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

Due to the more vigorous regulations on carbon gas emissions and fuel economy, Fuel cell electric vehicles (FCEV) are becoming more popular in the automobile industry. This paper presents a neural network-based maximum power point tracking (MPPT) controller for 1.26-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 function network (RBFN) algorithm for tracking the maximum power point of the PEMFC. High switching-frequency and high voltage-gain dc-dc converters are essential for the propulsion of FCEV. In order to attain high voltage-gain, a three-phase 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 MPPT controller is compared with the fuzzy logic controller in MATLAB/Simulink platform.