• MS. PRACHI N. SAMARTH
• PROF.CHETAN BOBADE

Fuel cell electric vehicles (FCEV) are becoming more common in the automotive industry as a result of tighter restrictions on carbon emissions and fuel economy. A neural network-based maximum power point tracking (MPPT) controller for a 1.26-kW proton exchange membrane fuel cell (PEMFC) that supplies electric vehicle powertrain through a high voltage-gain dc-dc boost converter is presented in this article. The suggested neural network MPPT controller tracks the PEMFC's maximum PowerPoint using a radial basis function network (RBFN) algorithm. For the propulsion of FCEVs, high switching-frequency and high voltage-gain dc-dc converters are needed. A three-phase high voltage-gain interleaved boost converter is also planned for the FCEV system in order to achieve high voltage gain. The interleaving technique reduces the power semiconductor devices' input current ripple and voltage tension. The fuzzy logic controller in the MATLAB/Simulink platform is compared to the performance review of the FCEV framework with the RBFN-based MPPT controller.

FUEL CELL,MPPT,RBFN ,IBC

"HIGH VOLTAGE GAIN WITH NEURAL NETWORK BASED MPPT BASED ELECTRIC VEHICLE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 7, page no.a494-a503, July-2021, Available :http://www.jetir.org/papers/JETIR2107060.pdf

"HIGH VOLTAGE GAIN WITH NEURAL NETWORK BASED MPPT BASED ELECTRIC VEHICLE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 7, page no. ppa494-a503, July-2021, Available at : http://www.jetir.org/papers/JETIR2107060.pdf