Energy Harvesting Model Research Articles

New Results for Pearson Type III Family of Distributions and Application in Wireless Power Transfer

The Pearson type III and the log Pearson type III distributions have been considered in several scientific fields, as in hydrology and seismology. In this article, we present new results for these distributions and we utilize them, for the first time in the literature, to investigate the statistical behavior of wireless power transfer, which can prolong the lifetime of Internet of Things networks, considering the nonlinear relationship between the received and harvested power, which can be precisely modeled by using the logistic function. Specifically, we present new closed-form expressions for the statistical properties of a general form of the Pearson type III and the log Pearson type III distributions and we utilize them to introduce a new member of the Pearson type III family, the logit Pearson type III distribution, through which the logit gamma and the logit exponential distributions are also defined. Moreover, we derive closed-form expressions for the probability density function, the cumulative distribution function and moments of the distributions of the sum, the log sum, and the logit sum of Pearson type III random variables. Furthermore, taking into account that the Pearson type III family of distributions is closely related to the considered nonlinear energy harvesting model the statistical properties of the distribution of the harvested power are derived, for both single input single output and multiple input single output scenarios with or without channel state information at the transmitter.

Towards carbon Neutrality: Prediction of wave energy based on improved GRU in Maritime transportation

Efficient use of renewable energy is one of the critical measures to achieve carbon neutrality. Countries have introduced policies to put carbon neutrality on the agenda to achieve relatively zero emissions of greenhouse gases and to cope with the crisis brought about by global warming. This work analyzes the wave energy with high energy density and wide distribution based on understanding of various renewable energy sources. This study provides a wave energy prediction model for energy harvesting. At the same time, the Gated Recurrent Unit network (GRU), Bayesian optimization algorithm, and attention mechanism are introduced to improve the model’s performance. Bayesian optimization methods are used to optimize hyperparameters throughout the model training, and attention mechanisms are used to assign different weights to features to increase the prediction accuracy. Finally, the 1-hour and 6-hour forecasts are made using the data from China's NJI and BSG observatories, and the system performance is analyzed. The results show that, compared with mainstream prediction algorithms, GRU based on Bayesian optimization and attention mechanism has the highest prediction accuracy, with the lowest MAE of 0.3686 and 0.8204, and the highest R2 of 0.9127 and 0.6436, respectively. Therefore, the prediction model proposed here can provide support and reference for the navigation of ships powered by wave energy.

Dynamic modeling and analysis of a tristable vortex-induced vibration energy harvester

Wind-induced vibration energy harvesters with unique dynamic characteristics and excellent energy harvesting performance have received increasing attention. This study establishes a dynamic model of a tristable vortex-induced vibration energy harvester (TVIVEH) and analyzes the output voltage and dynamic characteristics numerically and experimentally. The results indicate that the working wind speed range can be widened and adjusted using a magnetic-coupled tristable configuration. More importantly, it is found that the TVIVEH has a multi-solution wind speed range, and the vibration orbit and output voltage at the same wind speed are significantly different under different initial conditions. At the multi-solution wind speed range, the TVIVEH under appropriate initial conditions can potentially realize vibration at the high-energy orbit, which can improve the energy harvesting efficiency. Overall, this study provides a framework for the analysis and the design of magnetically coupled wind-induced vibration energy harvesters.

Smart dual battery management system for expanding lifespan of wireless sensor node

AbstractWireless sensor nodes have huge energy demand for their operations; they are deployed in remote locations for various applications like weather, industrial, satellite, construction, banking, and medical. Sensor nodes require continuous or uninterrupted power supply during their life cycles. When the available renewable power sources are not sufficient to run the system, the batteries are required to deliver a continuous and uninterrupted power supply. The main focus of proposed model is to design and develop a smart dual battery management system along with a hybrid energy harvesting model that can provide reliable and efficient power support to the sensor node. The problem under consideration also focuses on reducing the state of health degradation of batteries by applying a smart battery charging methodology using an ANFIS (adaptive neuro‐fuzzy inference system) controller. The proposed power management system ensures and meets the expected objectives such as switching of power sources, smart battery charging methodology (constant current and constant voltage [CC‐CV]), and dual battery power support using ANFIS controller. The result was obtained through the simulation and hardware prototype of the proposed system work flawlessly to meet the desired objective with partial charging and discharging of batteries for the prevention of battery degradation and also enhance the lifespan of the batteries.

