Abstract
Electric Vehicles (EVs), by reducing the dependency on fossil fuel and minimizing the traffic-related pollutants emission, are considered as an effective component of a sustainable transportation system. However, the massive penetration of EVs brings a big challenge to the establishment of charging infrastructures. This paper presents the approach to locate charging stations utilizing the reconstructed EVs trajectory derived from the Cellular Signaling Data (CSD). Most previous work focused on the commute trips estimated from the number of jobs and households between traffic analysis zones (TAZs). This paper investigated the large-scale CSD and illustrated the method to generate the 24-hour travel demand for each EV. The complete trip in a day for EV was reconstructed through merging the time sequenced trajectory derived from simulation. This paper proposed a two-step model that grouped the charging demand location into clusters and then identified the charging station site through optimization. The proposed approach was applied to investigate the charging behavior of medium-range EVs with Cellular Signaling Data collected from the China Unicom in Tianjin. The results indicate that over 50% of the charging stations are located within the central urban area. The developed approach could contribute to the planning of future charging stations.
Highlights
According to statistics, the transportation sector contributes over a half of the oil consumption and a quarter of the CO2 emissions, which is considered one of the factors resulting in the Greenhouse effect [1]
A two-step optimization model considering driver convenience was developed to determine the sites of the charging stations was developed, and the effective charging station layouts based on a different clustering approach were compared with a real-world case study
In order to deal with the placement issues of Electric vehicles (EVs) charging stations, this paper presents a two-step optimization model to locate the charging stations utilizing the reconstructed EV trajectories derived from simulation
Summary
The transportation sector contributes over a half of the oil consumption and a quarter of the CO2 emissions, which is considered one of the factors resulting in the Greenhouse effect [1]. To provide electrical energy for EVs, charging stations and battery technology considering actual EV field trips are attracting more and more attention from researchers [6,7]. Appropriate planning of charging station sites and scales is critical to reduce the adverse impacts and improve the service quality of EVs. Basically, there are two types of charging station: slow and fast. This paper illustrates the approach to locate the public charging stations utilizing the reconstructed EVs trajectory derived from simulation. A two-step optimization model considering driver convenience was developed to determine the sites of the charging stations was developed, and the effective charging station layouts based on a different clustering approach were compared with a real-world case study. Variables the number of charging stations the number of possible locations (demand clusters)where a station could be established the travel time from charging stations i to the demand cluster j decision variable i f station i is assigned to the cluster j the remaining battery state the battery capacity the average energy consumption of EV
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