Drone Charging Research Articles

Wireless Electrification System for Photovoltaic Powered Autonomous Drone Charging

Wireless Electrification System for Photovoltaic Powered Autonomous Drone Charging

Integrated truck drone delivery services with an optimal charging stations

Battery-powered drones, or unmanned aerial vehicles (UAVs), present significant market potential for faster, economical, and eco-friendly urban delivery solutions. Many firms are investing in drone logistics ventures to capitalize on their capabilities. However, the limited range of drone deliveries, dictated by battery capacity, poses a significant challenge. Hybrid delivery systems combining trucks and drones have gained attention to overcome this challenge. Traditional models assume trucks park at customer sites while drones handle additional deliveries, yet this approach may hinder drone efficiency due to constraints in parking availability. Alternative approaches like battery swapping and mobile charging stations on trucks have been explored but exhibit limitations such as blind spots and substantial battery reserves. We propose establishing dedicated drone charging stations and optimizing drone routing for efficient deliveries to address these issues We present a MINLP (Mixed Integer Non-Linear Programming) model aimed at identifying the most cost-effective solution that optimizes both transportation efficiency and charging infrastructure investment. Quantitative experiments demonstrate the efficacy of this approach, alongside sensitivity analysis aiding economic decision-making across various operational scenarios. Notably, our experiments show a 15% reduction in total costs when extending the operational lifespan of charging stations from 2 to 4 years.

Using Game Theory to Improve Drone Operations

The integration of game theory into optimizing the selection of drone charging stations and scheduling their operations is a revolutionary advance in unmanned vehicle logistics. Our research explores this frontier by emphasizing methodological innovation through the use of payoff matrices and Nash equilibrium to address the complex and changing requirements of drone operations. This research not only provides a strategic framework for resource optimization, but also highlights new ways to apply game theory to critical areas such as adaptive routing and swarm intelligence in drone management. By combining theoretical game models with practical applications of drones, we present a perspective that is poised to redefine drone operational strategies, paving the way for future research in this area.

A Novel Charging Station on Overhead Power Lines for Autonomous Unmanned Drones

Innovative drone-based technologies provide novel techniques to guarantee the safety and quality of power supply and to perform these tasks more efficiently. Electric multirotor drones, which are at the forefront of technology, face significant flight time limitations due to battery capacity and weight constraints that limit their autonomous operation. This paper presents a novel drone charging station that harvests energy from the magnetic field present in power lines to charge the drone’s battery. This approach relies on a charging station that is easy to install by the drone on an overhead AC power line without modifying the electrical infrastructure. This paper analyses the inductive coupling between the energy harvester and the power line, electrical protection, the power electronics required for maximum power point tracking and the mechanical design of the charging station. A drone that perches on a cable, an end effector for installation procedures and the charging maneuver are described, along with discussion of the robotic and electrical tests performed in a relevant environment. Finally, a lightweight drone charging station capable of harvesting 145 W of power from a 600 A line current is reported.

Building integrated photovoltaic powered wireless drone charging system

The future of urban drone-based transportation and delivery depends upon the efficient operation of its charging infrastructure. Working against gravity draws substantial energy from the drone’s battery, requiring frequent recharging. Currently, battery swapping is the most preferred method for drone charging. Charging multiple drones using the wired charging method has complications such as untangling, tripping over problems, wear and tear, and requires human intervention. This limits the drones from becoming fully autonomous and self-sufficient. To address these problems, an innovative Building Integrated Photovoltaic (BIPV) structure with wireless drone charging capabilities is designed to optimize the usage of rooftop space for multi-drone landings and utilize the wall space for efficient Photovoltaic (PV) based charging. This study compares the charging output from five PV panel mountings: a rooftop panel, a vertical PV panel, a 30° inclined PV panel, an inclined PV panel with a black reflective surface, and an inclined PV panel with a white reflective surface. The 30° inclined PV panel charges the 12.6 V/5.2 Ah drone’s LiPo battery in 31.29 min compared to vertically placed panels, which take 36.9 min. PV panel with a black reflective surface yields 10.09 % more energy compared to a white reflective surface PV panel. The output of the 30° inclined PV panel is connected to the wireless charging system, which transfers 120 W across a vertical displacement of 0–10 mm with an efficiency of 95.70 %.

