Flight trajectory and energy management coupled optimization for hybrid electric UAVs with adaptive sequential convex programming method

Improving energy efficiency has important significance for hybrid electric UAVs. This paper aims at improving energy efficiency by maximizing utilization solar energy, reducing demand power and optimizing power allocation. The flight trajectory optimization and energy management are considered synth...

Full description

Saved in:
Bibliographic Details
Published in:Applied energy Vol. 364; p. 123166
Main Authors: Tian, Weiyong, Liu, Li, Zhang, Xiaohui, Shao, Jiaqi
Format: Journal Article
Language:English
Published: Elsevier Ltd 15.06.2024
Subjects:
Energy efficiency
Energy management
Hybrid electric UAVs
Hydrogen energy
Sequential convex programming
Trajectory optimization
Sequential convex programming
Hydrogen energy
Energy efficiency
Hybrid electric UAVs
Energy management
Trajectory optimization
ISSN:0306-2619, 1872-9118
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Improving energy efficiency has important significance for hybrid electric UAVs. This paper aims at improving energy efficiency by maximizing utilization solar energy, reducing demand power and optimizing power allocation. The flight trajectory optimization and energy management are considered synthetically, and the coupled model is established. Adaptive sequential convex programming method (ASCP) is further proposed to solve this coupled model. The adaptive discretization method, fuzzy-based trust region update mechanism and two-stages solving strategy are proposed to improve its optimality and convergence. The optimized flight trajectory and global optimal battery SOC trajectory can be applied to trajectory tracking control and online energy management. Numerical simulation results show that ASCP can improve energy efficiency of hybrid electric UAVs. Compared with uniform discretized sequential convex programming (UDSCP) and Gauss pseudo-spectral method (GPM), it can improve solar energy by 9.3% and 24.1%. Energy management experiment results indicate that ASCP has excellent energy saving effect. Compared with nonlinear model predictive control (NMPC), fuzzy logic state machine (FLSM) and passive energy management strategy (PEMS), it can save hydrogen by 13.5%, 18.3%, and 22.2%, respectively. This work is conducive to promoting the application of renewable energy in UAVs. •Energy efficiency is improved by flight trajectory and energy management coupled optimization.•The flight trajectory optimization and energy management coupled model is established.•Adaptive sequential convex programming method is proposed to solve the coupled model.•Fuzzy logic control is proposed to adaptively adjust the sizes of trust region.
ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2024.123166