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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 596 Documents
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Innovations and advancements in solar tracker systems: A comprehensive review Essa, Mohamed El-Sayed M.; Hassan, Alyaa Abdo; El-Kholy, Elwy E.; Ahmed, Mohamed Mostafa Ramadan
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 16, No 1 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2025.1004

Abstract

This review paper demonstrates an in-depth discussion of the technological development in different solar tracking systems, which is one of the important components of solar power generation. These systems play a distinctive role in raising the energy generated by moving solar panels towards the sun. This paper is concerned with shedding light on the different classifications of tracking systems, control methods, and major modern smart components, including remote system monitoring and control, with an emphasis on the importance of increasing efficiency and cost-effectiveness. The study of solar tracker systems is crucial to consolidate current knowledge, recognize gaps in research, and foster revolution in this area. It offers a comprehensive thoughtful of up-to-date developments in control systems, classifications, and evolving technologies such as the internet of things (IoT) and artificial intelligence (AI). The study highlights limitations in prevailing systems, directorial future studies and, research to improve scalability, reliability and, affordability. Besides, they play a critical role in encouraging sustainability by capitalizing on the utilization of solar energy and donating to global goals of renewable energy. Moreover, this review paper acts as a valuable and distinct resource for industry leaders, policymakers, and researchers, offering awareness of the greatest practices and inspiring collaboration of interdisciplinary for progressing technologies of solar tracking.
Enhanced solar PV cell parameter identification via particle swarm optimization (PSO) with weighted objective function Ghosh, Bikshan; Mandal, Sharmistha
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 16, No 1 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2025.1033

Abstract

This work aims to identify the parameters of solar photovoltaic (PV) cells, which can then be used for modeling PV systems and designing controllers. The dynamic equation governing correlation among current and voltage at the output terminals of a solar cell is predominantly dependent on different parameters of the single diode model (SDM) or double diode model (DDM) representation of that solar PV cell. Without easy access to this information, accurately modeling PV systems for further studies becomes difficult. So, to identify those parameters with greater accuracy and less complexity, particle swarm optimization (PSO) in conjunction with the weighted objective function (WOF) has been proposed in this paper. This proposition of multi-objective optimization with a metaheuristic algorithm is found to give very satisfactory results while reducing any further modification in conventional PSO and with faster convergence.
Congestion management of power transmission line with advanced interline power flow controller Bhukya, Baddu Naik; Chinda, Padmanabha Raju; Rayapudi, Srinivasa Rao; Bondalapati, Swarupa Rani
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 16, No 1 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2025.795

Abstract

The growing reliance on renewable energy sources (RES), alongside the surge in electricity consumption, has intensified the challenges associated with congestion management in power transmission lines. This article investigates the use of an advanced interline power flow controller (AIPFC) combined with artificial intelligence (AI) and machine learning (ML) methods to tackle congestion management challenges. The aim is to establish a dependable and effective power system, all while reducing the costs associated with congestion management. Algorithms in AI and ML are utilized to create models aimed at predicting and managing congestion, whereas optimization techniques are applied to identify the most effective operation of AIPFC and strategies for alleviating congestion. The IEEE 30-bus system is utilized as a test case to assess the proposed methodology. A comparative analysis is performed, evaluating the effectiveness of the AI/ML-based approach in relation to traditional congestion management techniques. The findings demonstrate that the incorporation of AIPFC alongside AI/ML methodologies markedly alleviates congestion within the power transmission lines of the IEEE 30-bus system. The proposed combination of model predictive control (MPC) and AIPFC (MPC-AIPFC), integrated with chaotic fuzzy particle swarm optimization (CFPSO), achieves the lowest fuel cost of $798.81/h, the minimum total power loss of 0.0855 pu, and demonstrates congestion mitigation under overload conditions. These results underscore the approach’s significant advancements in reducing cost, optimizing power flow, and relieving congestion compared to traditional methods.
Advances in building energy management systems (BEMS): A comprehensive review with bibliometric analysis and future research directions Sihombing, Very; Yandri, Erkata; Pramono, Kukuh Priyo; Ariati, Ratna
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 16, No 1 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2025.961

Abstract

Building energy management systems (BEMS) are essential for enhancing energy efficiency and sustainability in buildings. This literature review analyzes BEMS research trends from 1982 to 2024, utilizing bibliometric analysis based on a dataset from Scopus. The study identifies key developments that influence all publications and emerging research topics in the field. While BEMS offers significant potential for real-time energy monitoring and control, challenges remain, including the need for standard protocols, improved cybersecurity, and cost-effective solutions for small buildings. This research highlights the importance of addressing these challenges to foster wider adoption of BEMS technology and contribute to a sustainable energy future. The findings aim to guide future research directions and enhance the implementation of BEMS in various building types.
Pico hydro propeller turbine prototype experimental study for very low head applications Subekti, Ridwan Arief; Mohd-Zawawi, Fazila; Ismail, Kamarulafizam; Ishak, Iskandar Shah; Sudibyo, Henny; Susatyo, Anjar; Pikra, Ghalya; Radiansah, Yadi; Aziz, Amiral; Fudholi, Ahmad
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 16, No 1 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2025.1111

