Dynamic programming-based energy optimization for the series hybrid electric fixed-wing VTOL UAV

Hybrid electric fixed-wing vertical take-off and landing (VTOL) UAVs are increasingly utilized for reconnaissance, patrol, and cargo transportation due to their VTOL capability and efficient cruise performance. Enhancing energy efficiency is essential for further advancing this type of aircraft. However, current research on hybrid-electric energy management primarily focuses on conventional aircraft or employs simplified optimization processes using basic rule strategies. The unique energy requirements and power system architecture of hybrid-electric fixed-wing VTOL UAVs result in energy management processes that differ significantly from those of traditional fixed-wing or rotary-wing UAVs. Research on suitable energy management strategies for this platform remains insufficient. To address this gap, a dynamic programming-based energy management strategy for hybrid electric fixed-wing VTOL UAVs is proposed in this paper. Three core models, a hybrid power system model, a fixed-wing VTOL UAV power demand model, and a hybrid energy management model oriented to the mission profile, are established. The application effects of the proposed dynamic programming strategy are compared with those of two state machine control models: basic rule and engine ON-OFF rule. This work is intended to provide a reference for the application of energy management strategy aimed at enhancing fuel economy and reducing operational costs during mission execution for hybrid electric fixed-wing VTOL UAVs.

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Publication Details

Journal
Energy Reports
Published
2025-12-01
DOI
https://doi.org/10.1016/j.egyr.2025.11.114
Primary Topic
Advanced Aircraft Design and Technologies
Type
article
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article

Dynamic programming-based energy optimization for the series hybrid electric fixed-wing VTOL UAV

Diyi Liu, Shipeng Yang, Guanxiong Li, Yuqi Tong et al.
Energy Reports
Advanced Aircraft Design and Technologies
article

Dynamic programming-based energy optimization for the series hybrid electric fixed-wing VTOL UAV

Diyi Liu, Shipeng Yang, Guanxiong Li, Yuqi Tong, Cheng He
article en

Abstract

Hybrid electric fixed-wing vertical take-off and landing (VTOL) UAVs are increasingly utilized for reconnaissance, patrol, and cargo transportation due to their VTOL capability and efficient cruise performance. Enhancing energy efficiency is essential for further advancing this type of aircraft. However, current research on hybrid-electric energy management primarily focuses on conventional aircraft or employs simplified optimization processes using basic rule strategies. The unique energy requirements and power system architecture of hybrid-electric fixed-wing VTOL UAVs result in energy management processes that differ significantly from those of traditional fixed-wing or rotary-wing UAVs. Research on suitable energy management strategies for this platform remains insufficient. To address this gap, a dynamic programming-based energy management strategy for hybrid electric fixed-wing VTOL UAVs is proposed in this paper. Three core models, a hybrid power system model, a fixed-wing VTOL UAV power demand model, and a hybrid energy management model oriented to the mission profile, are established. The application effects of the proposed dynamic programming strategy are compared with those of two state machine control models: basic rule and engine ON-OFF rule. This work is intended to provide a reference for the application of energy management strategy aimed at enhancing fuel economy and reducing operational costs during mission execution for hybrid electric fixed-wing VTOL UAVs.

Energy ReportsVol. 14
Sichuan University (CN), China Aerodynamics Research and Development Center (CN), Numerical Method (China) (CN), Shanghai Academy of Spaceflight Technology (CN), Nanjing University of Aeronautics and Astronautics (CN)
Openalex Percentile: Top 65%
Advanced Aircraft Design and Technologies
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Dynamic programming-based energy optimization for the series hybrid electric fixed-wing VTOL UAV — Diyi Liu, Shipeng Yang, et al. · Energy Reports (2025) | TGRS Research Map | TGRS