Abstract

Alternative vehicles to Internal Combustion Engines (ICE), for instance the electric vehicle is becoming popular. Electric Vehicles (EV) are pollution free and cost effective because the fossil fuel cost increases day by day. These factors make people passion for electric vehicles. Electrical energy demand necessitates charging of electric vehicles using renewable energy. Among the different renewable energy resources, Photovoltaic (PV) cells are suitable for EV. The PV output power capacity is still low, so efforts continue to develop the PV converter and its controller, aiming for higher power-extracting efficiency. The PV system requires a proper DC-DC converter with optimum controller to deliver its maximum power. This study analyses the various DC-DC converters such as buck, boost, cuk and modified cuk converters to find the solution for maximum efficiency. In this study in addition to converters, various Maximum Power Point Tracking (MPPT) methods, such as Perturb and Observe, Incremental Conductance along with a proposed algorithm called Brain Emotional Learning Based Intelligent Controller (BELBIC) has been analyzed. The operation of the BELBIC is based on the emotion processing mechanism in the brain. This intelligent control is stimulated by the limbic system of the mammalian brain. The performance analysis of the converters and MPPT methods are simulated using MATLAB/SIMULINK. Furthermore, experimental results are presented in order to validate the modified cuk converter with proposed BELBIC algorithm.

Keywords:

BELBIC , DC-DC converters , modified cuk converter, MPPT , PV system,

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Competing interests

The authors have no competing interests.

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