Optimal location of electric vehicle charging stations using genetic algorithm

Chen, Shuangshuang; Shi, Yue; Chen, Xingyu; Qi, Feng

doi:10.1109/apnoms.2015.7275344

Cited by 22 publications

(13 citation statements)

References 8 publications

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“…Several research projects have aimed to identifying ideal places for situating CSs using different emphases and targets, e.g., using optimization algorithms [9][10][11] or more spatially based, geostatistical approaches [12,13]. The minimization of trip length or travel times [14,15] is a widely used target criterion for CS location models.…”

Section: State Of the Art Of Cs Location Modelsmentioning

confidence: 99%

Electric Charging Demand Location Model—A User- and Destination-Based Locating Approach for Electric Vehicle Charging Stations

2019

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In recent years, with the increased focus on climate protection, electric vehicles (EVs) have become a relevant alternative to conventional motorized vehicles. Even though the market share of EVs is still comparatively low, there are ongoing considerations for integrating EVs in transportation systems. Along with pushing EV sales numbers, the installation of charging infrastructure is necessary. This paper presents a user- and destination-based approach for locating charging stations (CSs) for EVs—the electric charging demand location (ECDL) model. With regard to the daily activities of potential EV users, potential positions for CSs are derived on a micro-location level in public and semipublic spaces using geographic information systems (GIS). Depending on the vehicle users’ dwell times and visiting frequencies at potential points of interest (POIs), the charging demand at such locations is calculated. The model is mainly based on a survey analyzing the average time spent per daily activity, regional data about driver and vehicle ownership numbers, and the georeferenced localization of regularly visited POIs. Optimal sites for parking and charging EVs within the POIs neighborhood are selected based on walking distance calculations, including spatial neighborhood effects, such as the density of POIs. In a case study in southeastern Germany, the model identifies concrete places with the highest overall demand for CSs, resulting in an extensive coverage of the electric energy demand while considering as many destinations within the acceptable walking distance threshold as possible.

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Section: State Of the Art Of Cs Location Modelsmentioning

confidence: 99%

Electric Charging Demand Location Model—A User- and Destination-Based Locating Approach for Electric Vehicle Charging Stations

2019

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“…For instance, the studies of Jia et al (2014) and S. Li et al (2016) rely on georeferenced data as input and present the results in form of vector maps with the punctual location of CS, but they do not apply any geospatial analysis for finding these locations. Instead, the authors of these studies propose the CS siting problem as mixed-integer linear program (MILP) with cost minimization functions as target equations, which were solved with the IBM ILOG CPLEX solver or with GA. Chen, Shi, Chen & Qi (2015); Hidalgo, Ostendorp & Lienkamp (2016); Salmon (2016) and Sebastiani, Luders & Fonseca (2016) also use GA for specifying the circumstances and parameters in general and identify the optimal CS locations with the input of e.g. network and trip length or the battery SOC.…”

Section: Optimization Algorithms and Specializationmentioning

confidence: 99%

“…While some research projects attempt to find the best CS location with an optimization approach using road and driving networks, other studies aim to identify these locations by calculating the spatial distribution of charging demand. Optimization algorithms such as genetic algorithms (Chen, Shi, Chen & Qi, 2015;Hidalgo, Ostendorp & Lienkamp, 2016;Salmon, 2016;Sebastiani, Luders & Fonseca, 2016) or integer programming (Asamer, Reinthaler, Ruthmair, Straub & Puchinger, 2016;Li, Huang & Mason, 2016;Wang, Yuen, Hassan, An & Wu, 2016) are often applied as techniques.…”

Section: Introductionmentioning

confidence: 99%

A review of spatial localization methodologies for the electric vehicle charging infrastructure

Pagany

Camargo

Dorner

2018

International Journal of Sustainable Transportation

117

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With view to the high share of the transport sector in total energy consumption, e-mobility should play an important role within the transition of the energy systems. Policymakers in several countries consider electric vehicles (EV) as an alternative to fossil-fueled vehicles. In order to allow for the development of EV, the charging infrastructure has to be set up at locations with high charging potential, identified by means of various criteria such as demand density or trip length. Many methodologies for locating charging stations (CS) have been developed in the last few years. First, this paper presents a broad overview of publications in the domain of CS localization. A classification scheme is proposed regarding modeling theory and empirical application; further on, models are analyzed, distinguishing between users, route or destination centricity of the approaches and outcomes. In a second step, studies in the field of explicit spatial location planning are reviewed in more detail, that is, in terms of their target criteria and the specialization of underlying analytical processes. One divergence of these approaches lies in the varying level of spatial planning, which could be crucial depending on the planning requirements. It is striking that almost all CS locating concepts are proposed for urban areas. Other constraints, such as the lack of extensive empirical EV traffic data for a better understanding of the driving behavior, are identified. This paper provides an overview of the CS models, a classification approach especially considering the problem's spatial dimension, and derives perspectives for further research. ARTICLE HISTORY

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“…There are two main approaches namely the spatial approach and the flow-based approach in the literature to simulating the optimal location of EVCSs. The spatial approach is an adaptation of well-known models such as p-median [Toregas, Swain and Revelle (1971); Campbell (1996); An, Zeng and Zhang (2014)] and coverage problems [Chen, Shi, Chen et al (2015) ; Wang, Ju, Gao et al (2018) ; Liu, Yang, Zhou et al (2019)] used for the facility location [Wang, Gao, Sherratt et al (2018); Wang, Gao, Liu et al (2019)]. The P-median problem is based on the minimum demand point and the average distance from the service station.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Decision-Making Approach for the Optimal Location of Electric Vehicle Charging Facilities

Liu¹,

Tang²,

Yang³

et al. 2019

Computers, Materials &Amp; Continua

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Electric vehicles (EVs) are recognized as one of the most promising technologies worldwide to address the fossil fuel energy resource crisis and environmental pollution. As the initial work of EV charging station (EVCS) construction, site selection plays a vital role in its whole life cycle. In this paper, a multi-objective optimization model for the location layout of EVCSs is established when considering various factors such as user demand, investment cost, soil locations, the emergency charging mileage limit, the actual road condition and service network reliability. The model takes the minimum investment cost and the minimum user charging cost as the dual objectives. On the basis of satisfying the user's charging demand and the capacity constraints of EVCSs, the redundant design of the charging pile and station is considered to ensure the reliability of the service network. In the allocation of user charging demand, in this paper, two factors of time and distance are considered, and the equal time load distance method is adopted to distribute traffic flow under the limitation of emergency charging mileage. When calculating the average travel speed of a road section, an accounting method based on the land price level is proposed considering the congestion. Then, the linear weighting method is applied to normalizing the multi-objective function, and the genetic algorithm is employed to solve the problem. Finally, a computational experiment is presented to demonstrate the applicability and effectiveness of the proposed approach. The results show that the proposed approach is a useful, practical, and effective way to find the optimal location of EVCSs.

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Optimal location of electric vehicle charging stations using genetic algorithm

Cited by 22 publications

References 8 publications

Electric Charging Demand Location Model—A User- and Destination-Based Locating Approach for Electric Vehicle Charging Stations

Electric Charging Demand Location Model—A User- and Destination-Based Locating Approach for Electric Vehicle Charging Stations

A review of spatial localization methodologies for the electric vehicle charging infrastructure

A Multi-Objective Decision-Making Approach for the Optimal Location of Electric Vehicle Charging Facilities

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