Obtaining Condition Monitoring Data for the Prognostics of the Flight Time of Unmanned Aerial Vehicles

Abstract

In recent years, the use of Unmanned Aerial Vehicles (UAVs) that can fly at low and medium altitudes has become widespread in the world. Knowing the airtime and the maximum range that the UAVs, which are used in critical missions, especially in the military field, are important for the reliability of the mission to be carried out. Therefore, in this study, the creation of a data set to calculate the flight time and range of the UAV using the prognostic method, which is one of the heuristic methods, is discussed. For this purpose, a fixed-wing UAV was used in this study to create the data set to be used in the prognostic methods. The UAV used in flights has a weight of 2.5 kg, a wingspan of 1.3 m, and a body length of 1 m. In addition, thanks to the control card used in the UAV, both manual and autonomous flights were made. The flight data of the UAV was transferred to the Ground Control Station (GGS) instantly. As a result, data sets were obtained from manual and autonomous flights to be used in the prognostic method. By using these data sets, it will be possible to calculate the duration and range of the UAV in the future flights.

Keywords

• UAV
• Flight Time
• Range Increase
• Prognostic

References

1. Andre, D., Appel, C., Soczka-Guth, T., and Sauer, D. U. (2013). Advanced mathematical methods of SOC and SOH estimation for lithium-ion batteries. Journal of Power Sources, 224, 20-27.
2. Arik, S., Turkmen, I., and Oktay, T. (2018). Redesign of morphing UAV for simultaneous improvement of directional stability and maximum lift/drag ratio. Advances in Electrical and Computer Engineering, 18(4), 57-62.
3. Coban, S., and Oktay, T. (2017). A review of tactical unmanned aerial vehicle design studies. The Eurasia Proceedings of Science Technology Engineering and Mathematics, 1, 30-35.
4. Coban, S. (2019). Different autopilot systems design for a small fixed wing unmanned aerial vehicle. Avrupa Bilim ve Teknoloji Dergisi, 17, 682-691.
5. Coban, S., Bilgic, H. H., and Oktay, T. (2019). Designing, dynamic modeling and simulation of ISTECOPTER. Journal of Aviation, 3(1), 38-44.
6. Eleftheroglou, N., Zarouchas, D., Loutas, T., Mansouri, S. S., Georgoulas, G., Karvelis, P., ... and Benedictus, R. (2019). Real time diagnostics and prognostics of UAV lithium-polymer batteries. In Proceedings of The Annual Conference of the PHM Society, 11 (1), 1-8.
7. Hu, C., Jain, G., Tamirisa, P., and Gorka, T. (2014, June). Method for estimating capacity and predicting remaining useful life of lithium-ion battery. In 2014 International Conference on Prognostics and Health Management, 1-8.
8. Keane, J. F., and Carr, S. S. (2013). A brief history of early unmanned aircraft. Johns Hopkins APL Technical Digest, 32(3), 558-571.

9. Khalid, H. M., Ahmed, Q., and Peng, J. C. H. (2015). Health monitoring of li-ion battery systems: A median expectation diagnosis approach (MEDA). IEEE Transactions on Transportation Electrification, 1(1), 94-105.
10. Konar, M. (2019). GAO Algoritma tabanlı YSA modeliyle İHA motorunun performansının ve uçuş süresinin maksimizasyonu, Avrupa Bilim ve Teknoloji Dergisi, 15, 360-367
11. Lee, S., Kim, J., Lee, J., and Cho, B. H. (2008). State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge. Journal of Power Sources, 185(2), 1367-1373.
12. Lin, C., Tang, A. and Wang, W. (2015). A review of SOH estimation methods in lithium-ion batteries for electric vehicle applications. Energy Procedia, 75, 1920-1925.
13. Mátyás, P., and Máté, N. (2019). Brief history of UAV development. Repüléstudományi Közlemények, 31(1), 155- 166.
14. Oktay, T., Arik, S., Turkmen, I., Uzun, M., and Celik, H. (2018). Neural network based redesign of morphing UAV for simultaneous improvement of roll stability and maximum lift/drag ratio. Aircraft Engineering and Aerospace Technology, 90(8), 1203-1212.
15. Saha, B., Poll, S., Goebel, K., and Christophersen, J. (2007, September). An integrated approach to battery health monitoring using bayesian regression and state estimation. In 2007 IEEE Autotestcon, 646-653.
16. Schacht-Rodríguez, R., Ponsart, J. C., Garcia-Beltran, C. D., Astorga-Zaragoza, C. M., and Theilliol, D. (2019, June). Mission planning strategy for multirotor UAV based on flight endurance estimation. In 2019 International Conference on Unmanned Aircraft Systems (ICUAS), 778-786.
17. Tran, M. K., Mevawalla, A., Aziz, A., Panchal, S., Xie, Y., and Fowler, M. (2022). A review of lithium-ion battery thermal runaway modeling and diagnosis approaches. Processes, 10(6), 1192.
18. Yan, W., Zhang, B., Dou, W., Liu, D., and Peng, Y. (2017). Low-cost adaptive lebesgue sampling particle filtering approach for real-time li-ion battery diagnosis and prognosis. IEEE Transactions on Automation Science and Engineering, 14(4), 1601-1611.
19. Zhang, Y., Xiong, R., He, H., and Pecht, M. G. (2018). Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries. IEEE Transactions on Vehicular Technology, 67(7), 5695-5705.
20. Wang, H., Wang, X., Wang, L., Wang, J., Jiang, D., Li, G., ... and Jiang, Y. (2015). Phase transition mechanism and electrochemical properties of nanocrystalline MoSe2 as anode materials for the high performance lithium- ion battery. The Journal of Physical Chemistry C, 119(19), 10197-10205.
21. Wu, C., Zhu, C., Sun, J., and Ge, Y. (2016). A synthesized diagnosis approach for lithium-ion battery in hybrid electric vehicle. IEEE Transactions on Vehicular Technology, 66(7), 5595-5603.