Enhanced piezoelectric energy harvesting power with thermoelectric energy assistance

Chen, Zhidong; Xia, Yinshui; Shi, Ge; Xia, Huakang; Wang, Xiudeng; Qian, Libo; Ye, Yidie

doi:10.1177/1045389x21991238

Cited by 7 publications

(6 citation statements)

References 29 publications

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“…PV-TEG � Less heat in the PV cell [46] � Stable power generation [50] � High fabrication cost [51] � Efficiency increase of up to 21.9 % [46] T-TENG � Low fabrication cost [52] � High energy-conversion efficiency [52] � Long service lifetime [49] � Lightweight [49] � Low environmental impact [49] TEG & PZT � High sensitivity and conversion efficiency [53] � Harvested power increase of up to 33.3 % [47] TEG & RF � Low fabrication cost [48] � Low conversion efficiency [48] � Suitable for low-power application scenarios [54] energy harvesting topology operates well in low-power ranges and is low cost, but it has a low conversion efficiency [48].…”

Section: Methods Characteristicsmentioning

confidence: 99%

“…The use of two energy harvesting methods is called a hybrid energy harvesting system. Hybrid energy harvesting systems use a combination of the TEG and one or more other energy sources, for example, PV [45, 46], PZT [47], RF signal generator [48], or TENG [49].…”

Section: Thermoelectric Generatorsmentioning

confidence: 99%

“…Hybrid TEG/PZT systems generate power [53] by converting vibrational energy through thermal excitation into electric energy. Compared to a single PZT harvesting method, this hybrid topology has up to 33.3% greater harvesting power [47], high sensitivity, and high conversion efficiency [53]. Another combination of power sources uses RF signals and thermal energy.…”

Section: Thermoelectric Generatorsmentioning

confidence: 99%

See 2 more Smart Citations

Thermoelectric energy harvesting for internet of things devices using machine learning: A review

Kucova,

Prauzek,

Konecny

et al. 2023

CAAI Trans on Intel Tech

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Initiatives to minimise battery use, address sustainability, and reduce regular maintenance have driven the challenge to use alternative power sources to supply energy to devices deployed in Internet of Things (IoT) networks. As a key pillar of fifth generation (5G) and beyond 5G networks,IoT is estimated to reach 42 billion devices by the year 2025. Thermoelectric generators (TEGs) are solid state energy harvesters which reliably and renewably convert thermal energy into electrical energy. These devices are able to recover lost thermal energy, produce energy in extreme environments, generate electric power in remote areas, and power micro‐sensors. Applying the state of the art, the authorspresent a comprehensive review of machine learning (ML) approaches applied in combination with TEG‐powered IoT devices to manage and predict available energy. The application areas of TEG‐driven IoT devices that exploit as a heat source the temperature differences found in the environment, biological structures, machines, and other technologies are summarised. Based on detailed research of the state of the art in TEG‐powered devices, the authors investigated the research challenges, applied algorithms and application areas of this technology. The aims of the research were to devise new energy prediction and energy management systems based on ML methods, create supervised algorithms which better estimate incoming energy, and develop unsupervised and semi‐supervised approaches which provide adaptive and dynamic operation. The review results indicate that TEGs are a suitable energy harvesting technology for low‐power applications through their scalability, usability in ubiquitous temperature difference scenarios, and long operating lifetime. However, TEGs also have low energy efficiency (around 10%) and require a relatively constant heat source.

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Section: Methods Characteristicsmentioning

confidence: 99%

Section: Thermoelectric Generatorsmentioning

confidence: 99%

Section: Thermoelectric Generatorsmentioning

confidence: 99%

See 1 more Smart Citation

Thermoelectric energy harvesting for internet of things devices using machine learning: A review

Kucova,

Prauzek,

Konecny

et al. 2023

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

show abstract

“…Advanced energy harvesting technology and diverse renewable energy sources offer many implementation forms for energy conversion, such as solar (Cao et al, 2014; Perez-Rosado et al, 2015; Wang et al, 2021a), thermal (Ji et al, 2021; Sarris et al, 2022), wind (Sirohi and Mahadik, 2011; Wang et al, 2019), vibration energy harvesters (VEHs) (Yang et al, 2021; Zhou and Zuo, 2018; Zhou et al, 2017), and their hybrid application (Chen et al, 2021; Pan et al, 2022). In recent years, with the progress in performance and efficiency, vibration energy harvesting is considered to be promising and becomes a research hotspot (Bosso et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Mechanically-driven energy harvester in railway freight system: Integrated modeling and performance analysis

Dong

Yu²

2023

Journal of Intelligent Material Systems and Structures

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Vibration energy harvesters (VEHs) convert mechanical energy of unpowered freight wagons into electricity and unlock more smart devices to improve operating conditions. In this paper, the nut-screw-based mechanically-driven VEHs (MD-VEHs) along with several mechanical motion rectifiers (MMRs) are proposed and compared in nonlinear railway freight system. Firstly, the working principles of harvesters with non-MMR, half-wave and full-wave MMRs are researched, and equivalent circuit models are established to better reveal the engagement and disengagement of one-way clutches, followed by the integrated model of wagon-harvester system which studies the suspension vibration response and assesses harvester performance. The harvester equivalent circuit model is validated on a horizontal test bench, and presents a good consistency with test data. In addition, the effects of harvester equivalent damping and inertia on performance are explored systematically. The analysis results indicate that full-wave MMR presents the best power performance under the same parameter configuration. The increase of flywheel inertia will decrease the power generation capacity of non-MMR configuration, but further enhance those of half-wave and full-wave MMRs and narrow the performance gap between both. This analysis based on system coupling will be instructive for the engineering design and application of railway VEHs.

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“…A more cost-effective and efficient alternative needs to be explored to improve the longterm functionality of the system. A buck-based synchronous charge extraction (BUCK-SECE) interface for the PZT was proposed in [8] and can be easily extended to multiple piezoelectric inputs. But there is still a limit to the output power of a single PZT.…”

Section: Introductionmentioning

confidence: 99%

Bidirectional Thermoelectric Assisted Synchronous Charge Extraction Circuit Based on Buck Structure for Piezoelectric Energy Harvesting

Wu,

Wang,

Xia

2023

J. Phys.: Conf. Ser.

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Energy harvesting technology is currently a research hotspot, which can effectively solve energy supply problems in fields such as wireless sensor networks, wearable devices, and smart homes. An energy harvesting system using a thermoelectric generator (TEG) for energy assistance of a piezoelectric transducer (PZT) is proposed. It employs a synchronous charge extraction circuit based on a buck structure (BUCK-SECE), which uses thermoelectric energy to inject energy into the piezoelectric element in both directions to boost the initial voltage of the BUCK-SECE, increasing its output power. The experimental results demonstrate that the utilization of thermoelectric energy can increase the output power of BUCK-SECE by a factor of 3.3 under low-vibration conditions and by a factor of 0.8 under high-vibration conditions. Therefore, the proposed energy harvesting system in this article can effectively enhance the output power of piezoelectric energy transducers and yield desirable performance in real-world applications.

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Enhanced piezoelectric energy harvesting power with thermoelectric energy assistance

Cited by 7 publications

References 29 publications

Thermoelectric energy harvesting for internet of things devices using machine learning: A review

Thermoelectric energy harvesting for internet of things devices using machine learning: A review

Mechanically-driven energy harvester in railway freight system: Integrated modeling and performance analysis

Bidirectional Thermoelectric Assisted Synchronous Charge Extraction Circuit Based on Buck Structure for Piezoelectric Energy Harvesting

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