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

Abstract

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 Radial Basis Function Network (RBFN) Algorithm For Tracking
The Maximum Power Point (MPP) 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 (IBC) 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
(FLC).