Linear Reciprocating Motion Energy Harvester Systems Based on Multiple Operation Modes: A Review

Gu, Xiangfeng; He, Lipeng; Wang, Shuangjian; Liu, Lei; Yi, Hong-Xun; Cheng, Guangming

doi:10.1007/s11664-023-10285-1

Cited by 3 publications

(3 citation statements)

References 50 publications

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“…In recent years, wireless sensor networks (WSNs) and microelectromechanical systems (MEMS) have been widely used in the industrial and consumer electronics. To reduce the impact of frequent battery replacement, the sustainable energy is needed to supply these low-power microelectronics [1,2]. Thus, vibration energy harvesting (VEH) based on piezoelectric transducer (PZT) has attracted much attention because of its advantages of high electromechanical coupling efficiency, high energy density, easy of miniaturization, and no electromagnetic interference [3,4,5].…”

Section: Introductionmentioning

confidence: 99%

Self-powered multi-PZTs energy harvesting interface based on extensible PSSHI rectification with power region optimization technique

Chen,

Xia,

Fang

et al. 2024

IEICE Electron. Express

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This paper proposes a self-powered multiple piezoelectric (multi-PZTs) energy harvesting interface based on extensible parallel synchronized switching harvesting on inductor (PSSHI) rectification with power region optimization technique. The extensible PSSHI rectifier can accept multi-PZTs voltages input with arbitrary phase, thus addressing the charge conflict problem. The power region optimizer can assist the rectifier to operate within the high output power region. Meanwhile, all active devices in the circuit are powered by the harvested energy. Finally, the experimental results show that the average charging power of the circuit is 559.8W, and the energy conversion efficiency is 80.7%. Compared with the extensible PSSHI rectifier without power region optimization, the average charging power in this circuit is increased by 94%. Additionally, the experimental tests indicate that the interface circuit can completely realize cold start and self-power supply.

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Section: Introductionmentioning

confidence: 99%

Self-powered multi-PZTs energy harvesting interface based on extensible PSSHI rectification with power region optimization technique

Chen,

Xia,

Fang

et al. 2024

IEICE Electron. Express

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“…[ 7 ] Of these, piezoelectric methods have been extensively studied due to their simple structure, lack of electromagnetic interference, and high energy conversion efficiency. [ 8 ]…”

Section: Introductionmentioning

confidence: 99%

“…[7] Of these, piezoelectric methods have been extensively studied due to their simple structure, lack of electromagnetic interference, and high energy conversion efficiency. [8] Vibration-based techniques present a diverse array of potential applications within the realm of piezoelectric energy harvesting (PEH). These methods harness energy from sources as varied as wind, water, vehicles, and even human locomotion.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Piezoelectric Tile Performance in Flat and Mountainous Terrains Through Deep Neural Network

Adhikari,

Singh,

Sagar

et al. 2023

Advcd Theory and Sims

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Piezoelectric tiles harvest mechanical vibrations and convert them into electrical energy, making them an attractive energy‐harvesting technology. However, their performance is heavily influenced by the terrain where they are installed. Traditional experimental methods for predicting their performance on different terrains are time‐consuming, so a computational approach is necessary to improve efficiency. To address this, a machine learning‐based approach is proposed using Artificial Neural network (ANN) and Deep neural network (DNN) with the Tanh activation function to predict piezoelectric tile performance in diverse terrains. The models are trained on an experimental dataset consisting of four terrains, including Flat terrain (FT) and Hilly Terrain (HT) 1, 2, 3 with road angles of 0, 3, 6, and 10 degrees. A finite element model is also established to optimize the piezoelectric tile and estimate the suitable parameter range to prevent damage to the tile during experiments. The results indicate that the DNN model performs better than the ANN model, achieving high accuracy in predicting piezoelectric tile performance on different terrains. These findings suggest that machine learning can provide a time and cost‐effective way for predicting the performance of piezoelectric tiles in varied terrains, thereby facilitating betters installation and maintenance decisions for piezoelectric tile systems.

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Optimal noise injection energy allocation and collection of energy harvesting attacker under quality-changeable environment

Dong,

Han,

Yuan

et al. 2024

Sci. China Inf. Sci.

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Linear Reciprocating Motion Energy Harvester Systems Based on Multiple Operation Modes: A Review

Cited by 3 publications

References 50 publications

Self-powered multi-PZTs energy harvesting interface based on extensible PSSHI rectification with power region optimization technique

Self-powered multi-PZTs energy harvesting interface based on extensible PSSHI rectification with power region optimization technique

Prediction of Piezoelectric Tile Performance in Flat and Mountainous Terrains Through Deep Neural Network

Optimal noise injection energy allocation and collection of energy harvesting attacker under quality-changeable environment

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