Energy Harvesting Model Research Articles

Simultaneous Wireless Information and Power Transfer in mmWave Networks Under User-Centric Base Station Clustering

User-centric base station (BS) deployment has been designed for the fifth-generation (5G) dense millimeter wave (mmWave) networks for alleviating the inter-cell interference and improving the cell-edge user experience. However, the system power consumption increases sharply with the network density. In this paper, we investigate a user-centric simultaneous wireless information and power transfer (SWIPT) mmWave system employing a time-switching protocol at users to allow both energy harvesting (EH) and data decoding. To enable user-centric BS cooperation, adaptive BS clustering model is used to determine the user’s serving cluster based on its channel condition. Considering both linear and non-linear EH models, we analyze the joint coverage, namely, the probability that the user harvests enough energy in a given time slot and receives the required data from its serving cluster. The random serving clusters and the correlation between the amount of harvested energy and received data rate make the joint coverage analysis more challenging. A tractable tight approximation of the joint coverage probability is thus derived for ultra-dense networks. A mathematical optimization model for the time switching coefficient is also developed to maximize the system joint rate and energy coverage performance. All mathematical expressions are validated by Monte-Carlo simulations. Our results show that the proposed analytical framework is accurate and efficient for the design and deployment of SWIPT-enabled user-centric mmWave networks.

Achieving Multi-Beam Gain in Intelligent Reflecting Surface Assisted Wireless Energy Transfer

Intelligent reflecting surface (IRS) is a promising technology to boost the efficiency of wireless energy transfer (WET) systems. However, for a multiuser WET system, simultaneous multi-beam energy transmission is generally required to achieve the maximum performance, which may not be implemented by using the IRS having only a single set of coefficients. As a result, it remains unknown how to exploit the IRS to approach such a performance upper bound. To answer this question, we aim to maximize the total harvested energy of a multiuser WET system subject to the user fairness constraints and the non-linear energy harvesting model. We first consider the static IRS beamforming scheme, which shows that the optimal IRS reflection matrix obtained by applying semidefinite relaxation is indeed of high rank in general as the number of energy receivers (ERs) increases, due to which the resulting rank-one solution by applying Gaussian Randomization may lead to significant loss. To achieve the multi-beam gain, we then propose a general time-division based novel framework by exploiting the IRS's dynamic passive beamforming. Moreover, it is able to achieve a good balance between the system performance and complexity by controlling the number of IRS shift patterns. Finally, we also propose a time-division multiple access (TDMA) based passive beamforming design for performance comparison. Simulation results demonstrate the necessity of multi-beam transmission and the superiority of the proposed dynamic IRS beamforming scheme over existing schemes.

Foot Step Electric Power Generation

The reduction in energy demand by portable electronics has rekindled interest in the use of renewable energy sources in the built environment. This technological study focuses on a sophisticated piezoelectric material energy harvesting method. Piezoelectric materials can be used to convert mechanical energy, such as environmental vibrations, into electrical energy, which can then be stored and used to power other devices. When mechanical stress is applied to a piezoelectric material, it creates an electrical charge. However, when an electric field is introduced, mechanical deformation occurs. The electrical density generated on the piezo can be stored in a rechargeable battery for later use. Piezoelectric materials offer a wide range of applications in real life. Following are some of the latest applications. Following are some of the latest applications. Alternative forms of energy are currently needed in travel terminals such as airports and railways around the world. Keeping prices low, maintaining friendly and productive relations with neighbors and ensuring a healthy environment for future generations requires cleaner and more sustainable electricity. Piezoelectric devices installed in the terminals make it possible to recover the kinetic energy of pedestrian traffic. That energy can then be used to balance some of the electricity needed to power the lighting systems. Low power electrical appliances have become more common in recent years. Technologies are widely used to make our daily lives easier. The idea of looking for an alternative renewable energy for the human environment has piqued our curiosity, as the energy consumption of these portable electronic devices has increased. I am trying to create a piezoelectric generator in this project. It can generate energy from the vibration and pressure found in the second sentence (like people walking). This study describes the use of piezoelectric materials to capture the energy of walking people, creating and harvesting vibrational energy. This approach can also be applied to massive vibration sources found in nature. This project is also the first step towards a piezoelectric energy harvesting model that is both cost-effective and easy to install.