Design of a GSM 900 Energy Harvesting Model for a Remote-Control Device

Design of a GSM 900 Energy Harvesting Model for a Remote-Control Device

Applicability of magnetic force models for multi-stable energy harvesters

Multi-stable piezoelectric energy harvesters have been exploited to enhance performance for extracting ambient vibrational energy from a broadband energy source. Since magnetic force plays a significant role in enhancing the dynamic behavior of harvesters, it is necessary to model and understand the significant influencing of structural parameters on magnetic force. Recently, several theoretical modeling methods, including magnetic dipole, improved dipole, magnetic current, and magnetic charge models, have been developed to calculate the magnetic force in multi-stable energy harvesters. However, the influence of structural parameters and magnet dimensions on the accuracy of magnetic force calculation for these methods has not been analyzed. Therefore, it is necessary to investigate the applicability of these methods under a range of operating conditions. New insights into the accuracy and application constraints of these methods are presented in this paper to calculate the impact of magnetic force on multi-stable energy harvesters. From the theoretical derivation of models and numerical results obtained, a quantitative assessment of errors under different structural parameters and magnet sizes is presented and compared to evaluate the application constraints. Moreover, experimental measurements are performed to verify the applicability of these modeling methods for bi-stable and tri-stable energy harvesters with different structural parameters.

Efficient bubble energy harvesting by promoting pressure potential energy release using helix flow channel

The bubble energy harvesting system can provide in-situ energy supply for the subsea observation equipment through two stages of energy conversion. However, the conversion efficiency of bubble potential energy to fluid energy in the first stage is much lower than that of fluid energy to electrical energy in the second stage, limiting the output capability of the bubble energy harvesting system. Here, a complete energy transfer and conversion model for the subsea bubble energy harvesting is established for the first time. Regardless of the type of transducer used in the system, increasing the released pressure potential energy can substantially improve the efficiency of bubble energy harvesting. Based on this mechanism, we proposed a method to improve the released pressure potential energy and verified the results using a bubble energy harvesting system with a 3D streamline. The flow velocity and fluid volume of the system can be controlled by adjusting the helix angle and cylindrical diameter. Compared with the original system, the fluid pressure potential energy released of a 3D-system with a helix angle of 50 degrees can increase by at least 26%. The output energy density of the 3D-system with a height of 1.6 m can reach 6.6 J/m3, which is 59 times higher than that of the existing technology. This technology can efficiently harvest energy from subsea gas sources, and has the potential to become a long-term in-situ power supply solution for subsea observation networks.

Multi-physics modeling of piezoelectric energy harvesters from vibrations for improved cantilever designs

This study investigated piezoelectric cantilevers for energy harvesting using multi-physics modeling. The cantilevers with multiple degree-of-freedoms (DOFs) were designed with higher potential to match multiple-frequency vibrations of structures. Finite element models (FEMs) of cantilevers with different design parameters were built and verified with laboratory measurements. As results, multiple vibration modes with different bending conditions were captured by FEM outputs under resonant frequencies. The parametric analysis was performed to analyze the effects of cantilever length, width, thickness and mass. It was found that adjusting the cantilever design acquired the resonant frequencies in a wide range of 5 Hz–35 Hz, which potentially fitted the vibration scenario of a typical bridge structure. Under the cantilever design principle proposed in this study, as the DOF was increased, the maximum voltage outputs or the power outputs still remained the same level, respectively at 30 V or 5 mW, with no extra coverage area required from the multiple-DOF cantilever. All trends of resonant frequency changes via design parameter adjustments captured in this study will also serve as references for achieving specific design optimization strategies on the multiple-DOF cantilever designs, which will be varied based on the structure vibration scenarios in the field.

Parametric Analysis of Nonlinear Bi-Stable Piezoelectric Energy Harvester Based on Multi-Scale Method

Given the geometric nonlinearity of the piezoelectric cantilever beam, this study establishes a distributed parameter model of the nonlinear bi-stable cantilever piezoelectric energy harvester, following the generalized Hamilton variational principle. The analytical expressions of the dynamic response were obtained for the energy harvesting system using Galerkin modal decomposition and the multi-scale method. The investigation focuses on how the performance of the energy harvesting system is influenced by the gap distance between magnets, external excited amplitude, mechanical damping ratio and external load resistance. The calculation results were compared with those obtained neglecting the geometric nonlinearity of the beam. The results show that the system responses contain jump and multiple solutions. The consideration of the geometrical nonlinearity significantly amplified the peak displacement and peak output power of the intra-well and inter-well motions. There is an evident hardening effect of the inter-well motion frequency response curve. By reasonable adjusting the parameters, it is possible to improve the output power of the piezoelectric energy harvesting system and broaden the operating frequency of the system.

