Verification of the Methodology for Determining the Relative Mass of Electric Aircraft Batteries

Аuthors

Dukhnovskiy D. A.

Moscow Aviation Institute (National Research University), 4, Volokolamskoe shosse, Moscow, А-80, GSP-3, 125993, Russia

e-mail: DukhnovskijDA@mai.ru

Abstract

The article describes computational-and-experimental verification of the previously developed technique for the batteries relative mass determining of the aircraft with electric power plant. The batteries relative mass is a function of the aircraft's flight range, altitude and airspeed, propulsion efficiency, battery energy density and the airframe lift-to-drag ratio. For the study accommodation and demonstrativeness of the results, verification was performed to establish conformity of the aircraft flight range values with other known parameters. Test flights of the experimental aircraft were conducted for verification.
Methods for determining values of the components of the expression under study  for the flight range determining were selected and substantiated in the course of the computational-and-experimental verification.
The aircraft airframe aerodynamic quality was being determined by modern computational methods, particularly by the computational aero-hydrodynamics (CFD). Computations were being performed with the Open Foam software package. The aircraft first type polar was the result of the computations, and as a consequence a graph of the of the airframe aerodynamic quality dependence on the angle of attack. It allowed determining hereafter the aircraft aerodynamic quality in flight.
The relative mass value of the batteries and takeoff weight of the experimental aircraft were being determined by weighing.
The efficiency values of the experimental aircraft power plant were obtained by statistical methods.
The values of the flight range, speed, and altitude were obtained during the test flights. An airplane type experimental unmanned aerial vehicle with electric power plant, equipped the instruments necessary for the said flight parameters recording was designed and manufactured for their realization.
As the result, it allowed establishing conformity between the flight range values, obtained with the expressions developed by the author, and the flight range values obtained during of the tests. This confirms the developed expressions fidelity.
A conclusion was made as well on the importance of accounting for the aircraft onboard equipment consumption in flight while the batteries relative mass determining at the early design stages.

Keywords:

unmanned aerial vehicle, battery weight, electric aircraft flight range, electric power plant

