An energy-management and scheduling of EV charging stations with solar PV - Fuel cell hybrid renewable energy resources using artificial intelligence technique.

The optimal management and scheduling of renewable energy-based Electric Vehicle (EV) charging stations has become a critical issue in recent years. Traditional Energy Management (EM)solutions are feasible for renewable energy-based charging stations. However, there are still a number of obstacles that need to be addressed, including lessening power supply disruptions, addressing Power Quality Issues (PQI), and properly allocating charging stations. Hence, this paper proposes EM and scheduling strategies for hybrid photovoltaic and Fuel Cell (FC)-based EV charging stations. By removing the disturbances with a Hamming-based Sliding Mode Disturbance Observer, the PV power generated is connected to the DC bus (H-SMDO). The Inter-valued Intuitionistic Triangular Membership function based Fuzzy Logic (ITM-FL) is designed for the EM to control the power generation with or without the support of an FC based on energy demand. According to the findings, the suggested model outperformed current EM techniques in terms of efficiency by 97.66%, and it is still a viable option for charging stations. [ABSTRACT FROM AUTHOR]