DESIGN OF AN EFFICIENT POWER MANAGEMENT SYSTEM FOR SOLAR-POWERED UAVS: A SYSTEMATIC APPROACH
Abstract
As the demand for sustainable and efficient energy sources increases, integrating solar power into UAV operations presents a viable solution. Conventional MPPT controllers for solar-powered UAVs suffer from slow response times and inefficiencies under dynamic environmental conditions. This study proposes an enhanced power management system integrating FLC with MPPT for superior energy efficiency. The proposed power management design is achieved with the implementation of the Perturb and Observe (P and O) algorithm coupled with a Proportional-Integral (PI) controller to achieve Maximum Power Point Tracking (MPPT). The P&O algorithm was chosen for its simplicity and widespread adoption in MPPT applications, while PI control ensures stable convergence to the MPP. The integration of FLC further improves adaptability in fluctuating irradiance conditions. This approach ensures that the UAV operates at optimal power output levels under varying environmental conditions, specifically temperature solar irradiance. Additionally, a Fuzzy Logic Control (FLC) mechanism is employed to dynamically adjust the power output based on real-time data, ensuring optimal performance and stability. The results gotten from the simulations show that the FLC and the PI based on P & O algorithm returned a settling time of 0.01 seconds and 0.45 seconds respectively. The result showed that the fuzzy logic controller achieved a settling time 97% faster than the PI based on P & O algorithm. This research contributes to the development of more sustainable UAV technologies, paving the way for broader applications in various fields, including environmental monitoring and disaster response.
References
Abouzeid, A. F., Eleraky, H., Kalas, A., Rizk, R., Elsakka, M. M., & Refaat, A. (2024). Experimental validation of a low-cost maximum power point tracking technique based on artificial neural network for photovoltaic systems. Scientific Reports, 14(1), 18280. https://doi.org/10.1038/s41598-024-67306-0 DOI: https://doi.org/10.1038/s41598-024-67306-0
Boukoberine, M. N., Zhou, Z., & Benbouzid, M. (2019). A critical review on unmanned aerial vehicles power supply and energy management: Solutions, strategies, and prospects. Applied Energy, 255, 113823. DOI: https://doi.org/10.1016/j.apenergy.2019.113823
Del Cerro, J., Cruz Ulloa, C., Barrientos, A., & de Len Rivas, J. (2021). Unmanned aerial vehicles in agriculture: A survey. Agronomy, 11(2), 203. DOI: https://doi.org/10.3390/agronomy11020203
Dolara, A., Faranda, R., & Leva, S. (2009). Energy Comparison of Seven MPPT Techniques for PV Systems. Journal of Electromagnetic Analysis and Applications, 01(03), 152162. https://doi.org/10.4236/jemaa.2009.13024 DOI: https://doi.org/10.4236/jemaa.2009.13024
Gang, B. G., & Kwon, S. (2018). Design of an energy management technique for high endurance unmanned aerial vehicles powered by fuel and solar cell systems. International Journal of Hydrogen Energy, 43(20), 97879796. DOI: https://doi.org/10.1016/j.ijhydene.2018.04.049
Gao, Y., Qiao, Z., Pei, X., Wu, G., & Bai, Y. (2023). Design of Energy-Management Strategy for Solar-Powered UAV. Sustainability, 15(20), 14972. https://doi.org/10.3390/su152014972 DOI: https://doi.org/10.3390/su152014972
Joseph, S. B., & Dada, E. G. (2018). Proportional-integral-derivative (PID) controller tuning for an inverted pendulum using particle swarm optimisation (PSO) algorithm. FUDMA JOURNAL OF SCIENCES, 2(2), 7278.
Lee, B., Kwon, S., Park, P., & Kim, K. (2014). Active power management system for an unmanned aerial vehicle powered by solar cells, a fuel cell, and batteries. IEEE Transactions on Aerospace and Electronic Systems, 50(4), 31673177. https://doi.org/10.1109/TAES.2014.130468 DOI: https://doi.org/10.1109/TAES.2014.130468
Mohsan, S. A. H., Khan, M. A., Noor, F., Ullah, I., & Alsharif, M. H. (2022). Towards the unmanned aerial vehicles (UAVs): A comprehensive review. Drones, 6(6), 147. DOI: https://doi.org/10.3390/drones6060147
murdoch2013. (n.d.).
Sarang, S. A., Raza, M. A., Panhwar, M., Khan, M., Abbas, G., Touti, E., Altamimi, A., & Wijaya, A. A. (2024). Maximizing solar power generation through conventional and digital MPPT techniques: a comparative analysis. Scientific Reports, 14(1), 8944. https://doi.org/10.1038/s41598-024-59776-z DOI: https://doi.org/10.1038/s41598-024-59776-z
Shiau, J.-K., Ma, D.-M., Gong, J. H., Shiau, J.-K., Ma, D.-M., & Yang, P.-Y. (n.d.). Design of a Solar Power Management System for an Experimental UAV PIN-YING YANG GENG-FENG WANG.
Ubadike, O. C., Abbe, G. E., Amoako, T. E., Bonet, M. U., Momoh, M. O., Adeboye, C. A., & Ter, K. P. (2024). Robust LQR-Based Autopilot Design for Hybrid Energy Harvesting UAVs. FUDMA Journal of Renewable and Atomic Energy, 1(2). https://doi.org/https://doi.org/10.33003/fjorae.2024.0102.07
Wu, J., Wang, H., Huang, Y., Su, Z., & Zhang, M. (2018). Energy management strategy for solar-powered UAV long-endurance target tracking. IEEE Transactions on Aerospace and Electronic Systems, 55(4), 18781891. DOI: https://doi.org/10.1109/TAES.2018.2876738
Xu, L., Huangfu, Y., Ma, R., Xie, R., Song, Z., Zhao, D., Yang, Y., Wang, Y., & Xu, L. (2022). A comprehensive review on fuel cell UAV key technologies: propulsion system, management strategy, and design procedure. IEEE Transactions on Transportation Electrification, 8(4), 41184139. DOI: https://doi.org/10.1109/TTE.2022.3195272
Copyright (c) 2025 FUDMA JOURNAL OF SCIENCES
This work is licensed under a Creative Commons Attribution 4.0 International License.
FUDMA Journal of Sciences
