Optimizing charging station locations for urban taxi providers

Abstract

Electric vehicles are gaining importance and help to reduce dependency on oil, increase energy efficiency of transportation, reduce carbon emissions and noise, and avoid tail pipe emissions. Because of short driving distances, high mileages, and intermediate waiting times, fossil-fuelled taxi vehicles are ideal candidates for being replaced by battery electric vehicles (BEVs). Moreover, taxis as BEVs would increase visibility of electric mobility and therefore encourage others to purchase an electric vehicle. Prior to replacing conventional taxis with BEVs, a suitable charging infrastructure has to be established. This infrastructure, which is a prerequisite for the use of BEVs in practice, consists of a sufficiently dense network of charging stations taking into account the lower driving ranges of BEVs. In this case study we propose a decision support system for placing charging stations to satisfy the charging demand of electric taxi vehicles. Operational taxi data from about 800 vehicles is used to identify and estimate the charging demand for electric taxis based on frequent origins and destinations of trips. Next, a variant of the maximal covering location problem is formulated and solved, aiming at satisfying as much charging demand as possible with a limited number of charging stations. Already existing fast charging locations are considered in the optimization problem. In this work, we focus on finding regions in which charging stations should be placed, rather than exact locations. The exact location within an area is identified in a post-optimization phase (e.g., by authorities), where environmental conditions are considered, e.g., the capacity of the power network, availability of space, and legal issues. Our approach is implemented in the city of Vienna, Austria, in the course of an applied research project conducted in 2014. Local authorities, power network operators, representatives of taxi driver guilds as well as a radio taxi provider participated in the project and identified exact locations for charging stations based on our decision support system.

Shared autonomous electric vehicle service performance: Assessing the impact of charging infrastructure

“…Each cell may contain only one charging station. As suggested by Asamer et al (2016), since a complete tessellation of the study area is required, hexagonal cells are used. The diameter of a hexagon cell is chosen to be one kilometer.…”

Electric Vehicles for Public Transportation in Power Systems: A Review of Methodologies

“…Some other works put focus on the charging station sites and sizing for ETs. Thus, in [64], a decision support system was studied for placing ET charging stations. The objective was to maximize charging demand satisfaction of ET drivers, based on the data of 800 taxi vehicles in Vienna.…”

Location Design of Electric Vehicle Charging Facilities: A Path-Distance Constrained Stochastic User Equilibrium Approach

“…Sweda and Klabjan [25] developed an agent-based decision support system and a variant maximal covering location problem for EV charging infrastructure deployment. Asamer et al [26], by using 800 electric taxis' operational data in the city of Vienna, Austria, proposed a two-phase decision support system. Nie and Ghamami [3] presented a conceptual optimization model to analyze travel by EV along a long corridor whose objective was to select the battery size and charging capacity (in terms of both the charging power at each station and the number of stations needed along the corridor) to meet a given level of service.…”