Abstract

There is a lot of untapped potential for low-head and very low-head (VLH) hydroelectric power in Indonesia. The challenge in developing VLH is that the locations are very difficult to access by vehicle. One example is in the interior of South Kalimantan Province, where it takes more than 12 hours to reach the location on foot. This paper discusses an experimental study of a pico hydro propeller turbine prototype for VLH applications which is suitable for use in remote areas of Indonesia. Its design is simple and lightweight, and it is made from PVC. The turbine's specifications include a power output of 250 W with a net head of 1.53 m. The turbine was designed with four different runner models, including variations in the number of blades and their geometric shapes. The runner models are type 1 and 2 with five and four blades, respectively, and type 3 (in a shallow configuration) and type 4 (in a steep configuration) with 3 blades. The generator used was a DC, 36 V, with a maximum power of 500 W, 2,500 rpm, and 1 phase. An AC lamp was used as the generator load, so an inverter from DC to AC was used in this test. The propeller turbine was tested in the laboratory. The experiments were conducted at various flow rates by adjusting the rotational speed of the supply pump and the electrical load using incandescent lamps. The test results are presented as graphs showing the relationship between flow rate and rotational speed, hydraulic and electrical power, and efficiency. The experimental results indicate that the turbine with a type 3 runner model featuring three blades in a gentle slope configuration has the highest efficiency, approximately 72.5 %.
Development of styrene butadiene rubber-butadiene rubber with a hyperelastic model for vehicle tire design Rachmat, Angki Apriliandi; Ramadhan, Muhammad Hisyam; Mardiyati, Yati; Suweca, I Wayan; Dirgantara, Tatacipta
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 16, No 1 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2025.1201

Abstract

This paper proposes a mathematical correlation between styrene butadiene rubber (SBR)-butadiene rubber (BR) composition and hyperelastic model parameters for numerical studies in vehicle tire design. Experimental, numerical, and curve-fitting methods were employed in this research. Experimental tests were conducted using tensile tests for SBR-BR. The numerical study of the SBR-BR tensile test was carried out using several classic hyperelastic models. The best hyperelastic model was selected based on the smallest deviation between numerical and experimental results. Curve-fitting was done between the best hyperelastic model parameters and the compound to obtain a new correlation, and it was validated. This research shows that the neo-Hookean model with 6 % deviation is the most suitable for the SBR-BR, and the mathematical correlation for SBR-BR composition and C10 is linearly correlated. SBR60 %-BR40 % shows the optimum composition for non-pneumatic tires with the characteristic of maximum tensile strength 16.71 MPa, elongation 251 %, and 200 % modulus 13.04 MPa.
Appendix MEV Vol 16 Iss 1 Pikra, Ghalya
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 16, No 1 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2025.1270

Abstract

Review of Kalman filter variants for SLAM in mobile robotics with linearization and covariance initialization Gusrial, Muhammad Haniff; Othman, Nur Aqilah; Ahmad, Hamzah; Hassan, Mohd Hasnun Arif
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 16, No 1 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2025.1096

Abstract

Simultaneous localization and mapping (SLAM) has become a foundational concept in robotics navigation which enabling autonomous systems to build maps of unknown environments while estimating their own position. This article aims to provide a comprehensive review of the SLAM concept in the context of mobile robotics navigation by focusing on theoretical principles, estimation problems, algorithms involved, and related applications. The existing literature is systematically analyzed and classified based on three main perspectives of navigation, which are localization, mapping, and path planning. Particular attention is given to Kalman filters and their variants in SLAM-based systems, along with crucial consideration of the linearization and covariance initialization. This article identifies the strengths and limitations of current SLAM approaches. Therefore, this article concludes by outlining research gaps and recommending directions for future exploration, with the intention of serving as a foundation for continued innovation in SLAM-based robotic navigation systems.
Electric wheelchair navigation based on hand gestures prediction using the k-Nearest Neighbor method Anam, Khairul; Nahela, Safri; Sasono, Muchamad Arif Hana; Rizal, Naufal Ainur; Putra, Aviq Nurdiansyah; Wahono, Bambang; Putrasari, Yanuandri; Wardana, Muhammad Khristamto Aditya; Salim, Taufik Ibnu
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 16, No 1 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2025.1229

Abstract

The advancement of technology in the medical field has led to innovations in assistive devices, including wheelchairs, to enhance the mobility and independence of individuals with disabilities. This study investigates the use of electromyography (EMG) signals from hand muscles to control a wheelchair using the k-Nearest Neighbor (kNN) classification method. kNN is a classification algorithm that identifies objects based on the proximity of similar objects in the feature space. The wheelchair control process begins with the development of a kNN model trained on EMG signal data collected from five respondents over 30 seconds. The data was processed using feature extraction techniques, namely Mean Absolute Value (MAV) and Root Mean Square (RMS), to identify motion characteristics corresponding to five types of movement: forward, backward, right, left, and stop. The extracted features were classified using the kNN algorithm implemented on a Raspberry Pi 3. The classification results were then used to control the wheelchair through an Arduino UNO microcontroller connected to a BTS7960 motor driver. The study achieved an average accuracy of 96% with the MAV feature and ? = 3. Furthermore, combining MAV and RMS features significantly improved classification accuracy. The highest accuracy was obtained using the combination of MAV and RMS features with ? = 3, demonstrating the effectiveness of feature selection and parameter tuning in enhancing the system's performance.
Back Cover MEV Vol 16 Iss 1 Pikra, Ghalya
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 16, No 1 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2025.1271

Abstract

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