Back to Search Start Over

Solar Array Power Prediction for Near-Space Vehicles Based on a Hybrid Method

Authors :
Yang Gao
Guoning Xu
Sheng Wang
Yongxiang Li
Rong Cai
Zhaojie Li
Yanchu Yang
Yanlei Zhang
Source :
IEEE Access, Vol 10, Pp 123233-123250 (2022)
Publication Year :
2022
Publisher :
IEEE, 2022.

Abstract

Compared with other energy sources in the near-space, solar energy is more renewable, stable, and accessible, which makes solar cells the main source of energy for long-endurance near-space vehicles. However, due to the limited solar cells laying area and complex flight conditions, near-space vehicles are facing tense energy supply situation during flight. Therefore, fast and accurately online power prediction of the solar array, which has important significance for power management, efficient use, and reliable operation of the energy system, is the key point to solving the situation at present. In this paper, a hybrid online power prediction method combined particle filter and power calculation model is proposed. Firstly, a comprehensive solar cell array power calculation model for the near-space vehicle is established. Then, the particle filter algorithm is used to estimate optimally the important parameters in the power calculation model with the flight data, to realize the dynamic, rapid, and accurate prediction of the power generation of the vehicle during flight. Finally, the data obtained from flight tests of the stratospheric aerostat and high-altitude scientific balloon are verified and compared with the other four models. The results demonstrate that the generation power prediction method proposed in this paper has higher prediction accuracy, which will be a good guiding significance for the actual flight of different types of near-space vehicles.

Details

Language :
English
ISSN :
21693536
Volume :
10
Database :
Directory of Open Access Journals
Journal :
IEEE Access
Publication Type :
Academic Journal
Accession number :
edsdoj.b30f6be45cf4a43af0a7022c8c08850
Document Type :
article
Full Text :
https://doi.org/10.1109/ACCESS.2022.3208173