Wireless Energy Transfer Technology Research Articles

Application of wireless energy delivery system in automobile charging

With the popularity of electric vehicles, wireless charging technology has become a key area to address the convenience of charging electric vehicles. The aim of this study is to explore the principles of wireless energy transfer technology and its application in the field of electric vehicles. We first provide a background introduction to the field of electric vehicles, emphasising the importance of wireless charging technology. Subsequently, we delve into the charging and discharging characteristics of power batteries to better understand the need for energy transfer. Then, we carefully selected wireless charging methods suitable for electric vehicles and developed a corresponding wireless charging system architecture. In this paper, we also provide theoretical analyses of two major wireless energy transfer technologies, including electromagnetic coupled wireless energy transfer and resonant wireless energy transfer, as well as exploring the principles of RF wireless energy transfer technology. Finally, we propose an architecture for a static wireless charging system including five modules: pre-processing, transmission, reception, charging control and battery load. The contribution of this study is to provide an in-depth research and a comprehensive theoretical foundation for wireless charging technology in the field of electric vehicles, which helps to improve the charging efficiency and convenience of electric vehicles.

A DRL-based Partial Charging Algorithm for Wireless Rechargeable Sensor Networks

Breakthroughs in Wireless Energy Transfer technologies have revitalized Wireless Rechargeable Sensor Networks. However, how to schedule mobile chargers rationally has been quite a tricky problem. Most of the current work does not consider the variability of scenarios and how many mobile chargers should be scheduled as the most appropriate for each dispatch. At the same time, the focus of most work on the mobile charger scheduling problem has always been on reducing the number of dead nodes, and the most critical metric of network performance, packet arrival rate, is relatively neglected. In this article, we develop a DRL-based Partial Charging algorithm. Based on the number and urgency of charging requests, we classify charging requests into four scenarios. And for each scenario, we design a corresponding request allocation algorithm. Then, a Deep Reinforcement Learning algorithm is employed to train a decision model using environmental information to select which request allocation algorithm is optimal for the current scenario. After the allocation of charging requests is confirmed, to improve the Quality of Service, i.e., the packet arrival rate of the entire network, a partial charging scheduling algorithm is designed to maximize the total charging duration of nodes in the ideal state while ensuring that all charging requests are completed. In addition, we analyze the traffic information of the nodes and use the Analytic Hierarchy Process to determine the importance of the nodes to compensate for the inaccurate estimation of the node’s remaining lifetime in realistic scenarios. Simulation results show that our proposed algorithm outperforms the existing algorithms regarding the number of alive nodes and packet arrival rate.

Optimizing beamforming in IoT-WET: A symbiotic-based approach under imperfect channel state information

This study delves into the energy optimization problem in low-power Internet of Things (IoT) networks utilizing wireless energy transfer (WET) technology. In such IoT systems, conventional optimization schemes relying on complete knowledge of channel state information (CSI) are often irrelevant for practical implementation due to their high complexity and energy consumption. To address this challenge and enhance the fairness of energy harvesting among IoT devices with imperfect CSI, we propose a novel beamforming design based on the symbiotic organisms search (SOS) algorithm. Through comprehensive numerical analysis, we demonstrate the effectiveness of our proposed scheme in terms of computational complexity, convergence speed, and energy harvested at each IoT device. The results highlight the significant advantages of our approach compared to conventional methods, paving the way for practical and efficient energy optimization in IoT networks.

Review of energy supply techniques for ice in-situ thermal mining in lunar permanently shadowed regions

The Moon's polar regions have abundant water ice resources. The mining and utilization of lunar polar water ice could serve as a propellant and survival resource for human exploration of deep space. The concept of thermal mining has been proposed to extract water ice resources. There are advantages and disadvantages to different mining methods. The energy problems faced by in situ resource utilization on the Moon are of great concern. The electrolysis of water and the heating of weathered layers require a continuous supply of energy. Wired energy transfer at the lunar poles would be very costly for launching conductors from Earth to the Moon. Wireless energy transfer technology is also well-developed today and has promising applications in the energy supply of lunar thermal mining. This paper describes the different methods of thermal mining and the various ways of supplying energy using solar energy for thermal mining.

