Location model of electric vehicle charging stations

Zhang, Yuxi; Qiu, Zheyong; Gao, Pengbing; Jiang, Shihao

doi:10.1088/1742-6596/1053/1/012058

Cited by 8 publications

(3 citation statements)

References 5 publications

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“…By researching this phenomenon, a more reasonable distribution of city functional areas was designed and proposed. To solve the costly problem of monitoring network-wide traffic dynamics with the link resolution, a probabilistic framework to estimate network-wide link travel time with trip-based data from automatic vehicle identification detectors was studied in Zhang et al ( 18 ). With the help of the accuracy of trip-based data, the travel time of each trip was allocated at a lower cost, and the average link travel time within the network was estimated online accordingly.…”

Section: Related Workmentioning

confidence: 99%

Agent-Based Model of Electric Vehicle Charging Demand for Long-Distance Driving in the State of Indiana

Chen

Kang

Koo

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Historically the U.S. transportation system has been continuously improved to adopt new policies and technologies. The transportation vehicle energy transition from fossil fuels to electricity is promoted by policymakers and major automakers with an expectation of enhancing sustainability aspects such as fossil fuel consumption reduction, carbon emissions reduction, and lower operations and maintenance costs. However, the electrification of the existing transportation infrastructure system requires substantial upgrades to overcome two major concerns from ordinary drivers and the public. One is the driver’s range anxiety based on the current capability of the electric vehicle (EV) technologies. The other is the availability of EV charging stations near the planned route. To address these two issues, we introduce an agent-based simulation model to project the consequences of electrification in the Indiana state highway system. Specifically, the model is developed to monitor the status of long-distance EV trips between different regions. The multi-agent engine method guarantees the model can adapt to diverse scenarios and complex environments. The simulation experiment verifies that the proposed model can provide the expected outcomes, including the numerical data of electric energy demand and the geospatial information (as location coordinates) of failed trips. By performing a GIS-based analysis of the results, the derived geospatial data can help state transportation agencies determine where to deploy the charging facilities to satisfy the overall charging demand. The proposed simulation framework offers a novel and strategic way to resolve the challenges for EV charging-related research and projects.

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Section: Related Workmentioning

confidence: 99%

Agent-Based Model of Electric Vehicle Charging Demand for Long-Distance Driving in the State of Indiana

Chen

Kang

Koo

et al. 2022

Transportation Research Record: Journal of the Transportation R

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show abstract

“…A data-driven and particle swarm optimization based heuristic algorithm was developed in order to locate the EVCS with consideration of round trips [21]. Improved Whale Optimization Algorithm was modeled by the researchers of [22], for locating EVCS by considering service risk capacity constraints. The MILP model and improved Whale Optimization Algorithm were compared as conclusion.…”

Section: Literature Reviewmentioning

confidence: 99%

A model for determining the locations of electric charge stations in Istanbul

Daşcıoğlu¹,

Tuzkaya²,

Kılıç³

2019

Pamukkale J Eng Sci

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ÖzThe studies about Electric Vehicles (EV) have gained importance and increased in the last years depending on the environmental concerns of the classic transportation systems. One of the problems to consider at this point is locating the proper points of electric charging stations for EVs. The primary objective of this research is to locate the electric charging stations in Istanbul considering the flow of the paths. The locations of electric charging stations are determined by using a mathematical model based on the flow-refuelling location model with the aim of maximizing the captured flow. The mathematical model is run for various values of "p" and the optimum locations are obtained.Klasik ulaştırma sistemlerinin çevresel kaygıları göz önünde bulundurulduğunda, elektrikli araçlarla (EA) ilgili çalışmalar önem kazanmış ve son yıllarda sayıca artmıştır. Bu noktada ele alınması gereken sorunlardan biri EA'lar için şarj istasyonlarının uygun yerlerinin belirlenmesidir. Bu çalışmanın temel amacı, İstanbul'daki elektrik şarj istasyonlarına ilişkin en uygun konumları yolların akışını değerlendirmeye alarak bulmaktır. Şarj istasyonlarının konumları, yolların kapsanan akışını maksimize etmeyi amaçlayan akış-yakıt ikmal lokasyon modeline dayanan bir model kullanılarak belirlenmiştir. Matematiksel model farklı "p" değerli için çözülmüş ve en iyi lokasyonlar belirlenmiştir.

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“…With an increasing number of new energy vehicles, the location of charging piles has become an important factor influencing consumers to buy new energy vehicles. Therefore, the optimal layout planning of charging stations is also a current hot issue [2]. In this study, taking Guangzhou as an example, the location of the charging pile clustering center was selected using the K-Means clustering algorithm.…”

Section: Introductionmentioning

confidence: 99%

Location and Layout of Electric Vehicle Charging Stations Based on K-Means Algorithm

Zhou,

Wang,

Huang

et al. 2023

J. Phys.: Conf. Ser.

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The development of the electric vehicle industry can be promoted by the reasonable layout of electric vehicle charging stations. In this study, the construction of charging piles for new energy vehicles in Guangzhou was discussed. Specifically, the location of the charging pile clustering center was selected using the K-Means clustering algorithm. The K-Means clustering results were analyzed through the elbow method to solve the optimal construction partition of charging piles. Simulation modeling revealed that when k=6, charging piles could be used more conveniently when being about 4.8665 km from the dense flow of new energy vehicles under the clustering center mode compared with the situation at the original distance.

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Location model of electric vehicle charging stations

Cited by 8 publications

References 5 publications

Agent-Based Model of Electric Vehicle Charging Demand for Long-Distance Driving in the State of Indiana

Agent-Based Model of Electric Vehicle Charging Demand for Long-Distance Driving in the State of Indiana

A model for determining the locations of electric charge stations in Istanbul

Location and Layout of Electric Vehicle Charging Stations Based on K-Means Algorithm

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