A Novel on Performance Analysis of Proton Exchange Membrane Fuel Cell System with Metaheuristic Optimization Based MPPT Controller

At present, all over the world, the automotive manufacturing industries are utilizing the fuel cell methodologies for supplying the energy to the electrical machines for continuous running of Electric Vehicles (EV) at various operating temperature conditions. Basically, the fuel stacks are combined with the battery management system for running the EV without any shortage of electrical power. The fuel cell generated voltage is very less when equalized with the current. Also, the fuel stack produces nonlinear characteristics. So, the finding of working point of the fuel stack on V-I curve, and its extraction of peak power from the fuel stack are quite difficult. In this article, a metaheuristic optimization related Maximum Power Point Tracking (MPPT) methods are applied to trace the Maximum Power Point (MPP) of the fuel stack system with low steady state oscillations. The metaheuristic adaptive Cuckoo Search Controller, plus Perturb & Observe-Particle Swarm Optimization (P&O-PSO) controllers are compared in terms of working efficiency, tracing time of MPP, dependency on fuel cell design, converter output voltage distortions, plus settling time of MPP. Also, a three-phase power converter is integrated with the fuel cell for enhancing the source voltage of the system. The merits of proposed DC-DC device are good voltage conversion ratio, more efficient, and easy design.