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<p>The rapid adoption of Electric Vehicles (EVs) has significantly increased energy demand, necessitating efficient and stable charging infrastructure powered by renewable energy. This paper proposes a novel, season-aware forecasting and optimization framework for solar-powered EV charging stations using Joint Fusion Layer–Bidirectional Long Short-Term Memory (JFL-BiLSTM) and Rosenbrock Function-based Sea-Horse Optimization (RF-SHO). The proposed system smooths DC power using Laguerre Polynomial-based Ramp Rate Method (LP-RRM) and manages grid interruptions through Generalized Space Vector Modulation-based Switching Regulators (GSVM-SR). Seasonal classification and feature extraction are performed on a real-world dataset, and the JFL-BiLSTM model forecasts EV charging demand with 98.61% accuracy, 98.01% precision, 98.95% recall, 98.02% specificity, and 98.10% F-measure. The RF-SHO algorithm selects optimal charging stations within 7815&#xa0;ms, outperforming baseline algorithms (e.g., SHO, SSO, ACO, EHO) in convergence speed and selection accuracy. Simulation results demonstrate significant improvements in load prediction, power smoothing, ripple reduction, and system stability. The proposed framework proves to be an effective and intelligent solution for dynamic EV charging management under seasonal and operational uncertainties, paving the way for robust, user-centric charging infrastructure.</p>

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A novel optimized machine learning algorithm approach for solar-powered xEV charging stations

  • 期刊论文

  • 发表日期：

    2025年8月22日

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  • Jatoth Rajender,
  • Manisha Dubey,
  • Yogendra Kumar,
  • Gumpu Srinivasulu

摘要

The rapid adoption of Electric Vehicles (EVs) has significantly increased energy demand, necessitating efficient and stable charging infrastructure powered by renewable energy. This paper proposes a novel, season-aware forecasting and optimization framework for solar-powered EV charging stations using Joint Fusion Layer–Bidirectional Long Short-Term Memory (JFL-BiLSTM) and Rosenbrock Function-based Sea-Horse Optimization (RF-SHO). The proposed system smooths DC power using Laguerre Polynomial-based Ramp Rate Method (LP-RRM) and manages grid interruptions through Generalized Space Vector Modulation-based Switching Regulators (GSVM-SR). Seasonal classification and feature extraction are performed on a real-world dataset, and the JFL-BiLSTM model forecasts EV charging demand with 98.61% accuracy, 98.01% precision, 98.95% recall, 98.02% specificity, and 98.10% F-measure. The RF-SHO algorithm selects optimal charging stations within 7815 ms, outperforming baseline algorithms (e.g., SHO, SSO, ACO, EHO) in convergence speed and selection accuracy. Simulation results demonstrate significant improvements in load prediction, power smoothing, ripple reduction, and system stability. The proposed framework proves to be an effective and intelligent solution for dynamic EV charging management under seasonal and operational uncertainties, paving the way for robust, user-centric charging infrastructure.