A BIO-INSPIRED OPTIMIZER BASED ANN CONTROLLER FOR EV CHARGING STATION WITH GRID TIED PV SYSTEM

Computer Science Engineering

Nowadays, scientists have more interest towards the challenge of establishing an extremely effective emission-free energy generating and transportation infrastructure to tackle serious demand for ecological disaster driven on by greenhouse gas emissions and global warming. The Electric Vehicles (EVs) are developed to tackle the problem of emission-free mobility, whereas Photovoltaic (PV) systems are deployed and expanded to address the need for carbon free power generation. The use of PV-powered electric vehicles further reduces the amount of carbon dioxide emitted into the atmosphere. Hence, in this work concentrates on towards hybrid PV system and grid with EV, that is strongly emit the harmful gases from atmosphere with less distortion. By the utilization of Integrated High Gain Boost-Cuk (IHGBC) Converter, the poor level of output voltage is stabilized and also it provides minimized switching loss. Here, Artificial Neural Network (ANN) based DC link voltage approach is used to control the IHGBC converter. The Salp Swarm Optimized algorithm is intended for tuning the hyper-parameters of ANN (SS-ANN). The regulation of single-phase Voltage Source Inverter (1ɸ VSI) linked to the grid is accomplished deploying a Proportional Integral (PI) controller. To verify the performance of suggested ANN, the MATLAB platform is implemented. The simulation findings demonstrate that ANN-based control technique outperforms other control approaches. The suggested converter obtains excellent efficiency of 94% with reduced THD value of 2.12%, making it suitable for usage in PV-based EV applications.

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