The Combined Strategy of Energy Replenishment and Data Collection in Heterogenous Wireless Rechargeable Sensor Networks

The wireless energy transfer technology provides an effective solution of the energy shortage of wireless sensor networks. However, facing with complicated network areas and various kinds of applications, using one type of mobile devices and one data transmission mode: mobile sink mode or multihop transmission, cannot satisfy the requirements of the wireless rechargeable sensor networks (WRSN). To address this, a novel data collection and relay strategy is proposed. Under this strategy, considering various kinds of traffic requirements and limitations of sensor nodes, we propose a jointly scheduling of moving vehicles (MVs) and unmanned aerial vehicles (UAVs) to adapt to the complex environment and improve network performance. Through planning charging path and designing data transmission policy, the proposed algorithm prolongs the network lifetime while guarantees the quality of service of heterogeneous traffics. As the differences in moving speed, charging rate, and the amount of responsible data, there is some spare time of the MV or the UAV in a cycle and a global charging. A collecting algorithm is proposed to further charge and collect data from the cluster out of the original path. Finally, the results verify that the proposed algorithm can effectively reduce the dead time of the network and improve the energy efficiency simultaneously.

Mobile Charging Scheduling Approach for Wireless Rechargeable Sensor Networks Based on Multiple Discrete-Action Space Deep Q-Network

Wireless energy transfer technology (WET)-enabled mobile charging provides an innovative strategy for energy replenishment in wireless rechargeable sensor networks (WRSNs), where the mobile charger (MC) can charge the sensors sequentially by WET according to the mobile charging scheduling scheme. Although there have been fruitful studies, they usually assume that all sensors will be charged fully once scheduled or charged to a fixed percentage determined by a charging upper threshold, resulting in low charging performance as they cannot adjust the charging operation on each sensor adaptively according to the real-time charging demands. To tackle this challenge, we first formulate the mobile charging scheduling as a joint mobile charging sequence scheduling and charging upper threshold control problem (JSSTC), where the charging upper threshold of each sensor can adjust adaptively. Then, we propose a novel multi-discrete action space deep Q-network approach for JSSTC (MDDRL-JSSTC), where MC is regarded as an agent exploring the environment. The state information observed by MC at each time step is encoded to construct a high-dimensional vector. Furthermore, a two-dimensional action is mapped to the charging destination of MC and the corresponding charging upper threshold at the next time step, using bidirectional gated recurrent units (Bi-GRU). Finally, we conduct a series of experiments to verify the superior performance of the proposed approach in prolonging the lifetime compared with the state-of-the-art approaches.

Principles and applications of wireless energy transmission

In this day and age, people's lives are inextricably linked to electricity, which is being used more and more in everyday life. The traditional transmission of electrical energy usually uses materials such as metal wires and cables as the transmission medium, and the losses and potential damage (line deterioration, tip discharges) caused by this transmission medium are inevitable. The introduction of radio has been very effective in reducing this situation, and the development of Wireless Power Transfer (WPT) technology has seen three major developments since then, with Tesla lighting a phosphorescent lamp at the Colombian World's Fair in 1893, igniting hopes for WPT. This new way of transmitting electrical energy has expanded the imagination of people in terms of power supply methods by making it possible to provide more freedom in situations where cable charging is not suitable.The working theory, fundamental design, and potential applications of wireless energy transmission technology are covered in this study. By doing this, it offers insight into the development and use of wireless energy transmission technology as it is now. In the end, it is envisaged that the market will adopt wireless energy transfer technology extensively.

Genetic algorithm‐based partial charging schedule of rechargeable sensor networks

SummaryThe use of rechargeable sensors is a promising solution for wireless sensor networks. On this type of network, mobile charging vehicles (MC) are used for charging sensors using wireless energy transfer (WET) technology. In on‐demand charging, a sensor transmits a charging request to the service station, and the MC visits the sensor to transfer energy. The key disadvantages of utilizing MC‐based WET are its high energy expenditure rate due to mobility, long service time, and slow charging rate. Because of these reasons, sensors deplete their energy and become dead before the MC reaches the requesting nodes to recharge. We have adapted a genetic algorithm‐based partial charging scheme to serve the charging requests. Our objective is to improve the survival ratio of the network. Using comprehensive simulations, we analyze the performance of our proposed method and compare it to two other existing approaches. The simulation results demonstrate that our proposed algorithm improves the survival ratio by up to 20 % by developing a dynamic energy threshold function for transmitting charging requests from the sensors and a partial charging schedule using a genetic algorithm.

