ML-Guided Coordinated EV Charging: LSTM Forecasting and Multi-Objective Optimization for Real-Time Grid Operation
Received: 6 March 2026 | Revised: 9 May 2026, 20 May 2026, and 24 May 2026 | Accepted: 25 May 2026 | Online: 17 June 2026
Corresponding author: Deepthi Janyavula
Abstract
In this paper, an integrated real-time framework for coordinated Electric Vehicle (EV) charging is proposed based on load forecasting and multi-objective optimization techniques. The framework integrates load forecasting using Long Short-Term Memory (LSTM) and multi-objective optimization to minimize peak load, charging cost, and grid stress while maintaining high user satisfaction under dynamic smart-grid conditions. The proposed framework combines multi-step LSTM forecasting with a convex optimization scheduler operating on a 15-min rolling horizon that incorporates feeder constraints, electricity tariffs, charger limits, and departure state-of-charge requirements. Unlike conventional charging strategies, the proposed method enables adaptive and grid-aware charging decision-making in real time. The framework is evaluated under various EV penetration scenarios ranging from 30 to 100 EVs and is compared with uncontrolled charging, off-peak charging, and load-balancing strategies. The results demonstrate an average 25% reduction in peak load, a 15–20% reduction in charging costs, smoother feeder operation, and a user satisfaction rate exceeding 95% in meeting charging requirements. Furthermore, the proposed framework improves operational stability, reduces computational burden, and enhances charging coordination compared with conventional forecasting and heuristic scheduling approaches. These findings demonstrate the feasibility and scalability of integrating Machine Learning (ML)-based forecasting with real-time optimization for future smart-grid and EV energy management systems.
Keywords:
charging optimization, Electric Vehicles (EVs), energy management, load forecasting, Long Short-Term Memory (LSTM), multi-objective optimization, real-time scheduling, smart grid, Vehicle-to-Grid (V2G)References
[1] B. Biswal, S. Deb, S. Datta, T. S. Ustun, and U. Cali, "Review on smart grid load forecasting for smart energy management using machine learning and deep learning techniques," Energy Reports, vol. 12, pp. 3654–3670, Dec. 2024.
[2] D. Liu, P. Zeng, S. Cui, and C. Song, "Deep Reinforcement Learning for Charging Scheduling of Electric Vehicles Considering Distribution Network Voltage Stability," Sensors, vol. 23, no. 3, Feb. 2023, Art. no. 1618.
[3] S. M. Hasanat et al., "Enhancing Load Forecasting Accuracy in Smart Grids: A Novel Parallel Multichannel Network Approach Using 1D CNN and Bi-LSTM Models," International Journal of Energy Research, vol. 2024, no. 1, July 2024, Art. no. 2403847.
[4] F. Çakıl and N. Aksoy, "Reinforcement learning-based multi-objective smart energy management for electric vehicle charging stations with priority scheduling," Energy, vol. 322, May 2025, Art. no. 135475.
[5] H. Xie et al., "Reinforcement learning for vehicle-to-grid: A review," Advances in Applied Energy, vol. 17, Mar. 2025, Art. no. 100214.
[6] S. Li, X. Yan, and G. Wang, "Multi Objective Optimization of Electric Vehicle Charging Strategy Considering User Selectivity," World Electric Vehicle Journal, vol. 16, no. 2, Feb. 2025, Art. no. 95.
[7] G. Chen, L. Yang, and X. Cao, "A deep reinforcement learning-based charging scheduling approach with augmented Lagrangian for electric vehicles," Applied Energy, vol. 378, Jan. 2025, Art. no. 124706.
[8] M. Bielewski et al., Clean Energy Technology Observatory: Battery Technology in the European Union - 2023 Status Report on Technology Development, Trends, Value Chains and Markets. Luxembourg: Publications Office of the European Union, 2023.
[9] S. Orfanoudakis, V. Robu, E. M. Salazar, P. Palensky, and P. P. Vergara, "Scalable reinforcement learning for large-scale coordination of electric vehicles using graph neural networks," Communications Engineering, vol. 4, no. 1, July 2025, Art. no. 118.
[10] M. S. Eltohamy et al., "A comprehensive review of Vehicle-to-Grid V2G technology: Technical, economic, regulatory, and social perspectives," Energy Conversion and Management: X, vol. 27, July 2025, Art. no. 101138.
[11] M. M. Khayyat and S. B. Slama, "Smart Grid 2.0: Modeling Peer-to-Peer Trading Community and Incentives for Prosumers in the Transactive Energy Grid," Engineering, Technology & Applied Science Research, vol. 14, no. 2, pp. 13470–13480, Apr. 2024.
[12] M. Trimboli and L. Avila, "Multi-objective reinforcement learning for electric vehicle charging," Sustainable Energy, Grids and Networks, vol. 45, Mar. 2026, Art. no. 102083.
[13] M. A. Kabir, M. R. Ahmed, and F. Ahmed, "Strategic US Energy Market Insights: A Data-Driven Analysis Of U.S. Fuel Consumption, Pricing Trends, and the Rise of Electric Vehicles," American Journal of Interdisciplinary Studies, vol. 6, no. 1, pp. 174–207, Apr. 2025.
[14] B. A. L. M. Hermans, S. Walker, J. H. A. Ludlage, and L. Özkan, "Model predictive control of vehicle charging stations in grid-connected microgrids: An implementation study," Applied Energy, vol. 368, Aug. 2024, Art. no. 123210.
[15] Z. Zhao, C. K. M. Lee, X. Yan, and H. Wang, "Reinforcement learning for electric vehicle charging scheduling: A systematic review," Transportation Research Part E: Logistics and Transportation Review, vol. 190, Oct. 2024, Art. no. 103698.
[16] S. Boubaker et al., "Multi-objective optimization framework for electric vehicle charging and discharging scheduling in distribution networks using the red deer algorithm," Scientific Reports, vol. 15, no. 1, Apr. 2025, Art. no. 13343.
[17] L. Guo et al., "A multi-objective spatio-temporal pricing method for fast-charging stations oriented to transformer load balancing," Frontiers in Energy Research, vol. 12, Apr. 2024, Art. no. 1362343.
[18] F. Chang, C. T. Ng, F. Chang, F. Wu, T. C. E. Cheng, and L. Zheng, "A multi-task-driven multi-trip electric vehicle routing optimisation method for smart factory," Journal of Engineering Design, pp. 1–32, July 2025.
[19] G. La Tona, M. Luna, and M. C. Di Piazza, "Day-ahead forecasting of residential electric power consumption for energy management using Long Short-Term Memory encoder–decoder model," Mathematics and Computers in Simulation, vol. 224, pp. 63–75, Oct. 2024.
[20] F. Dakheel and M. Çevik, "Optimizing Smart Grid Load Forecasting via a Hybrid Long Short-Term Memory-XGBoost Framework: Enhancing Accuracy, Robustness, and Energy Management," Energies, vol. 18, no. 11, June 2025, Art. no. 2842.
[21] J. Qian et al., "Federated Reinforcement Learning for Electric Vehicles Charging Control on Distribution Networks," IEEE Internet of Things Journal, vol. 11, no. 3, pp. 5511–5525, Feb. 2024.
[22] D. Janyavula, "Dataset_repo." Zenodo, June 15, 2026.
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Copyright (c) 2026 Deepthi Janyavula, V. Gautam Kumar, S. N. Saxena
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