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INDONESIA
Mechatronics, Electrical Power, and Vehicular Technology
Published by Lembaga Ilmu Pengetahuan Indonesia
ISSN : 20873379     EISSN : 20886985     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Mechatronics, Electrical Power, and Vehicular Technology (hence MEV) is a journal aims to be a leading peer-reviewed platform and an authoritative source of information. We publish original research papers, review articles and case studies focused on mechatronics, electrical power, and vehicular technology as well as related topics. All papers are peer-reviewed by at least two referees. MEV is published and imprinted by Research Center for Electrical Power and Mechatronics - Indonesian Institute of Sciences and managed to be issued twice in every volume. For every edition, the online edition is published earlier than the print edition.
Arjuna Subject : -
Articles 16 Documents
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Search results for , issue "Vol 15, No 2 (2024)" : 16 Documents clear
Preface MEV Vol 15 Iss 2 Pikra, Ghalya
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 15, No 2 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2024.1144

Abstract

Ergonomic analysis of gaming mouse using electromyography and subjective assessment Zafs, Rizqie Anandita; Riyadi, Slamet; Sanjaya, Kadek Heri; Sya’Bana, Yukhi Mustaqim Kusuma; Dewi, Nugrahaning Sani; Tajalli, Muqorob; Yusuf, Sharfiden Hassen
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 15, No 2 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2024.1110

Abstract

First Person Shooter (FPS) gamers usually use mice intensively, which may lead to musculoskeletal disorders. The objective of this study is to investigate the effect of the three most popular gaming mice designs on arm muscle activity and subjective perception to find the most optimum design. Subjects who participated in this study were ten healthy young adult males (age 18-24 years) selected from FPS gamers. The mice were tested when the subjects played the FPS Valorant game. The activity of forearm muscles, namely flexor carpi radialis (FCR) and palmaris longus (PL), was measured using electromyography (EMG). One-way analysis of variance (ANOVA) was used to compare the muscular activities when using the three mice. This study also measured the subjective perception of the participants. The study reveals that the high activation of muscles confirms the risk of Carpal Tunnel Syndrome (CTS). However, the difference in muscular activities between the three mice was found to be insignificant. On the other hand, the qualitative analysis suggests that the third mouse showed the evenest distribution of muscular activity both at low cycle and high cycle performance. Further study using psychophysiological methods is necessary to measure subjective preferences.
Three-axis flexible tube sensor with LSTM-based force prediction for alignment of electric vehicle charging ports Saputra, Hendri Maja; Pahrurrozi, Ahmad; Baskoro, Catur Hilman Adritya Haryo Bhakti; Nor, Nur Safwati Mohd; Ismail, Nanang; Rijanto, Estiko; Yazid, Edwar; Zain, Mohd Zarhamdy Md; Darus, Intan Zaurah Mat
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 15, No 2 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2024.1104

Abstract

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.
Energy consumption, CO2, and cost analysis of hybrid and battery electric motorcycle Yuwono, Taufik; Sukra, Kurnia Fajar Adhi; Soewono, Respatya Teguh; Indriatmono, Dedy; Fuad, Nur Muhamad; Ma'ruf, Muhammad; Samanhudi, Ramadhani Deniartio; Kurniawan, Ade; Nugroho, Rudi Cahyo; Wahidin, Agus; Hayoto, Vebriyanti; Suryantoro, Muchammad Taufiq; Mokhtar, Mokhtar; Hidayat, Muhammad Novel; Wahono, Bambang; Pratama, Mulia; Nur, Arifin; Dimyani, Ahmad; Suherman, Suherman; Wardana, Muhammad Khristamto Aditya; Praptijanto, Achmad; Putrasari, Yanuandri; Prawara, Budi; Budianto, Hari
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 15, No 2 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2024.989

Abstract

The electrification of the two-wheel vehicle segment is an important strategy for decarbonising the transportation sector. This study aimed to assess the hybridisation of gasoline motorcycles with battery electric systems as an option for decarbonisation. A gasoline motorcycle that had been converted to a hybrid motorcycle was evaluated in several aspects: energy consumption, greenhouse gas (GHG) emission, and cost of energy. The vehicle was tested under the United Nations economic commission for europe (UNECE) Regulation No.40 and compared to a battery electric motorcycle. The test in internal combustion engine (ICE) mode consumed 233.31 Wh/km of specific energy, emitted 60.69 gCO2/km and cost 1.65 US-cent/km on average. The test in hybrid mode consumed specific energy at 6 % higher and 4 % lower specific energy consumption than ICE, thus not improving the carbon dioxide (CO2) emission and operating cost. In electric battery mode, energy consumption was saved by 83 %, with 35 % lower CO2 and 74 % cost savings. The battery electric motorcycle runs more efficiently with 88 % lower energy consumption, 53.8 % lower CO2 and saved cost by 82 %. If the hybrid controller is improved in future development, it could lower specific energy consumption by 41.7 %, reduce CO2 by 11.2 % and save cost by 35.7 %.
Appendix MEV Vol 15 Iss 2 Pikra, Ghalya
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 15, No 2 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2024.1145

Abstract

Non-linear model predictive control with single-shooting method for autonomous personal mobility vehicle Pratama, Rakha Rahmadani; Baskoro, Catur Hilman Adritya Haryo Bhakti; Setiawan, Joga Dharma; Dewi, Dyah Kusuma; Paryanto, Paryanto; Ariyanto, Mochammad; Saputra, Roni Permana
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 15, No 2 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2024.1105

Abstract

The advancement of autonomous vehicle technology has markedly evolved during the last decades. Reliable vehicle control is one of the essential technologies in this domain. This study aims to develop a proposed method for controlling an autonomous personal mobility vehicle called SEATER (Single-passenger Electric Autonomous Transporter), using Non-linear Model Predictive Control (NMPC). We propose a single-shooting technique to solve the optimal control problem (OCP) via non-linear programming (NLP). The NMPC is applied to a non-holonomic vehicle with a differential drive setup. The vehicle utilizes odometry data as feedback to help guide it to its target position while complying with constraints, such as vehicle constraints and avoiding obstacles. To evaluate the method's performance, we have developed the SEATER model and testing environment in the Gazebo Simulation and implemented the NMPC via the Robot Operating System (ROS) framework. Several simulations have been done in both obstacle-free and obstacle-filled areas. Based on the simulation results, the NMPC approach effectively directed the vehicle to the desired pose while satisfying the set constraints. In addition, the results from this study have also pointed out the reliability and real-time performance of NMPC with a single-shooting method for controlling SEATER in the various tested scenarios.

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