Research on Energy Management of Hybrid Unmanned Aerial Vehicles to Improve Energy-Saving and Emission Reduction Performance

Bai, Mingliang; Yang, Wenjiang; Song, Dongbin; Kosuda, Marek; Szabó, Stanislav; Lipovský, Pavol; Kasaei, Afshar

doi:10.3390/ijerph17082917

Cited by 24 publications

(18 citation statements)

References 44 publications

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“…This will encourage landowners to invest in this technology, seeing the long-term investment/return value. In recent years, there have been technical advances on prolonging the airtime of UAVs using solar power [195], hybrid fuel cell systems [196], and petrol-electric hybrid systems [197]. Such developments can have potential applicability in the field of UAVsSS.…”

Section: Development Of Unmanned Aerial Vehicle (Uav) and Robotics Technologiesmentioning

confidence: 99%

UAV-Supported Forest Regeneration: Current Trends, Challenges and Implications

Mohan

Richardson²,

Gopan³

et al. 2021

Remote Sensing

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Replanting trees helps with avoiding desertification, reducing the chances of soil erosion and flooding, minimizing the risks of zoonotic disease outbreaks, and providing ecosystem services and livelihood to the indigenous people, in addition to sequestering carbon dioxide for mitigating climate change. Consequently, it is important to explore new methods and technologies that are aiming to upscale and fast-track afforestation and reforestation (A/R) endeavors, given that many of the current tree planting strategies are not cost effective over large landscapes, and suffer from constraints associated with time, energy, manpower, and nursery-based seedling production. UAV (unmanned aerial vehicle)-supported seed sowing (UAVsSS) can promote rapid A/R in a safe, cost-effective, fast and environmentally friendly manner, if performed correctly, even in otherwise unsafe and/or inaccessible terrains, supplementing the overall manual planting efforts globally. In this study, we reviewed the recent literature on UAVsSS, to analyze the current status of the technology. Primary UAVsSS applications were found to be in areas of post-wildfire reforestation, mangrove restoration, forest restoration after degradation, weed eradication, and desert greening. Nonetheless, low survival rates of the seeds, future forest diversity, weather limitations, financial constraints, and seed-firing accuracy concerns were determined as major challenges to operationalization. Based on our literature survey and qualitative analysis, twelve recommendations—ranging from the need for publishing germination results to linking UAVsSS operations with carbon offset markets—are provided for the advancement of UAVsSS applications.

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Section: Development Of Unmanned Aerial Vehicle (Uav) and Robotics Technologiesmentioning

confidence: 99%

UAV-Supported Forest Regeneration: Current Trends, Challenges and Implications

Mohan

Richardson²,

Gopan³

et al. 2021

Remote Sensing

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“…In the application of UAV, most of the EMS are aimed at energy saving and emission reduction [21][22][23], including some research on the exploiting of solar energy [23], which is a very green and promising research field. However, as the current technology is not mature enough and extremely dependent on the natural conditions, it cannot be widely used.…”

Section: Introductionmentioning

confidence: 99%

Energy Management of Hybrid UAV Based on Reinforcement Learning

et al. 2021

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In order to solve the flight time problem of Unmanned Aerial Vehicles (UAV), this paper proposes a set of energy management strategies based on reinforcement learning for hybrid agricultural UAV. The battery is used to optimize the working point of internal combustion engines to the greatest extent while solving the high power demand issues of UAV and the response problem of internal combustion engines. Firstly, the decision-making oriented hybrid model and UAV dynamic model are established. Owing to the characteristics of the energy management strategy (EMS) based on reinforcement learning (RL), which is an intelligent optimization algorithm that has emerged in recent years, the complex theoretical formula derivation is avoided in the modeling process. In terms of the EMS, a double Q learning algorithm with strong convergence is adopted. The algorithm separates the state action value function database used in derivation decisions and the state action value function-updated database brought by the decision, so as to avoid delay and shock within the convergence process caused by maximum deviation. After the improvement, the off-line training is carried out with a large number of flight data generated in the past. The simulation results demonstrate that the improved algorithm can show better performance with less learning cost than before by virtue of the search function strategy proposed in this paper. In the state space, time-based and residual fuel-based selection are carried out successively, and the convergence rate and application effect are compared and analyzed. The results show that the learning algorithm has stronger robustness and convergence speed due to the appropriate selection of state space under different types of operating cycles. After 120,000 cycles of training, the fuel economy of the improved algorithm in this paper can reach more than 90% of that of the optimal solution, and can perform stably in actual flight.

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“…Moreover, the Q‐learning method is used in Ref. 44 and showed that the hybrid system can decrease pollutants.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous components sizing and flight scheduling for an hybrid aerial vehicle as a multi‐energy mobile microgrid

Salehpour

Zarenia

Wang

et al. 2021

Int Trans Electr Energ Syst

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Summary This article proposes a component‐sizing problem for a hybrid unmanned aircraft alongside a coordinated energy consumption and flight scheduling. The goal is to simultaneously minimize the fuel consumption, the gaseous emission, and the overall costs of components (capital and operating costs) including fuel cell, battery, electric motor, and thermal engines. To this end, a non‐linear programming is proposed, and by an efficient decomposition method, it is solved under GAMS. The numerical results are compared with some evolutionary algorithms and showed that the proposed optimization framework exerts influence on the overall costs, energy efficiency, and computation time.

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Research on Energy Management of Hybrid Unmanned Aerial Vehicles to Improve Energy-Saving and Emission Reduction Performance

Cited by 24 publications

References 44 publications

UAV-Supported Forest Regeneration: Current Trends, Challenges and Implications

UAV-Supported Forest Regeneration: Current Trends, Challenges and Implications

Energy Management of Hybrid UAV Based on Reinforcement Learning

Simultaneous components sizing and flight scheduling for an hybrid aerial vehicle as a multi‐energy mobile microgrid

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