Crowdsourcing Based Framework for Vehicle-to-Vehicle Power Transfer

Abstract

With the recent developments in the transportation sector, Electric Vehicles (EVs) have become a promising solution to entirely replace traditional internal combustion engine vehicles. However, the growth rate of installing Charging Station (CS) is much lower than the growth rate of EVs comparing the year-to-year increase. Therefore, EV owners face 'Range Anxiety' which is the fear that their remaining charge would not be enough to reach their destination. Vehicle-to-Vehicle (V2V) charging is currently being used in exchanging energy between EVs. V2V is an application of crowdsourcing where the task requesters are the energy requesters, the workers are the energy providers, and the task published is the charging request initiated by the energy requester. Currently, most of the existing energy V2V frameworks depend on a centralized server that manages the energy exchange. Centralized solutions have some limitations such as single point of failure, privacy leakage, or misbehaving platform. To overcome these limitations, Blockchain was proposed as a decentralized, immutable, transparent, and traceable ledger of transactions. In this thesis, a blockchain-based crowdsourcing framework for V2V charging is proposed. The framework is built to connect requesters to providers without the need for a central server or a third-party entity. The proposed framework features user registration, maintaining user information, clustering requesters (charging tasks), collecting preferences lists from requesters and providers, selects, through Gale-Shapely matching, which requester will charge from which provider at which meeting point based on their preferences. Upon completion of the charging task, the provider will get the payment amount that was transferred from the requester. These features are implemented using three types of smart contracts: User Management Contract, Task Management Contract, and Solution Management Contract. These smart contracts guarantee the transparency of the system while overcoming the main limitations of the centralized solutions while achieving a comparable performance.

Date of Award	May 2022
Original language	American English