Electric vehicle charging stations’ location in urban transportation networks: A heuristic methodology

Abstract

The lack of public charging infrastructure has been one of the main barriers preventing the technological transition from traditional vehicles to electric vehicles. To accelerate this technological transition, it is necessary to elaborate optimal charging station location strategies to increase the user confidence, and maintain investment costs within acceptable levels. However, the existing works for this purpose are often based on multipath considerations or multi‐objective functions, that result in taxing computational efforts for urban transportation networks. This article presents a heuristic methodology for urban transportation networks, that considers the deployment of the charging stations for coverage purposes, and the fulfilment of user preferences and constraints as two separated processes. In this methodology, a Reallocation Algorithm is formulated to prioritize the selection of Locations of Interest, and to reduce the number of stations with overlapping covering areas. The methodology results are compared to those drawn from a Greedy Algorithm based on a multipath consideration, in an extensive metropolitan transportation network. The results show that the proposed methodology significantly reduce the computational time required for solving the location problem, and furthermore, allows for similar results to those obtained when considering k = 2 and k = 3 deviation paths.

From Frustration to Innovation: Exploring the Lived Experiences of E-Bike Users on Limited Charging Infrastructure

“…Charging Station Concerns and Benefits: Electric bike users who experienced challenge and hard time in finding charging stations may encounter various issues that negatively affect their overall travel experience. According to Franco et al (2021) [18] , one significant challenge and problem that can seriously hinder the adoption of electric bikes is the inconvenience and uncertainty of finding a charging station if needed. Thus, developing charging infrastructure can be beneficial among electric bike users in various ways.…”

Modeling and simulation of charging characteristics of electric vehicle group under the mode of autonomous driving‐shared travel

“…First of all, the average charging cycle T of EV is calculated, that is, the average time required from the beginning of charging to the next charging, as shown in (12). T d is the average driving time, as shown in (13), where L is the average endurance mileage, v is the average free velocity of the region; T c is the average charging time, and is calculated by SOC from 20% to 80% according to the model assumption, as shown in (14), where C is the average battery capacity of EV, and P c is the average charging power.…”

“…Reference [11] analyzes the actual charging data and divides the city into the residence area, work area and business area and models the driving behavior of EV. References [12,13] studies the optimal location and sizing of charging stations by comprehensibly considering EV charging demand distribution, driving behavior, renewable generation and other factors. However, the above reference only use simple map partitioning or virtual network, which cannot reflect the real traffic characteristics of EVs.…”

Charging Infrastructure for Electric Vehicles Considering Their Integration into the Smart Grid

“…A heuristic methodology for urban transportation networks is presented in [23], which considers the deployment of charging stations for coverage purposes and the fulfillment of user preferences and constraints as two separate processes. This methodology uses a reallocation algorithm to prioritize selecting locations of interest and reducing the number of stations with overlapping coverage areas.…”