An Accurate Magnetic Equivalent Circuit Model for Modeling and Analysis of Variable Reluctance Energy Harvesters

An Accurate Magnetic Equivalent Circuit Model for Modeling and Analysis of Variable Reluctance Energy Harvesters

Energy Efficiency Optimization for MISO-NOMA SWIPT System with Heterogeneous QoS Requirements

This letter solves the energy efficiency optimization problem for the simultaneous wireless information and power transfer (SWIPT) systems with non-orthogonal multiple access (NOMA), multiple input single output (MISO) and power-splitting structures, where each user may have different individual quality of service (QoS) requirements about information and energy. Nonlinear energy harvesting model is used. Alternate optimization approach is adopted to find the solution, which shows a fast convergence behavior. Simulation results show the proposed scheme has higher energy efficiency than existing dual-layer iteration and throughput maximization methods.

Wireless-powered backscatter communications for smart sustainable cities: A secrecy analysis

Due to its ability to enable self-sustainable communications, wirelessly powered backscatter communication (WP-BackCom) has been deemed a promising technology for supporting smart sustainable cities. We note that the existing works on analyzing the secrecy performance of WP-BackComs have largely ignored the spatial randomness of nodes that is used to model a large-scale network. To fill this gap, this paper evaluates the secrecy outage probability of the large-scale WP-BackComs based on a tractable framework of stochastic geometry (SG). In particular, the imperfect successive interference cancellation (SIC) at each gateway (GW) and eavesdropper (Eve), and the non-linear energy harvesting (EH) model and energy-causality constraint at each backscatter user (BU) are considered. The energy outage probability and secrecy outage probability expressions are derived in the general case at first, then the closed-form expressions are obtained under the special scenario, i.e., the no-noise and the typical propagation loss scenario. Numerical simulations validate the analytical results and examine the impacts of various parameters on the secrecy performance. In particular, there exists an optimal reflection coefficient for secrecy outage probability minimization under certain conditions, and the optimal reflection coefficient increases with the increasing SIC coefficient at GW.

Long-Range Optical Wireless Information and Power Transfer

Simultaneous wireless information and power transfer (SWIPT) is a remarkable technology to support both the data and the energy transfer in the era of Internet of Things (IoT). In this article, we proposed a long-range optical wireless information and power transfer system utilizing retro-reflectors, a gain medium, a telescope internal modulator to form the resonant beam, achieving high-power and high-rate SWIPT. We adopt the transfer matrix, which can depict the beam modulated, resonator stability, transmission loss, and beam distribution. Then, we provide a model for energy harvesting and data receiving, which can evaluate the SWIPT performance. Numerical results illustrate that the proposed system can simultaneously supply 0–9 W electrical power and 18-bit/s/Hz spectral efficiency over 20-m distance.

An Accurate Model of Magnetic Energy Harvester in the Saturated Region for Harvesting Maximum Power: Analysis, Design, and Experimental Verification

To reduce the size and weight of the magnetic energy harvester (MEH), the magnetic core should be designed to work in the maximum power region, i.e., in the saturated region. However, the conventional analysis model of the MEH, which ignores the phase difference between the primary and secondary currents, is only suitable for the unsaturated region. When the core is in the saturated region, the results of the numerical calculation severely deviate from the experimental observations due to the non-negligible phase difference. To address this vital problem, this article establishes an excitation current model (ECM) to calculate the phase difference caused by the magnetizing inductance. An accurate model of the MEH is presented based on the ECM in this article, which can calculate the harvesting power of the MEH in the saturated region. Besides, a strategy for finding the maximum power point is proposed, which can be used for the designing of the MEH. Finally, an experimental prototype is constructed to verify the effectiveness of the proposed analysis model and the design method of the MEH. The experimental results show that the proposed analysis model can maintain high accuracy when the magnetic core enters the saturated region, and the MEH designed by the proposed model can reach the expected power with a compact size. Compared with the theoretical value, the output voltage deviation of the MEH is only 0.4%, and the output power deviation is only 0.8%.

