Simulative design and performance analysis of hybrid optimization technique for PEM fuel cell stack based EV application

Abstract

The fuel cell power generation systems are using in most of the present hybrid Electric Vehicle (EV) technology. The output characteristics of fuel cell arenonlinear. Also, the fuel operating point fluctuate continuously based on its operating temperature condition. In order to track and stabilize the operating point fuel cell, an Artificial Neuro Fuzzy Inference System-Genetic Algorithm Optimization (ANFIS-GAO) method is used. The merits of this proposed Maximum Power Point Tracking Technique (MPPT) are high extraction power capacity, high reliability, less oscillations, and fast tracking speed. The fuel cell is having a drawback of high output current and its less output voltage generation capability. To overcome this issue, in this work, an interleaved dc-dc converter circuit is used to enhance the fuel cell energy. The advantages of the proposed dc-dc converter are low voltage stress across the switches, less input current ripples, faster response, reduced electromagnetic emission, and high reliability. The proposed hybrid power point tracking technique performance is evaluated against with the Variable Step-Perturb & Observe (VS-P&O) algorithm. The MATLAB/Simulink environment is used for the performance analysis of proposed system.