A Self-powered Acceleration Sensor Based On Energy Harvesting

The Internet of Things and related sensor technology is a key driving force for the rapid development of industrial informatization and intelligence.The requirement of wireless,sustainable,and self-powered becomes increasingly important for sensor networks includ ing thousands even to millions of sensor nodes with different functionalities.For these purposes,developing technologies of self-powered sensors with the ambient environmental energy harvesting is highly desirable to solve the energy supply for sensors.Currently,enormous self-powered acceleration,pressure,and velocity sensors have been extensively studied by many researchers.Among them,self-powered acceleration sensor plays a crucial and essential role in many fields,such as wearable electronics,vehicle safety,and vibration monitoring.However,the applications of the reported self-powered acceleration sensors were limited by the low sensitivity.Therefore,the development of self-powered acceleration sensors with high sensitivity is vital for intelligent electronic products and industry.For this reason,we focus on the high-sensitivity and self-powered acceleration sensor fabrication,and the detailed work of this thesis is as following:(1)To develop a high-sensitivity and self-powered acceleration sensor utilizing the environmental energy,we first explore environmental energy harvesting technology.Based on the Wang-model's electrical double layer effect,we design a novel liquid-based triboelectric nanogenerator(L-TENG).A theoretica and a structure model are established to illuminate the droplet motion energy harvesting mechanism of the L-TENG.Guided by the above models,the designed L-TENG is fabricated by Micro Electro-Mechanical Systems(MEMS)compatible process technology,which achieve wafer-level and large-scale fabrication.The output voltage of the as-fabricated L-TENG has been increased from 19.1 m V to 57.5 m V by decreasing the electrode width and its interval.However,the optimized L-TENG is still unable to fabricate the high-sensitivity and self-powered acceleration sensor owing to the disadvantages of low output voltage and poor repeatability.We further explore the solid-based triboelectric nanogenerator.Based on the previous research of our group,a silk-fibroin triboelectric nanogenerator with the advantages of large-scale fabrication has been developed by a simple spray-coating process.The output voltage of silk-fibroin TENG is three orders of magnitude higher than that of L-TENG,and it has the potential to improve the sensitivity of the self-powered acceleration sensor.(2)Based on the as-fabricated silk-fibroin TENG,a high-sensitivity and self-powered acceleration sensor is developed.C urrently,the research on self-powered acceleration sensor based on TENG still has the theoretical defects.In this work,a novel V-Q-a theoretical model is presented to illuminate the working principle of the self-powered acceleration sensor.Guided by the V-Q-a model,the schematic of acceleration sensor is designed,and an acceleration sensor based on the silk-fibroin TENG is fabricated.The sensor exhibits a high sensitivity of 20.4 V/(m/s~2)in range of 1to 11 m/s~2,which also has a high-power density of 371.8 m W/m~2.These experimental results show a good agreement with the theoretical analysis.Compared with the reported self-powered acceleration sensor based on TENG,the as-fabricated acceleration sensor has increased sensitivity by more than 5 times.Moreover,our sensor has many application prospects including wearable alarming devices and vibration detection systems attributing to its higher sensitivity.(3)A novel intelligent umpiring system based on arrayed as-fabricated sensor nodes is developed to expand the application of the self-powered acceleration sensor.The referee of table tennis umpires the competition mainly based on visual observation and experience,which may impact on competition results due to the referee's subjective uncertainty and imprecision.In this paper,a novel intelligent umpiring system is presented to enhance the competition accuracy based on arrayed self-powered acceleration sensor nodes.Firstly,a sensor node array model is established to clearly illuminate the working mechanism of the proposed umpiring s ystem.Then,an improved particle swarm optimization,level-based competitive swarm optimization,is designed to optimize the arrayed sensor nodes distribution by redefining the representations and update rules for position and velocity of particle.The optimized results show that the number of sensors is reduced from 58to 51 by the proposed algorithm.In the end,the reliability of the optimized nodes distribution of the table tennis umpiring system has been verified theoretically,which achieves a precisely detecting of the ball collision point with an even error distance below 3.5 mm.The designed intelligent umpiring system will promote the real-time accuracy of the table tennis competition umpiring.