Autonomous drone charging station planning through solar energy harnessing for zero-emission operations

• Solar energy from building envelopes can extend UAVs coverage and reliability in last-mile delivery applications. • This integration can omit GHG emissions in parcel delivery while improving building energy efficiency. • A multi-objective optimization model is proposed that maximizes UAV coverage and minimizes the total cost of energy and decarbonization. • A flexible energy use model is applied to a digital twin to generate minimum-energy 3D trajectories. • RI-SHOT multi-objective optimization algorithm is used to solve all the delivery demands. • GHG emissions for the entire UAV charging network are omitted compared to a grid-connected operation. The introduction of Unmanned Arial Vehicles (UAVs) in smart city operations is considered a sustainable technological solution due to the promised significant greenhouse gas emission reductions. This study developed an integrated multi-objective charging infrastructure coverage optimization model that integrates UAV-based operations with solar energy harnessing from building envelopes. This model maximizes UAVs’ coverage range and minimizes the total cost of energy and decarbonization. The model is based on a flexible energy use model for UAVs calibrated to experimental measurements to generate a minimum-energy trajectory. We also developed an origin-destination (O-D) demand geo-referenced in a digital-twin model to replicate real-world operation. Overall, 12,532 simulated daily missions in a large-sized city are modelled. The results show that the proposed system can eliminate GHG emissions. Furthermore, the system can significantly reduce the decarbonization price through associated savings and excess generated electricity. The proposed approach demonstrates avenues to advance smart cities and capitalize on renewable energy.

Scheduling and Securing Drone Charging System Using Particle Swarm Optimization and Blockchain Technology

Unmanned aerial vehicles (UAVs) have emerged as a powerful technology for introducing untraditional solutions to many challenges in non-military fields and industrial applications in the next few years. However, the limitations of a drone’s battery and the available optimal charging techniques represent a significant challenge in using UAVs on a large scale. This problem means UAVs are unable to fly for a long time; hence, drones’ services fail dramatically. Due to this challenge, optimizing the scheduling of drone charging may be an unusual solution to drones’ battery problems. Moreover, authenticating drones and verifying their charging transactions with charging stations is an essential associated problem. This paper proposes a scheduling and secure drone charging system in response to these challenges. The proposed system was simulated on a generated dataset consisting of 300 drones and 50 charging station points to evaluate its performance. The optimization of the proposed scheduling methodology was based on the particle swarm optimization (PSO) algorithm and game theory-based auction model. In addition, authenticating and verifying drone charging transactions were executed using a proposed blockchain protocol. The optimization and scheduling results showed the PSO algorithm’s efficiency in optimizing drone routes and preventing drone collisions during charging flights with low error rates with an MAE = 0.0017 and an MSE = 0.0159. Moreover, the investigation to authenticate and verify the drone charging transactions showed the efficiency of the proposed blockchain protocol while simulating the proposed system on the Ethereum platform. The obtained results clarified the efficiency of the proposed blockchain protocol in executing drone charging transactions within a short time and low latency within an average of 0.34 s based on blockchain performance metrics. Moreover, the proposed scheduling methodology achieved a 96.8% success rate of drone charging cases, while only 3.2% of drones failed to charge after three scheduling rounds.

