Design and performance analysis of hybrid MPPT controllers for fuel cell fed DC-DC converter systems

Abstract

Fuel cell-based power generation is the most utilized renewable energy source in the automotive industry because of its features clean energy, and less environmental pollution. The fuel cell output power is mainly depending on the operating temperature of the fuel cell. The fuel cell gives nonlinear voltage versus current characteristics. As a result, the extraction of maximum power from the fuel stack is very difficult. In order to extract the peak power from the fuel cell, a Maximum Power Point Tracking (MPPT) controller is used at various working temperature conditions of the fuel cell. The main contribution of this study is the introduction and comparative performance analysis of different hybrid MPPT controllers for selecting the optimum duty cycle for the fuel cell-fed boost converter system. The studied MPPT controllers are Adaptive Adjustable Step-based Perturb and Observe (AAS-P&O) controllers, Variable Step Value-Radial Basis Function Controller (VSV-RBFC), Adaptive Step Hill Climb (ASHC) based fuzzy technique, Variable P&O with Particle Swarm Optimization (VP&O-PSO), and Variable Step Grey Wolf Algorithm (VSGWA) based fuzzy logic controller. These hybrid MPPT controllers’ comparative performance analysis has been done in terms of tracking speed of MPP, oscillations across MPP, design complexity of controller, ability to handle fast changes of temperature values, and accuracy of MPP tracking. From the simulative performance results, it is identified that the VSGWA-based fuzzy controller gives superior performance when compared to the other controllers.