Optimal deployment of public charging stations for plug-in hybrid electric vehicles

He, Fang; Wu, Di; Yin, Yafeng; Guan, Yongpei

doi:10.1016/j.trb.2012.09.007

Cited by 465 publications

(194 citation statements)

References 27 publications

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“…He et al [10] used an active-set algorithm for the optimal deployment of charging stations for plug-in hybrid vehicles, with the objective of maximizing of the social welfare. They recommend that public charging locations and electricity prices should be taken into account at the planning phase.…”

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.

View full text Add to dashboard Cite

show abstract

“…Vehicle range is the key factor for determining the locations and number of refueling stations for completing a journey, especially a long-distance one. The location of switching/recharging stations for accommodating electric/hybrid vehicles has been the scope of recent research (including but not limited to Kuby and Lim (2005), Lin (2009), Wang andWang (2010), MirHassani and Ebrazi (2012), He et al (2013), Nie and Ghamami (2013), Xi et al (2013)). It is not hard to see that the refueling station placement problem with its inherent coverage and range limitations is closely related with the RLP.…”

Section: Applications/limitations In Transportation Settingsmentioning

confidence: 99%

Regenerator Location Problem and survivable extensions: A hub covering location perspective

Yıldız

Karaşan

2015

Transportation Research Part B: Methodological

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Survivable network design Branch and cut a b s t r a c tIn a telecommunications network the reach of an optical signal is the maximum distance it can traverse before its quality degrades. Regenerators are devices to extend the optical reach. The regenerator placement problem seeks to place the minimum number of regenerators in an optical network so as to facilitate the communication of a signal between any node pair. In this study, the Regenerator Location Problem is revisited from the hub location perspective directing our focus to applications arising in transportation settings. Two new dimensions involving the challenges of survivability are introduced to the problem. Under partial survivability, our designs hedge against failures in the regeneration equipment only, whereas under full survivability failures on any of the network nodes are accounted for by the utilization of extra regeneration equipment. All three variations of the problem are studied in a unifying framework involving the introduction of individual flow-based compact formulations as well as cut formulations and the implementation of branch and cut algorithms based on the cut formulations. Extensive computational experiments are conducted in order to evaluate the performance of the proposed solution methodologies and to gain insights from realistic instances.

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“…Several papers deal with PHEV-related subjects, including, but not limited to, range requirement analysis (Pearre et al, 2011;Franke and Krems, 2013), PHEV impacts on the electricity network (Sioshansi and Miller, 2011;Nurre et al, 2014;Arslan and Karaşan, 2013;Galus et al, 2010;Weiller, 2011;Axsen et al, 2011), battery capacity analysis (Albertus et al, 2008;Catenacci et al, 2013;Scrosati and Garche, 2010), environmental impacts (Stephan and Sullivan, 2008;Smith, 2010;Marshall et al, 2013), market analysis (Gardner et al, 2013;Khan and Kockelman, 2012;Axsen and Kurani, 2013;Eppstein et al, 2011) and location optimization of recharging infrastructure (He et al, 2013;Wang and Lin, 2009;MirHassani and Ebrazi, 2012). A summary of recent studies in the literature related to the economics of PHEVs is presented in Table 1.…”

Section: Literature Reviewmentioning

confidence: 99%

Impacts of battery characteristics, driver preferences and road network features on travel costs of a plug-in hybrid electric vehicle (PHEV) for long-distance trips

Arslan¹,

Yıldız²,

Karaşan³

2014

Energy Policy

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We investigate the travel costs of CVs, HEVs and PHEVs for long-distance trips. We analyze the impacts of battery, driver and road network characteristics on the costs. We provide critical managerial insights to shape the investment decisions about PHEVs. Drivers' stopping intolerance may hamper the cost and emission benefits of PHEVs. Negative effect of intolerance on cost may be overcome by battery capacity expansion. a r t i c l e i n f o b s t r a c tIn a road network with refueling and fast charging stations, the minimum-cost driving path of a plug-in hybrid electric vehicle (PHEV) depends on factors such as location and availability of refueling/fast charging stations, capacity and cost of PHEV batteries, and driver tolerance towards extra mileage or additional stopping. In this paper, our focus is long-distance trips of PHEVs. We analyze the impacts of battery characteristics, often-overlooked driver preferences and road network features on PHEV travel costs for long-distance trips and compare the results with hybrid electric and conventional vehicles. We investigate the significance of these factors and derive critical managerial insights for shaping the future investment decisions about PHEVs and their infrastructure. In particular, our findings suggest that with a certain level of deployment of fast charging stations, well established cost and emission benefits of PHEVs for the short range trips can be extended to long distance. Drivers' stopping intolerance may hamper these benefits; however, increasing battery capacity may help overcome the adverse effects of this intolerance.

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Optimal deployment of public charging stations for plug-in hybrid electric vehicles

Cited by 465 publications

References 27 publications

Electric vehicles charging infrastructure location: a genetic algorithm approach

Electric vehicles charging infrastructure location: a genetic algorithm approach

Regenerator Location Problem and survivable extensions: A hub covering location perspective

Impacts of battery characteristics, driver preferences and road network features on travel costs of a plug-in hybrid electric vehicle (PHEV) for long-distance trips

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