The recent growth of the IoT (Internet of Things) technologies makes these connected devices suitable for monitoring and diagnostic in different applications. Through these devices, a wireless sensor network has become a smart solution for monitoring structures, vehicles, and other devices. Each node in the network can be placed in an inaccessible or unsafe location for human intervention and provide a real-time data stream, useful for the diagnostic and maintenance of the structure. In this context, the power node becomes a fundamental problem since the replacement of batteries is a disadvantage both for environmental disposal and for the related costs. Thus, the interest in the so-called AIOT (Autonomous Internet of Things) is growing, and the energy harvester generators can be a possible solution to this problem. In this scenario, an inductive linear generator having a non-symmetrical gravitational suspension is presented. The main characteristics of the generator and the magnetic suspension are introduced with the description of the Matlab/Simulink model that simulates the same behavior. In this work, a first study of the duty cycle of the generator to power a wireless sensor node for industrial application is presented as well. This study is carried out with a particular focus on the acceleration frequency evaluation of railway vehicles to better understand the possible effective power that can be extracted from the harvester. The relevance of this work lies in the fact that the generator sizing cannot be separated from the detailed knowledge of the energy source and of the sensing/monitoring system that must be powered.
In this paper, the dynamic experimental identification of an inductive energy harvester for the conversion of vibration energy into electric power is presented. Recent advances and requirements in structural monitoring and vehicle diagnostic allow defining Autonomous Internet of Things (AIoT) systems that combine wireless sensor nodes with energy harvester devices properly designed considering the specific duty cycle. The proposed generator was based on an asymmetrical magnetic suspension and was addressed to structural monitoring applications on vehicles. The design of the interfaces of the electric, magnetic, and structural coupled systems forming the harvester are described including dynamic modeling and simulation. Finally, the results of laboratory tests were compared with the harvester dynamic response calculated through numerical simulations, and a good correspondence was obtained.
The growing spread of IoT (Internet of Things) and monitoring system based on micro electro-mechanical system (MEMS) concerns also the railway systems. The recent developments have made it possible to study and realize innovative integrated systems using connected devices. The technologies allow to collect real-time data from assets, thus providing fundamental information regarding the operation conditions and offering an evaluation of safety and durability of the monitored device. The major innovation of these devices is the use of energy harvesters to support or replace their power supply, making the monitoring device completely autonomous and drastically lowering the maintenance costs. The work presented in this paper is the product of the research group knowledge in vibrational energy harvesting. In particular, we studied a two-degrees-of-freedom (2DOF) gravitational vibration-based energy harvester (GVEH), characterized by the absence of the magnet on the top end of the tube, exploiting gravity as a restoring force. Different masses configuration were tested in order to find the best configuration optimizing power output, frequency bandwidth and overall performances. The time-domain simulations realized in Matlab/Simulink environment are supported with Multiboduy simulations for a better understanding of the system dynamics.
Energy harvesting is a promising technique for supplying low-power devices as an alternative to conventional batteries. Energy harvesters can be integrated into Autonomous Internet of Things (AIoT) systems to create a wireless network of sensor nodes for real-time monitoring of assets. This paper shows a design and optimization methodology for gravitational vibration-based electromagnetic energy harvesters (GVEHs) of different sizes considering the design constraints of its real application. The configuration, analytical model, and electro-mechanical coupling of these devices are described in detail. A numerical model is developed in the Ansys Maxwell FEM environment to derive the non-linear stiffness and damping of the asymmetric magnetic suspension. Experimental laboratory tests on three harvester prototypes are compared to numerical results of dynamic simulations in MATLAB/Simulink for the validation of the proposed model through error estimation. The fully-parametric validated model is used to perform sensitivity analyses on the device’s mechanical characteristics of natural frequency and magnet equilibrium position by varying the fixed and moving magnets dimensions. The set of magnets composing the magnetic spring is chosen complying with the application design constraints of size and resonance frequency tuning. Coil parameters of length and number of turns are optimized for maximum output power generation. The optimized device simulated performances are compared to other devices in the literature in terms of NPD, a significant index that evaluates power density under different excitation amplitudes. The optimized harvester presents the highest NPD value of 2.61, achieving an improvement of 52% with respect to the best harvester amongst the three tested prototypes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.