Optimized Power Management Unit for IoT Applications

Buck-boost DC–DC converters are useful as DC grid interfaces for renewable energy resources. In the classical buck-boost converter, output voltages smaller than the input voltage (the buck region) are observed for duty cycles between 0 and 0.5. Several recent buck-boost converters have been designed to present higher voltage gains. Nevertheless, those topologies show a reduced duty-cycle range, leading to output voltages in the buck region, and thus require the use of very low duty cycles to achieve the lower range of buck output voltages. In this work, we propose a new buck-boost DC-DC converter that privileges the buck region through the extension of the duty-cycle range, enabling buck operation. In fact, the converter proposed here allows output voltages below the input voltage even with duty cycles higher than 0.6. We present the analysis, design, and testing of the extended buck-boost DC-DC converter. Several tests were conducted to illustrate the characteristics of the extended buck-boost DC-DC converter. Test results were obtained using both simulation software and a laboratory prototype.

Potential evaluation of hybrid nanofluids for solar thermal energy harvesting: A review of recent advances

This two-part paper reviews recent innovations in circuit design that have accelerated the miniaturization of sensor nodes. In this second part of the paper, we focus on key building blocks of miniaturized sensor nodes, such as data transceivers, energy harvesters, power management units, and digital logic circuits. System level design considerations are also discussed to provide guidelines for the design of a miniaturized system. As an example prototype design, a 2.7-cm3 global navigation satellite system (GNSS) logger is proposed. This paper includes a die-stacked sensor platform composed of an ARM cortex M0 processor, energy harvester, power management unit, solar cell, optical receiver, sensor layer, and RF transmitter that exploits the discussed design techniques for ultra-low power operation. The GNSS logger can store GNSS signals of >1 k positions on a single battery charging without additional energy harvesting.

A high‐efficiency power management unit (PMU) with wide dynamic range for radio frequency (RF) energy harvesting system is proposed in this paper. A digital zero current switching (ZCS) and a digital maximum power point tracking (MPPT) circuits are adopted. A sub‐threshold current reference circuit and a subthreshold voltage detection circuit are proposed to reduce the power consumption of always‐on control circuits and improve the conversion efficiency at low input power. A dual‐oscillator is designed to extend the frequency range and widen the input power range with optimized power consumption under low‐end band and high‐end band, respectively. Switch size and on‐time are adjusted according to the input power, which optimizes the conversion efficiency in a wide power range. Proposed PMU is implemented in a standard 180 nm CMOS process. The post‐layout simulation results show that proposed PMU can achieve cold‐start at the input voltage of 280 mV, the power consumption to maintain the PMU is only 9.6 nW. The input power range of the PMU is 11 nW–677 μW. When the input power is 50 nW, a power conversion efficiency (PCE) of 65% is achieved, and the peak PCE can reach 93% at 23.5 μW.

In this paper, a new quasi-Y source buck-boost dc-dc converter is proposed. The proposed converter uses a quasi-Y impedance network, which consists of a three-winding transformer, or three tightly coupled inductors. The proposed converter inherits all the advantages of the original quasi-Y source dc-dc converter such as continuous input current, lack of saturation in the magnetic core of the transformer, small duty cycle and high gain. The advantage of the proposed converter is adding the buck-boost ability to the conventional structure, which is crucial for some applications such as wind turbines and battery charging systems, which demand a buck-boost converter in their structure. The mathematical analyses are provided in order to investigate the operation principles of the converter. Also, in order to verify the proper functioning of the proposed converter, simulations are done in PSCAD/EMTDC and the results and waveforms are provided in the paper.

The Internet of Things (IoT) is a network of interconnected physical devices, vehicles, and buildings that are embedded with sensors, software, and network connectivity, enabling them to collect and exchange data. This exchange of data between the physical and digital worlds allows for a wide range of applications, from smart homes and cities to industrial automation and healthcare. However, a key challenge faced by IoT nodes is the limited availability of energy to support their operations. Typically, these nodes can only function for a few days based on their duty cycle. This paper introduces a solution that aims to ensure the sustainability of IoT applications by addressing this energy challenge. Thus, we develop a design of a hybrid sustainable energy system designed specifically for IoT nodes, using solar photovoltaic (PV) and wind turbines (WT) chosen for their multiple benefits and complementarity. The system uses the single-ended primary-inductance converter (SEPIC) and is controlled using a hybrid approach, combining Harris Hawks Optimization and Particle Swarm Optimization (HHHOPSO). Each SEPIC converter boost the electrical energy generated to attain the required voltage level when charging the battery. The proposed methodology is implemented in MATLAB/Simulink and its performance is measured using appropriate metrics. In terms of efficiency and average power, the results show that the suggested method outperforms previous strategies. Our system powers also many sensor nodes, leading to a high level of sustainability and lowering the carbon footprint associated with traditional energy sources.

