Harvested Energy Maximization Research Articles

Triple-junction Dion-Jacobson perovskite tandem solar cells: advancing efficiency via interface optimization and broadened light absorption spectrum

Abstract Dion-Jacobson (DJ) perovskites have emerged as game-changers in the world of photovoltaics due to their unique combination of advantages like cost-effective fabrication, tailorable bandgap, and exceptional efficiency. Further, in this study, MXene contacts have been utilized with 2D-perovskites solar cells to leverage the advantages of both, leading to improved stability, superior charge transport, and adjustable energy bandgap without the occurrence of pinhole effects. To further tap into the immense potential of perovskites, this study explores the power of a triple-junction All DJ perovskite tandem solar cell (ADJT-PSC) architecture. This design captures a broader spectrum of sunlight, maximizing energy harvesting and unlocking new possibilities for photovoltaic devices. In this symphony of light and power, 1.5-pentamethylenediamine (PeDA) methylammonium lead iodide (PeDAMAPb2I7) (1.98 eV), PeDAMA5Pb6I19 (1.6 eV), CsLaTa2Se7 (1.1 eV) take as the absorber layers in the top, middle, and bottom subcells, respectively. Moreover, optimizing the thickness of each perovskite layer is crucial for maximizing efficiency. This study conducted a thorough analysis and identified the ideal 350 nm/550 nm/200 nm thicknesses for the top/middle/bottom layers, respectively. Furthermore, the study explored the art of doping variation in the electron and hole transport layers. By meticulously adjusting the doping levels, ADJT-PSC can achieve a maximum efficiency of 35%.

A reconfigurable architecture for maximizing energy harvesting of thermoelectric generators in non-stationary conditions

The use of Thermoelectric Generators (TEGs) has proliferated across a multitude of applications for energy harvesting. As more modules are employed to recover greater amounts of energy, the temperature mismatch between them increases. This results in each module operating at a distinct maximum power point, thereby reducing the overall system efficiency. Furthermore, in dynamic applications such as automotive scenarios, the temperatures of the thermoelectric generators are not constant, and the maximum power point accordingly shifts. A fixed architecture is unable to cope with these fluctuating situations. Therefore, this paper introduces a reconfigurable architecture capable of harnessing maximum energy at any given moment, improving energy recovery compared to a fixed architecture. Optimization techniques, lean methodologies, and clustering approaches are employed to efficiently design the reconfigurable TEG, which enables modification of the electrical connections inside the TEG modules and the number of Maximum Power Point Tracking (MPPT) modules. A use case is presented where the reconfigurable TEG is compared with fixed, yet optimized, TEG configurations under mixed driving modes. In this specific case, the results demonstrate that the reconfigurable TEG achieves enhanced performance in dynamic environments with two MPPTs under mixed scenarios, reaching an efficiency of 96.3% and a 0.29% improvement in energy recovery.

Bifacial perovskite/silicon heterojunction tandem solar cells based on FAPbI3-based perovskite via hybrid evaporation-spin coating

Combining the two technologies of tandem solar cells and bifacial solar cells has a great potential to maximize energy harvesting while minimizing material and surface usage. Mid-bandgap perovskites (1.50–1.60 eV) are important for fulfilling current matching in bifacial perovskite/silicon heterojunction tandem solar cells. Herein, efficient (>20 %) and stable planar FAPbI3-based perovskite (1.54 eV) solar cells have been fabricated via a hybrid evaporation-spin coating process. X-ray diffraction and electron microscopy data reveal the formation of highly crystalline (001) perovskite domains. The fabricated high-quality perovskite films lead to a more homogenized contact potential difference at the film surface and a reduction in non-radiative losses, resulting in a quasi-Fermi-level splitting (half-cell) of 1.16 eV, rendering 120 mV non-radiative losses. Transferring these films into tandem devices atop single-side textured silicon heterojunction bottom cells, we obtain an efficiency of >24 % under AM1.5 G illumination for monofacial devices with an active area of 1.21 cm2. Furthermore, the bifacial devices generate >27 mW cm−2 power output with 15 % rear illumination fraction.

