While PHEV/BEV technology has developed rapidly in recent years, there exist great uncertainties in terms of market acceptance. Previous studies evaluated factors that impact PHEV/BEV adoption, including infrastructure support, economies of scale, word of mouth effects (influence from other people’s perception of electric vehicles), age of current vehicle, consumer income, and travel patterns.

In particular, consumer travel patterns (i.e., travel behavior) have increasingly received significant attention, because they directly determine whether PHEV/BEV is acceptable to consumers and how it is utilized for daily travels. However, previous research has predominately used aggregated travel pattern data, such as the often cited National Household Travel Survey (NHTS), which assumes that everyone follows the same travel pattern as the aggregated average and neglects the heterogeneity of individual users and their specific travel patterns. Recent attempts to differentiate the impacts of individual travel patterns on PHEV/BEV market acceptance have also been constrained by the size of travel pattern samples (usually in the dozens or hundreds) due to the difficulty in collecting travel behavior data from the private fleet.

Fortunately, the rapid development of information and communications technology has increasingly made a massive amount of travel behavior data available at a much larger scale. The availability of these “big data” (commonly referring to large-scale data sets) on individual travel patterns, especially for public fleets, represents untapped opportunities to better understand how individual travel behavior affects the PHEV/ BEV market acceptance and the associated environmental impacts.

—Cai and Xu

In their study, Cai and Xu used a large-scale data set containing real-time trajectories of 10,375 taxis in Beijing (approximately 15% of the taxi fleet) retrieved by GPS systems for one week to explore the impacts of individual travel patterns to PHEV acceptance and associated implications on GHG emissions.

Each data point includes a unique taxi ID, the time (to the seconds) of the recording, and the position (longitude and latitude) of the taxi at the specific time. Depending on the GPS device setup in each vehicle, the frequency of recording ranges from five seconds to ten minutes—but stays consistent for the same vehicle.

To clean up the data, they applied a filter to eliminate 1) empty data points; 2) duplicate data points; 3) taxis with less than seven data points; and 4) unreasonable off-the-chart positions.

Because taxis do not have uniformly regular parking times—and some drivers pair up to drive the same taxi in rotation to minimize costs—“daily driving distance” is not a good metric to characterize taxi trips, the team noted. Taxis may have significantly different starting and ending times of each “day” and the length of a “day” may also be different from taxi to taxi (e.g., one-driver taxi versus two-driver taxi).

To address this issue, Cai and Xu introduced the concept of “driving segments”: the total distance driven between two major resting periods when the vehicle is parked with a predetermined length threshold. One segment may contain several separate trips, which is similar to “trip chains” used in previous studies. Resting periods between segments represent charging opportunities.

For the study, they ranged the predetermined resting threshold from 30 min to eight hours to test the impact of charging opportunities on PHEVs adoption. In the journal paper, they focused on two extreme cases: the “home-charging only” scenario and the “ubiquitous charging” scenario. The home-charging only scenario represents a relatively conservative case that vehicles can only be charged at home thus requiring a longer resting period eight hours in this study). The ubiquitous charging scenario represents an optimistic case that public charging stations are ubiquitously available, allowing drivers to charge their vehicles as long as they have more than half an hour of resting time.

Among the findings of the study were:

• The largest gasoline displacement (1.1 million gallons per year) can be achieved by adopting PHEVs with modest electric range (approximately 80 miles) with current battery cost, limited public charging infrastructure, and no government subsidy.

• While the battery range is one of the major concerns from consumers’ perception, the study shows that a larger battery actually decreases the VMT electrification rate when the unit battery cost is higher than $200/kWh. Only when the unit battery cost is lower than $200/kWh, extended electric drive range can increase the adoption and thus electrification rate.

• Government subsidies can be more effective to increase the VMT electrification rate and gasoline displacement if targeted to PHEVs with modest electric ranges (80 to 120 miles).

• Beijing belongs to the North China Grid with a GHG emission factor of 236.7 g CO₂-eq/km traveled. GHG emissions of conventional gasoline vehicles are approximately 224.4 g CO₂-eq/km. While taxi fleet electrification can increase greenhouse gas emissions by up to 115 kiloton CO₂-eq per year with the current grid in Beijing, emission reduction of up to 36.5 kiloton CO₂-eq per year can be achieved if the fuel cycle emission factor of electricity can be reduced to 168.7 g/km.

• At current battery cost (approximately $500/kWh), a larger battery does not necessarily imply higher rate of adoption, utilization, and electrification of PHEVs due to the heterogeneous individual travel patterns.

• Subsidy can effectively increase the VMT electrification rate by filling the gap between fuel cost savings and the premium cost of PHEVs. Results show that focusing on PHEVs with modest electric ranges (80 to 120 miles) can most efficiently boost VMT electrification with a fixed amount of budget.

Our study demonstrates how individual travel patterns, charging opportunities, and battery size influence life cycle GHG emissions due to PHEV adoption and utilization at the individual vehicle level. It also sheds lights on the utilization of large-scale vehicle trajectory data for enhancing assessment of environmental impacts of PHEV/BEVs.

—Cai and Xu

Resources

• Hua Cai and Ming Xu (2013) Greenhouse Gas Implications of Fleet Electrification Based on Big Data-Informed Individual Travel Patterns. Environmental Science & Technology doi: 10.1021/es401008f