Tracking National Household Vehicle Usage by Type, Age, and Area in Support of Market Assessments for Plug-In Hybrid Electric Vehicles ()
1. Introduction
According to the Energy Information Administration (EIA), in 2009 the transportation sector was responsible for 70% of petroleum consumption and 33% of GHG emissions in the United States [2]. Within the transportation sector, light-duty vehicles account for nearly 60% of its petroleum consumption [3]. By reducing fossil fuel use per mile of service delivered, sustainable transport leads to greater energy security and reduced greenhouse gas (GHG) emissions. Among several initiatives supported by the US government, one is to diversify transportation energy sources by using electricity to drive light-duty vehicles.
Although specifics are important, at a broad conceptual level, the technology for plug-in hybrid electric vehicles (PHEVs) is similar to that in regular hybrid electric vehicles (HEVs), except that they employ bigger batteries, which are recharged through electric vehicle supply equipment by drawing electricity from the grid.
Regarding specifics, there are three basic powertrain configurations and operational capabilities: series, parallel, and series-parallel (power-split) [4]. When the gridto-vehicle series operational capability is implemented in a PHEV, stored grid-supplied battery energy propels the vehicle initially. This phase of operation is called chargedepleting (CD) operation. The more power and energy that are available in the battery pack, the greater the vehicle’s ability to operate all electrically during CD. All PHEVs considered in this paper have some degree of ability to operate all electrically with grid electricity. The parallel and series-parallel tend to have much less power than the series configuration and are more likely to operate with both the engine and battery simultaneously providing power during CD operation. The three designs are distinguished from one another with respect to their operational capabilities when the engine comes on.
In a series PHEV, only the electric motor directly drives the wheels; the engine does not. There is a second electric machine, which operates as a generator (an electric machine can be reversed in rotational direction and can operate either as a generator or motor). The series configuration is an engine-to-generator-to-motor-to-wheels pathway. After the battery energy is depleted to a predetermined level, an internal combustion engine (ICE) turns the generator, which supplies current to the electric motor, which then rotates the vehicle’s drive wheels. When excess electric energy is available, the generator recharges the battery pack. Since it must do all the work of moving the vehicle, the electric motor of a series PHEV must be larger than that of the other two types.
By adding the conventional engine-to-transmission-towheels pathway, the parallel design can simultaneously transmit power to the drive wheels from both the internal combustion engine and the battery. Compared to the series PHEV, the parallel operational capability primarily uses the conventional mechanical link from the engine to the wheels, eliminating the ability of the engine to support simultaneous series operation. This restriction allows the parallel PHEV to use only one electric machine (motor/generator) of less power than in the series configuration, thereby reducing cost. By using two electric machines, the series-parallel design has the flexibility to operate the onboard engine to support either series or parallel mode, or both simultaneously. A specialized mechanical step splits engine power into two pathways: one to the wheels and another to the generator. For this reason, the series-parallel system is called a “power split”. Compared to the series PHEV, both the parallel and series-parallel are able to use much less electric machine and battery pack power, thus cutting costs. For a given level of acceleration capability, the series-parallel is more expensive than the parallel, but it is more efficient.
A PHEV travels its initial miles by making use of energy from the grid, which has been stored in the battery. If the power of the battery pack and electric machines is sufficient, propulsion may happen all electrically. However, as electric power capability is reduced to cut PHEV cost (as in parallel or parallel-series configurations), the battery power must often be supplemented by engine power during CD operation. Thus, PHEV designs will vary with respect to the share of electricity and fuel used as the battery pack is discharged. All PHEVs operate in “hybrid mode” (as an HEV) on fossil fuels once the battery is depleted, although they can differ in the way they do so, according to powertrain configuration, as previously discussed.
Because electricity is generated through the use of coal, nuclear, natural gas, hydro, and wind sources, widespread acceptance of PHEVs could diversify energy sources used in the transportation system. It is fairly well understood that the reduction in petroleum use by PHEVs increases with a corresponding increase in their onboard energy storage, which increases nonlinearly (less well known) with the size of the employed batteries. In many (but not all) cases, the increase in vehicle weight associated with bigger batteries partially offsets the potential reduction in petroleum use by PHEVs during engine operation. Another poorly understood attribute is that the power of the battery pack and electric machines is an important factor in the ability of PHEVs to electrify miles. Also in a nonlinear fashion, the higher the electrical power, the more electricity is used per mile of CD operation. More power means fewer miles until depletion of the pack and lower fossil fuel use during depletion, which translates into greater electrification potential. Clearly, the PHEV technology can cover a wide variety of options with respect to technical attributes, such as the battery chemistry, the amount of grid electricity that can be stored in the battery, and the powertrain and fuel choices. In addition, the driving behavior of consumers, such as driving aggressiveness and daily travel distance, could also significantly affect the energy use and the GHG effects of PHEVs.