Adaptive Control for Underwater Simultaneous Lightwave Information and Power Transfer: A Hierarchical Deep-Reinforcement Approach

In this work, we consider a point-to-point underwater optical wireless communication scenario where an underwater sensor (US) transmits its sensing data to a remotely operated vehicle (ROV). Before the US transmits its data to the ROV, the ROV performs simultaneous lightwave information and power transfer (SLIPT), delivering both control data and lightwave power to the US. Under the considered scenario, our objective is to maximize energy harvesting at the US while supporting predetermined communication performance between the two nodes. To achieve this objective, we develop a hierarchical deep Q-network (DQN)–deep deterministic policy gradient (DDPG)-based online algorithm. This algorithm involves two reinforcement learning agents: the ROV and US. The role of the ROV agent is to determine an optimal beam-divergence angle that maximizes the received optical signal power at the US while ensuring a seamless optical link. Meanwhile, the US agent, which is influenced by the decision of the ROV agent, is responsible for determining the time-switching and power-splitting ratios to maximize energy harvesting without compromising the required communication performance. Unlike existing studies that do not account for adaptive parameter control in underwater SLIPT, the proposed algorithm’s adaptive nature allows for the dynamic fine-tuning of optimization parameters in response to varying underwater environmental conditions and diverse user requirements.

Energy harvesting using rooftops in urban areas: Estimating the electricity generation potential of PV and wind turbines considering the surrounding environment

Building rooftops, rich in sunlight and with high wind speeds, are among the most ideal locations for installing renewable energy systems (RES) in urban areas. However, not all rooftops are suitable for RES installation. Therefore, this study aimed to maximize energy harvesting using limited rooftop space. To this end, four scenarios were established based on a district in Seoul, South Korea, and the annual electricity generation of the RES was then estimated. This process was refined further into seven scenarios to assess economic feasibility. The principal findings of this research are as follows. First, depending on the type of RES installed on rooftops, it is possible to generate between 755,124 kWh and 3,236,918.3 kWh of electricity annually. Second, in terms of economic feasibility, scenarios predominantly featuring photovoltaic (PV) installations proved economically viable, whereas those mainly involving vertical axis wind turbines (VAWTs) did not guarantee economic feasibility. Third, for VAWTs scenarios with lower economic viability, adjusting the Renewable Energy Certificate weight and providing government subsidies up to USD1,131/kW could improve the profitability. These results provide a foundation for RES operators to design appropriate projects based on economic evaluations, and for policymakers to formulate effective policies to better promote the diffusion of RES.

Design and investigation of small-scale long-distance RF energy harvesting system for wireless charging using CNN, LSTM, and reinforcement learning

Introduction: The energy supply challenge in wireless charging applications is currently a significant research problem. To address this issue, this study introduces a novel small-scale long-distance radio frequency (RF) energy harvesting system that utilizes a hybrid model incorporating CNN, LSTM, and reinforcement learning. This research aims to improve RF energy harvesting and wireless charging efficiency.Method: The methodology of this study involves data collection, data processing, model training and evaluation, and integration of reinforcement learning algorithms. Firstly, RF signal data at different distances are collected and rigorously processed to create training and testing datasets. Next, the CNN-LSTM model is trained using the prepared data, and model performance is enhanced by adjusting hyperparameters. During the evaluation phase, specialized test data is used to assess the accuracy of the model in predicting RF energy harvesting and wireless charging efficiency. Finally, reinforcement learning algorithms are integrated, and a reward function is defined to incentivize efficient wireless charging and maximize energy harvesting, allowing the system to dynamically adjust its strategy in real time.Results: Experimental validation demonstrates that the optimized CNN-LSTM model exhibits high accuracy in predicting RF energy harvesting and wireless charging efficiency. Through the integration of reinforcement learning algorithms, the system can dynamically adjust its strategy in real time, maximizing energy harvesting efficiency and charging effectiveness. These results indicate significant progress in long-distance RF energy harvesting and wireless charging with this system.Discussion: The results of this study validate the outstanding performance of the small-scale long-distance RF energy harvesting system. This system is not only applicable to current wireless charging applications but also demonstrates potential in other wireless charging domains. Particularly, it holds significant prospects in providing energy support for wearable devices, Internet of Things (IoT), and mobile devices.