Explicit Design of Impedance Matching Networks for Robust MHz WPT Systems With Different Features

This article develops explicit design of an impedance matching network (IMN) to enhance the robustness of megahertz (MHz) wireless power transfer (WPT) systems against variations in their final load and coil coupling. Unlike the existing trial-and-error-based solutions, analytical derivations are conducted to calculate the parameters of a two-port T- or <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\bf \Pi$</tex-math></inline-formula> -IMN. The load-pull technique is first applied to determine a target impedance ray that represents a desired loading condition of the power amplifier. Based on the analytical derivations, the three degrees of the design freedom of the IMN are then fully utilized to accurately match the starting point and direction of the target impedance ray. Different features of a final MHz WPT system, such as load-independent output voltage and high tolerance to coil misalignment, are further achieved by having different target impedance rays. Performance enhancement of an experimental 6.78-MHz WPT system is investigated under largely varied final load (10–50 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf{\Omega }$</tex-math></inline-formula> ) and coil coupling (0–53%). With an explicitly designed IMN, the maximum efficiency drops are reduced from 18% to 6% under the changing final load and 21% to 3.9% under the changing coil coupling (0–40%), respectively. An actual application of wireless drone charging is also included to demonstrate the practicability of the proposed IMN design

Charging Stations Distribution Optimization using Drones Fleet for Disaster Prone Areas

A disaster is an unforeseen calamity that causes damage to property or brings about a loss of human life. Quick response and rapid distribution of vital relief items into the affected region could save precious lives. In this regard, disaster management comes into play, which is highly dependent on the topography of the disaster-hit area. If the disaster-hit area has little or no road connectivity, the use of drones in such areas becomes essential for the delivery of health packages. Since the battery capacity of the drone is limited, there is a need of charging stations that should be transported using road infrastructure and pre-installed in disaster-prone areas, as access to these areas may be denied once the disaster hits. In this article, a simulation model was used to optimize the number and location of drone charging stations for deployment in a disaster-prone area in the pre-disaster scenario, aiming at the distribution of relief items to disaster-hit areas in the post-disaster scenario. We consider the relative priority of locations where a preference is given to the locations that have higher priority levels. An optimal number of charging stations and optimal routes have also been determined by using our optimization model. To illustrate the use of our model, numerical examples have been simulated for different sizes of the disaster-hit area and the number of targets. In our numerical simulation, it was observed that the drone's maximum distance capacity is the key factor in determining the optimal grid size, which directly correlates to the number of charging stations.

Drone Charging Stations Deployment in Rural Areas for Better Wireless Coverage: Challenges and Solutions

While fifth-generation (5G) cellular is meant to deliver gigabit peak data speeds, low latency, and connection to billions of devices, and 6G is already on the way, half of the world population living in rural areas are still facing challenges connecting to the internet. Compared to urban areas, users in rural areas are greatly impacted by low income, high cost of backhaul connectivity, limited resources, extreme weather, and natural geographical limitations. Therefore, how to connect the rural areas and the difficulties of providing connectivity draw great attention. This article first provides a brief discussion about existing technologies and strategies for enhancing the network coverage in rural areas, and their advantages, limitations, and cost. Next, we mainly focus on the UAV-assisted network in resource-limited regions. Considering the limitation of the onboard battery of UAVs and the electricity supply scarcity in some rural regions, we investigate the possibility and performance enhancement of the deployment of renewable energy (RE) charging stations. We outline three practical scenarios, and use simulation results to demonstrate that RE charging stations can be a possible solution to address the limited onboard battery of UAVs in rural areas, especially when they can harvest and store enough energy. Finally, future works and challenges are discussed.

Wireless Drone Charging Station Using Class-E Power Amplifier in Vertical Alignment and Lateral Misalignment Conditions

Recent major advancements in drone charging station design are related to the differences in coil design between the material (copper or aluminum) and inner thickness (diameter design) to address power transfer optimization and increased efficiency. The designs are normally challenged with reduced weight on the drone’s side, which can lead to reduced payload or misalignment position issues between receiver and transmitter, limiting the performance of wireless charging. In this work, the coil combination was tested in vertical alignment from 2 cm to 50 cm, and in lateral misalignment positions that were stretched across 2, 5, 8, 10, and 15 cm ranges. Simulated and experimental results demonstrated improved transfer distances when the drone battery load was 100 Ω. With the proposed design, the vertical transfer power that was achieved was 21.12 W, 0.460 A, with 81.5% transfer efficiency, while the maximum lateral misalignment air gap that was achieved was 2 cm with 19.22 W and 74.15% efficiency. This study provides evidence that the developed circuit that is based on magnetic resonant coupling (MRC) is an effective technique towards improving power transfer efficiency across different remote and unmanned Internet of Things (IoT) applications, including drones for radiation monitoring and smart agriculture.

