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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 14 Documents
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Search results for , issue "Vol 15, No 1 (2024)" : 14 Documents clear
Robust remaining useful life prediction of lithium-ion battery with convolutional denoising autoencoder Yuliani, Asri Rizki; Pardede, Hilman Ferdinandus; Ramdan, Ade; Zilvan, Vicky; Yuwana, Raden Sandra; Amri, M Faizal; Kusumo, R. Budiarianto Suryo; Pramanik, Subrata
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 15, No 1 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2024.905

Abstract

Using lithium-ion (Li-ion) batteries exceeding their useful lifetime may be dangerous for users, and hence, developing an accurate prediction system for batteries that remain useful for life is necessary. Many deep learning models, such as gated recurrent units and long short-term memory (LSTM), have been proposed for that purpose and have shown good results. However, their performance when dealing with noisy data degrades significantly. This may hamper their implementations for the real world since battery data are prone to noise. In this paper, we develop a robust prediction model in a noisy environment for predicting the remaining useful life (RUL) of Li-ion batteries. We propose a denoising autoencoder (DAE) utilized to remove noise from the data. The DAE is built with convolutional layers instead of traditional feed-forward networks here. We combine DAE with LSTM as the predictor. The proposed framework is evaluated using artificially corrupted battery data provided by National Aeronautics and Space Administration (NASA). The results reveal that our proposed method improves robustness when data contain various types of noise. A comparative study using the traditional approach has also been conducted. Our evaluation shows that convolutional layers are more effective than the traditional approach and that the original composition of the DAE was built using traditional feed-forward networks. DAE with convolutional layers has the best average performance with MSE of 0.61 and is the most consistent model.
Front Cover MEV Vol 15 Iss 1 Pikra, Ghalya
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 15, No 1 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2024.1023

Abstract

Photovoltaic energy harvesting booster under partially shaded conditions using MPPT based sand cat swarm optimizer Abdilla, Moch Rafi Damas; Windarko, Novie Ayub; Sumantri, Bambang
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 15, No 1 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2024.857

Abstract

Photovoltaic (PV) systems perform a vital role in addressing the worldwide energy crisis and fulfilling the escalating energy demand. The variability in irradiance, temperature, and unpredictable weather conditions possess a direct impact on the productivity of PV systems. Furthermore, the existence of partially shaded conditions intensifies the complexity of PV systems, resulting in significant power degradation. These conditions present significant challenges for PV systems to achieve maximum power output and produce optimal energy. To address the prevailing challenges, this study introduces a maximum power point tracking (MPPT) control methodology utilizing a sand cat swarm optimizer (SCSO). This ingenious strategy adapts the sand cat hunting style. The investigation centers on optimizing energy harvesting in PV systems, with a specific emphasis on enhancing precision, rapid convergence, and minimizing oscillations. The suggested SCSO performance is evaluated under a variety of weather situations, including both instances of partially shaded and uniform irradiance. The SCSO results are juxtaposed with other existing bio-inspired algorithms, such as grey wolf optimization (GWO), particle swarm optimization (PSO), and tunicate swarm algorithm (TSA). The proposed SCSO technique achieves 99.94 % tracking accuracy on average and shows superior performance, with faster tracking response and less power oscillation. Moreover, the proposed SCSO generates significantly more energy than the rest compared algorithms. The performance of the suggested method is further validated through a hardware-based experimental assessment, demonstrating an optimal level of tracking performance.
Smart watering of ornamental plants: exploring the potential of decision trees in precision agriculture based on IoT Pratama, Hafiyyan Putra; Hadi Putri, Dewi Indriati; Putri, Hafiziani Eka; Irawan, Elysa Nensy; Kautsar, Makna A’raaf
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 15, No 1 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2024.963

Abstract

Ornamental plant farmers face various challenges due to climate change and environmental stress that significantly affect plant health and growth. This research overcomes these challenges by developing an intelligent watering system that uses internet of things (IoT) technology and decision trees (DTs) algorithms to optimize the use of planting land by ensuring plants grow in the most optimal conditions, both in terms of water and nutrients and increase land productivity. The system is built by integrating various sensors to monitor soil moisture, air humidity, temperature, and light intensity in real-time. The collected data is used to automate watering schedules and provide recommendations on suitable plant species based on the soil nutrient content of nitrogen (N), phosphorus (P), and potassium (K). The use of the DTs algorithm helps in analyzing the data from the sensors and providing recommendations on the most suitable plants for the land. The smart watering system was tested in three zones, each simulating a different watering scenario, and successfully maintained optimal conditions for plant growth in each zone. The machine learning (ML) model with the DTs algorithm can predict the right type of ornamental plants based on the existing land conditions in three watering zones, with an accuracy of 89 %, 90 %, and 91 %, respectively. Furthermore, farmers can follow these recommendations to minimize damage and death of plants so that the level of productivity on the land becomes optimal.
An experimental investigation of an energy regeneration suspension Tho, Nguyen Huu; Danh, Le Thanh
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 15, No 1 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2024.811