Energy efficient transceiver design for SWIPT systems with non-orthogonal multiple access and power splitting

This paper studies the energy efficient (EE) optimization for the simultaneous wireless information and power transfer (SWIPT) systems combined with non-orthogonal multiple access for multi-user support. Power splitting at receiver is adopted with nonlinear energy harvesting (EH) model. Our goal is to pursue high EE for information transmission by jointly design the transmit beamforming and power splitting coefficients while satisfying quality of service requirements, such as maximum transmit power budget, minimum data rate, and minimum harvested energy per terminal. The coupling of variables make it the non-convex even with linear EH models, which is difficult to solve. Firstly, after approximating the nonlinear EH model in the optimization objective by linear one, the nonlinear fractional function is transformed into a linear subtractive form with two groups of coupled variables. After transformations, the original problem is approximately solved in an alternate way. Through the iterative procedure, semi-definite relaxation and arithmetic–geometric mean inequality are adopted to deal with the non-convex constraints. Simulation results reveal that our proposed method has a faster convergence speed and a higher energy efficiency than the benchmarks.

Geometric nonlinear model and optimization of vortex-induced energy harvester with variable beam width

This paper proposes a geometric nonlinear analytical model of a vortex-induced energy harvester (VIEH) with variable beam width in order to improve the output performance. The analytic method is adopted to establish the geometric nonlinear model of VIEH. Based on the geometric nonlinear analytical model, the beam structure is then optimized to improve the output. The results show that the harvested power and average power density within a specific resistance range can be increased through the beam width variation of the VIEH. Nonlinear hardening behaviors can be observed in the outputs varied by the flow velocity of a VIEH with variable beam width.

Performance on Cluster Backscatter Communication Networks With Coupled Interferences

This article presents an analytical model to analyze the communication performance of cluster backscatter communication networks (CBackCom Nets) by considering their unique interferences. In CBackCom Nets, interferences are from both backscatter transmitters (BTs) and carrier emitters (CEs), i.e., RF signal emitters. Because BTs are distributed in clusters around CEs, interferences from BTs and interferences from CEs constitute coupled interferences. In addition, since BTs conduct backscatter communications by reflecting RF signals from CEs, interfering signals from BTs, and interfering signals from CEs are power-correlated, leading to the particularity and complexity of coupled interferences of CBackCom Nets. In contrast to previous studies that analyze the performance of CBackCom Nets ignoring coupled interferences, this article develops a novel interference analysis approach to analyze their coupled interferences, and then analyze performance, including coverage probability and spatial throughput of a cluster. Our numerical results show that our analytical model can obtain more accurate results than prior analytical models. In addition, our results reveal the relationship between the communication performance and multiple factors, such as the node density, the energy harvesting model, the interferences from CEs, and the cluster size, offering insightful implications for constructing and configuring CBackCom Nets.

Machine Learning Strategy to Achieve Maximum Energy Harvesting and Monitoring Method for Solar Photovoltaic Panel Applications

The choice of the optimal orientation of the solar panels is by far one of the most important issues in the practical application of solar installations. The use of phase changing materials (PCMs) is an efficient approach of storing solar thermal energy. Because PCMs are isothermal in nature, they provide better density energy storage and the capacity to function across a wide temperature range. Unfortunately, this feature is very rare on various solar power panels; however, ignoring it can reduce the performance of the panels to unacceptable levels. The fact is that the angle of incidence of rays on the surface greatly affects the reflection coefficient and, consequently, the role of unacceptable solar energy. In this paper, a smart energy harvesting model was proposed. In the case of glass, when the angle of incidence varies vertically from its surface to 30, the reflection coefficient is practically unchanged and slightly less than 5%, i.e., more than 95% of the radiation goes inwards. Furthermore, the reflection increase is noticeable, and the area of the reflected radiation by 60 doubles to almost 10%. At an angle of incidence of 70, it reflects 20% of the radiation, and at 80, 40%. For most other objects, the dependence of the reflection magnitude on the angle of incidence is approximately the same.

