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

Mohammed G. Gronfula,

Khairy Sayed

Energies, Journal Year: 2025, Volume and Issue: 18(7), P. 1781 - 1781

Published: April 2, 2025

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

Language: Английский

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

Mohammed G. Gronfula,

Khairy Sayed

Energies, Journal Year: 2025, Volume and Issue: 18(7), P. 1781 - 1781

Published: April 2, 2025

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

Language: Английский

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