Abhinav Saxena
Department of Electrical Engineering, J.C. Bose University of Science and Technology, YMCA, Faridabad, Haryana, India.

Mohd. Majid
Department of Mechanical Engineering, Sant Longowal Institute of Engineering & Technology, Longowal, 148106, Punjab, India.

Rajat Kumar
Department of Electrical Engineering, Dayalbagh Educational Institute, Dayalbagh, Agra, 282005, Uttar Pradesh, India.

Mohammed Amir
School of Engineering, The University of Liverpool, Liverpool L69 7ZX, United Kingdom.

Majed A. Alotaibi
Saudi Electricity Company Chair in Power System Reliability and Security, King Saud University, Riyadh 11421, Saudi Arabia. & Department of Electrical Engineering, College of Engineering, King Saud University, Saudi Arabia.

Hasmat Malik
Department of Electrical Power Engineering, Faculty of Electrical Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru, 81310, Malaysia. & Department of Electrical Engineering, Graphic Era (Deemed to be University), Dehradun, 248002, Uttarakhand, India. & Intelligent Prognostic Private Limited, Delhi, India.

Taha Selim Ustun
Smart Grid Cybersecurity Laboratory, Fukushima Renewable Energy Institute, National Institute of Advanced Industrial Science and Technology, Koriyama, Japan.

Asyraf Afthanorhan
Artificial Intelligence Research Center for Islamic and Sustainability (AIRIS), Universiti Sultan Zainal Abidin (UniSZA), Malaysia. & Faculty of Computing and Information Technology, Sohar University, Oman.

DOI https://doi.org/10.33889/IJMEMS.2025.10.6.098

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Abstract

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%).

Keywords- Artificial neural network, Genetic algorithm, Smart charging, State of charge control, Solar-based Electric vehicle, Vehicle dynamics.

Citation

Saxena, A., Majid, M., Kumar, R., Amir, M., Alotaibi, M. A. Malik, H., Ustun, T. S. & Afthanorhan, A. (2025). A Dynamic Framework of Solar Based Electric Vehicle Charging Station with Artificial Neural Network and Genetic Algorithm Techniques. International Journal of Mathematical, Engineering and Management Sciences, 10(6), 2103-2124. https://doi.org/10.33889/IJMEMS.2025.10.6.098.