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Ehab Bayoumi

Prof. Dr. Ehab Bayoumi

Professor · Mechatronics and Robotics
Author
APID
13359
ORCID
0000-0002-3131-5043
Scopus
24401038400
Google Scholar
https://scholar.google.com/citations?user=Kh4OpXQAAAAJ&hl=en
Location
Cairo, Al Qāhirah, Egypt
Expertise
Power electronics; Renewable energy; Smart grids; Microgrids; Power quality;; Grid integration; Inverter-based resources; Electrical engineering; Energy sustainability; System stability; Electric Vehicles; Charing of Electric Vehicles, LI-ion Battery Managements
Areas of interest
Power electronics; Renewable energy; Smart grids; Microgrids; Power quality;; Grid integration; Inverter-based resources; Electrical engineering; Energy sustainability; System stability; Electric Vehicles; Charing of Electric Vehicles, LI-ion Battery Managements
Member since
August 2026

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Publications

  1. A Review of Data-Driven Approaches and Datasets for Battery State of Health Estimation," SAE Int. J. Elec. Veh. 15(2), 2026,
  2. Active disturbance rejection-based decentralised sensor fault-tolerant control in DC microgrids. Sci Rep 16, 12468 (2026).
  3. Awad, H., & Bayoumi, E. H. E. (2026). Electrical Grid Architectures for Smart Cities from Digitalized Power Systems to AI-Enabled Urban Energy Ecosystems. Smart Cities, 9(6), 96.
  4. Ehab H.E. Bayoumi, Yasser Nassar, M.Saber Eltohamy, Hilmy Awad, State of charge estimation in lithium-ion batteries: A critical review of traditional and AI-driven methods with pathways to real-world implementation, Global Energy Interconnection, 2026, ISSN 2096-5117,
  5. Frequency Control of Smart Grids Under Complex Hybrid Deception Attacks via Point-by-Point Model-Reference Tracking. Energies, 19(16), 3795.
  6. Hassan, M. F., Soliman, H. M., El Sheikhi, F. A., Lee, S., & Bayoumi, E. H. E. (2026). Frequency Control of Smart Grids Under Complex Hybrid Deception Attacks via Point-by-Point Model-Reference Tracking. Energies, 19(16), 3795.
  7. Mohamad, A.M.I., Ibrahim, A.M. & Bayoumi, E.H.E. Active disturbance rejection-based decentralised sensor fault-tolerant control in DC microgrids. Sci Rep 16, 12468 (2026).
  8. Nyachionjeka, K. and Bayoumi, E., "A Review of Data-Driven Approaches and Datasets for Battery State of Health Estimation," SAE Int. J. Elec. Veh. 15(2), 2026,
  9. Nyachionjeka, K., Abd-Elrady, E., & Bayoumi, E. H. E. (2026). Physics-Informed Cross-Domain Deep Learning for Laboratory-to-Field Battery Remaining Useful Life Estimation Under Operational Shifts and Target-Label Scarcity. Batteries, 12(8), 274.
  10. Physics-Informed Cross-Domain Deep Learning for Laboratory-to-Field Battery Remaining Useful Life Estimation Under Operational Shifts and Target-Label Scarcity. Batteries, 12(8), 274.
  11. Safe-Calibrated TCN–Transformer Transfer Learning for Reliable Battery SoH Estimation Under Lab-to-Field Domain Shift. World Electric Vehicle Journal, 17(3), 149.
  12. State of charge estimation in lithium-ion batteries: A critical review of traditional and AI-driven methods with pathways to real-world implementation, Global Energy Interconnection, 2026, ISSN 2096-5117,