一种基于在线辨识的多模式氢能无人机能量管理方法
By using online feature identification methods, a fuel cell polarization curve model was established and noise adaptive parameter identification was designed. Combined with a flight mode recognizer, the energy management problem of hydrogen-powered drones in highly dynamic environments and multi-mode switching was solved, thereby improving the safety and efficiency of the system.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HANGZHOU INNOVATION RES INST OF BEIJING UNIV OF AERONAUTICS & ASTRONAUTICS
- Filing Date
- 2024-04-11
- Publication Date
- 2026-07-17
AI Technical Summary
Existing energy management strategies are ill-suited for hydrogen-powered drones in handling highly dynamic environments, unknown measurement noise, and multi-mode switching. They cannot simultaneously guarantee system safety and efficiency, especially when fuel cell characteristics drift and measurement noise are unknown. Traditional methods are computationally burdensome and have slow response times, making them unsuitable for the needs of drones.
By adopting an online feature identification method, a noise-adaptive online parameter identification method is designed by establishing a fuel cell polarization curve model and using a variational Bayesian-extended Kalman filter for parameter identification. Combined with a flight mode fast identifier, online energy management strategies for different modes are designed to achieve real-time tracking of fuel cell output characteristics and rapid mode switching.
It improves the accuracy of fuel cell output characteristic tracking, optimizes operating point settings, reduces computational burden, enhances system safety and energy efficiency, and reduces hydrogen consumption and fuel cell power supply pressure.
Smart Images
Figure CN118047071B_ABST