ENERGY EFFICIENT FEDERATED TRANSFORMER DRL FRAMEWORK FOR OPTIMIZING PV STORAGE AND EV CHARGING IN COUPLED TRANSPORTATION AND ENERGY SYSTEMS

Shuo Zhang, Haiping Liang, Xiaoqing Guo

View Full Paper

References

  1. [1] M. T. Sarker, M. A. Qwaid, S. J. Shern, and G. Ramasamy, “AI-driven optimization framework for smart EV charging systemsintegrated with solar PV and BESS in high-density residentialenvironments,” World Electric Vehicle Journal, vol. 16, no. 7,p. 385, 2025.
  2. [2] X. Zhao, H. He, J. Li, Z. Wei, R. Huang, and H. Yue, “Muti-physics cosonstrained PEV charging management for solar PVpenetrated energy-transportation Nexus: A novel DRL-basedapproach combining a cyber-physical system,” Journal of En-ergy Storage, vol. 106, 2025.
  3. [3] S. H. Abdallah and S. Tounsi, “Optimal design and controlof Ac-Dc-Dc wind energy system for electric vehicle batteriesrecharging stations,” International Journal of Power and En-ergy System, vol. 44, pp. 1–9, 2024.
  4. [4] S. Singh, V. Subburaj, K. Sivakumar, R. A. Kumar, M. S.Muthuramam, R. Rastogi, and A. Rajaram, “Optimum PowerForecasting Technique for Hybrid Renewable Energy SystemsUsing Deep Learning,” Electric Power Components and Sys-tems, pp. 1–18, 2024.
  5. [5] A. Hameed, S. M. Danish, A. Ranjha, and G. Srivastava,“Block-FeST: Blockchain-enhanced federated sparse transform-ers for privacy-preserving RES forecasting in internet of vehi-cles systems,” IEEE Internet of Things Journal, vol. 12, no. 14,pp. 27510–27518, 2025.
  6. [6] M. Hemalatha and R. Rengaraj, “Prognostic and power schedul-ing technique for EV using optimized adaptive deep belief net-work,” Journal of Energy Storage, vol. 108, p. 114943, 2025.
  7. [7] D. R. Dipto, M. T. Rahman, S. K. Shib, A. Shufian, and R. Is-lam, “Dynamic load forecasting in EV charging systems us-ing deep neural networks,” in 2025 4th International Confer-ence on Robotics, Electrical and Signal Processing Techniques(ICREST), (Dhaka, Bangladesh), pp. 81–85, 2025.
  8. [8] K. Ramu, G. Sreenivasulu, R. S. Dixit, S. L. Choudhary, K. V.Rao, S. K. Suman, M. Shuaib, and A. Rajaram, “Smart solarpower conversion: Leveraging deep learning MPPT and hybridcascaded H bridge multilevel inverters for optimal efficiency,”Biomedical Signal Processing and Control, vol. 105, p. 107582,2025.
  9. [9] A. Prajapati, P. Paraye, B. K. Ahirwar, et al., “Artificial in-telligence integration in solar-powered EV charging systems:challenges, opportunities, and future perspectives,” Journal ofThermal Analysis and Calorimetry, vol. 150, pp. 12103–12134,2025.
  10. [10] Z. Wang, “Federated learning for power cyber-physical sys-tems: Toward secure, resilient, and explainable intelligence,”Preprints, 2025.
  11. [11] F. Zeng, X. Yuan, S. Lv, H. Miao, H. Han, and Y. Pan, “A noveldetection algorithm for anomalies and attacks on electric vehi-cle charging stations using distributed data privacy protection,”International Journal of High Speed Electronics and Systems,p. 2540588, 2025.
  12. [12] S. Aravindan and A. Rajaram, “Energy aware multi attributetrust model for secure MANET IoT environment,” MultimediaTools and Applications, vol. 83, pp. 85637–85662, 2024.
  13. [13] S. Chinnaperumal, S. K. Raju, A. H. Alharbi, et al., “Decen-tralized energy optimization using blockchain with battery stor-age and electric vehicle networks,” Scientific Reports, vol. 15,p. 5940, 2025.19
  14. [14] M. Cavus, H. Ayan, M. Sari, O. Akbulut, D. Dissanayake,and M. Bell, “Enhancing smart grid reliability through data-driven optimisation and cyber-resilient EV integration,” Ener-gies, vol. 18, no. 17, p. 4510, 2025.
  15. [15] R. Jin and H. Liu, “Privacy-aware electric vehicle load forecast-ing via blockchain-based federated transfer learning,” Complex& Intelligent Systems, vol. 11, p. 376, 2025.
  16. [16] F. Jeribi, M. Shuaib, S. Alam, and A. Rajaram, “Blockchainenabled monitoring of public works projects using piezoelectrictransducers,” Mechanics of Advanced Materials and Structures,pp. 1–15, 2025.
  17. [17] S. Ray, K. Kasturi, and M. R. Nayak, “Multi-objective elec-tric vehicle charge scheduling for photovoltaic and battery en-ergy storage based electric vehicle charging stations in distri-bution network,” Green Energy and Intelligent Transportation,p. 100296, 2025.