A Feedback Analytic Algorithm for Maximal Solar Energy Harvesting of InP Stepped Nanocylinders

A Feedback Analytic Algorithm for Maximal Solar Energy Harvesting of InP Stepped Nanocylinders

Energy scavenging from the diurnal cycle with a temperature-doubler circuit and a self-adaptive photonic design

Abstract A temperature-doubler circuit is the functional equivalent of a voltage-doubler in the thermal domain. Effective temperature-doubler circuits could benefit energy scavenging from fluctuating thermal resources, e.g. the diurnal cycle. However, the current paradigm relies on static photonic designs of the selective solar absorber or blackbody emitter, which aims at maximizing energy harvesting from either the sun or outer space, but not from both. Furthermore, photonic and thermal optimizations have not yet been coupled to maximize the power output. Here we develop a general framework to optimize the energy acquisition and conversion simultaneously to maximize a temperature-doubler’s power output under a realistic solar-thermal boundary condition. With an ideal self-adaptive absorber/emitter to fully exploit the thermodynamic potential of both the sun and outer space, the theoretical limit of the temperature-doubler circuit’s average output power in a diurnal cycle is found to be 168 W m−2, a 12-fold enhancement as compared to the blackbody emitter. We provide a numerical design of such a self-adaptive absorber/emitter, which, combined with a thermoelectric generator, generate 2.3 times more power than the blackbody emitter in a synthetic “experiment”. The model further reveals that, as compared to traditional thermal circuits, the key merit of the temperature-doubler is not to enhance the total power generation, but to convert the fluctuating thermodynamic input to a continuous and stable power output in a 24 h day-night cycle.

Outer isolated detached resonance curve and its implications of a two-stage nonlinear dynamic system

This paper presents an innovative study of a two-stage nonlinear dynamic system, providing insights into the occurrence of Large-Amplitude Long-Duration Vibration State (LALDVS) and outer isolated detached resonance curves (OIDRCs). The Lagrange equations and the incremental harmonic balance method (IHBM) are used to uncover the fundamental mechanism of OIDRCs and to discover two and three OIDRCs that have not been previously reported. Multiple OIDRCs could have significant practical implications in engineering, as they indicate the possibility of unpredictable and potentially hazardous vibrations under certain operating conditions. The existence of OIDRCs depends on the jump frequency point being a Neimark-Sacker bifurcation, and the resulting phase diagram provides vital insights into the stability and bifurcation behaviors of the system. This work also explores the effect of stiffness on the nonlinear response behavior of the system and identifies the range of stiffness values that lead to LALDVS. The correctness of the theoretical calculation results is validated by comparing them with vibration experiment and simulation calculations. These results have practical implications for engineering and energy harvesting research, including identifying potential hazards associated with vibration and designing systems that maximize energy harvesting efficiency. Overall, these findings are innovative and have significant implications for predicting potentially hazardous vibrations in engineering and the design of energy harvesting systems.

Multi-objective optimization for energy efficient cooperative communication in energy-constrained overlay Cognitive Radio Networks

With the rapid proliferation of wireless devices, energy efficiency and addressing spectrum scarcity have become critical global concerns. To address these challenges, this paper explores radio-frequency energy harvesting in Cognitive Radio Networks (CRNs) as a solution which can significantly extend the battery life of mobile devices. In this setup, energy-constrained secondary users (SUs) use Simultaneous Wireless Information and Power Transfer (SWIPT) technology to harvest energy from primary signals and aid primary transmission. We propose a joint time-switching and power-splitting technique to maximize energy harvesting, achieving a 97.7% accurate sub-optimal solution compared to the optimal (benchmark) result. Furthermore, we investigate optimal power allocation during cooperative communication and secondary transmission, resulting in a near-optimal solution with 98.5% accuracy. We also introduce a cooperative communication framework using the Hedonic game concept, enabling SUs to establish preferred partnerships with suitable primary users (PUs). Numerical results indicate that this cooperative framework enhances the secondary network’s overall utility, average SU satisfaction, and SU participation when compared to the non-cooperative approach.

Application of nonlinear stiffness mechanism on energy harvesting from vortex-induced vibrations

