Zhenhong Lin

Promoting the Market for Plug-In Hybrid and Battery Electric Vehicles: Role of Recharge Availability

Much recent attention has been drawn to providing adequate recharge availability as a means to promote the battery electric vehicle (BEV) and plug-in hybrid electric vehicle (PHEV) market. The possible role of improved recharge availability in developing the BEV-PHEV market and the priorities that different charging options should receive from the government require better understanding. This study reviews the charging issue and conceptualizes it into three interactions between the charge network and the travel network. With travel data from 3,755 drivers in the National Household Travel Survey, this paper estimates the distribution among U.S. consumers of (a) PHEV fuel-saving benefits by different recharge availability improvements, (b) range anxiety by different BEV ranges, and (c) willingness to pay for workplace and public charging in addition to home recharging. With the Oak Ridge National Laboratory MA3T model, the impact of three recharge improvements is quantified by the resulting increase in BEV-PHEV sales. Compared with workplace and public recharging improvements, home recharging improvement appears to have a greater impact on BEV-PHEV sales. The impact of improved recharging availability is shown to be amplified by a faster reduction in battery cost.

Assessing Energy Impact of Plug-In Hybrid Electric Vehicles: Significance of Daily Distance Variation over Time and Among Drivers

Accurate assessment of the impact of plug-in hybrid electric vehicles (PHEVs) on petroleum and electricity consumption is a necessary step toward effective policies. Variations in daily vehicle miles traveled (VMT) over time and among drivers affect PHEV energy impact, but the significance is not well understood. This paper uses a graphical illustration, a mathematical derivation, and an empirical study to examine the cause and significance of such an effect. The first two methods reveal that ignoring daily variation in VMT always causes underestimation of petroleum consumption and overestimation of electricity consumption by PHEVs; both biases increase as the assumed PHEV charge-depleting (CD) range moves closer to the average daily VMT. The empirical analysis based on national travel survey data shows that the assumption of uniform daily VMT over time and among drivers causes nearly 68% underestimation of expected petroleum use and nearly 48% overestimation of expected electricity use by PHEVs with a 40-mi CD range (PHEV40s). Also for PHEV40s, consideration of daily variation in VMT over time but not among drivers—similar to the way the utility factor curve is derived in SAE Standard SAE J2841—causes underestimation of expected petroleum use by more than 24% and overestimation of expected electricity use by about 17%. Underestimation of petroleum use and overestimation of electricity use increase with larger-battery PHEVs.

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.

Stochastic Modeling of Battery Electric Vehicle Driver Behavior: Impact of Charging Infrastructure Deployment on the Feasibility of Battery Electric Vehicles

A stochastic modeling approach is proposed to characterize battery electric vehicle (BEV) drivers’ behavior. The approach uses longitudinal travel data and thus allows more realistic analysis of the impact of the charging infrastructure on BEV feasibility. BEV feasibility is defined as the probability that the ratio of the distance traveled between charges to the BEV range is kept within a comfort level (i.e., drivers are comfortable with driving the BEV when the batterys state of charge is above a certain level). When the ratio exceeds the comfort level, travel adaptation is needed–-use of a substitute vehicle, choice of an alternative transportation mode, or cancellation of a trip. The proposed stochastic models are applied to quantify BEV feasibility at different charging infrastructure deployment levels with the use of GPS-based longitudinal travel data collected in the Seattle, Washington, metropolitan area. In the Seattle case study, the range of comfort level was found to be critical. If BEV drivers were comfortable with using all the nominal range, about 10% of the drivers needed no or little travel adaptation (i.e., they made changes on less than 0.5% of travel days), and almost 50% of the drivers needed travel adaptation on up to 5% of the sampled days. These percentages dropped by half when the drivers were only comfortable with using up to 80% of the range. In addition, offering opportunities for one within-day recharge can significantly increase BEV feasibility, provided that the drivers were willing to make some travel adaptation (e.g., up to 5% of drivers in the analysis).

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.

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.

Performance, Cost, and Market Share of Conventional Vehicle Efficiency Technologies? Retrospective Comparison of Regulatory Document Projections for Corporate Average Fuel Economy and Greenhouse Gas Standards

This study conducted an analysis of regulatory documents on current energy- and greenhouse gas–relevant conventional vehicle efficiency technologies in the corporate average fuel economy standards (2017 to 2025) and greenhouse gas rulemaking context by NHTSA and EPA. The focus was on identifying what technologies today—as estimated now (2015 to 2016)—receive higher or lower expectations with regard to effectiveness, cost, and consumer adoption than what experts projected during the 2010 to 2011 rulemaking period. A broad range of conventional vehicle efficiency technologies, including gasoline engine and diesel engine, transmission, accessory, hybrid, and vehicle body technologies, was investigated in this analysis. Most assessed technologies were found to have had better competitiveness than expected with regard to effectiveness or costs, or both, with costs and market penetration more difficult to predict than technology effectiveness.

Assessing Energy Impacts of Connected and Automated Vehicles at the U.S. National Level—Preliminary Bounds and Proposed Methods

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.

Quantitative Evaluation of MD/HD Vehicle Electrification using Statistical Data

This paper presents a wide-ranging analysis of Class 3-8 commercial vehicle electrification by means of developing a framework tool which uses a quantitative method of estimating electric vehicle energy consumption and appropriate charging considerations. The Fleet DNA composite statistics on real-world driving behavior is used to evaluate feasible or market-ready battery electric vehicle (BEV) technologies in medium- and heavy-duty (MD/HD) applications. In the paper, ten representative Class 3-8 commercial vehicle electrifications have been evaluated as a function of various service coverages, including applications in port drayage tractors, refuse trucks, delivery trucks, buses, and bucket trucks. The results indicate significant energy savings and fuel cost savings across all MD/HD vehicle electrifications. The majority of MD BEVs, with the exception of Class 3 bucket trucks, achieve better than a 5-year payback with 50–75% service coverage. For HD BEVs, with the exception of the Class 8 port drayage tractors, the 90% service coverage results in a 10-year or longer payback time, while the 50% service coverage yields a 7–8 year payback. Class 8 port drayage tractors should achieve payback in no more than a 3.5 years with 50–75% service coverage. Thus, the analysis indicates a highly feasible potential for Class 3-6 MD vehicles to be electrified, and feasible opportunities for electrification in Class 7-8 HD short-distance applications.