Detailed investigation of sinusoidal vibration on triboelectric energy harvester

Singh

Env Prog and Sustain Energy

et al. 2023

Self Cite

A simple and affordable triboelectric device is fabricated to harvest mechanical energy and convert it to electrical energy. Based on a double-dielectric-layered (Nylon and PTFE) structure, a lateral sliding mode triboelectric energy harvester (SM-TEH) has been fabricated. The fabricated SM-TEH device is linked to the slider-crank mechanism for smooth operation. The dimension of the fabricated device has been used as 10.1 cm Â 10.1 cm. An experimental study has been conducted for various rotational speeds at different external resistance. Additionally, an electrical model has been developed to validate the experimental results. After that, it has been carefully examined to see how the material, structure, and experimental parameters may affect the output performance of the SM-TEH. Later, a thorough investigation of the effects of the dual factors, including the effective triboelectric layer thickness, contact area, external resistance, and rotational speed (in rpm), was conducted. The relationship between conversion efficiency and external resistance is investigated to obtain the maximum efficiency at optimum external resistance. Further, the cost analysis of the SM-TEH device is presented to study the commercialization of the device for practical application. The feasibility of the designed SM-TEH is demonstrated by lighting on 40 red light-emitting diodes (LEDs). The AC signals generated are then converted into DC signals using a bridge rectifier. Capacitors of various sizes (1, 2.2, 3.3, 10, 47 μF) store the output voltage without external load resistance.

Section: Practical Applications Of Sm‐tehmentioning

confidence: 99%

Section: Practical Applications Of Sm-tehmentioning

confidence: 99%

Experiment and simulation of sliding mode triboelectric energy harvester based on slider‐crank mechanism

Singh

Env Prog and Sustain Energy

et al. 2023

Self Cite

“…Therefore, alternative, clean, and renewable energy sources must be explored. In the past decade, a plethora of energy harvesting techniques have emerged, including triboelectric, [ 3 ] electromagnetic, [ 4 ] piezoelectric, [ 5 ] thermoelectric, [ 6 ] and solar energy. [ 7 ] Of these, piezoelectric methods have been extensively studied due to their simple structure, lack of electromagnetic interference, and high energy conversion efficiency.…”

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

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.

Recent Advances in Mechanical Vibration Energy Harvesters Based on Triboelectric Nanogenerators

Dong

et al. 2023

Small

With the development of autonomous/smart technologies and the Internet of Things (IoT), tremendous wireless sensor nodes (WSNs) are of great importance to realize intelligent mechanical engineering, which is significant in the industrial and social fields. However, current power supply methods, cable and battery for instance, face challenges such as layout difficulties, high cost, short life, and environmental pollution. Meanwhile, vibration is ubiquitous in machinery, vehicles, structures, etc., but has been regarded as an unwanted by‐product and wasted in most cases. Therefore, it is crucial to harvest mechanical vibration energy to achieve in situ power supply for these WSNs. As a recent energy conversion technology, triboelectric nanogenerator (TENG) is particularly good at harvesting such broadband, weak, and irregular mechanical energy, which provides a feasible scheme for the power supply of WSNs. In this review, recent achievements of mechanical vibration energy harvesting (VEH) related to mechanical engineering based on TENG are systematically reviewed from the perspective of contact–separation (C‐S) and freestanding modes. Finally, existing challenges and forthcoming development orientation of the VEH based on TENG are discussed in depth, which will be conducive to the future development of intelligent mechanical engineering in the era of IoT.