  18. [18] T. N. Prabhu, B. Mohankumar, C. Ranjeethkumar, and A. Ra-jaram, “Deep reinforcement learning-based detection frameworkfor false data injection attacks in power systems,” InternationalJournal of Renewable Energy Research (IJRER), vol. 14, no. 2,pp. 311–323, 2024.
  19. [19] S. Sykiotis, “Deep learning innovations for energy efficiency:Advances in non-intrusive load monitoring and EV charg-ing optimization for a sustainable grid,” arXiv preprintarXiv:2505.04367, 2025.
  20. [20] J. J. Sonia, G. A. Kumar, E. R. Kumar, K. N. Raju, V. Sudha,P. R. Kshirsagar, V. Tirth, and A. Rajaram, “Intelligent trafficprediction system using hybrid convolutional neural networksfor smart cities,” Multimedia Tools and Applications, vol. 84,pp. 31919–31937, 2025.
  21. [21] S. Ray, K. Kasturi, and M. R. Nayak, “Multi-objective elec-tric vehicle charge scheduling for photovoltaic and battery en-ergy storage based electric vehicle charging stations in distri-bution network,” Green Energy and Intelligent Transportation,p. 100296, 2025.
  22. [22] V. Devaraj, G. Diwakar, P. Subhashini, V. V. Kumar,K. Rayudu, K. B. Teja, and A. Rajaram, “Integrating AI-drivenfault detection and protection technique for electric power com-ponents and systems,” International Journal of Renewable En-ergy Research (IJRER), vol. 14, no. 2, pp. 293–303, 2024.
  23. [23] S. Prathiba, P. Anitha, A. S. Kamaraja, et al., “Optimizing EVcharging with improved energy storage: Boosting efficiency andstability with a renewable energy-integrated ultra-high voltageDC-DC converter,” Iranian Journal of Science and Technology,Transactions of Electrical Engineering, 2025.
  24. [24] M. Karthikeyan and D. Manimegalai, “Enhancing voltage con-trol and regulation in smart micro-grids through deep learning-optimized EV reactive power management,” Energy Reports,vol. 13, pp. 1095–1107, 2025.
  25. [25] R. P. Singh, N. Nagabhooshanam, Y. Thakur, D. Nathiya,A. Praveena, U. L. N. Kumar, and A. Rajaram, “AI driven dis-covery of alloyed plasmonic nanodisks for broadband solar ab-sorption and charge carrier multiplication,” Plasmonics, 2025.
  26. [26] M. Sithambaram, P. Rajesh, F. H. Shajin, and I. R. Rajeswari,“Grid connected photovoltaic system powered electric vehiclecharging station for energy management using hybrid method,”Journal of Energy Storage, vol. 108, 2025.
  27. [27] L. Jin, S. Zhong, B. Su, D. Zhou, Q. Wang, and X. Yu, “EV-integrated and grid-connected hybrid renewable energy system:a two-stage optimization strategy,” Energy, 2025.
  28. [28] A. Abazari, M. Ghafouri, D. Jafarigiv, R. Atallah, and C. Assi,“Data-driven framework for mitigating EV-based load-alteringattacks on LFC model of microgrid,” IEEE Transactions onConsumer Electronics, 2025.
  29. [29] M. C¸e¸cen, “Optimal integration of electric vehicle charging sta-tions into a renewable-supported multi-energy system,” ElectricPower Systems Research, vol. 247, p. 111832, 2025.
  30. [30] S. Sykiotis, “Deep learning innovations for energy efficiency:Advances in non-intrusive load monitoring and EV charg-ing optimization for a sustainable grid,” arXiv preprintarXiv:2505.04367, 2025.
  31. [31] A. F. G¨uven and E. Y¨ucel, “Sustainable energy integrationand optimization in microgrids: enhancing efficiency with elec-tric vehicle charging solutions,” Electrical Engineering, vol. 107,pp. 1541–1573, 2025.
  32. [32] T. Alharbi, A. Abdalrahman, M. H. Mostafa, and L. Alkhalifa,“Joint optimization of EV charging and renewable distributedenergy with storage systems under uncertainty,” IEEE Access,vol. 13, pp. 76838–76856, 2025.
  33. [33] M. H. Alsharif, F. Alsaif, M. K. Singla, S. Manna, and M.-K.Kim, “Techno-economic optimization and environmental analy-sis of a solar-powered Electric Vehicles (EVs) charger system fora greener transportation ecosystem,” Energy Reports, vol. 13,pp. 5803–5814, 2025.
  34. [34] D. Wang, X. Chen, X. Liu, Y. Li, Z. Piao, and H. Li, “Dynamictariff adjustment for electric vehicle charging in renewable-richsmart grids: A multi-factor optimization approach to load bal-ancing and cost efficiency,” Energies, vol. 18, no. 16, p. 4283,2025.

Important Links:

Go Back