Acceptability, energy consumption, and costs of electric vehicle for ride-hailing drivers in Beijing. (15th September 2019)

• Record Type: Journal Article
• Authors: Tu, Wei; Santi, Paolo; Zhao, Tianhong; He, Xiaoyi; Li, Qingquan; Dong, Lei; Wallington, Timothy J.; Ratti, Carlo
• Is Part Of: Applied energy. Volume 250(2019)
• Page Start: 147
• Page End: 160
• Publication Date: 2019-09-15
• Subjects: Ride-hailing -- Urban mobility -- GPS trajectories -- Electrification -- Machine learning -- Big data; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2019.04.157
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: The EV acceptability in ride-hailing drivers is quantified. Ride-hailing drivers is less favored electric vehicles. The power load profile generated by ride-hailing EVs has a diurnal pattern. The payback time for the ride-hailing adoption of BEVs and PHEVs is 4.3–12.8 years. Abstract: The acceptability, energy consumption, and environmental benefits of electric vehicles are highly dependent on travel patterns. With increasing ride-hailing popularity in mega-cities, urban mobility patterns are greatly changing; therefore, an investigation of the extent to which electric vehicles would satisfy the needs of ride-hailing drivers becomes important to support sustainable urban growth. A first step in this direction is reported here. GPS-trajectories of 144, 867 drivers over 104 million km in Beijing were used to quantify the potential acceptability, energy consumption, and costs of ride-hailing electric vehicle fleets. Average daily travel distance and travel time for ride-hailing drivers was determined to be 129.4 km and 5.7 h; these values are substantially larger than those for household drivers (40.0 km and 1.5 h). Assuming slow level-1 (1.8 KW) or moderate level-2 (7.2 KW) charging is available at all home parking locations, battery electric vehicles with 200 km all electric range (BEV200) could be used by up to 47% or 78% of ride-hailing drivers and electrify up to 20% or 55% of total distance driven by the ride-hailing fleet. With level-2 charging available at home, work, and public parking, the acceptance ceiling increases to up to 91% of drivers and 80% of distance. Our study suggests that long range BEVs and widespread level-2 charging infrastructure are needed for large-scale electrification of ride-hailing mobility in Beijing. The marginal benefits of increased all electric range, effects on charging infrastructure distribution, and payback times are also presented and discussed. Given the observed heterogeneity of ride-hailing vehicle travel, our study outlines the importance of individual-level analysis to understand the electrification potential and future benefits of electric vehicles in the era of shared smart transportation.