This study presents a modern version of the economic load dispatch (MELD) problem with the contribution of renewable energies and conventional energy, including wind, solar and thermal power plants. In the study, reduction of electricity generation cost is the first priority, while the use of multiple fuels in the thermal power plant is considered in addition to the consideration of all constraints of power plants. Two meta-heuristic algorithms, one conventional and one recently published, including Particle swarm optimization (PSO) and Equilibrium optimizer (EO), are applied to determine the optimal solutions for MELD. A power system with ten thermal power plants using multiple fossil fuels, one wind power plant, and three solar power plants is utilized to evaluate the performance of both PSO and EO. Unlike other previous studies, this paper considers the MELD problem with the change of load demands over one day with 24 periods as a real power system. In addition, the power generated by both wind and solar power plants varies at each period. The results obtained by applying the two algorithms indicate that EO is completely superior to PSO, and the solutions found by EO can satisfy all constraints. Particularly in Case 1 with different load demand values, EO achieves better total electricity production cost (TEGC) than PSO by 0.75%, 0.87%, 0.13%, and 0.45% for the loads of 2400 MW, 2500 MW, 2600 MW and 2700 MW. Moreover, EO also provides a faster response capability over PSO through the four subcases although EO and PSO are run by the same selection of control parameters. In Case 2, the high efficiency provided by EO is still maintained, though the scale of the considered problem has been substantially enlarged. Specifically, EO can save $51.2 compared to PSO for the minimum TEGC. The savings cost is equal to 0.33% for the whole schedule of 24 hours. With these results, EO is acknowledged as a favourable search method for dealing with the MELD problem. Besides, this study also points out the difference in performance between a modern meta-heuristic algorithm (EO) and the classical one (PSO). The modern metaheuristic algorithm with special structure is highly valuable for complicated problem as MELD.

This paper presents a hybrid buck-boost DC-DC converter structure to address the limitations of single-output and low conversion ratios (CR) in traditional energy harvesting systems. To reduce the CR of the inductive boost and enhance the total CR of the converter while improving efficiency, the proposed converter cascades a boost circuit with a reconfigurable switched capacitor (SC), and also includes a supercapacitor. The advantage of this architecture is the ability to use low-voltage low-threshold transistors with lower parasitic capacitance and on-resistance, which achieve high-voltage output while reducing switching and conduction losses. The supercapacitor can store energy under a light load and provide additional energy replenishment in buck mode under heavy load. The reconfigurable SC circuit can enable a 2V or 3V output by configuring the switch array. Adaptive switching frequency control was used to achieve a balance between load capability and switching loss. The proposed DC-DC converter is designed with a 0.18 µm CMOS process and has maximum conversion efficiencies of 91.83%, 89.42%, and 88.09% for the inductive boost stage, 2V SC output stage, and 3V SC output stage, respectively. The output voltage ripple is less than 20 mV at 1V output and less than 30 mV at 2V output and 3V output. The input voltage of the converter is as low as 20 mV. The converter features multi-output, high-conversion-ratio, and enhanced efficiency, which are suitable for energy-harvesting applications.

In this article, we provide a brief overview of solar photovoltaic and thermal energy, wind turbines with vertical and horizontal axes, and other sustainable energy production systems as well as energy storage systems. In some remote areas away from easy access to electricity and fresh water, a self-contained and self-sustainable off-grid energy production and storage farm is very much needed. In this so-called sustainable energy farm, solar photovoltaic and solar thermal energies along with wind energy can be harvested and stored in a secured battery warehouse with mobile wireless surveillance systems with unpredictable coherent motions. In this battery-based energy storage system, special fire protection measures must also be employed with heat sensors and early detection and alarm systems. The alarm system will be connected to both local fire stations and automated fire extinguishing systems. A subsequent paper will present the details of this wireless fireproof alarm powered by sustainable energy resources. The main purpose of this paper is to document, provide references, and inform about the state-of-the-art achievements in the field of renewable energies, particularly, solar, wind, and other green energy resources.