Circuit-Level Modeling and Simulation of Wireless Sensing and Energy Harvesting With Hybrid Magnetoelectric Antennas for Implantable Neural Devices.

A magnetoelectric antenna (ME) can exhibit the dual capabilities of wireless energy harvesting and sensing at different frequencies. In this article, a behavioral circuit model for hybrid ME antennas is described to emulate the radio frequency (RF) energy harvesting and sensing operations during circuit simulations. The ME antenna of this work is interfaced with a CMOS energy harvester chip towards the goal of developing a wireless communication link for fully integrated implantable devices. One role of the integrated system is to receive pulse-modulated power from a nearby transmitter, and another role is to sense and transmit low-magnitude neural signals. The measurements reported in this paper are the first results that demonstrate simultaneous low-frequency wireless magnetic sensing and high-frequency wireless energy harvesting at two different frequencies with one dual-mode ME antenna. The proposed behavioral ME antenna model can be utilized during design optimizations of energy harvesting circuits. Measurements were performed to validate the wireless power transfer link with an ME antenna having a 2.57 GHz resonance frequency connected to an energy harvester chip designed in 65nm CMOS technology. Furthermore, this dual-mode ME antenna enables concurrent sensing using a carrier signal with a frequency that matches the second 63.63 MHz resonance mode. A wireless test platform has been developed for evaluation of ME antennas as a tool for neural implant design, and this prototype system was utilized to provide first experimental results with the transmission of magnetically modulated action potential waveforms.

Modeling and analysis of directional energy harvesting and spectrum sharing communications in massive D2D networks

This paper considers both Energy Harvesting and Spectrum Sharing in Massive Device-to-Device Networks (EH-SS-MD2D Nets) to realize resource-utilization efficiency, where massive D2D networks consist of a large number of low-power devices that sporadically transmit a small volume of data. In contrast to previous study investigating EH-SS networks equipped with omni-directional antennas, we employ directional antennas in our EH-SS-MD2D Nets (namely directional EH-SS-MD2D Nets) to improve network performance. In particular, we develop a theoretic model to analyze both the transmission probability and connection probability of Second Users (SUs) in directional EH-SS-MD2D Nets. Our theoretic model is general for both EH-SS-MD2D Nets equipped with directional antennas and omni-directional antennas. All of our theoretic results are presented in closed-form solutions. Our analytical model is verified by extensive simulations. Numerical results indicate that EH-SS-MD2D Nets equipped with directional antennas can significantly improve the connection probability compared with omni-directional antennas. In addition, results show that the connection probability can be maximized by choosing an optimal density of PTs. Moreover, the connection probability of directional EH-SS-MD2D Nets can be improved by decreasing the antenna beamwidth of the main lobe of SUs. Our model and results can offer beneficial implications for constructing directional EH-SS-MD2D Nets.

Optimization for IRS-Assisted MIMO-OFDM SWIPT System With Nonlinear EH Model

Simultaneous wireless information and power transfer (SWIPT) has emerged as an appealing solution to prolonging the lifetime of low-power Internet of Things (IoT) networks. Meanwhile, an intelligent reflecting surface (IRS) can reconstruct a favorable wireless propagation environment for IoT terminals to achieve high spectrum and energy efficiencies. To take full advantage of these two technologies, this article studies the optimization of an IRS-assisted multiple-input and multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) SWIPT system with nonlinear energy harvesting (EH) model. In particular, we aim to maximize the achievable data rate by jointly designing the transmit precoding matrices, the IRS matrix, as well as the power splitting (PS) ratio subject to the transmit power and harvested power constraints. Since the formulated problem is highly nonconvex, we develop an alternating optimization (AO)-based algorithm to find a high-quality suboptimal solution. Moreover, we further design a heuristic algorithm based on a two-stage optimization strategy to reduce the computational complexity. Simulation results verify that the proposed AO-based algorithm can significantly improve the achievable data rate compared to conventional benchmarks, and the proposed heuristic low-complexity algorithm can achieve comparable performance to the AO-based algorithm.

