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Greenhouse Gas Implications of Fleet Electrification Based on Big Data-Informed Individual Travel Patterns
journal contribution
posted on 2013-08-20, 00:00 authored by Hua Cai, Ming XuEnvironmental implications of fleet
electrification highly depend
on the adoption and utilization of electric vehicles at the individual
level. Past research has been constrained by using aggregated data
to assume all vehicles with the same travel pattern as the aggregated
average. This neglects the inherent heterogeneity of individual travel
behaviors and may lead to unrealistic estimation of environmental
impacts of fleet electrification. Using “big data” mining
techniques, this research examines real-time vehicle trajectory data
for 10,375 taxis in Beijing in one week to characterize the travel
patterns of individual taxis. We then evaluate the impact of adopting
plug-in hybrid electric vehicles (PHEV) in the taxi fleet on life
cycle greenhouse gas emissions based on the characterized individual
travel patterns. The results indicate that 1) 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; 2) reducing battery cost has the largest impact on increasing
the electrification rate of vehicle mileage traveled (VMT), thus increasing
gasoline displacement, followed by diversified charging opportunities;
3) 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); and 4) 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. Although the results are based on a specific
public fleet, this study demonstrates the benefit of using large-scale
individual-based trajectory data (big data) to better understand environmental
implications of fleet electrification and inform better decision making.
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gasoline displacementvehicle trajectory dataimpactlife cycle greenhouse gas emissionstravel patternsgreenhouse gas emissionsVMT electrification ratePHEVbattery costtaxi fleet electrificationGreenhouse Gas ImplicationsCOfuel cycle emission factorfleet electrificationTravel PatternsEnvironmental implications
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