Charging infrastructure planning for promoting battery electric vehicles: An activity-based approach using multiday travel data

Dong, Jing; Liu, Changzheng; Lin, Zhenhong

doi:10.1016/j.trc.2013.11.001

Cited by 453 publications

(210 citation statements)

References 26 publications

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“…Many research efforts are ongoing to improve both problems of which the first one will be more deeply discussed in Section 3. In literature [9][10][11][12][13][14][15] the range anxiety is identified as mainly a psychological barrier since most people drive less kilometers a day than the range of current EVs. This problem is enhanced by the long charging time of an EV as well as the lack of abundantly available charging stations for electric vehicles.…”

Section: State Of the Art-hevmentioning

confidence: 99%

Cost Projection of State of the Art Lithium-Ion Batteries for Electric Vehicles Up to 2030

et al. 2017

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Abstract:The negative impact of the automotive industry on climate change can be tackled by changing from fossil driven vehicles towards battery electric vehicles with no tailpipe emissions. However their adoption mainly depends on the willingness to pay for the extra cost of the traction battery. The goal of this paper is to predict the cost of a battery pack in 2030 when considering two aspects: firstly a decade of research will ensure an improvement in material sciences altering a battery's chemical composition. Secondly by considering the price erosion due to the production cost optimization, by maturing of the market and by evolving towards to a mass-manufacturing situation. The cost of a lithium Nickel Manganese Cobalt Oxide (NMC) battery (Cathode: NMC 6:2:2 ; Anode: graphite) as well as silicon based lithium-ion battery (Cathode: NMC 6:2:2 ; Anode: silicon alloy), expected to be on the market in 10 years, will be predicted to tackle the first aspect. The second aspect will be considered by combining process-based cost calculations with learning curves, which takes the increasing battery market into account. The 100 dollar/kWh sales barrier will be reached respectively between 2020-2025 for silicon based lithium-ion batteries and 2025-2030 for NMC batteries, which will give a boost to global electric vehicle adoption.

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Section: State Of the Art-hevmentioning

confidence: 99%

Cost Projection of State of the Art Lithium-Ion Batteries for Electric Vehicles Up to 2030

et al. 2017

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“…Li et al [23] employed genetic algorithm for facility location to minimize the total costs. Dong et al [24] proposed a genetic algorithmic framework to minimize Brange anxiety^, defined as the total number of missed trips in the network, employing GPS data from conventional vehicles and a household travel choice survey.…”

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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“…[10] explored the optimal mix of public Level 2 and 3 EVSE located along a travel route. Other work concerned with the optimal placement of EVSE within cities have been developed in [11][12][13][14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Electricity costs for a Level 3 electric vehicle fueling station integrated with a building

2017

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Charging infrastructure planning for promoting battery electric vehicles: An activity-based approach using multiday travel data

Cited by 453 publications

References 26 publications

Cost Projection of State of the Art Lithium-Ion Batteries for Electric Vehicles Up to 2030

Cost Projection of State of the Art Lithium-Ion Batteries for Electric Vehicles Up to 2030

Electric vehicles charging infrastructure location: a genetic algorithm approach

Electricity costs for a Level 3 electric vehicle fueling station integrated with a building

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