Analyzing battery electric vehicle feasibility from taxi travel patterns: The case study of New York City, USA

Abstract

Electric taxis have the potential to improve urban air quality and save driver's energy expenditure. Although battery electric vehicles (BEVs) have drawbacks such as the limited range and charging inconvenience, technological progress has been presenting promising potential for electric taxis. Many cities around the world including New York City, USA are taking initiatives to replace gasoline taxis with plug-in electric vehicles. This paper extracts ten variables from the trip data of the New York City yellow taxis to represent their spatialtemporal travel patterns in terms of driver-shift, travel demand and dwell, and examines the implications of these driving patterns on the BEV taxi feasibility. The BEV feasibility of a taxi is quantified as the percentage of occupied trips that can be completed by BEVs of a given driving range during a year. It is found that the currently deployed 280 public charging stations in New York City are far from sufficient to support a large BEV taxi fleet. However, adding merely 372 new charging stations at various locations where taxis frequently dwell can potentially make BEVs with 200-and 300-mile ranges feasible for more than half of the taxi fleet. The results also show that taxis with certain characteristics are more suitable for switching to BEV-200 or BEV-300, such as fewer daily shifts, fewer drivers assigned to the taxi, shorter daily driving distance, fewer daily dwells but longer dwelling time, and higher likelihood to dwell at the borough of Manhattan.

Can Charging Infrastructure Used Only by Electric Taxis Be Profitable? A Case Study From Karlsruhe, Germany

“…With battery capacities below 30 kWh, already 75% of the taxis could be electric and would lead to global cost reductions of 20%. For New York City, USA, Hu et al [18] state a need for more public chargers that would allow electrifying more than half of the taxi fleet with BEV. Deyang et al [19] use simulation to determine techno-economically optimal battery sizes for electric taxis based on taxi data from Shanghai, China.…”

Effects of Charging Infrastructure and Non-Electric Taxi Competition on Electric Taxi Adoption Incentives in New York City

“…Several studies have shown that the operations of an e-taxi fleet are highly dependent on the availability of quick charging infrastructure. As a result, there are several studies that try to optimize the location of charging stations (e.g., [17,[25][26][27][28][29]).…”

“…For example, Asamer et al ( 27 ) concluded that only 30 charging stations were needed to cover over 90% of the 800 e-taxis in Vienna but they do not account for the cost of queue delay for their charging stations. Hu et al required having over 650 charging stations to adequately support 50% fleet adoption of e-taxis in New York City ( 29 ). They ignore trip chaining behavior of taxis and assume that taxis continue along the trajectories observed from the data, recharging whenever they are within 1 mi of a charging station with state of charge (SOC) below 50%.…”

Spatial Markov chain model for electric vehicle charging in cities using geographical information system (GIS) data

“…The buffer zone is estimated by specifying a buffer distance around a parking lot. The buffer distance-also called the air distance-was used before in parking models such as [25], and optimizing charging stations for taxis in [26]. In our model, the buildings within the buffer distance are considered to be visited by the drivers parking in this specific parking lot.…”