Secure MIMO systems with nonlinear energy harvesting using optimal transmit antenna selection over [formula omitted] fading channels

This paper investigates the secrecy performance of multiple-input multiple-output systems with a nonlinear energy harvesting (EH) scheme used at the transmitter to improve energy efficiency and extend the lifetime of energy-constrained systems. Most of the current literature investigates cognitive radio networks with linear EH model. However, in order to realize more realistic scenarios due to hardware constraints, such as the saturation and sensitivity characteristics of the EH components, a nonlinear EH model is examined here and compared with a simpler linear EH scheme. In particular, a practical nonlinear energy harvester model with a source, a destination, an active eavesdropper, and a power beacon is considered under generalized α−μ fading conditions. Here, the eavesdropper can overhear the data transmission messages when the source communicates with the destination using wiretap channels. Furthermore, the source employs all its antennas to combine the radio frequency signals emitted by the power beacon via the maximal ratio combining (MRC) scheme. Moreover, the MRC scheme is used at the destination and eavesdropper to improve the signal’s quality (i.e., at the receiver side). Closed-form expressions for the lower bound and asymptotic security performance are investigated. Next, optimal antenna selection (OAS) scheme and traditional space–time transmission (STT) scheme are compared based on whether the channel state information (CSI) of the wiretap link is available. In addition to deriving the secrecy diversity order and secrecy array gain, asymptotic closed-form analytical expressions for security performance and closed-form analytical expressions for the probability of strictly positive secrecy capacity are also derived for various selection schemes. Finally, numerical results indicate that, compared to the linear EH scheme with just minimal trade-offs, the nonlinear EH scheme represents more realistic and dependable outcomes.

HBMFTEFR: Design of a Hybrid Bioinspired Model for Fault-Tolerant Energy Harvesting Networks via Fuzzy Rule Checks

Designing energy harvesting networks requires modelling of energy distribution under different real-time network conditions. These networks showcase better energy efficiency, but are affected by internal & external faults, which increase energy consumption of affected nodes. Due to this probability of node failure, and network failure increases, which reduces QoS (Quality of Service) for the network deployment. To overcome this issue, various fault tolerance & mitigation models are proposed by researchers, but these models require large training datasets & real-time samples for efficient operation. This increases computational complexity, storage cost & end-to-end processing delay of the network, which reduces its QoS performance under real-time use cases. To mitigate these issues, this text proposes design of a hybrid bioinspired model for fault-tolerant energy harvesting networks via fuzzy rule checks. The proposed model initially uses a Genetic Algorithm (GA) to cluster nodes depending upon their residual energy & distance metrics. Clustered nodes are processed via Particle Swarm Optimization (PSO) that assists in deploying a fault-tolerant & energy-harvesting process. The PSO model is further augmented via use of a hybrid Ant Colony Optimization (ACO) Model with Teacher Learner Based Optimization (TLBO), which assists in value-based fault prediction & mitigation operations. All bioinspired models are trained-once during initial network deployment, and then evaluated subsequently for each communication request. After a pre-set number of communications are done, the model re-evaluates average QoS performance, and incrementally reconfigures selected solutions. Due to this incremental tuning, the model is observed to consume lower energy, and showcases lower complexity when compared with other state-of-the-art models. Upon evaluation it was observed that the proposed model showcases 15.4% lower energy consumption, 8.5% faster communication response, 9.2% better throughput, and 1.5% better packet delivery ratio (PDR), when compared with recently proposed energy harvesting models. The proposed model also showcased better fault prediction & mitigation performance when compared with its counterparts, thereby making it useful for a wide variety of real-time network deployments.