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The buck-boost DC-AC inverter generates an alternating output voltage as the differential voltage of two DC-DC individual buck-boost converters that are driven with two 180/spl deg/ phase-shifted DC-biased sinusoidal references. The peak value of the inverter alternating output voltage does not depend on the direct input voltage. In addition, an advantage over the Boost DC-AC inverter is that the output voltages of both Buck-Boost converters are also independent of the direct input voltage. The difficulty of the topology lies in the control of both buck-boost DC-DC converters as they are required to work with variable operating-points. A double-loop control strategy is proposed for the buck-boost DC-DC converter with a new inner control loop for the inductor current and also a new outer control loop for the output voltage. These control schemes include different compensations that make possible a fast and accurate control of both converters with variable operating points. With these compensations, the controllers are easy to design, implement and develop. Furthermore, feedforward loops are included to increase the robustness of the inverter to external disturbances in both the input voltage and output load. The proposed control strategy is designed and implemented on a prototype 1.5 kW buck-boost DC-AC inverter. The control strategy is validated by means of both simulation and experimental tests. The results show that the proposed strategy achieves a robust, reliable and accurate control of the inverter even in hard situations such as nonlinear loads, sudden load changes and transient short circuits.

This paper presents a novel method for making Capacitive De-Ionizatrion (CDI) water purification more efficient and cheaper, using a buck-boost DC-DC converter. The energy recovery is superior (51%) versus maximum reported results in the literature (30%) [1]. First, the concept of a CDI cell is introduced. Second, the existing methods for energy recovery are reported showing their advantages and disadvantages. Then, the novel method using a buck-boost converter is presented. Simulation results are provided. Experimental data verified the new technique and agrees with simulation.

This paper presents the study on the impedance matching method in two-stage converters for single phase PV-grid system. The use of PV systems was to obtain the electrical power from the sunlight energy. The system consisted of a Buck-Boost DC-DC converter and a five-level inverter. A Buck-Boost DC-DC converter was used as a means of impedance matching to obtain the maximum power that, in this case, through a method by using the incremental conductance current control algorithm. Meanwhile a five-level inverter was used as an interface to the utilities. By using this technique, the system came to be simple. The impedance of the power grid, a Buck-Boost DC-DC converter, and a five-level inverter were seen by PV mostly in the area of R<sub>MPP</sub>, enabling the maximum power produced by the PV to be delivered to the grid. To demonstrate the effectiveness of the design, the analysis and simulation results, furthermore, were provided

This study investigates the optimization of the size of a solar-wind hybrid microgrid using Particle Swarm Optimization (PSO) to improve energy production efficiency, economic feasibility, and overall sustainability. By using past solar and wind resource data, load demand profiles, and system component specifications, the PSO algorithm effectively maximized the capabilities of solar panels and wind turbines. The findings indicate a significant rise in daily energy production, with a 15% enhancement in solar panel capability and a 12% boost in wind turbine capability. The increased energy production plays a crucial role in dealing with the natural irregularity of renewable resources, hence enhancing the resilience and self-reliance of the microgrid. The economic calculations demonstrate significant improvements in the economic feasibility of the microgrid designs. The Levelized Cost of Energy (LCOE) undergoes a significant 10% decrease, suggesting a more economically efficient energy generation. Moreover, the payback time for the original expenditure is reduced by 15%, indicating faster returns on investment. The economic improvements highlight the practical advantages of using PSO for microgrid size, in line with the goal of creating sustainable energy solutions while minimizing economic costs. The improved performance of Particle Swarm Optimization (PSO) is shown by a thorough comparison study with other optimization approaches, such as Genetic Algorithms (GA) and Simulated Annealing (SA). The superior convergence rate of PSO, together with a 15% enhancement in solution quality relative to GA and SA, underscores the efficiency and efficacy of PSO in traversing the complex solution space associated with microgrid size. PSO’s comparative advantage makes it an effective tool for tackling the intricacies of integrating renewable energy, highlighting its potential for extensive use in microgrid design and optimization. The sensitivity evaluations demonstrate that the solutions optimized by the PSO are resilient even when important parameters vary, thereby highlighting the stability and dependability of the approach. In addition to technical and economic factors, the study evaluates the environmental consequences and social aspects of the optimum microgrid designs. The land use efficiency has seen a 10% enhancement, demonstrating the optimum application of area for renewable energy infrastructure. In addition, there is a 7% improvement in community approval, which demonstrates the algorithm’s ability to effectively handle social aspects and promote a comprehensive and socially acceptable approach to renewable energy projects.

As the aviation has realized the seriousness of pollution and emission issues, people have taken efforts to use renewable energy on planes or UAVs. This paper focused on the applications of solar and hydrogen energy to UAVs. A hybrid power system, consisting of solar cells, fuel cells and lithium batteries, was discussed. To achieve the hybridization of power sources, a prototype of a power management unit (PMU) was fabricated. After the installation of a test system for synthesizing power sources, PMU and load, a series of ground tests were executed to verify the mathematical model of lithium battery and the reliability of the hardware. Ground data confirmed the feasibility of hybrid power system.