Mobile Charging Sequence Scheduling for Optimal Sensing Coverage in Wireless Rechargeable Sensor Networks

In wireless rechargeable sensor networks (WRSNs), a novel approach to energy replenishment is offered by the utilization of mobile chargers (MCs), which charge nodes via wireless energy transfer technology. However, previous research on mobile charging schemes has commonly prioritized charging efficiency as a performance index, neglecting the importance of quality of sensing coverage (QSC). As the network scale increases, the MC’s charging power becomes unable to meet the energy needs of all nodes, leading to a decline in network QSC when nodes’ energy is depleted. To solve this problem, we study the problem of mobile charging sequence scheduling for optimal network QSC (MSSQ) and propose an improved quantum-behaved particle swarm optimization (IQPSO) algorithm. With the attraction of potential energy in quantum space, this algorithm will adaptively adjust the contraction expansion coefficient iteratively, leading to a global optimal solution for the mobile charging sequence. Extensive simulation results demonstrate the superiority of IQPSO over the widely used QPSO and Greedy algorithms in terms of network QSC, especially in large-scale networks.

WIRELESS ENERGY TRANSMISSION IN LIMITED POWER SYSTEMS. PROBLEM ANALYSIS AND WAYS TO SOLUTION

The development of wireless energy transfer technologies occurs in several directions, each of which is characterized by its own set of driving forces and factors motivating the development. These factors are different and must be explicitly taken into account in our analysis, because in the dynamically changing world of knowhow and high-tech innovative projects, there is a clear tendency to reduce the time distance between a technical idea and its commercially in demand implementation. The paper provides an analysis of existing approaches in wireless transmission of electrical energy (WPT). The choice of one of the promising directions in wireless energy transmission technologies is justified – energy transmission via microwave radio signals. The main problems in this area are formulated and the possibility of supplying energy to remote autonomous devices using low-power high-frequency signals is considered. The results of the analysis are aimed at developing working prototypes of wireless power transmission devices. The current level of development of the production of electronic components makes it possible to create devices with dimensions of several millimeters, while the dimensions of the final devices are determined by the dimensions of antennas and batteries.

IRS-Assisted Short Packet Wireless Energy Transfer and Communications

In this letter, we analyse and optimize an intelligent reflecting surface (IRS)-assisted ultra-reliable and low-latency communications (uRLLC) system supported by wireless energy transfer (WET) technology, in which short packets are used in both the WET and wireless information transfer (WIT) phases. We first present the statistical features of the signal-to-noise ratio (SNR) of the system. Then, we derive an approximate closed-form expression of the average packet error probability (APEP). Additionally, we optimize the channel uses in the WET and WIT phases to maximize the effective throughput (ET) of the system. Finally, the effectiveness of the proposed solution is verified by Monte-Carlo simulation.

Performance analysis for wireless-powered IoT networks with hybrid non-orthogonal multiple access

Aim: In this paper, we study a wireless-powered Internet of Things (IoT) network, where a hybrid access point (HAP) charges IoT devices with wireless energy transfer technology (WET) and collects their data by wireless information transfer (WIT). Methods: To improve spectral efficiency, we propose a hybrid non-orthogonal multiple access (NOMA)-based transmission scheme. On the one hand, NOMA technology is applied for WIT. On the other hand, when some devices transmit data, the HAP can simultaneously charge the other devices, namely concurrent WET and WIT, such that the other devices can harvest more energy to achieve a better rate with some rate loss of these devices due to interference. %During the transmission of some devices, the WET is simultaneously conducted, such that other devices can harvest more energy to achieve a better rate with a rate loss of these devices due to the interference. How to divide devices into the interference and non-interference groups, namely device grouping, and how to pair devices, e.g., device pairing, becomes critical issues in terms of the achieved network throughput and fairness. Results: We first formulate the network throughput maximization problem by optimizing the pairing and grouping policies. To simplify the analysis, we then investigate two specific hybrid NOMA-based transmission schemes. In the former, all devices are firstly paired based on the max-min criterion, where the "best" device is paired with the "worst" one, and then grouped in either ascending or descending order; this is referred to as the first-pairing-then-grouping (FPTG) scheme. In the latter, devices are first grouped and then paired; this is referred to as the first-grouping-then-pairing (FGTP) scheme. By applying the order statistics theory, we theoretically analyze the achieved network throughput and derive the corresponding pairing and grouping policies. Furthermore, we study the max-min fairness of the proposed hybrid NOMA-based scheme. Conclusion: Simulation results validate the significant improvement of the proposed hybrid NOMA-based scheme in terms of network throughput and fairness.

