Fuzzy Based Smart Charging Station for Electric Vehicle Application to Mitigate Peak Power Demand

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This thesis proposes a fuzzy based smart charging station (SCS) for electric vehicle application to mitigate the peak power demand of typical city. The SCS, as proposed in this thesis is a place where the EVs come together to charge (drawn power from grid) or discharge (support the grid) their batteries based on the distribution node (DN) voltage and the available energy in the EVs batteries. The SCS has various charging points, which enables EVs of different battery ratings to charge and discharge. However, allowing the EVs to discharge or charge without any control may lead to voltage deviation in the DN. If multiple EVs can be discharged or charged in a controlled fashion, then they can act as distributed energy storage system (DESS). A proper implementation of controlled charging and discharging can mitigate the peak power demand of the DN by providing power to grid (peak shaving) or drawn power from the grid (valley filling). In this thesis, the active power control strategies have been modeled for V2G system to enable peak shaving and valley filling. If EVs batteries are intended to perform charging or discharging operations, it would get subjected to varying node voltage conditions. Such frequent charging/discharging operating conditions affect the internal circuit parameters due to change in state of charge, charge rate of the battery. Moreover, the capacity of EVs batteries would decrease due to frequent charging/discharging process at different charge rate. Therefore, a precise model of EV battery is very much required, to predict the performance of EVs in real-time V2G interaction. Hence, an electric equivalent circuit based battery model and capacity fade/loss model suitable for EVs in V2G applications has been developed. By using the BM, the mathematical modeling and control of contactless based SCS in V2G scenario has been designed. Fuzzy logic controllers and aggregator have been used to control the power flow between the SCS and the grid.
Supervisor: Praveen Kumar