Electric Power Prediction of 2D FE Magnetic Spring Energy Harvester Based on Coil using Support Vector Machines

Magnetic spring energy harvesters are considered to be a promising solution for powering small electronic devices because they employ the magnetic interaction between two fixed permanent magnets and one that is levitated vertically. This paper presents the two dimensional (2D) Finite Element Method (FEM) model of magnetic spring energy harvester based on coil layout. The simulation provides an optimized arrangement of number of turn and geometrical parameters of coil in the magnetic spring. The method based on Support Vector Machine (SVM) is proposed to predict electrical power of magnetic spring and to establish its dependence on the coil parameters. Furthermore an experimental analysis was performed to evaluate the electrical performance of the proposed magnetic spring system.

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Published
2024-11-26
DOI
https://doi.org/10.1109/esars-itec60450.2024.10819928
Primary Topic
Magnetic Bearings and Levitation Dynamics
Type
article
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article

Electric Power Prediction of 2D FE Magnetic Spring Energy Harvester Based on Coil using Support Vector Machines

T. Trawiński, Joanna Bijak, Fabio Viola, Zygmunt Kowalik et al.
Magnetic Bearings and Levitation Dynamics
article

Electric Power Prediction of 2D FE Magnetic Spring Energy Harvester Based on Coil using Support Vector Machines

T. Trawiński, Joanna Bijak, Fabio Viola, Zygmunt Kowalik, Grazia Lo Sciuto, Giacomo Capizzi
article en

Abstract

Magnetic spring energy harvesters are considered to be a promising solution for powering small electronic devices because they employ the magnetic interaction between two fixed permanent magnets and one that is levitated vertically. This paper presents the two dimensional (2D) Finite Element Method (FEM) model of magnetic spring energy harvester based on coil layout. The simulation provides an optimized arrangement of number of turn and geometrical parameters of coil in the magnetic spring. The method based on Support Vector Machine (SVM) is proposed to predict electrical power of magnetic spring and to establish its dependence on the coil parameters. Furthermore an experimental analysis was performed to evaluate the electrical performance of the proposed magnetic spring system.

Silesian University of Technology (PL), University of Catania (IT), University of Palermo (IT)
Affordable and clean energy
Openalex Percentile: Top 70%
Magnetic Bearings and Levitation Dynamics
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