An approach for the optimal planning of electric vehicle infrastructure for highway corridors

Sathaye, Nakul; Kelley, Scott

doi:10.1016/j.tre.2013.08.003

Cited by 153 publications

(54 citation statements)

References 18 publications

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“…Xi et al [11] found that while location depends on the optimization criteria, the service levels are not. Sathaye and Kelley [12] explored a way to locate stations on highway corridors. They developed a continuous algorithm that minimizes the deviation of the vehicles from the expected trips.…”

Section: Literature Reviewmentioning

confidence: 99%

Electric vehicles charging infrastructure location: a genetic algorithm approach

Efthymiou

Chrysostomou

Morfoulaki

et al. 2017

Eur. Transp. Res. Rev.

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Introduction As part of the overall goal of carbon emissions reduction, European cities are expected to encourage the electrification of urban transport. In order to prepare themselves to welcome the increased number of electric vehicles circulating in the city networks in the near future, they are expected to deploy networks of public electric vehicle chargers. The Electric Vehicle Charging Infrastructure Location Problem is an optimization problem that can be approached by linear programming, multi-objective optimization and genetic algorithms. Methods In the present paper, a genetic algorithm approach is presented. Since data from electric vehicles usage are still scarce, origin -destination data of conventional vehicles are used and the necessary assumptions to predict electric vehicles' penetration in the years to come are made. The algorithm and a user-friendly tool have been developed in R and tested for the city of Thessaloniki. ResultsThe results indicate that 15 stations would be required to cover 80% of the estimated electric vehicles charging demand in 2020 in the city of Thessaloniki and their optimal locations to install them are identified. Conclusions The tool that has been developed based on the genetic algorithm, is open source and freely available to interested users. The approach can be used to allocate charging stations at high-level, i.e. to zones, and the authors encourage its use by local authorities of other cities too, in Greece and worldwide, in order to deploy a plan for installing adequate charging infrastructure to cover future electric vehicles charging demand and reduce the electric vehicle Bdriver anxiety( i.e. the driver's concern of running out of battery) encouraging the widespread adoption of electromobility.

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Section: Literature Reviewmentioning

confidence: 99%

Electric vehicles charging infrastructure location: a genetic algorithm approach

Efthymiou

Chrysostomou

Morfoulaki

et al. 2017

Eur. Transp. Res. Rev.

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show abstract

“…Adaptation is important when implementing novel infrastructure technologies, since demand can be difficult to predict (Brownstone et al, 2010). Similar arguments have been made for the analysis of a variety of transportation problems and previously for electric vehicle infrastructure deployment (Sathaye and Kelley, 2013). Nevertheless, it should be noted that approximate models must be specified carefully so that there is no loss of relevant information.…”

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confidence: 66%

The optimal design and cost implications of electric vehicle taxi systems

Sathaye¹

2014

Transportation Research Part B: Methodological

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“…The use of electric vehicles in the logistics operations led to several new problems flourish in the literature such as pollution-routing problem (Bektas ß and Demir et al, 2012Demir et al, , 2014Franceschetti et al, 2013;Kramer et al, 2015;Koç et al, 2014), green vehicle routing problem (Erdogan and Miller-Hooks, 2012;Ć irović et al, 2014;Lin et al, 2014;Felipe et al, 2014), location optimization of alternative fuel stations (Kuby and Lim, 2005;Wang and Lin, 2009;Wang and Wang, 2010;Wang and Lin, 2013;Sathaye and Kelley, 2013;Xi et al, 2013;Li and Huang, 2014;, and optimal routing problems (Goeke and Schneider, 2015;Schneider et al, 2014;Felipe et al, 2014). The common objective of these studies is to establish the environmental and cost impacts of electric vehicles from the logistics perspective.…”

Section: Related Literaturementioning

confidence: 99%

Minimum cost path problem for Plug-in Hybrid Electric Vehicles

Arslan

Yıldız

Karaşan

2015

Transportation Research Part E: Logistics and Transportation Re

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a b s t r a c tWe introduce a practically important and theoretically challenging problem: finding the minimum cost path for PHEVs in a road network with refueling and charging stations. We show that this problem is NP-complete and present a mixed integer quadratically constrained formulation, a discrete approximation dynamic programming heuristic, and a shortest path heuristic as solution methodologies. Practical applications of the problem in transportation and logistics, considering specifically the long-distance trips, are discussed in detail. Through extensive computational experiments, significant insights are provided. In addition to the charging infrastructure availability, a driver's stopping tolerance arises as another critical factor affecting the transportation costs.

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An approach for the optimal planning of electric vehicle infrastructure for highway corridors

Cited by 153 publications

References 18 publications

Electric vehicles charging infrastructure location: a genetic algorithm approach

Electric vehicles charging infrastructure location: a genetic algorithm approach

The optimal design and cost implications of electric vehicle taxi systems

Minimum cost path problem for Plug-in Hybrid Electric Vehicles

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