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    An Electric Vehicle Charging Reservation Approach Based on Blockchain

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    Date
    2020-12-01
    Author
    Cao, Sheng
    Dang, Sixuan
    Du, Xiaojiang
    Guizani, Mohsen
    Zhang, Xiaosong
    Huang, Xiaoming
    ...show more authors ...show less authors
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    Abstract
    The popularity of electric vehicles depends on convenient and efficient charging services. At present, none of existing charging services allow users to reach charging stations at desirable time and charge immediately when they arrive without waiting. This paper proposes a charging reservation service approach based on the consortium blockchain and smart contract technology. Users can choose the charging station and charging time period with no charging congestion, which is based on the charging information recorded in the consortium blockchain composed of stations located in distributed regions in a city. To ensure a user arrives at the charging station on time and charge within due time as he/she has reserved, a personalized pricing scheme for reward and punishment by utilizing smart contract is proposed. We take the past charging behavior into consideration when deciding current charging price of each user, which can provide individualized prices for different users. This approach can not only greatly reduce the user's waiting time, but also offer high cost-effective charging services for good behavior users. We carry out experimental verification under multiple sets of parameter settings, illustrate the variations in three aspects including user's initial score, violation rate and intensity of reward and punishment, thus the feasibility of our approach is proved. Our work is a credible charging paradigm based on trust mechanism via blockchain, which has the potential to become an industry service standard for electric vehicle charging.
    URI
    https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85100380278&origin=inward
    DOI/handle
    http://dx.doi.org/10.1109/GLOBECOM42002.2020.9322093
    http://hdl.handle.net/10576/36391
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    • Computer Science & Engineering [‎2428‎ items ]

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