Abstract

Energy absorbed from road bumps in traditional suspensions is dissipated as heat. An energy regeneration suspension (ERS) has the capability to capture and store this energy in batteries. It has the potential to be used in several categories of vehicles, encompassing cars, trucks, buses, and even trains. ERS technology shows significant promise in enhancing the fuel efficiency and environmental sustainability of vehicles. In this paper, the design of an ERS that converts kinetic energy into electrical energy is presented. The primary objective is to identify key design parameters that result in high magnetic intensity levels in the air gap of the ERS model. Optimizing these parameters is essential to maximize the advantages of ERS while minimizing any drawbacks. The study investigates the impact of different magnetic permeability materials in the ERS model using ANSYS software. A test rig is established based on the analysis results to assess the energy regeneration efficiency of the ERS model under various excitations. Experimental results demonstrate that ERS models with higher permeability inner sleeves exhibit superior energy regeneration efficiency.
Design of image classification system for fabric inspection process using Raspberry Pi Nugroho, Emmanuel Agung; Setiawan, Joga Dharma; Munadi, Munadi; Diki, Diki
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 15, No 1 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2024.863

Abstract

This research is designed as a prototype of defect inspection system on fabric production using machine learning-based image processing technology using the open source Google teachable machine application integrated with Raspberry Pi-3B. The prototype of fabric defect inspection system is built by utilizing two rollers that function as a fabric roll house before and after the inspection process. On both rollers, a fabric is stretched to be inspected, so that from a roll of fabric with a certain length, it can be seen how many defects occur on the fabric. The inspection system is carried out using a web camera with a certain level of lighting connected to a raspberry pi as a control device. Raspberry Pi functions as an image processing device and fabric rolling motor controller. In addition to the category of fabric in good condition, this system classifies into two categories of defects, namely slap defects and sparse defects. The test results show that this system has an average frame per second (FPS) of 4.85, an average inference time of 181.1 ms, with an accuracy of image classification results of 98.4 %.
Preface MEV Vol 15 Iss 1 Pikra, Ghalya
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 15, No 1 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2024.1024

Abstract

Comparative performance evaluation of dual-axis solar trackers: Enhancing solar harvesting efficiency Jaafar, Saman Sarkawt; Maarof, Hiwa Abdlla; Hamasalh, Hardi Bahman; Ahmed, Kardo Muhammed
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 15, No 1 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2024.808

Abstract

Nowadays, renewable energy is a much discussable topic because of its important specifications such as pollution and un end sources. This paper emphasizes the importance and capabilities of the dual axis solar tracking system (DASTS) of solar cell technologies. The study carried out a practical process of a dual-axis system design, which practically demonstrates the influence of dual-axis solar trackers, including their operational principles, advantages, and associated challenges. Furthermore, it has been shown that dual-axis solar trackers can significantly increase solar energy yield by capturing maximum sunlight, making them an important advancement in improving the efficiency and effectiveness of solar energy generation. The paper also presents a performance evaluation of a solar tracker system that improved energy output by 45 % compared to the fixed solar panel (FSP) while also reducing the numerical calculation (NC) efficiency of DASTS by 3.16 %. Based on these findings, solar tracker systems can provide sustainable and environmentally friendly energy solutions that are both feasible and substantial.
Design of intelligent cruise control system using fuzzy-PID control on autonomous electric vehicles prototypes Saputro, Joko Slamet; Anwar, Miftahul; Adriyanto, Feri; Ramelan, Agus; Yusuf, Putra Maulana; Irsyadi, Fakih; Firmansyah, Rendra Dwi; Putri, Tri Wahyu Oktaviana
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 15, No 1 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2024.877

Abstract

Electric vehicles provide a solution for using alternative fuels, namely, electricity. Electric vehicles are used for short distances and intercity travel over long distances, increasing the risk of accidents. Cruise Control is a technology embedded in vehicles to maintain stable speeds; this system will automatically adjust the vehicle's speed when motion changes cause changes in vehicle speed. This study aims to apply lidar sensors to detect distance in the Intelligent Cruise Control (ICC) system using the Fuzzy-PID control method. Testing results were obtained at safe distance inputs of 5, 6, and 7 meters with various object distances. All the tests were carried out; the response systems were obtained with an average settling time of 5 seconds and an average overshoot of 1.53%. Therefore, the proposed Fuzzy-PID method works well for controlling Intelligent Cruise Control systems in autonomous electric vehicle prototypes.
Appendix MEV Vol 15 Iss 1 Pikra, Ghalya
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 15, No 1 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2024.1025

Abstract

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