References

1. Malenkov A.A. Design solutions selection while developing a system of unmanned flying vehicles in conditions of multi-target uncertainty. Aerospace MAI Journal, 2018, vol. 25, no. 2, pp. 7-15. URL: https://vestnikmai.ru/eng/publications.php?ID=92727
2. Shvetsova S.V., Shvetsov A.V. Unmanned aerial vehicles integration into modern infrastructure systems operation. Aerospace MAI Journal, 2021, vol. 28, no. 3, pp. 186-193. DOI: 10.34759/vst-2021-2-186- 193
3. Schäfer A.W., Barrett S.R.H., Doyme K. et al. Technological, economic and environmental prospects of all-electric aircraft. Nature Energy, 2019, vol. 4, no. 2, pp. 160-166. DOI: 10.1038/s41560-018-0294-x
4. Traub L.W. Range and endurance estimates for battery-powered aircraft. Journal of Aircraft, 2011, vol. 48, no. 2, pp. 703-707. DOI: 10.2514/1.C031027
5. Dukhnovskiy D.A. Formation of the area of possible existence of electrified airplanes. 2020 New Trends in Aviation Development (NTAD). IEEE, 2020, pp. 49-53. DOI: 10.1109/NTAD51447.2020.9379118
6. Dukhnovskiy D. Methodology for determining the takeoff mass of all-electric aircraft at the early stages of design. Aerospace Systems, 2024, vol. 7, pp. 93–101. DOI: 10.1007/s42401-023-00242-5
7. Suvanjumrat C. Comparison of turbulence models for flow past NACA0015 airfoil using OpenFOAM. Engineering Journal, 2017, vol. 21, no. 3, pp. 207-221. DOI: 10.4186/ej.2017.21.3.207
8. Kekina P., Suvanjumrat C. A comparative study on turbulence models for simulation of flow past NACA 0015 airfoil using OpenFOAM. 3rd International Conference on Mechatronics and Mechanical Engineering (ICMME 2016). MATEC Web of Conferences. 2017, vol. 95: 12005. DOI: 10.1051/matecconf/20179512005
9. Vogeltanz T. Comparison of open-source CFD software for aerodynamic analysis of mini-UAV. 2015 IEEE/AIAA 34th Digital Avionics Systems Conference (DASC). DOI: 10.1109/DASC.2015.7311438
10. Ponweiser T., Stadelmeyer P., Karásek T. Fluid-structure simulations with OpenFOAM for aircraft designs. Partnership for Advanced Computing in Europe, 2014. URL: http://www.prace-ri.eu/IMG/pdf/wp172.pdf
11. Miyamasu M., Akatsu K. Efficiency comparison between Brushless dc motor and Brushless AC motor considering driving method and machine design. IEEJ Journal of Industry Applications, 2011, vol. 2, no. 1, pp. 79-86. DOI: 10.1109/IECON.2011.6119584
12. Lu S.-M. A review of high-efficiency motors: Specification, policy, and technology. Renewable and Sustainable Energy Reviews, 2016, vol. 59, pp. 1-12. DOI: 10.1016/j.rser.2015.12.360
13. Green C.R., McDonald R.A. Modeling and test of the efficiency of electronic speed controllers for Brushless Dc Motors. 15th AIAA Aviation Technology, Integration, and Operations Conference. 2015: 3191. DOI: 10.2514/6.2015-3191
14. Harrington A.M., Kroninger C. Characterization of small dc brushed and brushless motors. Army research lab aberdeen proving ground md vehicle technology directorate. Technical Report. ARL-TR-6415. 2013. DOI: 10.21236/ada577582
15. ELkholy M.M., El-Hay E.A. Efficient dynamic performance of brushless DC motor using soft computing approaches. Neural Computing and Applications, 2020, vol. 32, no. 2, pp. 6041-6054. DOI: 10.1007/s00521-019-04090-3
16. Jegajothi B., Geethamahalakashmi G., Raja A., Mahendran N. An efficient metaheuristic optimization based fuzzy controller for brushless DC drives lifetime expansion // Materials Today: Proceedings. 2022. Vol. 56, Part 6, pp. 3343-3348. DOI: 10/1016/i.matpr2021.10.176
17. Brandt J.B., Selig M.S. Propeller performance data at low reynolds numbers. 49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition, 2011, p. 1255. DOI: 10.2514/6.2011-1255
18. Patel Y., Gaurav A., Srinivas K., Singh Y. A review on design and analysis of the propeller used in UAV. International Journal of Advanced Production and Industrial Engineering, 2017, vol. 605, pp. 20-23.
19. Duan D., Wang Z., Wang Q., Li J. Research on integrated optimization design method of high-efficiency motor propeller system for UAVs with multi-states. IEEE Access, 2020, vol. 8, pp. 165432-165443. DOI: 10.1109/ACCESS.2020.3014411
20. Zhu H., Jiang Z., Zhao H. et al. Aerodynamic performance of propellers for multirotor unmanned aerial vehicles: Measurement, analysis, and experiment. Shock and Vibration, 2021, vol. 2021, no. 3, pp. 1-11. DOI: 10.1155/2021/9538647
21. Zinenkov Yu.V., Fedotov M.M., Raznoschikov V.V., Lukovnikov A.V. An approach to the aircraft propeller mathematical modeling. Aerospace MAI Journal, 2023, vol. 30, no. 4, pp. 140–149. URL: https://vestnikmai.ru/publications.php?ID=177615
22. Zinenkov Yu.V., Fedotov M M., Raznoschikov V.V., Lukovnikov A.V. Specifics of Aircraft Screw Propulsion Unit Thrust Computing by the Airscrew Aerodynamic Characteristics. Aerospace MAI Journal, 2024, vol. 31, no. 1, pp. 105-113.