A Switched Modular Multi-Coil Array Transmitter Pad With Coil Rectenna Sensors to Improve Lateral Misalignment Tolerance in Wireless Power Charging of Drone Systems

In this article, a Switched Modular Multi-coil Array transmitter pad integrated with coil RecTenna (SMMART) is designed to present an intelligent wireless drone charging system that improves lateral misalignment tolerance. The resultant optimized design constitutes four independent transmitter modules, wherein each includes four spatially distributed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf{2\times2}$</tex-math> </inline-formula> array coils. Therefore, the proposed design induces a maximal uniform voltage in the Rx coil region. Moreover, the optimization procedure accounts for the miniaturization of transmitter coil size without compromising the misalignment tolerance by overlapping various transmitter modules. In addition, a novel coil-based detection system for wireless chargeable drone applications is proposed to activate the desired transmitter module based on the position of the receiver coil. The detection system consists of an array of coil rectennas integrated with a switching circuit to reduce the undesired magnetic field, enhancing the link efficiency. The prototype of the charging pad is fabricated using Litz wire, and the S21-based link efficiency is measured using an experimental setup. Besides, the fabricated coil rectenna sensor array system demonstrates a real-application model of the detection system. Hence, the proposed transmitter pad improves lateral misalignment tolerance and is considered a potential wireless drone charging system design.

Algorithms for Delivery of Data by Drones in an Isolated Area Divided into Squares

Drones are frequently used for the delivery of materials or other goods, and to facilitate the capture and transmission of data. Moreover, drone networks have gained significant interest in a number of scenarios, such as in quarantined or isolated areas, following technical damage due to a disaster, or in non-urbanized areas without communication infrastructure. In this context, we propose a network of drones that are able to fly on a map covered by regular polygons, with a well-established mobility schedule, to carry and transfer data. Two means exist to equidistantly cover an area with points, namely, grouping the points into equilateral triangles or squares. In this study, a network of drones that fly in an aerial area divided into squares was proposed and investigated. This network was compared with the case in which the area is divided into equilateral triangles. The cost of the square drone network was lower than that of the triangular network with the same cell length, but the efficiency factors were better for the latter. Two situations related to increasing the drone autonomy using drone charging or battery changing stations were analyzed. This study proposed a Delay Tolerant Network (DTN) to optimize the transmission of data. Multiple simulation studies based on experimental flight tests were performed using the proposed algorithm versus five traditional DTN methods. A light Wi-Fi Arduino development board was used for the data transfer between drones and stations using delivery protocols. The efficiency of data transmission using single-copy and multiple-copy algorithms was analyzed. Simulation results showed a better performance of the proposed Time-Dependent Drone (TD-Drone) Dijkstra algorithm compared with the Epidemic, Spray and Wait, PRoPHET, MaxProp, and MaxDelivery routing protocols.

Optimization Design of Resonance Coils With High Misalignment Tolerance for Drone Wireless Charging Based on Genetic Algorithm

The misalignment between the transmitting (Tx) coil and the receiving (Rx) coil is an urgent problem in the drone charging system using wireless power transfer technology. The coupling coefficient is so low when the resonance coils is not aligned, which degrades the drone wireless charging performance. In this article, the optimization processes of the coupling mechanism are presented in detail. First, we illustrate the great superiority of unequally spaced Tx coil, compared with traditional uniformly spaced one. Then, a genetic algorithm to optimize the side lengths of the Tx coils is highlighted by discussing the main design considerations and methods in a comprehensive manner. Furthermore, the Rx coil with varying radius size per turn is proposed in terms of the distribution of magnetic field generated by the optimized Tx coil. Experimental results show that the optimized coils have better coupling performance due to small mutual inductance change under the condition of misalignment. Power transfer efficiencies in different misalignment situations are measured and the best power transfer efficiency improvement achieved by the proposed system can reach 56.23&#x0025; in contrast with traditional coils. This drone charging system can transfer a maximum power of 100 W with the remarkable transfer efficiency of 92.41&#x0025;.