Smart Application Division and Time Allocation Policy for Computational Offloading in Wireless Powered Mobile Edge Computing

Limited battery life and poor computational resources of mobile terminals are challenging problems for the present and future computation-intensive mobile applications. Wireless powered mobile edge computing is one of the solutions, in which wireless energy transfer technology and cloud server’s capabilities are brought to the edge of cellular networks. In wireless powered mobile edge computing systems, the mobile terminals charge their batteries through radio frequency signals and offload their applications to the nearby hybrid access point in the same time slot to minimize their energy consumption and ensure uninterrupted connectivity with hybrid access point. However, the smart division of application into k subtasks as well as intelligent partitioning of time slot for harvesting energy and offloading data is a complex problem. In this paper, we propose a novel deep-learning-based offloading and time allocation policy (DOTP) for training a deep neural network that divides the computation application into optimal number of subtasks, decides for the subtasks to be offloaded or executed locally (offloading policy), and divides the time slot for data offloading and energy harvesting (time allocation policy). DOTP takes into account the current battery level, energy consumption, and time delay of mobile terminal. A comprehensive cost function is formulated, which uses all the aforementioned metrics to calculate the cost for all k number of subtasks. We propose an algorithm that selects the optimal number of subtasks, partial offloading policy, and time allocation policy to generate a huge dataset for training a deep neural network and hence avoid huge computational overhead in partial offloading. Simulation results are compared with the benchmark schemes of total offloading, local execution, and partial offloading. It is evident from the results that the proposed algorithm outperforms the other schemes in terms of battery life, time delay, and energy consumption, with 75% accuracy of the trained deep neural network. The achieved decrease in total energy consumption of mobile terminal through DOTP is 45.74%, 36.69%, and 30.59% as compared to total offloading, partial offloading, and local offloading schemes, respectively.

Research progress of air-cooled heat dissipation technology

Air cooling is a kind of cooling method, which uses air as a medium to cool objects that need to be cooled. It is usual to increase the surface area of the object that needs to be cooled, or to increase the rate of air flowing through the object per unit time, or to use the two methods. In recent years, there have been some emerging technologies that improve the heat dissipation efficiency by improving the heat transfer efficiency of the air-cooled front end. Based on the analysis of the current research trend of air-cooled heat dissipation technology, a detailed review of typical air-cooled heat dissipation technologies from three aspects of materials, structure and emerging technologies is expected to provide relevant references for the further research and optimization of laser wireless energy transfer technology.

An efficient partial charging scheme using multiple mobile chargers in wireless rechargeable sensor networks

The advent of mobile charging with wireless energy transfer technology has perpetuated the omnipresent wireless rechargeable sensor networks. The existing literature finds that on-demand recharging of the sensor nodes (SNs) can significantly improve the charging performance while using multiple charging vehicles (MCVs) and multi-node charging model. Most of the existing schemes ignore the heterogeneous rate of SNs’ energy consumption, partial charging of the SNs, and joint optimization of multiple network attributes and thus these schemes are deprived from the benefits of prolonging network lifetime to its maximum extent. To this end, this paper addresses all the aforementioned issues and proposes an on-demand multi-node charging scheme for the SNs following a partial charging model. The working of the proposed scheme is twofold. First, charging schedules of the MCVs are generated through optimal halting points by integrating non-dominated sorting genetic algorithm (NSGA-II) and multi-attribute decision making (MADM) approach. Then the charging time at each halting point is decided for the SNs with the help of a partial charging timer. We carry out extensive simulations on the proposed scheme and the results are compared with some existing schemes using various performance metrics. The results confirm the superiority of the proposed scheme over the existing ones.

Stochastic Latency Guarantee in Wireless Powered Virtualized Sensor Networks

How to guarantee the data rate and latency requirement for an application with limited energy is an open issue in wireless virtualized sensor networks. In this paper, we integrate the wireless energy transfer technology into the wireless virtualized sensor network and focus on the stochastic performance guarantee. Firstly, a joint task and resource allocation optimization problem are formulated. In order to characterize the stochastic latency of data transmission, effective capacity theory is resorted to study the relationship between network latency violation probability and the transmission capability of each node. The performance under the FDMA mode and that under the TDMA mode are first proved to be identical. We then propose a bisection search approach to ascertain the optimal task allocation with the objective to minimize the application latency violation probability. Furthermore, a one-dimensional searching scheme is proposed to find out the optimal energy harvesting time in each time block. The effectiveness of the proposed scheme is finally validated by extensive numerical simulations. Particularly, the proposed scheme is able to lower the latency violation probability by 11.6 times and 4600 times while comparing with the proportional task allocation scheme and the equal task allocation scheme, respectively.

