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AI-Driven Predictive Control for Dynamic Energy Optimization in Flying Cars

Author

Listed:
  • Mohammed Gronfula

    (Electrical Engineering Department, College of Engineering, Alasala Colleges, Dammam 31483, Saudi Arabia)

  • Khairy Sayed

    (Electrical Engineering Department, Sohag University, Sohag 82524, Egypt
    Electrical Engineering Department, Grove School of Engineering, City University of New York, New York, NY 10031, USA)

Abstract

This study presents an AI-driven energy management system (EMS) for a hybrid electric flying car, integrating multiple power sources—including solid-state batteries, Li-ion batteries, fuel cells, solar panels, and wind turbines—to optimize power distribution across various flight phases. The proposed EMS dynamically adjusts power allocation during takeoff, cruise, landing, and ground operations, ensuring optimal energy utilization while minimizing losses. A MATLAB-based simulation framework is developed to evaluate key performance metrics, including power demand, state of charge (SOC), system efficiency, and energy recovery through regenerative braking. The findings show that by optimizing renewable energy collecting, minimizing battery depletion, and dynamically controlling power sources, AI-based predictive control dramatically improves energy efficiency. While carbon footprint assessment emphasizes the environmental advantages of using renewable energy sources, SOC analysis demonstrates that regenerative braking prolongs battery life and lowers overall energy use. AI-optimized energy distribution also lowers overall operating costs while increasing reliability, according to life-cycle cost assessment (LCA), which assesses the economic sustainability of important components. Sensitivity analysis under sensor noise and environmental disturbances further validates system robustness, demonstrating that efficiency remains above 84% even under adverse conditions. These findings suggest that AI-enhanced hybrid propulsion can significantly improve the sustainability, economic feasibility, and real-world performance of future flying car systems, paving the way for intelligent, low-emission aerial transportation.

Suggested Citation

  • Mohammed Gronfula & Khairy Sayed, 2025. "AI-Driven Predictive Control for Dynamic Energy Optimization in Flying Cars," Energies, MDPI, vol. 18(7), pages 1-35, April.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:7:p:1781-:d:1626562
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    References listed on IDEAS

    as
    1. Khairy Sayed & Ahmed Kassem & Hedra Saleeb & Ali S. Alghamdi & Ahmed G. Abo-Khalil, 2020. "Energy-Saving of Battery Electric Vehicle Powertrain and Efficiency Improvement during Different Standard Driving Cycles," Sustainability, MDPI, vol. 12(24), pages 1-26, December.
    2. Shang, Yitong & Li, Duo & Li, Yang & Li, Sen, 2025. "Explainable spatiotemporal multi-task learning for electric vehicle charging demand prediction," Applied Energy, Elsevier, vol. 384(C).
    3. Yang, Chao & Lu, Zhexi & Wang, Weida & Wang, Muyao & Zhao, Jing, 2023. "An efficient intelligent energy management strategy based on deep reinforcement learning for hybrid electric flying car," Energy, Elsevier, vol. 280(C).
    4. Wang, Shengyou & Zhuge, Chengxiang & Shao, Chunfu & Wang, Pinxi & Yang, Xiong & Wang, Shiqi, 2023. "Short-term electric vehicle charging demand prediction: A deep learning approach," Applied Energy, Elsevier, vol. 340(C).
    5. Liu, Ming & Hao, Han & Sun, Xin & Qu, Xiaobo & Wang, Kai & Qian, Yuping & Hao, Xu & Xun, Dengye & Geng, Jingxuan & Dou, Hao & Deng, Yunfeng & Du, Shilong & Liu, Zongwei & Zhao, Fuquan, 2024. "Exploring the key technologies needed for the commercialization of electric flying cars: A levelized cost and profitability analysis," Energy, Elsevier, vol. 303(C).
    Full references (including those not matched with items on IDEAS)

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