This study investigates the potential of vortex-induced vibration (VIV) as a renewable energy source, achieved when fluid flow interacts with a bluff body, inducing self-sustained oscillations through vortex shedding in the wake. While VIV research has traditionally focused on understanding its mechanisms and mitigating detrimental effects, interest in VIV energy harvesting has surged as a means to convert marine hydrokinetic (MHK) energy into usable electrical power. The nonlinear effects of two linear oblique springs on VIV energy harvesting are explored using the wake oscillator model, encompassing bistable and Duffing hardening stiffness. The study examines the response and energy harvesting performance while considering the impact of undeformed spring length, structural damping, and initial conditions on VIV energy conversion. Findings show that nonlinear stiffness application in the VIV system can broaden the synchronization bandwidth or reduce the VIV initiation flow speed. Bistable stiffness may broaden the synchronization velocity range, while Duffing hardening stiffness efficiently reduces the VIV initiation speed with small energy harvesting loss. Combining both stiffness types with appropriate control strategies presents a promising approach for achieving a broad synchronization VIV bandwidth and low initiation flow speed. Key parameters, such as the nondimensional parameter defining spring system obliquity and the ratio between undeformed spring length and cylinder diameter, significantly influence VIV response and energy harvesting. Moreover, optimal structural damping is vital to maximize energy harvesting efficiency, and understanding and controlling initial conditions are crucial for optimizing VIV synchronization bandwidth and energy harvesting efficiency for both bistable and Duffing hardening stiffness. This study provides valuable insights into VIV system dynamics and energy conversion potential with nonlinear springs, offering promising avenues for enhancing energy harvesting efficiency and inspiring further applications of nonlinear effects in VIV energy converters.

Exploring the Effect of Meteorological Factors on Predicting Hourly Water Levels Based on CEEMDAN and LSTM

The magnitude of tidal energy depends on changes in ocean water levels, and by accurately predicting water level changes, tidal power plants can be effectively helped to plan and optimize the timing of power generation to maximize energy harvesting efficiency. The time-dependent nature of water level changes results in water level data being of the time-series type and is essential for both short- and long-term forecasting. Real-time water level information is essential for studying tidal power, and the National Oceanic and Atmospheric Administration (NOAA) has real-time water level information, making the NOAA data useful for such studies. In this paper, long short-term memory (LSTM) and its variants, stack long short-term memory (StackLSTM) and bi-directional long short-term memory (BiLSTM), are used to predict water levels at three sites and compared with classical machine learning algorithms, e.g., support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). This study aims to investigate the effects of wind speed (WS), wind direction (WD), gusts (WG), air temperature (AT), and atmospheric pressure (Baro) on predicting hourly water levels (WL). The results show that the highest coefficient of determination (R2) was obtained at all meteorological factors when used as inputs, except at the La Jolla site. (Burlington station (R2) = 0.721, Kahului station (R2) = 0.852). In the final part of this article, the complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) algorithm was introduced into various models, and the results showed a significant improvement in predicting water levels at each site. Among them, the CEEMDAN-BiLSTM algorithm performed the best, with an average RMSE of 0.0759 mh−1 for the prediction of three sites. This indicates that applying the CEEMDAN algorithm to deep learning has a more stable predictive performance for water level forecasting in different regions.

An experimental study on hybrid control of a solar tracking system to maximize energy harvesting in Jordan

Due to its capacity to maximize the power produced by photovoltaic (PV) panels, solar tracking systems have grown in popularity. However, erratic weather conditions, like cloudy or overcast days, change the panel's preferred orientation. On sunny days, the panel is turned to face the sun's radiation; however, on cloudy or overcast days, diffused radiation alters the optimal position. This has increased the necessity for new search algorithms that control the orientation of the panel to generate the most power feasible regardless of the weather conditions. This study aims at developing a sun-tracking system that can adjust the solar panel's orientation to generate the maximum possible electrical output from solar energy in Jordan, regardless of the local climate. To achieve this; a hybrid controller has been created that combines an open-loop sun monitoring system with a dynamic feedback controller built on an active search algorithm. The suggested algorithm is built on the real-time measurement of the PV panels' waning solar irradiance by using small solar cell 0.6 W. The system switches to the recently proposed circular path finding method to locate the Maximum Power Point Tracking (MPPT) if the instantaneous irradiance level falls below a predetermined threshold (DNI irradiance). As a result of the testing phase, the generated power of a solar panel increased by about 35%, confirming the effectiveness of the newly proposed sun tracker hybrid control system. During experimental test, positive irradiance current noises are appeared, using average filter taking the mean of 100 values for one measurement.