Stochastic Geometry Based Performance Study for Wireless-Powered Backscatter Communications

Wirelessly powered backscatter communication (WP-BackCom) enables battery-free Internet of Things (IoT) nodes to passively modulate and backscatter information on the energy signals transmitted by power beacons (PBs) and to harvest energy for realizing energy self-sustainability. It has been deemed a promising solution for the large-scale deployment of IoT nodes. In this paper, we exploit stochastic geometry (SG) to develop a tractable framework to evaluate the successful transmission probability in WP-BackCom while considering the imperfect successive interference cancellation (SIC) at each gateway (GW) and the non-linear energy harvesting (EH) model and energy-causality constraint at each backscatter user (BU). The successful transmission probability is defined as the probability for the event that not only the BU can harvest sufficient power from PBs for sustaining its operation but also the backscattered information can be successfully decoded by the GW. We derive the active probability into a closed form and then obtain the successful transmission probability based on the random spectrum access (RSA) policy. The closed-form expression for the successful transmission probability is derived under three special cases. We also devise a closed-form expression for the sub-optimal reflection coefficient to obtain a sub-optimal successful transmission probability. The analysis reveals that the sub-optimal reflection coefficient increases with the SIC coefficient. Numerical simulations validate the correctness of the above theoretical derivations and examine the impacts of various parameters on the successful transmission probability.

Joint Optimization of Trajectory, Task Offloading, and CPU Control in UAV-Assisted Wireless Powered Fog Computing Networks

This paper studies a rotary-wing unmanned aerial vehicle (UAV)-assisted wireless powered fog computing network, where a UAV serves as both a mobile wireless energy source and a mobile fog server to provide charging and computing services to a group of ground sensors. The UAV first broadcasts radio frequency (RF) signals to charge the sensors, and then the sensors use the harvested energy to complete their tasks by local computing and fog computing, where the partial offloading policy is employed. In order to achieve green communication system design, an optimization problem is formulated to minimize the UAV’s energy consumption by jointly optimizing the UAV’s trajectory, the task offloading allocation and computing resource allocation, where the piecewise nonlinear energy harvesting (EH) model, the charging requirements of sensors and velocity constraints of UAV are taken into account together. To solve the non-convex problem, an iterative method is designed based on the successive convex approximation (SCA) theory. Simulation results show that our presented scheme converges within a few iterations, by using which the UAV’s energy consumption can be greatly reduced compared to benchmark schemes. It is also observed that the harvested energy by the linear EH model have obvious bias compared to that obtained by the nonlinear EH model. And in the case of nonlinear EH model, as circuit saturation threshold increases, the UAV moves closer to the sensors such that their tasks can be completed, which may result in a higher energy consumption of the UAV. Besides, the velocity variation of UAV is relatively small by introducing the velocity constraint, which avoids drastic steering of the UAV in our considered scenario.

α-Fairness-Maximizing User Association in Energy-Constrained Small Cell Networks

Renewable energy source (RES)-powered base stations have received tremendous research interest in recent years because they can expand network coverage without building a power grid. This paper proposes a novel user association (UA), resource allocation (RA), and dynamic power control (PC) scheme to maximize the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> -fairness in RES-assisted small cell networks. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> -fairness is a general notion that flexibly adjusts the balance between the throughput, proportional fairness, and max-min fairness according to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> . Nevertheless, none of the existing studies has proposed UA, RA, and PC to maximize the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> -fairness due to its NP-hardness. Furthermore, fixed-policy-based PC designs cannot consider time-varying environments (e.g., energy harvesting models and wireless channels) of the RES-assisted networks. We first provide a Lagrangian duality-based algorithm to solve the UA and RA problem for a fixed PC. Next, we propose a dynamic PC scheme based on deep reinforcement learning (DRL) that chooses the best PC considering the time-varying environments. However, because the UA and RA algorithm executed in each step of the dynamic PC requires a long computation time, we aim to accelerate the computation of the UA and RA with DRL. Inspired by the Lagrangian duality, we design a DRL-based UA and RA with a low-dimensional continuous variable by relaxing the UA variable, the cardinality of which increases exponentially with the number of base stations and users. The simulation results show that the proposed scheme achieves a 100 times shorter computation time than the optimization-based schemes by computing only two neural networks. In particular, although there have been numerous studies on the proportional fairness maximization, the proposed scheme outperforms the optimization-based schemes in the throughput, proportional fairness, and max-min fairness metrics.