Plug-in electric drive’s ability to eliminate oil use has become increasingly attractive since 2007 as 1) technical and economic feasibility has improved; 2) oil prices have increased significantly on average; and 3) oil prices have become more volatile. Starting in 2010, almost all of the major vehicle manufacturers offered—or planned to soon offer—PHEVs for sale to the mass market. Although the broad PHEV technology offers great promise, many questions about details remain unanswered. This paper examines the use of household vehicles to support assessment of the market potential of the many different PHEV technology options.
2. Contribution
To our best knowledge, no similar study has been conducted to assess the vehicle utilization by demographic factors. We have searched Google Scholar and TRID (http://www.trid.trb.org/) by using the following key words: vehicle utilization rates and vehicle usage rates.
New vehicle technologies are expensive at the early stage of implementation and require high usage to pay off. This paper helps decision makers and manufacturers identify the proper market niche of early vehicle models on the basis of the usage rate. Moreover, this paper details the reasons why vehicles were not used on travel days, which provides alternative perspectives to identify potential markets for PHEV powertrains.
3. Searching for High-Usage Vehicles
This paper examines usage of household vehicles—by type, age, and area—to support assessment of the market potential of PHEVs. However, the information obtained in the study is applicable to any costly powertrain that sharply reduces fuel costs, whether by use of a lessexpensive fuel or by higher efficiency. High usage rates are needed for such technologies to pay off. The paper complements a paper presented in 2011 [5], in which the 2001 National Household Transportation Survey (NHTS) was used to examine vehicle records, separating those records into groups of vehicles 1) older and newer than 10 years of age, and 2) above and below 50 miles of use per day.
One issue for the purchaser of such vehicles is the warranted life of the battery pack, which is at present eight years for the Nissan Leaf and Chevrolet Volt. If a pack replacement were necessary, and diminished rates of use were anticipated after the warranty period, the costs might be prohibitive and lead to a need to scrap the vehicle. Accordingly, the target market was drivers who used their vehicles intensively (high miles per day and many days per year), so that such vehicle would otherwise have its end of useful life at about the same time as the end of the useful life of the PHEV or EV battery (i.e., about 8 years). Another consideration was battery “cycle” life, which is believed to be about 3000 cycles. However, Vyas et al. assumed 5000 cycles to be possible by the year 2020 [6]. An assumption of charging overnight once for 90% of days for 10 years would lead to 3285 cycles, and so vehicles with a pack cycle life of 5000 cycles could be charged more than once per day on average, but not twice each day. The issue of charging a second time during the day has been addressed in Vyas et al. 2009 [7] and Elgowainy et al. 2012 [8]. Because calendar life (years of pack life) and cycle life have different causal mechanisms, a capability for greater cycle life may not lead to longer calendar life. Thus, there may be an incentive to use expensive PHEVs and EVs as intensively as possible, particularly if 5000 cycles or more can be obtained within a 10-year calendar life.
One aspect of use that has not been investigated is the proportion of days that a vehicle is in operation. We are aware that the 90% assumption used in the computations described above is optimistic and probably not typical. This paper is intended to address that assumption in the context of the 10-year life break point assumptions made previously [5]. In this paper, we analyze the probability of daily use to enhance our understanding of the market for personal-use PHEVs and EVs.
According to the 2009 NHTS [1], about 40% of vehicles on the survey travel day are reported as “not used”. At first glance, it appears that many households do not travel by personal vehicle. As shown in Table 1, of the 60.9% of vehicles used, 19.5% are vehicles greater than 10 years old, while 41.4% are relatively new vehicles (≤10 years old). However, of the 39.4% vehicles not used, percentages of old vehicles and new vehicles are almost identical. Besides vehicle age, there are many other factors that can affect vehicle usage, such as vehicle type, residential area type, and travel day.
