A Dynamic Framework of Solar Based Electric Vehicle Charging Station with Artificial Neural Network and Genetic Algorithm Techniques

Electric vehicles (EVs) are one of the best replacements of conventional vehicles due to environmental friendly nature. The poor State of Charge (SOC in %) control and large settling time & peak overshoot of speed are the few research gap which need to be address due to continuous degradation of battery. The paper demonstrates the conceptual design and implementation of a solar-powered electric vehicle that uses soft computing methods for smart control. The implementation of a dynamic solar-powered electric vehicle charging stations combining smart control & soft computation methods requires careful, optimal design, charging the economy, & regular upkeep. Nonetheless, it can offer an environmentally friendly, long-term EV charging option and perhaps reduce continuing operational costs. Because electric vehicles emit no pollutants, this work also assists global efforts to reduce emissions of greenhouse gases and combat climate change. In this paper, electric vehicle charging has been assessed at various voltage levels. Subsequently, an electric vehicle is controlled by DC motor. In addition to state of charging of electric vehicle which is measured in terms of state of charging, the speed of electric vehicle is analyzed. In order to attain the smooth operation of speed and SOC, an objective function has been developed. Further, the objective function has been controlled by using artificial neural network and genetic algorithm. It is observed that SOC (%) shows better and smoother performance with GA in comparison to ANN and existing methods. In addition to this, settling time and peak overshoot of the speed has been improved a lot with GA (2.5 sec, 2%) and ANN (3.1 sec, 2.7%).