تجزیه و تحلیل هزینه از وسایل نقلیه هیبرید الکتریکی پلاگین با استفاده از اطلاعات سفر طولی مبتنی بر GPS

Using spatial, longitudinal travel data of 415 vehicles over 3–18 months in the Seattle metropolitan area, this paper estimates the operating costs of plug-in hybrid electric vehicles (PHEVs) of various electric ranges (10, 20, 30, and 40 miles) for 3, 5, and 10 years of payback period, considering different charging infrastructure deployment levels and gasoline prices. Some key findings were made. (1) PHEVs could help save around 60% or 40% in energy costs, compared with conventional gasoline vehicles (CGVs) or hybrid electric vehicles (HEVs), respectively. However, for motorists whose daily vehicle miles traveled (DVMT) is significant, HEVs may be even a better choice than PHEV40s, particularly in areas that lack a public charging infrastructure. (2) The incremental battery cost of large-battery PHEVs is difficult to justify based on the incremental savings of PHEVs' operating costs unless a subsidy is offered for large-battery PHEVs. (3) When the price of gasoline increases from $4/gallon to $5/gallon, the number of drivers who benefit from a larger battery increases significantly. (4) Although quick chargers can reduce charging time, they contribute little to energy cost savings for PHEVs, as opposed to Level-II chargers.

Electrification of transportation is widely regarded as an effective solution to energy security, climate change, and air quality (Ohnishi, 2008 and National Research Council (NRC), 2010, 2013). The EV Everywhere Grand Challenge, announced by President Obama in March 2012, aims “to produce plug-in electric vehicles (PEVs) as affordable and convenient for the American family as gasoline-powered vehicles by 2022” (USDOE, 2013). However, fast growth of the PEV market faces two barriers. One is the high cost of battery packs. For example, according to a US Department of Energy (DOE) report (2013), the battery cost was $500/kWh in 2012. Another barrier is the lack of public charging facilities (Lin, 2012). Though plug-in hybrid electric vehicles (PHEVs) also have issues such as battery safety, durability, bulkiness, etc., they are less dependent on charger availability, compared to battery electric vehicles (BEVs). PHEVs can operate on gasoline when the battery is depleted. An adequate charging infrastructure, however, can increase a PHEV's share of driving on electricity, thus increasing energy savings and promoting consumer acceptance. This paper aims to study the impacts of battery cost and charging infrastructure coverage on market acceptance of PHEVs. PHEVs combine an internal combustion engine (ICE) with a battery which can be charged with grid electricity. PHEVs can operate in the charge-depleting (CD) mode, in which little or no fuel is consumed and little or no tailpipe pollutants are emitted. After the CD range is exhausted, PHEVs can continue to operate in the charge-sustaining (CS) mode, using the ICE as the major power source, in virtually the same fashion as that of a hybrid electric vehicle (HEV). Having the ability to partially substitute electricity for gasoline, PHEVs can reduce lifecycle greenhouse gas (GHG) emissions compared with conventional vehicles, unless the grid electricity comes from coal (Hawkins and Singh, 2012). A less controversial merit of PHEVs is enhancing the energy security of the nation (Vyas et al., 2009 and Lin and Greene, 2011). These benefits come from operating PHEVs in the CD mode. Therefore, it is important to make a full use of the CD mode in PHEVs' operations. The maximum distance that a fully charged PHEV can operate in the CD mode, known as the CD range, is determined by the effective battery capacity.1 To take full advantage of a PHEV, motorists would hope to operate the vehicle mostly in the CD mode and return home with an empty battery. A long CD range is usually associated with a large and more expensive battery pack. Depending on their travel needs, different motorists might prefer batteries of different sizes. Lin (2012) estimated the optimal electric range for each individual in a national driver sample by tradeoffs of battery cost and energy cost, forming a national distribution of optimal ranges due to variation of driving patterns. Clearly, the impacts of the battery capacity and public charging facility coverage are highly correlated. With an extensive coverage of charging facilities that allow frequent charges, small batteries may meet motorists' needs; on the other hand, if the government subsidy to PEVs increases, customers may prefer buying PEVs with large batteries, and thus reduce the need for investment in public charging facilities. Therefore, it is necessary to incorporate such correlations in the study of the long-term benefits and costs of PHEVs. For example, Peterson and Michalek, 2012 employed the 2009's National Household Travel Survey (NHTS) data to investigate the cost of adopting PHEVs with different CD ranges, considering increasing battery capacity and infrastructure coverage. Zhang et al. (2011) used the 2009's NHTS data taken in the South California to study energy consumption of PHEVs with different CD ranges under three charger coverage scenarios. These NHTS data were converted to a typical one-day travel pattern data. The results were compared with that of conventional gasoline vehicles (CGVs) and hybrid electric vehicles (HEVs), showing that a HEV could reduce 45% fuel consumption (in gallons) compared to a CGV and PHEV40 can help reduce additional 70% fuel consumption (in gallons), compared to a HEV. Furthermore, using the same dataset, Zhang et al. (2013) studied the operating costs of PHEVs and BEVs, assuming optimal charging strategies based on a time-of-use (TOU) electricity rate (which varies by season of a year) within a day. However, the NHTS data are aggregate data based on a cross-sectional survey, which cannot reflect the longitudinal variation in travel patterns of motorists. Furthermore, the NHTS data were collected through phone interview. The accuracy of the travel temporal and spatial information is low. Based on one school-day travel data collected in Austin, Texas, in 2005 or 2006, Dong and Lin (2012) studied the fuel savings and total energy cost of PHEVs under several hypothetical coverage levels of public chargers. These data were recorded by global-positioning-system (GPS) devices installed in vehicles. Therefore, they promise a high accuracy of the temporal and spatial information. However, studies in travel demand modeling and analysis have suggested great variations in motorists' trip-making behavior, including daily variations in the trip frequency, trip length, trip chaining, departure time choice and its connections with demographic variables (Pas and Sundar, 1995, Elango et al., 2007 and Lin et al., 2012). Specifically, daily vehicle miles traveled (DVMT) varies from one day to another for a particular motorist and also varies among motorists. Both the day-to-day variation in the DVMT and motorist heterogeneity could significantly impact the energy consumption of PHEVs (Lin and Greene, 2011). In this paper, we focus on the impacts of two factors – battery capacity and charger coverage – on the energy costs from the perspective of motorists (i.e., we do not consider the cost of building public charger facilities) based longitudinal travel data of multiple motorists. By assuming different scenarios of charger coverage, we want to answer two questions: (1) How much energy cost savings over the long term could PHEVs bring compared with CGVs or HEVs? (2) Is a large-capacity battery worth buying for motorists, considering the trade-off between incremental battery costs and operating cost savings?

