PROJECT TITLE :

High Voltage Gain Interleaved Boost Converter with Neural Network Based MPPT Controller for Fuel Cell Based Electric Vehicle Applications

ABSTRACT:

Due to the additional vigorous regulations on carbon gas emissions and fuel economy, Fuel cell electrical vehicles (FCEV) are changing into more widespread in the automobile business. This paper presents a neural network-primarily based most power point tracking (MPPT) controller for 1.twenty six-kW proton exchange membrane fuel cell (PEMFC), supplying electric vehicle powertrain through a high voltage-gain dc-dc boost converter. The proposed neural network MPPT controller uses a radial basis perform network (RBFN) algorithm for tracking the most power purpose of the PEMFC. High switching-frequency and high voltage-gain dc-dc converters are essential for the propulsion of FCEV. In order to achieve high voltage-gain, a three-section high voltage-gain interleaved boost converter is also designed for FCEV system. The interleaving technique reduces the input current ripple and voltage stress on the power semiconductor devices. The performance analysis of the FCEV system with RBFN-based mostly MPPT controller is compared with the Fuzzy Logic controller in MATLAB/Simulink platform.