Low-speed, low induction multi-blade rotor for energy efficient small wind turbines

To promote the deployment of small wind turbines (SWTs), thorough understanding of design parameter implications is essential. In-depth research is required to comprehend the influence of design TSR on off-design wind speed performance of multi-blade SWTs. In-house built blade element momentum algorithm was employed, which considered Reynolds number dependence of aerodynamic coefficients and correlated well with experimental results. Peak power coefficients were produced for 0.9, 1.5, 2 and 3 m diameter S826 rotors with blade numbers from 2 to 12 and design TSR of 2–10. Remarkably, regardless of diameter, greatest CP,max values were achieved around design TSR of 4. For peak efficiency, smaller the diameter, narrower the blade number and design TSR range. High-speed rotors have wider TSR range for high power coefficient. Yet, it was shown that operating TSR of low-speed rotors deviates less from design TSR as wind speed varies. It was revealed that low-speed (with a threshold design TSR of 3), low-induction multi-blade rotors provide high CP,max, better efficiency at off-design, shorter starting time and lower wind speed than three-bladed high-speed rotor. A small boost in operational TSR was found to effectively mitigate loss in off-design performance. These are key features to maximize energy harvesting.

Experimental study on energy harvesting maximization from the flow-induced vibration of a highly confined bluff body

Flow-induced vibrations of rigid prisms supported elastically were studied experimentally in a free-surface water channel with a high blockage (2/5). The study focused on finding the prism cross-sectional shape that maximizes the efficiency of energy harvesting. Seven cross-sectional shapes were tested: square, circular, 45° tilted square, equilateral triangle, isosceles 120° triangle, D-section, and C-section. All other dimensionless parameters of the problem, mass ratio, damping, blockage ratio, reduced velocity range, and the Reynolds (Re) number (characteristic velocity times characteristic length divided by kinematic viscosity) range (400–1070), were kept unchanged. By doing so, the effect of the cross-sectional shape was isolated. D-section proved to be the geometry with the highest values of energy transfer efficiency. A hysteresis loop was present in its oscillatory response (dimensionless oscillation amplitude vs reduced velocity). This loop was characterized by two branches, (+) and (−), meaning a bi-valued amplitude response for each reduced velocity. Regarding temporal patterns of wake topology and body motion, it was found that synchronization occurs in the (+) branch, but not in the (−). Regarding vortex shedding modes, particle image velocimetry was used for identification purposes, and it was found that the 2P mode is the dominant mode in the (+) branch, while the 2S mode pervades the (−). Finally, a new relative reduced velocity definition was introduced, and, when re-plotting the experimental results, it was found that the hysteresis loop disappears, thereby providing a more compact mathematical description of the observed phenomena.

Harnessing Solar Power with a Smart Flower Prototype: Design and Fabrication

Abstract: Globally, renewable energy has grown remarkably. In the upcoming years, significant growth in the use of renewable energy is anticipated in India. With the majority of installations, solar energy led the renewable energy market, followed by wind energy. This demonstrates India's dedication to making the switch to a cleaner, more sustainable energy future. This research article discusses the design and fabrication of a prototype of a smart solar electricity generator flower i.e., the Smart Solar Flower (SSF), a unique and effective solar energy solution. It examines the features, advantages, and design of the SSF. The SSF, which was designed after flowers, autonomously tracks the sun to maximize energy harvesting. Photovoltaic panels, a solar tracking system, energy storage, a smart control unit, a self-cleaning mechanism, and sensors are some of its parts. The scalable and adaptive SSF may support energy-dependent systems, charge electric vehicles, power devices, and provide lighting. Intelligent sensor technology, machine learning, and Internet of Things connectivity enable autonomous adjustment based on the weather and energy consumption. Reduced reliance on fossil fuels, lower carbon emissions, and a sustainable future are all advantages for the environment. Installation expenses, energy savings, and money production are taken into account for economic viability. Efficiency, durability, and cost are explored along with challenges and prospects for the future. The SSF has significant promise for generating sustainable energy and revolutionizing the use of solar power to tackle global energy concerns.

