Three-axis flexible tube sensor with LSTM-based force prediction for alignment of electric vehicle charging ports

This paper introduces a novel three-axis flexible tube sensor designed for force measurement in electric vehicle (EV) charging port alignment, utilizing long short-term memory (LSTM) networks. The research aims to develop and validate a flexible and accurate sensor system capable of predicting multi...

Full description

Saved in:
Bibliographic Details
Main Authors: Hendri Maja Saputra, Ahmad Pahrurrozi, Catur Hilman Adritya Haryo Bhakti Baskoro, Nur Safwati Mohd Nor, Nanang Ismail, Estiko Rijanto, Edwar Yazid, Mohd Zarhamdy Md Zain, Intan Zaurah Mat Darus
Format: Article
Language:English
Published: Indonesian Institute of Sciences 2024-12-01
Series:Journal of Mechatronics, Electrical Power, and Vehicular Technology
Subjects:
Online Access:https://mev.brin.go.id/mev/article/view/1104
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1849716367871180800
author Hendri Maja Saputra
Ahmad Pahrurrozi
Catur Hilman Adritya Haryo Bhakti Baskoro
Nur Safwati Mohd Nor
Nanang Ismail
Estiko Rijanto
Edwar Yazid
Mohd Zarhamdy Md Zain
Intan Zaurah Mat Darus
author_facet Hendri Maja Saputra
Ahmad Pahrurrozi
Catur Hilman Adritya Haryo Bhakti Baskoro
Nur Safwati Mohd Nor
Nanang Ismail
Estiko Rijanto
Edwar Yazid
Mohd Zarhamdy Md Zain
Intan Zaurah Mat Darus
author_sort Hendri Maja Saputra
collection DOAJ
description This paper introduces a novel three-axis flexible tube sensor designed for force measurement in electric vehicle (EV) charging port alignment, utilizing long short-term memory (LSTM) networks. The research aims to develop and validate a flexible and accurate sensor system capable of predicting multi-axis forces during alignment. The sensor integrates a magnetic sensor at the center of a flexible tube to capture three-dimensional (3-D) magnetic field variations corresponding to force changes. Fabricated using thermoplastic polyurethane (TPU) via 3-D printing technology, the sensor leverages machine learning to predict force values along the , , and  axes ( , , ). Finite element method (FEM) analysis was conducted to assess the deflection characteristics of the flexible tube under various force conditions. Experimental results demonstrate that integrating LSTM significantly enhances the accuracy of force prediction, achieving an R² score exceeding 97 % for all axes, with mean squared error (MSE) values of 0.2819 for the -axis, 0.3567 for the -axis, and 2.8086 for the -axis. The sensor is capable of measuring forces up to 30 N without exceeding its elastic limits. These findings highlight the sensor’s potential for improving alignment accuracy and reliability in automated EV charging systems.
format Article
id doaj-art-785eb4241d3c458ab7ccfd987e2c3a39
institution DOAJ
issn 2087-3379
2088-6985
language English
publishDate 2024-12-01
publisher Indonesian Institute of Sciences
record_format Article
series Journal of Mechatronics, Electrical Power, and Vehicular Technology
spelling doaj-art-785eb4241d3c458ab7ccfd987e2c3a392025-08-20T03:13:00ZengIndonesian Institute of SciencesJournal of Mechatronics, Electrical Power, and Vehicular Technology2087-33792088-69852024-12-0115220821910.55981/j.mev.2024.1104361Three-axis flexible tube sensor with LSTM-based force prediction for alignment of electric vehicle charging portsHendri Maja Saputra0Ahmad Pahrurrozi1Catur Hilman Adritya Haryo Bhakti Baskoro2Nur Safwati Mohd Nor3Nanang Ismail4Estiko Rijanto5Edwar Yazid6Mohd Zarhamdy Md Zain7Intan Zaurah Mat Darus8-Research Center for Smart Mechatronics, National Research and Innovation Agency - BRIN -Faculty of Mechanical Engineering, Universiti Teknologi Malaysia - UTMUIN Sunan Gunung DjatiResearch Center for Smart Mechatronics, National Research and Innovation Agency - BRINFaculty of Mechanical Engineering, Universiti Teknologi Malaysia - UTMUIN Sunan Gunung DjatiResearch Center for Smart Mechatronics, National Research and Innovation Agency - BRINResearch Center for Smart Mechatronics, National Research and Innovation Agency - BRINUniversiti Teknologi Malaysia - UTMUniversiti Teknologi Malaysia - UTMThis paper introduces a novel three-axis flexible tube sensor designed for force measurement in electric vehicle (EV) charging port alignment, utilizing long short-term memory (LSTM) networks. The research aims to develop and validate a flexible and accurate sensor system capable of predicting multi-axis forces during alignment. The sensor integrates a magnetic sensor at the center of a flexible tube to capture three-dimensional (3-D) magnetic field variations corresponding to force changes. Fabricated using thermoplastic polyurethane (TPU) via 3-D printing technology, the sensor leverages machine learning to predict force values along the , , and  axes ( , , ). Finite element method (FEM) analysis was conducted to assess the deflection characteristics of the flexible tube under various force conditions. Experimental results demonstrate that integrating LSTM significantly enhances the accuracy of force prediction, achieving an R² score exceeding 97 % for all axes, with mean squared error (MSE) values of 0.2819 for the -axis, 0.3567 for the -axis, and 2.8086 for the -axis. The sensor is capable of measuring forces up to 30 N without exceeding its elastic limits. These findings highlight the sensor’s potential for improving alignment accuracy and reliability in automated EV charging systems.https://mev.brin.go.id/mev/article/view/1104finite element method (fem) analysisflexible tube sensorforce measurementlong short-term memory (lstm) neural networkthree-axis force prediction.
spellingShingle Hendri Maja Saputra
Ahmad Pahrurrozi
Catur Hilman Adritya Haryo Bhakti Baskoro
Nur Safwati Mohd Nor
Nanang Ismail
Estiko Rijanto
Edwar Yazid
Mohd Zarhamdy Md Zain
Intan Zaurah Mat Darus
Three-axis flexible tube sensor with LSTM-based force prediction for alignment of electric vehicle charging ports
Journal of Mechatronics, Electrical Power, and Vehicular Technology
finite element method (fem) analysis
flexible tube sensor
force measurement
long short-term memory (lstm) neural network
three-axis force prediction.
title Three-axis flexible tube sensor with LSTM-based force prediction for alignment of electric vehicle charging ports
title_full Three-axis flexible tube sensor with LSTM-based force prediction for alignment of electric vehicle charging ports
title_fullStr Three-axis flexible tube sensor with LSTM-based force prediction for alignment of electric vehicle charging ports
title_full_unstemmed Three-axis flexible tube sensor with LSTM-based force prediction for alignment of electric vehicle charging ports
title_short Three-axis flexible tube sensor with LSTM-based force prediction for alignment of electric vehicle charging ports
title_sort three axis flexible tube sensor with lstm based force prediction for alignment of electric vehicle charging ports
topic finite element method (fem) analysis
flexible tube sensor
force measurement
long short-term memory (lstm) neural network
three-axis force prediction.
url https://mev.brin.go.id/mev/article/view/1104
work_keys_str_mv AT hendrimajasaputra threeaxisflexibletubesensorwithlstmbasedforcepredictionforalignmentofelectricvehiclechargingports
AT ahmadpahrurrozi threeaxisflexibletubesensorwithlstmbasedforcepredictionforalignmentofelectricvehiclechargingports
AT caturhilmanadrityaharyobhaktibaskoro threeaxisflexibletubesensorwithlstmbasedforcepredictionforalignmentofelectricvehiclechargingports
AT nursafwatimohdnor threeaxisflexibletubesensorwithlstmbasedforcepredictionforalignmentofelectricvehiclechargingports
AT nanangismail threeaxisflexibletubesensorwithlstmbasedforcepredictionforalignmentofelectricvehiclechargingports
AT estikorijanto threeaxisflexibletubesensorwithlstmbasedforcepredictionforalignmentofelectricvehiclechargingports
AT edwaryazid threeaxisflexibletubesensorwithlstmbasedforcepredictionforalignmentofelectricvehiclechargingports
AT mohdzarhamdymdzain threeaxisflexibletubesensorwithlstmbasedforcepredictionforalignmentofelectricvehiclechargingports
AT intanzaurahmatdarus threeaxisflexibletubesensorwithlstmbasedforcepredictionforalignmentofelectricvehiclechargingports