회전익 드론의 접촉식 배터리 충전스테이션에 관한 연구

회전익 드론(Rotary wing drone)은 인간이 수행하기 힘든 상황의 각종 제약요소들을 극복할 수 있기 때문에 물류운송, 관측, 감시, 측량과 같은 다양한 분야에 적용하기 위해 연구개발 되고 있다. 하지만 이러한 임무를 수행할 때 배터리 기술의 한계로 인한 회전익 드론의 짧은 비행시간은 가장 큰 걸림돌이 되고 있으며, 이를 극복하기 위하여 다양한 방식의 드론 충전스테이션(Drone charging stations)과 운영 방법이 제안되고 있다. 본 논문은 산불 감시의 지속적인 임무 수행을 위한 접촉식 충전플랫폼 개발을 제시한다. 접촉식 충전플랫폼은 드론에 연결된 자동 충전장치와 충전플랫폼 인터페이스를 이용하여 드론의 비행횟수를 효과적으로 늘릴 수 있는 기술이다. 충전베드는 계란판형 구조와 접촉식 충전 단자로 설계되었다. 드론에 장착된 충전단자는 원추형으로 구성되어 충전대의 계란판형 구조에 형성된 복수의 충전 슬롯(Slot) 중 하나에 슬라이딩을 통해 삽입되어 랜덤으로 충전된다. 접촉 충전의 효율을 확인하기 위해 접촉 저항 측정을 통해 충전 가능성을 확인하였다.

Scheduling drone charging for multi-drone network based on consensus time-stamp and game theory

Drones or Unmanned Aerial Vehicles (UAVs) can be highly efficient in various applications like hidden area exploration, delivery, or surveillance and can enhance the quality of experience (QoE) for end-users. However, the number of drone-based applications are not very high due to the constrained flight time. The weights of the drones need to be kept less, and intuitively they cannot be loaded with big batteries. Frequent recharging and battery replacement processes limit the appropriate use of drones in most applications. A peer-to-peer distributed network of drones and charging stations is a highly promising solution to empower drones to be used in multiple applications by increasing their flight time. The charging stations are limited, and therefore, an adequate, fair, and cost-optimal scheduling algorithm is required to serve the most needed drone first. The proposed model allows the drones to enter into the network and request for a charging time slot from the station. The stations are also the part of the same network, this work proposes a scheduling algorithm for drones who compete for charging slots with constraints of optimizing criticality and task deadline. A game-theoretic approach is used to model the energy trading between the drones and charging station in a cost-optimal manner. Numerical results based on simulations show that the proposed model provides a better price for the drones to get charged and better revenue for the charging stations simultaneously.

A Novel Data Forwarding Strategy for a Drone Delay Tolerant Network with Range Extension

Amazon, Uber Eats, and United Parcel Service (UPS) are planning to launch drone delivery services in the near future. Indeed, recently, Google has received Federal Aviation Administration (FAA) approval for its Wings delivery platform. Amazon claims that a drone logistics network is more cost-efficient and quicker than a motor vehicle delivery network. In this paper, we propose a data delivery service by the drone network in addition to parcel delivery. We propose Heuristic Flight Path Planning (HFPP) that plans a drone’s flight path based on parcel delivery destination as well as data delivery destinations (waypoints). We further extend the solution to include drone charging stations for range extension. Our simulation studies show that our proposed method has delivered the data and consignments such that HFPP delivers up to 33% more data packets compared with Encounter-Based Routing (EBR), Epidemic, and a similar path planning method. Also, HFPP reduces the data delivery delays by up to 72% while the overhead ratio is low.