Piezoelectric small scale generator: towards near-Joule output energy generation

Research on piezoelectric microgenerators harvesting energy from vibrations led to an abundant literature, with various strategies to optimize the frequency range and output power. In contrast, for very low frequency range (<10 Hz) and/or for non-harmonic mechanical source, the large majority of the strategies are not adapted. This work deals with a small scale piezoelectric generator where the input mechanical source consists of a single force application in the range of hundreds of Newtons (i.e. typical human weight). Contrary to harmonic mechanical sources, such an application context necessitates harvesting as much as energy as possible in a single cycle. This was achieved by assembling several piezoelectric stacks within a mechanical amplification system, and to use the electric field and stress levels close to the limits of the piezoelectric elements. Ericsson cycle (i.e. thermodynamic cycle comprising two iso-electric field and two iso-stress steps) was applied to the piezoelectric material and later two device prototypes were developed in order to quantify the harvesting capabilities. Finally, in a realistic application point of view, a passive electrical interface based on Bennet’s doubler was implemented and compared to the Ericsson cycles in terms of output energy. This electrical energy management strategy successfully allowed working at ultra-high electric field (>2 kV mm−1) enabling a converted energy density close to the ultimate value. An maximal energy density of 320 mJ cm−3 was reached using Ericsson cycles, and 130 mJ cm−3 using Bennet’s doubler (∼40% of the ultimate energy density). The device comprising ∼2.4 cm3 of piezoelectric material, the net output energy converted and stored per cycle reached 320 mJ. Still, the work presented here can be adapted to other range of forces and displacements for maximizing energy harvesting.

On‐board energy scheduling optimization algorithm for nanosatellites

SummaryThis work proposes a real‐time and online energy scheduling optimization algorithm for nanosatellites operating with a direct energy transfer (DET) architecture. The goal of the algorithm is to optimize energy utilization by maximizing energy harvesting, meeting task deadlines and priorities, and ensuring a minimum quality of service (QoS) for the system. The algorithm is composed of a modified perturb and observe algorithm to reach the best energy efficiency point of the solar panels, a task prioritization algorithm to guarantee their times and minimum QoS, and a backpack optimization problem to maximize the energy efficiency of the satellite. To demonstrate the effectiveness of the proposed algorithm, simulations of a nanosatellite operating in a given orbit, attitude, and thermal parameters were conducted and compared to other task scheduling strategies. The results showed that the proposed energy scheduling algorithm had the best performance in terms of energy balance and average power consumption, with an energy balance up to 16% higher compared to the other tested strategies. This demonstrates the ability of the proposed algorithm to optimize power consumption and energy utilization, as well as efficiently schedule tasks to maximize energy harvesting. The proposed energy scheduling optimization algorithm has the potential to be a useful tool for the design and optimization of future satellite missions and could potentially guide the design of electrical power systems for new CubeSat missions operating with a DET architecture. The algorithm's ability to optimize energy utilization and ensure a minimum QoS for the system could improve the overall efficiency and performance of the satellite.

Cleaning frequency of the solar PV power plant for maximum energy harvesting and financial profit

Cleaning frequency of the solar PV power system plays a major role in energy harvesting. This paper proposed an optimized cleaning frequency for the PV power plants. The objective is to maximize energy harvesting and increase financial profit. The proposed technique is tested in roof top grid connected 20 kW PV power plant in Cairo, Egypt. Experimental results are collected for one year throw the period from January 2021 to December 2021 based on real time monitoring under the regular cleaning of the PV plant every fifteen days. The collected data and results are used for deriving a formula to find the optimal frequency for cleaning the PV cells. This formula can be used for any PV power plant under different operating conditions. Therefore, the formula can detect the financial losses due to the dust accumulated on the surfaces of PV panels. The model proposed in this research is verified by the comparison between the generated power from the photovoltaic power plant and the cost of cleaning (water and cleaning workers) in case of manual cleaning, taking into consideration the various operating conditions though whole the period of the study.

Maximizing Energy Harvesting in a Miniaturized Implant with an Integrated Coil Receiver

In this work, a fully integrated energy harvester employing an on-chip coil and a voltage multiplier rectifier is proposed. The differential, multi-stage rectifier is optimized recurring to a genetic algorithm with the objective of maximize the power delivered to the load (W power transfer level) while minimizing area and start-up voltage. The rectifier is one of the most critical components in wireless-powered systems, generating a stable DC supply voltage, its efficiency is the main limiting factor in the achievable power budget. When implemented inside an implant, the circuit should occupy the minimum possible area and present a low start-up power as in the context of most applications, the amount of energy that reaches the receiver is extremely small. Simulation results show that the proposed circuit performs with an efficiency of 38% delivering a stable 1.16 V DC voltage to a 100 kΩ load, providing an output power of 14.9 W and solely occupying an area of 8750 m. Post-fabrication experimental results are presented, where a small inductive link employing a fully integrated receiver coil is used with the rectifier. A maximum power transfer of 12.5 W is achieved, with the inductive link operating at 915 MHz.