Changzheng Liu

Estimation of Energy Use by Plug-In Hybrid Electric Vehicles; Validating Gamma Distribution for Representing Random Daily Driving Distance

The fuel and electricity consumptions of plug-in hybrid electric vehicles (PHEVs) are sensitive to the variation of daily vehicle miles traveled (DVMT). Although some researchers have assumed that DVMT follows a gamma distribution, such an assumption has yet to be validated. On the basis of continuous travel data from the Global Positioning System for 382 vehicles, each tracked for at least 183 days, the authors of this study validated the gamma assumption in the context of PHEV energy analysis. Small prediction errors caused by the gamma assumption were found in PHEV fuel use, electricity use, and energy cost. Validating the reliability of the gamma distribution paves the way for its application in energy use analysis of PHEVs in the real world. The gamma distribution can be easily specified with few pieces of driver information and is relatively easy for mathematical manipulation. Validation with real world travel data enables confident use of the gamma distribution in a variety of applications, such as the development of vehicle consumer choice models, the quantification of range anxiety for battery electric vehicles, the investigation of the role of charging infrastructure, and the construction of online calculators that provide personal estimates of PHEV energy use.

MODELING THE DEMAND FOR E85 IN THE UNITED STATES

How demand for E85 might evolve in the future in response to changing economics and policies is an important subject to include in the National Energy Modeling System (NEMS). This report summarizes a study to develop an E85 choice model for NEMS. Using the most recent data from the states of Minnesota, North Dakota, and Iowa, this study estimates a logit model that represents E85 choice as a function of prices of E10 and E85, as well as fuel availability of E85 relative to gasoline. Using more recent data than previous studies allows a better estimation of non-fleet demand and indicates that the price elasticity of E85 choice appears to be higher than previously estimated. Based on the results of the econometric analysis, a model for projecting E85 demand at the regional level is specified. In testing, the model produced plausible predictions of US E85 demand to 2040.

PHEV Energy Use Estimation: Validating the Gamma Distribution for Representing the Random Daily Driving Distance

The fuel and electricity consumptions of plug-in hybrid electric vehicles (PHEVs) are sensitive to the variation of daily vehicle miles traveled (DVMT). Although some researchers have assumed that DVMT follows a gamma distribution, such an assumption has yet to be validated. On the basis of continuous travel data from the Global Positioning System for 382 vehicles, each tracked for at least 183 days, the authors of this study validated the gamma assumption in the context of PHEV energy analysis. Small prediction errors caused by the gamma assumption were found in PHEV fuel use, electricity use, and energy cost. Validating the reliability of the gamma distribution paves the way for its application in energy use analysis of PHEVs in the real world. The gamma distribution can be easily specified with few pieces of driver information and is relatively easy for mathematical manipulation. Validation with real world travel data enables confident use of the gamma distribution in a variety of applications, such as the development of vehicle consumer choice models, the quantification of range anxiety for battery electric vehicles, the investigation of the role of charging infrastructure, and the construction of online calculators that provide personal estimates of PHEV energy use.

Consumer Choice of E85 Denatured Ethanol Fuel Blend: Price Sensitivity and Cost of Limited Fuel Availability

The promotion of greater use of E85, a fuel blend of 85% denatured ethanol, by flex-fuel vehicle owners is an important means of complying with the Renewable Fuel Standard 2. A good understanding of factors affecting E85 demand is necessary for effective policies that promote E85 and for developing models that forecast E85 sales in the United States. In this paper, the sensitivity of aggregate E85 demand to E85 and gasoline prices is estimated, as is the relative availability of E85 versus gasoline. The econometric analysis uses recent data from Minnesota, North Dakota, and Iowa. The more recent data allow a better estimate of nonfleet demand and indicate that the market price elasticity of E85 choice is substantially higher than previously estimated.

Estimation of Energy Use by Plug-In Hybrid Electric Vehicles

The fuel and electricity consumptions of plug-in hybrid electric vehicles (PHEVs) are sensitive to the variation of daily vehicle miles traveled (DVMT). Although some researchers have assumed that DVMT follows a gamma distribution, such an assumption has yet to be validated. On the basis of continuous travel data from the Global Positioning System for 382 vehicles, each tracked for at least 183 days, the authors of this study validated the gamma assumption in the context of PHEV energy analysis. Small prediction errors caused by the gamma assumption were found in PHEV fuel use, electricity use, and energy cost. Validating the reliability of the gamma distribution paves the way for its application in energy use analysis of PHEVs in the real world. The gamma distribution can be easily specified with few pieces of driver information and is relatively easy for mathematical manipulation. Validation with real world travel data enables confident use of the gamma distribution in a variety of applications, such as the development of vehicle consumer choice models, the quantification of range anxiety for battery electric vehicles, the investigation of the role of charging infrastructure, and the construction of online calculators that provide personal estimates of PHEV energy use.

Feebates and Fuel Economy Standards: Impacts on Fuel Use in Light-Duty Vehicles and Greenhouse Gas Emissions

This study evaluates the potential impacts of a national feebate system, a market-based policy that consists of graduated fees on low-fuel-economy (or high-emitting) vehicles and rebates for high-fuel-economy (or low-emitting) vehicles. In their simplest form, feebate systems operate under three conditions: a benchmark divides all vehicles into two categories—those charged fees and those eligible for rebates; the sizes of the fees and rebates are a function of a vehicles deviation from its benchmark; and placement of the benchmark ensures revenue neutrality or a desired level of subsidy or revenue. A model developed by the University of California for the California Air Resources Board was revised and used to estimate the effects of six feebate structures on fuel economy and sales of new light-duty vehicles, given existing and anticipated future fuel economy and emission standards. These estimates for new vehicles were then entered into a vehicle stock model that simulated the evolution of the entire vehicle stock. The results indicate that feebates could produce large, additional reductions in emissions and fuel consumption, in large part by encouraging market acceptance of technologies with advanced fuel economy, such as hybrid electric vehicles.