Selection of piezoeloectric material and fiber volume fraction to maximize the electrical power produced by macro-fiber composite energy harvesters

A piezoelectric Macro fiber composite (MFC) materials developed in the last decade found widespread applications such as vibration sensing, actuation and structural health monitoring. Unlike conventional ceramic piezoelectric materials (PZT), these composites show many advantages such as flexibility, reliability and high actuation capacity. However, increasing their energy harvesting capabilities still remains a challenge. Modeling and simulation of electrical energy harvesters using MFC patches provide a mean for geometrical optimization and appropriate choice of the MFC composite. This paper proposes a linear analytical model for prediction of the electro-mechanical response of bimorph harvesters using MFC patches. Homogenization technique is used to describe the equivalent piezoelectric properties of the composite structure of the MFC patch. This paper investigates the effect of the volume fraction of the fibers and the material choice of the piezoelectric fibers and epoxy matrix on the generated electrical power. It has been found that an increased fiber volume fraction causes a decrease of the voltage, the power and the velocity amplitudes for a range of load resistance. However, an increase in the fiber volume fraction FVF is achieved by an increase of the current amplitude for a range of load resistance. Maximum amount of power is generated by the bimorph MFC harvester for an FVF of 86% which corresponds to the reference configuration. The piezoelectric fiber material has a significant influence on the output power. In fact, the SONOX-P502 generated the greatest electrical power. However, the material of the matrix has a negligible effect on the generated power.

Power Minimization in Multi-Antenna Transmitter Aided Bidirectional Tag-to-Device Communications for IoT

Bidirectional device-to-device (D2D) communications with energy-constrained devices hold the potential to revolutionize the low-power communication landscape and provide a means to realize the aim of Internet-of-Things (IoT). In this paper, we aim to explore a communication environment where a multi-antenna transmitter facilitates communication between two users equipped with two different technologies — backscattering and radio frequency based energy harvesting. Under such a setting, we aim to minimize the transmit power over a set of constraints by considering the traditional linear and the more practical nonlinear (sigmoid) energy harvesting model. Henceforth, we propose a novel optimal solution approach, exploiting the geometric interpretation of constraints and interdependency of optimization variables and the performance metric, to solve the formulated non-convex problem for the multi-antenna transmitter case for both the energy harvesting models considered. In addition, we revisit the power minimization problem for the single-antenna transmitter case, solved in our previous work, to demonstrate the applicability of the current solution approach. Furthermore, to establish a fair balance between the performance and the computation-intensive nature of the proposed optimal solution, we present two low complexity sub-optimal solution approaches leveraging the analysis carried out for the single antenna transmitter case. Finally, using the numerical simulations, we assess the performance of our system and present a comparison between the proposed solution approaches by varying some key parameters of our simulation setting.

Optimal Design of a Novel Piezoelectric Vibration Energy Harvester

A novel piezoelectric vibration energy harvester structure is designed. The output voltage is employed as the objective function, and the structural parameters of the vibration energy harvester device are optimized by Taguchi method. The best parameter combination of A3(95mm), B3(45mm), C1(60mm), D2(35mm) and E1(50mm) is determined. Through the analysis of variance, it is concluded that parameter A has the most obvious effect on the response of the piezoelectric energy harvester, with the contribution rate reaching 26.02%, and the influences of B, C, D and E decrease successively. The finite element model of the piezoelectric vibration energy harvester is established, then the load impedance matching characteristics and acceleration dependence of voltage are discussed.

Generalized Analysis Method for Magnetic Energy Harvesters

Large-scale deployment of low-power sensing and computing units calls for unique power management solutions to overcome the inconvenience, costs, and waste problems associated with batteries. Energy harvesting offers an exciting solution to the battery problem, enabling circuits that can power themselves on-site from available ambient energy. Magnetic energy harvesters (MEHs), configured as current transformers, extract energy from the magnetic fields surrounding current-carrying power lines. As maximum power harvest occurs when a magnetic core is on the verge of saturation or saturated to some degree, modeling of magnetic energy harvesters is inherently difficult and nonlinear. This article proposes generalized analytical methods for modeling magnetic energy harvester behavior and validates these methods along with existing circuit model techniques. Intuition for core saturation behavior is presented and agreement with existing models is discussed. The analysis is motivated by addressing the feasibility of a split core magnetic energy harvester to power a microcontroller unit, and the models are experimentally validated for multiple harvester cores.