This paper employed the longitudinal travel data from 749,828 trips made by 415 vehicles during 3–18 months from the Seattle metropolitan area, to address (1) how much energy cost could be saved for PHEVs compared with CGVs or HEVs, and (2) whether a large-battery PHEV can be justified based on the incremental energy cost savings. The dataset provides detailed spatial and temporal information of trips, which makes it possible for us to incorporate specific location-based public charging coverage in the analysis of PHEV operating costs. The data come from a travel choice study which aims to investigate how motorists change their travel behavior in response to tolling that varies by location and time of day. All participants in the study used CGVs. In this paper, we directly employed this dataset and assumed that their travel behavior would not change if they were to switch to a different vehicle technology, such as PHEVs or HEVs. The average DVMT based on our dataset is close to that of the 2009's NHTS data. However, it is not clear if conclusions from this study can be applied to other regions. Another caveat is that we do not consider the cost of building public charging infrastructure. In future studies, we expect to consider infrastructure cost and examine cost-effectiveness of different infrastructure scenarios. This study generates several methodological contributions. First, we used a longitudinal travel dataset to capture variability of travel behavior over time and among motorists. Second, we considered endogenous charging decisions. Third, PHEV ranges were assigned to motorists based on individual typical driving patterns, in order to avoid assigning ranges that are not suitable for individual motorists. By using realistic data for vehicle and infrastructure technologies and carefully setting up the infrastructure deployment scenarios, we have made several important observations. First, whether PHEVs have lower energy costs than those of CGVs or HEVs depends on coverage of public chargers. It is found from the travel data that PHEVs could help save around 60% or 40% in energy costs, compared with CGVs or HEVs, respectively. Mid-range PHEVs (20 or 30 miles of CD range) may achieve the lowest energy cost, if the infrastructure coverage is limited (to home or downtown). When more public chargers are available, the savings could be even more. On the other hand, without large coverage of public chargers, HEVs may even be a better choice than large-battery PHEVs. The general results are well consistent with other studies, such as Khan and Kockelman (2012) and Peterson and Michalek (2012). The incremental battery cost of long-range PHEVs is difficult to justify based on the PHEV's incremental operating costs savings unless a government subsidy is offered for large-battery PHEVs. It is found that if the gasoline price is less than $4/gallon, even at battery cost of $200/kWh and for a period of 10 years, PHEV10s are the best option in most cases (without subsidy). However, the battery technology development for the next 10 years is not considered. The price of gasoline has a significant impact on the cost-effectiveness of PHEVs, especially those with a large battery. When the price increases, PHEVs could provide a greater energy cost savings than CGVs and HEVs. More importantly, it was found that when it increases from $4/gallon to $5/gallon, the number of PHEV users who benefit from having a larger battery increases significantly under each scenario of charger coverage. Therefore, the subsidy policy and gasoline price are two key factors that impact the acceptance of large-battery PHEVs, which use less gasoline and save more energy. Policy-makers should pay attention to these two factors. If chargers are immediately available when a PHEV motorist arrives and parks, the effect of having quick DC chargers (50 kW) would be almost negligible in terms of long-term energy savings, compared with the normal Level-II chargers (6 kW). The assumption of immediate availability is less realistic if more PHEVs and BEVs are on the road. The most important advantage of quick chargers lies in the fact that they can reduce the charging time dramatically. Therefore, they cut the recharging time for the previous PHEV, and make the charger more likely to be available for the next one.