Energy consumption and efficiency degradation predictive analysis in unmanned aerial vehicle batteries using deep neural networks

• Autorzy: Al-Haddad Luttfi A. , Łukaszewicz Andrzej , Majdi Hasan Sh. , Holovatyy Andriy , Jaber Alaa Abdulhady , Al-Karkhi Mustafa I. , Giernacki Wojciech

Identyfikatory

DOI

10.12913/22998624/201346

• Języki publikacji: EN

Abstrakty

EN

The unmanned aerial vehicles (UAVs) needs efficient energy management to ensure optimal performance and flight time. In this paper, the energy consumption and efficiency degradation in DJI Mini 2 drone batteries by the use of a deep neural network (DNN) for predictive analysis, was concern. The research conducted repeated flights and monitoring battery discharge from 100% to 27% over 20 trials. Experimental conditions, including flight duration and environmental factors, were controlled to ensure repeatability and to minimize any external influences on the recorded data. Data were stored onto AIRDATA (drone logbook) and then recollected for new labeling. The initial flights demonstrated similar, near constant performance, while following flights showed a gradual reduction in flight time (performance degradation). To ensure comparable power usage and minimize external influences, hover mode was selected for all flights. Next, on this data the DNN was trained using the metrics of mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), coefficient of variation of the root mean squared error (CVRMSE), and determination coefficient (R²). The trained model achieved the MSE of 0.352%, RMSE of 0.593%, MAE of 0.324%, MAPE of 0.857%, CVRMSE of 0.743%, and R² of 0.981. The obtained results show the DNN’s ability to predict future power consumption for the UAV that in turn provides insights for energy management and extension of battery life. The paper contributes to the development of sustainable UAV operations by better knowledge about battery performance for in-flight conditions.

Słowa kluczowe

EN

deep neural network; UAV; battery; flight duration; energy consumption

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2025
• Tom: Vol. 19, no. 5
• Strony: 21--30
• Opis fizyczny: Bibliogr. 23 poz., fig., tab.

Twórcy

autor

Al-Haddad Luttfi A.

• Mechanical Engineering Department, University of Technology-Iraq, Baghdad, Iraq

• https://orcid.org/0000-0001-7832-1048

autor

Łukaszewicz Andrzej

• Institute of Mechanical Engineering, Faculty of Mechanical Engineering, Bialystok University of Technology, ul. Wiejska 45C, 15-351 Bialystok, Poland

• https://orcid.org/0000-0003-0373-4803

autor

Majdi Hasan Sh.

• Department of Chemical Engineering and Petroleum Industries, Al‐Mustaqbal University College, Hillah, Iraq

• https://orcid.org/0000-0001-6752-4835

autor

Holovatyy Andriy

• Department of Computer-Aided Design Systems, Lviv Polytechnic National University, 79013 Lviv, Ukraine

• https://orcid.org/0000-0001-6143-648X

autor

Jaber Alaa Abdulhady

• Mechanical Engineering Department, University of Technology-Iraq, Baghdad, Iraq

• https://orcid.org/0000-0001-5709-195X

autor

Al-Karkhi Mustafa I.

• Mechanical Engineering Department, University of Technology-Iraq, Baghdad, Iraq

autor

Giernacki Wojciech

• Faculty of Automatic Control, Robotics and Electrical Engineering, Institute of Robotics and Machine Intelligence, Poznan University of Technology, Poland

• https://orcid.org/0000-0003-1747-4010

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).