Rate-Splitting Multiple Access in a MISO SWIPT System Assisted by an Intelligent Reflecting Surface

Rate-splitting multiple access (RSMA) and the intelligent reflecting surface (IRS) are novel technologies to improve spectrum and energy efficiencies in multi-antenna networks. In this paper, we investigate a multi-user multiple-input single-output (MISO) simultaneous wireless information and power transfer (SWIPT) system assisted with an IRS and using RSMA. In particular, the IRS assists with data and energy transmission from the base station (BS) to power-splitting (PS) users under a non-linear energy harvesting (EH) model. Our objective is to minimize the transmission power subject to rate and EH requirements at the users. To solve the non-convex problem, we propose a bilevel programming problem in which a genetic algorithm is used to solve the outer optimization problem, and the inner optimization problem is addressed with the semidefinite relaxation (SDR) method. In addition, to study a practical scenario of imperfect channel state information (CSI), we consider a robust beamforming design based on imperfect cascade channels at the BS. Then, we propose a solution based on the S-procedure with the SDR technique. Simulation results show that our proposed RSMA-based scheme provides a significant reduction in transmission power compared to the space-division multiple access (SDMA) and non-orthogonal multiple access (NOMA) methods under perfect and imperfect CSI.

A single-degree-of-freedom mathematical model for energy harvesting from cavity flow oscillations

Flow-induced vibrations have proven to be a valuable power source for wireless sensors and MEMS devices. In this study, the energy harvesting aspects of an open cavity flow is explored and modelled as a single-degree-of-freedom system. Experiments with flow over a cavity of length-to-depth ratio of 3, with an incoming velocity of 30 m/s was taken as a case study to validate the model. It was observed in experiments that three distinct flow oscillation frequencies of significant amplitudes, corresponding to the different modes of oscillation were generated. To harvest this oscillatory power, a piezoelectric cantilever beam, mounted on the aft wall was subjected to unsteady pressure forces. To harvest efficiently, the natural frequency of the cantilever beam was tuned to lock in with the flow oscillation frequencies. The frequencies of cavity oscillations agreed well with Rossiter’s model in the experiments. A single-degree-of-freedom system assuming lumped parameters was used for modelling. The forcing term was described using a periodic function that uses empirical values for amplitude. The model was able to predict the overall trend and values of the average power and RMS voltage generated across a resistor load with reasonable accuracy. Using the model, the effects of cavity length, incoming velocity and resonant frequency were also studied.

Joint Communication and Trajectory Design for Intelligent Reflecting Surface Empowered UAV SWIPT Networks

Aiming at the limited battery capacity of widely deployed low-power smart devices in the Internet-of-things (IoT), this paper proposes a novel intelligent reflecting surface (IRS) empowered unmanned aerial vehicle (UAV) simultaneous wireless information and power transfer (SWIPT) network framework, in which IRS is used to reconstruct the wireless channel to enhance the wireless energy transmission efficiency and coverage area of the UAV SWIPT networks. In this paper, we formulate an achievable sum-rate maximization problem by jointly optimizing UAV trajectory, successive interference cancellation (SIC) decoding order, UAV transmit power allocation, power splitting (PS) ratio and IRS reflection coefficient while taking account of user non-orthogonal multiple access (NOMA) and a non-linear energy harvesting model. Due to the coupling of optimization variables, this problem is a complex non-convex optimization problem, and it is challenging to solve it directly. We first transform the problem, and then apply the alternating optimization (AO) algorithm framework to divide the transformed problem into four sub-problems to solve it. Specifically, by applying successive convex approximation (SCA), penalty function method and difference-convex (DC) programming, UAV trajectory, SIC decoding order, UAV transmit power allocation, PS ratio and IRS reflection coefficient are alternately optimized until the convergence is achieved. Numerical simulation results verify the effectiveness of our proposed algorithm compared to other algorithms.