Advanced Drone Battery Charging System

In this work, an advanced drone battery charging system is developed. The system is composed of a drone charging station with multiple power transmitters and a receiver to charge the battery of a drone. Resonance inductive coupling-based wireless power transmission technique is used. With limits of wireless power transmission in inductive coupling, it is necessary that the coupling between a transmitter and receiver must be strong for efficient power transmission but for a drone, it is normally hard to land it properly on a charging station or a charging device to get maximum coupling for efficient wireless power transmission. Normally some sensors are used to align the transmitter and receiver for proper coupling but in this system, a novel method based on Hill climbing algorithm is proposed to control the coupling between the transmitter and a receiver without using any sensor. The feasibility of the proposed algorithm is checked using MATLAB. A practical test bench is developed for the system and several experiments are conducted under different scenarios. The system is fully automatic and works well to eliminate the poor landing and coupling effect which hinders the efficient wireless power transmission.

Wireless Power Transfer With Magnetic Resonator Coupling and Sleep/Active Strategy for a Drone Charging Station in Smart Agriculture

Drones can be used in agriculture applications to monitor crop yield and climate conditions and to extend the communication range of wireless sensor networks in monitoring areas. However, monitoring the climate conditions in agriculture applications faces challenges and limitations, such as drone flight time, power consumption, and communication distance, which are addressed in this study. Wireless power transfer (WPT) can be used to charge drone batteries. WPT using a magnetic resonant coupling (MRC) technique was considered in this study because it allows high transfer power and efficiency with tens of centimeters, power transfers can be achieved in misalignment situations, charging several devices simultaneously, and unaffected by weather conditions. WPT was practically implemented based on a solar cell using a proposed flat spiral coil (FSC) in the transmitter circuit and multiturn coil (MTC) in a receiver circuit (drone) for the alignment and misalignment of two coils at different distances. FSC and MTC improved power transfer and efficiency to 20.46 W and 85.25%, respectively, at 0 cm with the loaded system under alignment condition. In addition, the two coils achieved appropriate transfer efficiencies and power for charging the drone battery under misaligned conditions. The maximum power transfer and efficiency were 17.1 W and 71% for the misalignment condition, at an air gap of 1 cm between two coils when the system was loaded with the drone battery. Moreover, the battery life of the drone was extended to 851 minutes based on the proposed sleep/active strategy relative to the traditional operation (i.e., 25.84 minutes). Consequently, a 96.9% battery power saving was achieved based on this strategy. Comparison results showed that the proposed system outperformed some present techniques in terms of the transfer power, transfer efficiency, and drone battery life. The proposed WPT technique developed in this study has been proven to solve the misalignment issue. Thus it offers a great opportunity as a key deployment component for the automation of farming practices toward the Internet of Farming applications.

Development of Intelligent Drone Battery Charging System Based on Wireless Power Transmission Using Hill Climbing Algorithm

In this work, an advanced drone battery charging system is developed. The system is composed of a drone charging station with multiple power transmitters and a receiver to charge the battery of a drone. A resonance inductive coupling-based wireless power transmission technique is used. With limits of wireless power transmission in inductive coupling, it is necessary that the coupling between a transmitter and receiver be strong for efficient power transmission; however, for a drone, it is normally hard to land it properly on a charging station or a charging device to get maximum coupling for efficient wireless power transmission. Normally, some physical sensors such as ultrasonic sensors and infrared sensors are used to align the transmitter and receiver for proper coupling and wireless power transmission; however, in this system, a novel method based on the hill climbing algorithm is proposed to control the coupling between the transmitter and a receiver without using any physical sensor. The feasibility of the proposed algorithm was checked using MATLAB. A practical test bench was developed for the system and several experiments were conducted under different scenarios. The system is fully automatic and gives 98.8% accuracy (achieved under different test scenarios) for mitigating the poor landing effect. Also, the efficiency η of 85% is achieved for wireless power transmission. The test results show that the proposed drone battery charging system is efficient enough to mitigate the coupling effect caused by the poor landing of the drone, with the possibility to land freely on the charging station without the worry of power transmission loss.