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<p>High penetration of electric vehicles (EVs) has posed serious threats to stability of power grids, battery life, and charging capacity during dynamic load situations. The given paper suggests an adaptive heuristic optimization model of a real time battery conscious charging in the form of hybrid Genetic Algorithm and Particle Swarm Optimization (GA-PSO) with a Model Predictive Control (MPC) framework. The framework integrates real time grid feedback and Demand Side Management (DSM) in order to maximize charging performance without damaging battery health and causing grid instability. The simulation outcomes prove the proposed framework offered the highest charging completion rate of 86.04%, the lowest battery degradation index of 0.172, and the lowest grid load deviation of 8.50%. Moreover, the system achieved a State of Charge (SoC) within 99.98%, the average charging cost decreased to ₹89.54 per session, and the optimization calculation reduced to 2.25&#xa0;s. The findings supports validity of the suggested framework to enhance the efficiency of charging, battery maintenance, and reliability of grids in smart electric vehicle charging systems.</p>

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Adaptive Heuristic Optimization Framework for Real-Time Battery-Aware Charging in Electric Vehicles Under Dynamic Load Conditions

  • 期刊论文

  • 发表日期：

    2026年6月23日

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  • V. Arun,
  • Senthil Kumar Sengottaiyan,
  • P. Balakrishnan,
  • M Vijayakumar,
  • Gokul Chandrasekaran,
  • S. Ragul

摘要

High penetration of electric vehicles (EVs) has posed serious threats to stability of power grids, battery life, and charging capacity during dynamic load situations. The given paper suggests an adaptive heuristic optimization model of a real time battery conscious charging in the form of hybrid Genetic Algorithm and Particle Swarm Optimization (GA-PSO) with a Model Predictive Control (MPC) framework. The framework integrates real time grid feedback and Demand Side Management (DSM) in order to maximize charging performance without damaging battery health and causing grid instability. The simulation outcomes prove the proposed framework offered the highest charging completion rate of 86.04%, the lowest battery degradation index of 0.172, and the lowest grid load deviation of 8.50%. Moreover, the system achieved a State of Charge (SoC) within 99.98%, the average charging cost decreased to ₹89.54 per session, and the optimization calculation reduced to 2.25 s. The findings supports validity of the suggested framework to enhance the efficiency of charging, battery maintenance, and reliability of grids in smart electric vehicle charging systems.