energy Policy article (Vol. 122 (2018): pp. 162-168) is copyrighted by Elsevier B.V. and only available by accessing it through Elsevier's website.
A Driving Cycle Detection Approach Using Map Service API
7/1/2018
Following advancements in smartphone and portable global positioning system (GPS) data collection, wearable GPS data have realized extensive use in transportation surveys and studies. The task of detecting driving cycles (driving or car-mode trajectory segments) from wearable GPS data has been the subject of much research. Specifically, distinguishing driving cycles from other motorized trips (such as taking a bus) is the main research problem in this paper. Many mode detection methods only focus on raw GPS speed data while some studies apply additional information, such as geographic information system (GIS) data, to obtain better detection performance. Procuring and maintaining dedicated road GIS data are costly and not trivial, whereas the technical maturity and broad use of map service application program interface (API) queries offers opportunities for mode detection tasks. The proposed driving cycle detection method takes advantage of map service APIs to obtain high-quality car-mode API route information and uses a trajectory segmentation algorithm to find the best-matched API route. The car-mode API route data combined with the actual route information, including the actual mode information, are used to train a logistic regression machine learning model, which estimates car modes and non-car modes with probability rates. The experimental results show promise for the proposed method's ability to detect vehicle mode accurately.
Authors: Zhu, L.; Gonder, J.D.
Notes:
This copyrighted publication can be downloaded from the Elsevier ScienceDirect website.
Cooperative and Integrated Vehicle and Intersection Control for Energy Efficiency (CIVIC-E2)
6/28/2018
Recent advances in connected vehicle technologies enable vehicles and signal controllers to cooperate and improve the traffic management at intersections. This paper explores the opportunity for cooperative and integrated vehicle and intersection control for energy efficiency (CIVIC-E2) to contribute to a more sustainable transportation system. We propose a two-level approach that jointly optimizes the traffic signal timing and vehicles' approach speed, with the objective being to minimize total energy consumption for all vehicles passing through an isolated intersection. More specifically, at the intersection level, a dynamic programming algorithm is designed to find the optimal signal timing by explicitly considering the arrival time and energy profile of each vehicle. At the vehicle level, a model predictive control strategy is adopted to ensure that vehicles pass through the intersection in a timely fashion. Our simulation study has shown that the proposed CIVIC-E2 system can significantly improve intersection performance under various traffic conditions. Compared with conventional fixed-time and actuated signal control strategies, the proposed algorithm can reduce energy consumption and queue length by up to 31% and 95%, respectively.
Authors: Hou, Y.; Seliman, S.M.S.; Wang, E.; Gonder, J.D.; Wood, E.; He, Q.; Sadek, A.W.; Su, L.; Qiao, C.
Assessing Energy Impacts of Connected and Automated Vehicles at the U.S. National Level - Preliminary Bounds and Proposed Methods
6/26/2018
Connected and automated vehicles (CAVs) can have tremendous impacts on transportation energy use. Using published literature to establish bounds for factors impacting vehicle demand and vehicle efficiency, we find that CAVs can potentially lead to a threefold increase or decrease in light-duty vehicle energy consumption in the United States. Much of this uncertainty is due to possible changes in travel patterns (in vehicle miles traveled) or fuel efficiency (in gallons per mile), as well as future adoption levels and patterns of use. This chapter details the factors which go into these estimates, and presents a methodological approach for refining this wide range of estimated fuel consumption.
Authors: Stephens, T.S.; Auld, J.; Chen, Y.; Gonder, J.; Kontou, E.; Lin, Z.; Xie, F.; Mohammadian, A.; Shabanpour, R.; Gohlke, D.
Notes:
This copyrighted publication can be accessed through Springer International Publishing's website.