Emerging new vehicle technologies offer opportunities to reduce the US transportation sector’s dependence on petroleum and possibly reduce greenhouse gas emissions. Prior experience with hybrid electric vehicle technology has shown that new technologies, such as PHEVs, will first be introduced in the passenger car [9]. Once the technologies are successfully introduced in passenger cars, they may be made available in other vehicle types. Analysis of the types of vehicles households use more frequently is needed to assist transportation analysts and decision makers. Knowing the vehicle usage by type, location, travel day, and population density would be helpful in estimating the benefits of new-technology vehicles in terms of the energy use and emission reductions associated with daily travel.
Many factors influence daily household vehicle usage; this study focuses on the following: vehicle type, vehicle age, travel day, residential location, and population density. Four vehicle types—car, van, SUV, and pickup— were selected. Vehicle age is divided into two groups: less than or equal to 10 years old (≤10 years) and greater than 10 years old (>10 years), while residential location is subdivided into metropolitan statistical area (MSA) and non-MSA. Population density in square kilometers includes five groups, ≤386, 387 - 1544, 1545 - 3860, 3861 - 9650, and >9650.
4. Identifying Unique Used/Non-Used Vehicle Records
The 2009 NHTS was conducted from March 2008 through May 2009. Information relating to sampled households, household members, vehicles owned, and travel during one day was collected. The survey was designed to collect travel data during a typical year and on all seven days of the week, including all holidays. The household sample consisted of a random national sample
Table 1. Pattern of household vehicle usage.
and several add-on samples (comprising additional households in selected areas where local planning entities paid to have the sample size expanded). Care was taken to assign weights such that the final sample would provide estimates representative of the national population. The NHTS dataset contains data for 150,147 households that own 309,163 vehicles. Various files provide detailed data relating to households, persons, vehicles, and daily (travel day) trips. This study utilized travel day trip and vehicle files, which also contain data related to characteristics of households and household members. Vehicles in the travel day file are sampled for only one day, making it impossible to track the weekly, monthly, or seasonal behavior of any single vehicle.
In this analysis, vehicles with or without reported travel time and distance are considered “vehicles used”. All others are called “vehicles not used”. We subdivided the “vehicles not used” into three sets: 1) left at home; 2) used other modes; and 3) no trips. The first “vehicle not used” set represents vehicles that were left at home while residents drove other household vehicles. The second set, “used other modes”, represents the vehicles left at home while household members used other travel modes, such as public transit, carpooling, bicycle, walking, or traveling as passengers in someone else’s vehicle. The last set, “no trips”, represents vehicles left at home because the household members did not make any trips. Within the “vehicles used” group, vehicles were subdivided as “with travel data” and “without travel data”. The “without travel data” set represents vehicles that were used for travel, but because the respondents did not report travel distance or time, these vehicles are often excluded from travelrelated analysis. For these five usage sets, vehicles were further subdivided into four vehicle types: car, van, SUV, and pickup. Each vehicle type was further subdivided into two age groups: 10 years or less (≤10) and over 10 years (>10). Finally, the vehicle usage was compared by household location: MSA and non-MSA.
We first created three subsets of day trip file records based on the trip information: driver-set 1, driver-set 2, and other. Driver-set 1 includes the driver trip records with reported travel distance and time, while driver-set 2 includes driver trip records without travel distance and/or time. The driver trip means the survey responder is the driver of this particular trip. The other subset contains all the non-driver trips. Next, we created unique files out of the first two files containing one record by household identification code (HOUSEID) and vehicle number (VEHID). Because a household may report many daily trips with detailed information for the same vehicle but may not do so for some trips, a few vehicles ended up in both of the driver files. We deleted the duplicates in driver-set 1 and driver-set 2 so that vehicle numbers are not duplicated.
Figure 1 shows the procedures used to match the vehicle file with the three trip files to identify unique “used” or “non-used” vehicle records. First, we matched the vehicle file with a trip file driver-set 1, without any duplicate records, by HOUSEID and VEHID. The matched records are the vehicles used with reported travel data. Care was taken to separate the non-matched vehicle records. Next, the first non-matched vehicle file was further matched with driver-set 2 by HOUSEID and VEHID. The matched records for this step are the vehicles used without reported trip distance and/or time, while the non-matched file includes the vehicles not used. The non-matched file from these two steps was matched with the combined file (driver-set 1 and driver-set 2) by unique HOUSEID only; the matched records are the vehicles left at home while household members used other vehicles to travel. Next, the non-matched file generated in this step was matched with the other file by HOUSEID; the matched records are the vehicles owned by the household members who traveled by using other modes (e.g., public transit, bicycle). Finally, the non-matched file of the last step includes vehicles that were not used because the household members did not travel at all.