With high penetration of electric vehicles (EVs), stability of the electric grid becomes a challenging task. A greater penetration level would demand a proper coordination amongst the various EVs as they charge or discharge to the grid. Coordination here refers to controlling the charging and discharging patterns of different EVs depending on their individual battery states and the present grid condition. Therefore, a good coordination between EVs is required for making the grid stable. With high penetration of EVs, the vehicle to grid (V2G) concept can be explored where excess energy of the battery can be supplied back to the grid in controlled fashion. Discharging EVs' battery energy to the grid in coordination can make V2G utilization as distributed energy storage. Charging EVs in coordination can flatten the voltage profile of a distribution node. In this work, a typical distribution system of a city is modeled to demonstrate V2G capabilities such as meeting peak demand and voltage sag reduction. The simulation of the distribution system with V2G capabilities are tested using fuzzy logic controller (FLC). Two controllers have been developed, namely the charging station controller and the V2G controller. Together they decide the proper energy flow between the EVs and the grid. Energy discharge to the grid from EVs or energy required for charging EVs is controlled and tested for the real time scenario.

This paper presents an electric circuit-based battery and a capacity fade model suitable for electric vehicles (EVs) in vehicle-to-grid applications. The circuit parameters of the battery model (BM) are extracted using genetic algorithm-based optimization method. A control algorithm has been developed for the battery, which calculates the processed energy, charge or discharge rate, and state of charge limits of the battery in order to satisfy the future requirements of EVs. A complete capacity fade analysis has been carried out to quantify the capacity loss with respect to processed energy and cycling. The BM is tested by simulation and its characteristics such as charge and discharge voltage, available and stored energy, battery power, and its capacity loss are extracted. The propriety of the proposed model is validated by superimposing the results with four typical manufacturers' data. The battery profiles of different manufacturers' like EIG, Sony, Panasonic, and Sanyo have been taken and their characteristics are compared with proposed models. The obtained battery characteristics are in close agreement with the measured (manufacturers' catalogue) characteristics.

This paper discusses the modeling of a multi charging station for Electric Vehicles (EVs) and its utilisation for grid support. Allowing the EVs to charge and discharge without any control may lead to voltage variations in the grid. However, if the charging/discharging of the EVs is done in an intelligent fashion, they can act as distributed energy sources and can smoothen the load profile of the distribution network by providing peak shaving and valley filling. The presented work mainly focuses on the control architecture for using the EVs' batteries as distributed energy storage systems. This enables in maintaining the node voltage within the prescribed limit by valley filling and peak shaving. The control architecture is based on fuzzy control techniques. The proposed architecture is implemented on the distribution network of Guwahati city. Dynamic load profile is used to check the effectiveness of the proposed architecture for flattening the load profile.