Maximum Power Point Tracking of PEM Fuel Cell with Interleaved Buck Boost Converter Using Jellyfish Search Algorithm

This research aims to enhance a maximum power point tracking (MPPT) for a proton exchange membrane fuel cell (PEMFC) employing a jellyfish search algorithm (JSA) integrated with an interleaved buck-boost converter (IBBC).The nonlinear characteristics of PEMFC often make it challenging for MPPT to achieve satisfactory control performance in reaching maximum power.Traditional or artificial intelligent methods commonly consider parameters such as pressure and temperature for controlling the converter to catch the maximum power.The proposed MPPT introduces JSA algorithm to enhance the operation of IBBC under varying hydrogen pressure, temperature, and membrane water content conditions.The interleaved scheme in buck boost converter is used to smooth the ripple of fuel cell output current.Simulations were performed to illustrate the power characteristics derived from the PEMFC system.The newly designed JSA-MPPT algorithm effectively tracks the maximum power point (MPP) swiftly across different PEMFC parameters and conditions.Simulation results of 1 kW PEMFC and MPPT with 6 Ohm/ 77.5 Volt resistive load using JSA method under various water content reached 99.98% of the MPP within 2.1 seconds.It is more accurate and faster than ANFIS and Cuckoo method.