A Fuzzy Logic and Artificial Neural Network-Based Intelligent Controller for a Vehicle-to-Grid System

With a shift toward increasing usage of electric vehicles (EVs) for mobility, the demand for power from the electric grid to recharge EVs is imminent to increase. Vehicle to grid (V2G) is an emerging technique used to integrate EVs with electric grid for meeting this demand and mitigating the impact of EV recharging on the grid. V2G use an extensive communication network to exchange data between various entities, including the grid, charging station (CSs), and the EVs. There are possibilities of errors in the data communicated between entities of the V2G system. These errors, if not addressed properly, would create unwanted disturbances in the electric grid. The V2G controller plays a significant role in the integration of EVs to the grid. It does so by deciding the right amount of power to be exchanged between them based on the inputs communicated by the grid and the CSs. Hence, this article proposes a two-layer framework intelligent controller comprising of a data integrity and correction check block in the first layer and a rule-based fuzzy logic controller (FLC) in the second layer. The proposed controller's effectiveness is validated using a simulation model, which comprises of an electricity distribution network and a communication network. Results demonstrate the intelligent controller's potential in reducing the impact of errors by deciding the right amount of power to be exchanged between the grid and EVs. A comparison between the conventional FLC and the proposed intelligent controller further corroborates the potential.

Automated summary

As the usage of electric vehicles (EVs) increases for mobility, the demand for power from the electric grid is expected to rise, leading to the emergence of Vehicle to Grid (V2G) technology. The V2G controller plays a crucial role in determining the power exchange between EVs and the grid, with this article proposing an intelligent controller framework based on data integrity and correction checks.