The development of each country depends on electricity. In this regard, conventional energy sources, e.g., diesel, petrol, etc., are decaying. Consequently, the investigations of renewable energy sources (RES) are increasing as alternate energy sources for the fulfillment of energy requirements. The output characteristics of RES are becoming non-linear. Therefore, the Maximum Power Point Tracking (MPPT) techniques are critical for extracting the Maximum Power Point (MPP) from RES, e.g., photovoltaic (PV) and wind turbines (WT). RES such as the Fuel Cell (FC) has been hailed as one of the major capable RES for automobile applications since they continually create electricity for the dc-link (even if one or both RES are not supplied by solar and wind, the FC will continue to supply to the load). Adaptive Neuro-Fuzzy Inference System (AN-FIS) MPPT for PV, WT, FC, and Hybrid RES is employed in this research article to solve this problem. The high step-ups (boost converters) are connected with PV and FC modules, and the buck converter is connected with the WT framework, to extract the maximum amount of power using MPPT algorithms. The performance of proposed frameworks based on MPPT algorithms is assessed in variable operating conditions such as Solar-Radiation (SR), Wind-Speed (WS), and Hydrogen-Fuel-Rate (HFR). A novel AN-FIS MPPT framework has enhanced the power of Hybrid RES at DC-link, and also reduced the simulation time to reach the MPP when compared to the perturb and observe (P-&-O), Fuzzy-Logic Controller (F-LC), and artificial neural network (AN-N) MPPTs.

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