iCharge: An IoT-Assisted Framework for Efficient Charging of the Electric-Vehicles

With increasing focus on the use of renewable energy as well as impact of transportation systems over climate and air pollution, Electric-Vehicles (EV) are going to be one of the major means of conveyance in the near future. However, due to limited availability of the Charging-Stations (CS), accomplishing quick and efficient charging of the EVs is still a serious bottleneck. Existing solutions for deciding where an EV should go for charging mostly use centralized support, e.g., cloud assisted decision making. However, such centralized solutions, although serve the goal quite efficiently, are highly prone to serious problems primarily because of single-point-of-failure. Dynamic charge-scheduling of the EVs, being one of the extremely important issues, in this work we take an endeavor to build an alternative approach that achieves the goal through a distributed IoT-assisted framework. In a nutshell, our proposed framework, iCharge, forms a dynamic IoT-system comprised of both the CSs and EVs. Through the IoT-setting, the EVs obtain the necessary status information from the CSs which they use for deciding the right CS for charging on-the-fly. To demonstrate the effectiveness of the proposed strategy we incorporate a Multi-Criteria Decision-Making (MCDM) algorithm for efficient decision making based on limited data. Through extensive simulation based experiments we show that iCharge is able to quite reliably schedule quite a large number of EVs over a smaller number of CSs in a well balanced manner and minimize the overall waiting time of the EVs at the CSs.