Vol. 4 No. 1 (2025): January
RESEARCH ARTICLES

Smart Charging for Electric Vehicles: Balancing Grid Stability and Driver Satisfaction

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  • A Sai Sreeja,
  • G Aravind Reddy,
  • A Lakshmidhar Reddy,
  • G Dastagiramma,
  • S Mohammed Ali
A Sai Sreeja
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
G Aravind Reddy
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
A Lakshmidhar Reddy
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
G Dastagiramma
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
S Mohammed Ali
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
DOI: https://doi.org/10.5281/zenodo.15163403

Published 2025-04-06

Keywords

  • Machine Learning,
  • Driver Satisfaction Prediction,
  • Smart Grid Optimization,
  • Data-Driven Decision Making,
  • User-Centric Charging Model,
  • Sustainable Transportation
    • ...More
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How to Cite

A Sai Sreeja, G Aravind Reddy, A Lakshmidhar Reddy, G Dastagiramma, & S Mohammed Ali. (2025). Smart Charging for Electric Vehicles: Balancing Grid Stability and Driver Satisfaction. International Journal of Computational Learning & Intelligence, 4(1), 401–407. https://doi.org/10.5281/zenodo.15163403

Copyright (c) 2025 A Sai Sreeja, G Aravind Reddy, A Lakshmidhar Reddy, G Dastagiramma, S Mohammed Ali

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

Electric vehicles (EVs) are a key component of modern transportation, yet optimizing their charging remains a challenge due to grid constraints and fluctuating renewable energy availability. This study proposes a machine learning-driven framework to enhance EV charging by prioritizing driver satisfaction. By considering factors such as socio-demographic attributes, battery State of Charge (SoC), charging station proximity, and dynamic pricing, the model predicts user satisfaction with high accuracy (87.9%). Trained on data from Hungarian EV users, the proposed optimization algorithm maximizes driver satisfaction while minimizing grid power costs. Simulation results demonstrate that the model achieves an average satisfaction rate of 98.5%, significantly outperforming traditional methods. Additionally, the approach maintains a stable average SoC around 50%, ensuring efficient energy use and grid stability. By dynamically allocating EVs to charging stations and leveraging renewable energy sources, this research presents a novel, user-centric strategy for smart EV charging, contributing to the sustainable evolution of transportation infrastructure.
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