An efficient power management control strategy for grid-independent hybrid renewable energy systems with hybrid energy storage: Hybrid approach

In light of rising environmental pollution, countries throughout the world are pushing electric vehicles (EVs). Grid able Electric Vehicles (GEVs) comprise plug-in hybrid electric vehicles and battery electric vehicles. Regulation problems and grid instability may result from EVs being more frequently plugged into the utility system for charging. In nations like India, the charging situations would not be synchronized, causing the grid's load demand to be unstable. This paper proposes a hybrid method for energy management of renewable energybased dc micro grids supporting electric vehicle charging systems. This work's originality is found in in its proposition of a hybrid technique for managing energy in a renewable energy-based DC micro grid that supports electric vehicle charging. The proposed strategy combines the Mud Ring Algorithm (MRA) and Fuzzy Logic Controller (FLC). Hence it is named as MRA-FLC. This approach is to ensure a consistent power supply while mitigating the premature degradation of storage devices. Two principal sources a battery storage system (BSS) and renewable energy sources (RES) is included in the planned DC micro grid. By integrating renewable energy systems with EVs, the aim is to reduce harmful emissions and enhance resource efficiency through energy storage. The objective is to develop a DC micro grid powered by non-polluting energy sources, effectively balancing power to meet both load demand and electric vehicle charging needs. This novel methodology has the potential to significantly improve the reliability, sustainability, and efficiency of hybrid renewable energy systems. By then, the proposed hybrid technique's performance is put into practice using the MATLAB platform, and it is contracted with other existing methodologies. The power value of proposed method is 15 x 105 W it is higher, emission of proposed is 0.701 g/km which is lower and the efficiency of proposed method is 96 % it is higher than the existing methods.