Modeling and simulation of thin-walled piezoelectric energy harvesters immersed in flow using monolithic fluid–structure interaction

A monolithic numerical scheme for fluid–structure interaction with special interest in thin-walled piezoelectric energy harvesters driven by fluid is proposed. Employing a beam/shell model for the thin-walled structure in this particular application creates a FSI problem in which an ( n − 1 ) -dimensional structure is embedded in an n -dimensional fluid flow. This choice induces a strongly discontinuous pressure field along the moving fluid–solid interface. We overcome this challenge within a continuous finite element framework by a splitting-fluid-domain approach. The governing equations of the multiphysics problem are solved in a simultaneous fashion in order to reliably capture the main dynamic characteristics of the strongly-coupled system that involves a large deformation piezoelectric composite structure, an integrated electric circuit and an incompressible viscous fluid. The monolithic solution scheme is based on the weighted residuals method, with the boundary-fitted finite element method used for the discretization in space, and the generalized- α method for discretization in time. The proposed framework is evaluated against reference data of two popular FSI benchmark problems. Two additional numerical examples of flow-driven thin-walled piezoelectric energy harvesters demonstrate the feasibility of the framework to predict controlled cyclic response and limit-cycle oscillations and thus the power output in typical operational states of this class of energy harvesting devices. • A fully monolithic model tailored to flow-driven thin-walled piezoelectric energy harvesters (PEHs). • A novel method to tackle the strongly discontinuous pressure field of the fluid field. • The computational framework is validated against classical FSI benchmark problems. • The importance of optimized PEH electrode coverage is revealed by two new benchmarks. • Reference implementation using the open-source finite element framework FEniCS.

RIS-Empowered Non-Linear Energy Harvesting Communications Over Nakagami-m Channels

We propose novel reconfigurable-intelligent-surface (RIS)-based energy harvesting (EH) systems with linear EH (L-EH) and non-linear EH (NL-EH) models where channels are exposed to Nakagami- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> fading. We also compare two RIS-based EH systems in terms of RIS locations, namely RIS-EH and RIS-information processing (RIS-IP). In the RIS-EH, RIS increases the amount of harvested energy, while in the RIS-IP, RIS is utilized to improve the information processing link. Closed-formed expressions of throughput, outage probability, and average harvested power are derived and confirmed via simulation. The L-EH model overestimates system performance, while the NL-EH model provides a realistic interpretation of EH system design.

Joint Subcarrier and Transmission Power Allocation in OFDMA-Based WPT System for Mobile-Edge Computing in IoT Environment

Mobile-edge computing (MEC) is expected to play an important role in the next-generation of Internet-of-Things (IoT) services with artificial intelligence (AI) by providing the sustainable computation capability of resource-constrained IoT devices. Since the finite battery lifetime has been a longstanding challenge of the MEC system for IoT services, the wireless power transfer (WPT) technology has been recently developed for the MEC system in order to support the perpetual operation of IoT devices. In this article, we introduce two resource allocation problems for OFDMA-based WPT-MEC systems: 1) a max–min energy fairness (MMEF) problem and 2) a power sum maximization (PSM) problem. These problems ensure high-performance computations for AI-based applications, where the network reliability and the tremendous power consumption may be required. Moreover, we incorporate a logarithmic nonlinear energy harvesting (EH) model into our formulated problems, which lead to nonconvex mixed-integer nonlinear programming (MINLP) problems. In order to resolve these NP-hard problems, we convert the proposed problems into their equivalent convex forms by applying the continuous relaxation method. The near-optimal solutions are thereby obtained in closed-form expressions by leveraging the Lagrangian duality method for each relaxed problem. Numerical results are presented to validate the merits of the proposed algorithms of MMEF&PSM over the alternative benchmark algorithm and provide significant insights on the effects of the key system parameters, including the number of IoT devices and power transmission subcarriers.

Joint offloading and beamforming design in full‐duplex multi‐hop SWIPT MEC systems under non‐linear energy harvesting model

Joint offloading and beamforming design in full‐duplex multi‐hop SWIPT MEC systems under non‐linear energy harvesting model