Bioinspired Cooperative Wireless Energy Transfer for Lifetime Maximization in Multihop Networks

To extend the lifetime of a multihop network by addressing energy shortages in wireless nodes, we apply wireless energy transfer (WET) technology to the multihop transmission. Considering a linear multihop topology, we establish a system model for a WET-enabled multihop transmission and formulate an optimization problem that obtains the optimal WET time of each node to maximize the lifetime of multihop networks. To solve this problem, we adopt a flocking model inspired by the similarity between flocking behaviors and WET-enabled multihop transmissions. Applying the underlying principles of the flocking model, we propose a bioinspired cooperative WET (BiCoWET) algorithm, in which each node adjusts its own WET time to equalize the lifetime of all nodes in a distributed manner. Theoretical analysis verifies that the proposed BiCoWET algorithm achieves optimality with exponential convergence. The intensive simulation shows that the proposed BiCoWET outperforms the conventional multihop transmission methods without WET and maximizes the network lifetime by equalizing the lifetime of all nodes.

A deep reinforcement learning-based on-demand charging algorithm for wireless rechargeable sensor networks

Wireless rechargeable sensor networks are widely used in many fields. However, the limited battery capacity of sensor nodes hinders its development. With the help of wireless energy transfer technology, employing a mobile charger to charge sensor nodes wirelessly has become a promising technology for prolonging the lifetime of wireless sensor networks. Considering that the energy consumption rate varies significantly among sensors, we need a better way to model the charging demand of each sensor, such that the sensors are able to be charged multiple times in one charging tour. Therefore, time window is used to represent charging demand. In order to allow the mobile charger to respond to these charging demands in time and transfer more energy to the sensors, we introduce a new metric: charging reward. This new metric enables us to measure the quality of sensor charging. And then, we study the problem of how to schedule the mobile charger to replenish the energy supply of sensors, such that the sum of charging rewards collected by mobile charger on its charging tour is maximized. The sum of the collected charging reward is subject to the energy capacity constraint on the mobile charger and the charging time windows of all sensor nodes. We first prove that this problem is NP-hard. Due to the complexity of the problem, then deep reinforcement learning technique is exploited to obtain the moving path for mobile charger. Finally, experimental simulations are conducted to evaluate the performance of the proposed charging algorithm, and the results show that the proposed scheme is very promising.

A survey of video capsule endoscope system based on wireless energy transfer technology

The wireless capsule endoscope system allows clinicians to directly observe the image of the inner wall of the human gastrointestinal tract and obtain the most intuitive information at the lesion. Compared with traditional endoscopic detection technology, its painless, non-invasive, safe and convenient, full gastrointestinal detection features make this technology a research focus in the field of medical devices at home and abroad. As a new non-invasive detection technology for gastrointestinal diseases, the working time, image frame rate and image quality of the existing wireless capsule endoscope system cannot fully meet the needs in clinical applications. The cause of these problems lies in the capsule. The internal space of the speculum is limited, and only the button battery can be used as the energy source. Therefore, it is necessary to design a video capsule endoscope based on wireless energy transmission technology. This article is a review of Zhu Zhanquan, Xue Kaifeng, Lu Ruiqi and other scholars.

The Charging Strategy of Mobile Charging Vehicles in Wireless Rechargeable Sensor Networks With Heterogeneous Sensors

Energy shortage obstructs the applications of the wireless rechargeable sensor network (WRSN). With the development of the wireless energy transfer technology, the mobile wireless charging vehicle (WCV) becomes a promising solution to solve that problem. However, the importance of different sensor nodes in the data transmission and uneven energy consumptions are often ignored. In this paper, the charging strategy of the WCV is studied in the WRSN considering these two phenomena. According to the importance of the sensor node, which is associated with the distance to the base station, we divide sensor nodes into two types: sensor nodes in ring 0 and sensor nodes in outer ring. We propose a novel charging model, the WCV adopts different charging strategies for different sensor nodes. To make the charging more efficient, the WCV charges sensor nodes one by one in ring 0 first, and then charges multiple sensor nodes simultaneously in outer ring. To estimate the lifetime of the network, a new metric named as the normalized dead time is proposed. Maximizing the lifetime of the network is modeled as minimizing the sum normalized dead time, and an efficient algorithm is proposed to minimize the sum normalized dead time through searching the optimal charging timeslots sequences. Then, through reassigning charging timeslots of sensor nodes, the proposed minimum travel cost algorithm minimizes the travel distance of the WCV and guarantee the lifetime of the network. We further deploy a cluster head node which has larger battery capacity in each cluster and can charge other sensor nodes within a limited distance. An algorithm is proposed to pre-distribute energy of the cluster head node. At last, the performance of proposed algorithms is verified by MATLAB. The results indicate that the performance of the WRSN can be improved by our proposed algorithms.