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

Abstract

As a result of the strict regulations on carbon emissions and the fuel economy, fuel cell electric vehicles (FCEV) vehicles are becoming increasingly popular in the automotive industry. This paper provides the Neural Network Maximum Power Point Tracking (MPPT) controller of the 1.26 kW Proton Exchange Membrane Fuel Cell (PEMFC), which provides electric vehicle powertrain using DC-DC power converters. The proposed neural network controls the MPPT Radial Basis Function Network (RBFN) using the PEMFC Maximum PowerPoint (MPP) tracking algorithm. High frequency switching and high DC-DC converting power are important for FCEV continuity. For maximum power gain, a three-phase power supply interleaved boost converter (IBC) is also designed for the FCEV system. The interleaving process reduces the current input pressure and electrical pressure in semiconductor electrical equipment. FCEV system performance analysis with RBFN based MPPT control compared to fuzzy logic controllers (FLC) on the MATLAB / Simulink platform.