A data-driven optimization-based approach for siting and sizing of electric taxi charging stations

Yang, Jie; Dong, Jing; Hu, Liang

doi:10.1016/j.trc.2017.02.014

Cited by 194 publications

(79 citation statements)

References 45 publications

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“…In 2016, the EV ownership in Shanghai has surpassed the 100,000, ranking the first in the world. By 2018, the number had increased to attain 239,000, accounting for 6% of the total vehicle ownership in Shanghai [7], in which percentages of BEV and PHEV were around 30% and 70%, respectively.One of the most pressing challenges is to deploy charging infrastructures in public locations considering the travel characteristics of EV drivers, so as to promote EVs and maximize the overall travel electrification [8][9][10]. Mismatch and disparity of charging demand and charging facility supply may lead to underutilized charging stations and points [11][12][13].…”

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confidence: 99%

A Charging Location Choice Model for Plug-In Hybrid Electric Vehicle Users

Yun

Sun

Zhang³

et al. 2019

Sustainability

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Electric vehicles (EVs) are promising alternatives to replace traditional gasoline vehicles. The relationship between available charging stations and electric vehicles has to be precisely coordinated to facilitate the increasing promotion and usage of EVs. This paper aims to investigate the choice of the charging location with global positioning system (GPS) trajectories of 700 Plug-in Hybrid Electric Vehicle (PHEV) users as well as the charging facility data in Shanghai. First, the recharge accessibility of each PHEV user was investigated, and 9% rely solely on public charging networks. Then, we explored the relationship between fuel consumption and the average distance between charging to analyze the environmental benefits of PHEVs. It was found that 16% PHEVs are similar to EVs, and 9% whose drivers rely solely on public charging stations are similar to internal combustion engine (ICE) vehicles. PHEV users were divided into four types based on the actual recharge access: home and workplace-based user (private + workplace + public), the home-based user (private + public), the workplace-based user (workplace + public), and the public-based user (public). Models were developed to identify and compare the factors that influence PHEV user's charging location choices (home, workplace, and public stations). The modeling and results interpretation were carried out for all PHEV users, home and workplace-based users, home-based users, and workplace-based users, respectively. The estimation results demonstrated that PHEV users tended to charge at home or workplace rather than public charging stations. Charging price, charging price tariff, the initial state of charge (SOC), dwell time, charging power, the density and size of public charging stations, the total number of public charging, vehicle kilometer travel (VKT) of the current trip and current day are the main predictors when choosing the charging location. Findings of this study may provide new insights into the operational strategies of the public charging station as well as the deployment of public charging facilities in urban cities.Sustainability 2019, 11, 5761 2 of 23 electrification through considerable investments in consumer incentives, charging infrastructures as well as technology research and development [4,5]. In order to promote the usage and adoption of EVs, Shanghai had made great investments to subsidize EV consumers and manufacturers, offered free and specific license plate, built public charging stations, and offered purchasing tax deductions and exemptions and other non-monetary incentives (e.g., free usage restriction policy during peak hours) [6]. In 2016, the EV ownership in Shanghai has surpassed the 100,000, ranking the first in the world. By 2018, the number had increased to attain 239,000, accounting for 6% of the total vehicle ownership in Shanghai [7], in which percentages of BEV and PHEV were around 30% and 70%, respectively.One of the most pressing challenges is to deploy charging infrastructures in public locations considering the tra...

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confidence: 99%

A Charging Location Choice Model for Plug-In Hybrid Electric Vehicle Users

Yun

Sun

Zhang³

et al. 2019

Sustainability

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“…We classify the charging stations according to the number of fast chargers for each charging station and the fixed investment cost is greatly depending on the level of the charging station [35]. s denotes the area occupied by a fast charger including the parking area.…”

Section: Total Social Costmentioning

confidence: 99%

Bi-Level Planning Model of Charging Stations Considering the Coupling Relationship between Charging Stations and Travel Route

Zang

Chen³

et al. 2018

Applied Sciences

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Abstract:The major factors affecting the popularization of electric vehicles (EV) are the limited travel range and the lack of charging infrastructure. Therefore, to further promote the penetration of EVs, it is of great importance to plan and construct more fast charging stations rationally. In this study, first we establish a travel pattern model based on the Monte Carlo simulation (MCS). Then, with the traveling data of EVs, we build a bi-level planning model of charging stations. For the upper model, with an aim to maximize the travel success ratio, we consider the influence of the placement of charging stations on the user's travel route. We adopt a hybrid method based on queuing theory and the greedy algorithm to determine the capacity of charging stations, and we utilize the total social cost and satisfaction index as two indicators to evaluate the optimal solutions obtained from the upper model. Additionally, the impact of the increase of EV ownership and slow charger coverage in the public parking lot on the fast charging demands and travel pattern of EV users are also studied. The example verifies the feasibility of the proposed method.

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“…Stochastic CS design was considered in [28][29][30][31][32][33][34]. In such studies, the system performance is usually measured by the average waiting time or the probability of getting blocked.…”

Section: Related Workmentioning

confidence: 99%

“…In the capacity planning of the CSs, each CS was modeled as an M/M/c queuing system and the maximum waiting time was used as the threshold for deciding the number of charging outlets in every CS. The work in [32] proposed a data-driven approach for an electric taxi CS sitting problem and an M/M/s/k queuing model for the CS sizing problem in the city of Changsha, where the maximum block rate was adopted as a threshold. A model for the optimal electric bus CS capacity and bus charging schedule was presented in the study [33].…”

Section: Related Workmentioning

confidence: 99%

A Hierarchical Optimization Model for a Network of Electric Vehicle Charging Stations

et al. 2017

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Abstract:Charging station location decisions are a critical element in mainstream adoption of electric vehicles (EVs). The consumer confidence in EVs can be boosted with the deployment of carefully-planned charging infrastructure that can fuel a fair number of trips. The charging station (CS) location problem is complex and differs considerably from the classical facility location literature, as the decision parameters are additionally linked to a relatively longer charging period, battery parameters, and available grid resources. In this study, we propose a three-layered system model of fast charging stations (FCSs). In the first layer, we solve the flow capturing location problem to identify the locations of the charging stations. In the second layer, we use a queuing model and introduce a resource allocation framework to optimally provision the limited grid resources. In the third layer, we consider the battery charging dynamics and develop a station policy to maximize the profit by setting maximum charging levels. The model is evaluated on the Arizona state highway system and North Dakota state network with a gravity data model, and on the City of Raleigh, North Carolina, using real traffic data. The results show that the proposed hierarchical model improves the system performance, as well as the quality of service (QoS), provided to the customers. The proposed model can efficiently assist city planners for CS location selection and system design.

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A data-driven optimization-based approach for siting and sizing of electric taxi charging stations

Cited by 194 publications

References 45 publications

A Charging Location Choice Model for Plug-In Hybrid Electric Vehicle Users

A Charging Location Choice Model for Plug-In Hybrid Electric Vehicle Users

Bi-Level Planning Model of Charging Stations Considering the Coupling Relationship between Charging Stations and Travel Route

A Hierarchical Optimization Model for a Network of Electric Vehicle Charging Stations

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