Power density improvement of the power conditioning circuit for combined piezoelectric and electrodynamic generators

Zessin, Henrik; Spies, Peter; Mateu, Loreto

doi:10.1088/1742-6596/773/1/012055

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…In the experiment, the environmental parameters are light intensity of 45700LUX and temperature of 5 DEG C. According to the curve in Figure 3, it is known that, with the increase of k value, the output power of photovoltaic cell and the output current of converter are both increased first and then decreased, and the maximum value is reached under the same condition of k=0.8 [9]. The experimental data show that the tracking of the maximum output power point (the maximum power point) of the solar panel can be realized by detecting the output current of the converter and by adjusting the k value.…”

Section: Mppt Experimental Datamentioning

confidence: 99%

Design of Solar Power Management Circuit Based on Wireless Sensor Network

Jianying

2017

Int. J. Onl. Eng.

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Abstract-To explore the design of solar power management circuit, the fuzzy logic control algorithm based on MPPT, which has fast control speed and good environment robustness, is adopted as the control algorithm. In addition, the MPPT solar battery charge and discharge power management circuit is designed and successfully applied in the on-line measurement projects of dielectric loss of wireless sensor network in Jilin Province LG Electronics Company. The results show that the charging efficiency of solar battery charge and discharge power management circuit can reach above 80%, and the current of static power management circuit is less than 1mA. In different light intensities, the dynamic power management is intelligently carried out. At last, it is concluded that the stability and reliability of circuit are quite high.Keywords-Wireless sensor, power management, circuit design IntroductionIn the research on solar power supply, the control algorithms of MPPT in the past are divided into the following three categories: indirect control method based on parameter selection, direct control method based on sampled data, and intelligent control method based on modern control theory. Among them, the algorithm based on parameter selection is comparatively simple, but it cannot adapt to the influence of environment change on the parameters of solar panel. The control method based on sampled data is dynamic, which makes the maximum power point tracking according to environmental conditions, but the tracking is too slow. The MPPT algorithm based on the modern control theory makes up for the shortcomings of the above two algorithms, and it can achieve better maximum power point tracking. Compared with the previous two algorithms, this algorithm is more complex. Fuzzy logic control algorithm has the characteristics of fast control speed, good environment robustness and simple algorithm realization. As a result, it is chosen as the control algorithm for further study.

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Section: Mppt Experimental Datamentioning

confidence: 99%

Design of Solar Power Management Circuit Based on Wireless Sensor Network

Jianying

2017

Int. J. Onl. Eng.

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“…The addition of a DC-DC converter has been shown to improve the conversion efficiency by a factor of seven. A more specialized power management system for piezoelectric generators is the “synchronized switch harvesting on inductor” (SSHI) circuit [25,26,27]. It consists of a switching device in parallel with a piezoelectric element.…”

Section: Introductionmentioning

confidence: 99%

Optimization of a Piezoelectric Energy Harvester and Design of a Charge Pump Converter for CMOS-MEMS Monolithic Integration

Duque

Leon-Salguero

Sacristán

et al. 2019

Sensors

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The increasing interest in the Internet of Things (IoT) has led to the rapid development of low-power sensors and wireless networks. However, there are still several barriers that make a global deployment of the IoT difficult. One of these issues is the energy dependence, normally limited by the capacitance of the batteries. A promising solution to provide energy autonomy to the IoT nodes is to harvest residual energy from ambient sources, such as motion, vibrations, light, or heat. Mechanical energy can be converted into electrical energy by using piezoelectric transducers. The piezoelectric generators provide an alternating electrical signal that must be rectified and, therefore, needs a power management circuit to adapt the output to the operating voltage of the IoT devices. The bonding and packaging of the different components constitute a large part of the cost of the manufacturing process of microelectromechanical systems (MEMS) and integrated circuits. This could be reduced by using a monolithic integration of the generator together with the circuitry in a single chip. In this work, we report the optimization, fabrication, and characterization of a vibration-driven piezoelectric MEMS energy harvester, and the design and simulation of a charge-pump converter based on a standard complementary metal–oxide–semiconductor (CMOS) technology. Finally, we propose combining MEMS and CMOS technologies to obtain a fully integrated system that includes the piezoelectric generator device and the charge-pump converter circuit without the need of external components. This solution opens new doors to the development of low-cost autonomous smart dust devices.

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