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All Journal Mechatronics, Electrical Power, and Vehicular Technology
Wardana, Muhammad Khristamto Aditya
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Electrical & Electronics Engineering

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Thermal efficiency and emission characteristics of a diesel-hydrogen dual fuel CI engine at various loads condition Putrasari, Yanuandri; Praptijanto, Achmad; Nur, Arifin; Santoso, Widodo Budi; Pratama, Mulia; Dimyani, Ahmad; Suherman, Suherman; Wahono, Bambang; Wardana, Muhammad Khristamto Aditya; Lim, Ocktaeck
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 9, No 2 (2018)
Publisher : Research Centre for Electrical Power and Mechatronics, Indonesian Istitutes of Sciences

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (177.288 KB) | DOI: 10.14203/j.mev.2018.v9.49-56

Abstract

Efforts to find alternative fuels and reduce emissions of CI engines have been conducted, one of which is the use of diesel hydrogen dual fuel. One of the goals of using hydrogen in dual-fuel combustion systems is to reduce particulate emissions and increase engine power. This study investigates the thermal efficiency and emission characteristics of a diesel-hydrogen dual fuel CI engine at various loads condition. The hydrogen was used as a secondary fuel in a single cylinder 667 cm3 diesel engine. The hydrogen was supplied to intake manifold by fumigation method, and diesel was injected directly into the combustion chamber. The results show that the performance test yielding an increase around 10% in the value of thermal efficiency of diesel engines with the addition of hydrogen either at 2000 or 2500 rpm. Meanwhile, emission analyses show that the addition of hydrogen at 2000 and 2500 rpm lead to the decrease of NOx value up to 43%. Furthermore, the smokeless emissions around 0% per kWh were occurred by hydrogen addition at 2000 and 2500 rpm of engine speeds with load operation under 20 Nm.
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 %.
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.
Co-Authors A.A. Ketut Agung Cahyawan W Achmad Praptijanto Ade Kurniawan Anam, M. Khairul Arifin Nur Bambang Wahono Budi Prawara Budianto, Hari Dimyani, Ahmad Fuad, Nur Muhamad Hayoto, Vebriyanti Hidayat, Muhammad Novel Indriatmono, Dedy Lim, Ocktaeck Ma'ruf, Muhammad Mokhtar, Mokhtar Nahela, Safri Nugroho, Rudi Cahyo Pratama, Mulia Putra, Aviq Nurdiansyah Rizal, Naufal Ainur Salim, Taufik Ibnu Samanhudi, Ramadhani Deniartio Santoso, Widodo Budi Sasono, Muchamad Arif Hana Soewono, Respatya Teguh Suherman, Suherman Sukra, Kurnia Fajar Adhi Suryantoro, Muchammad Taufiq Wahidin, Agus Yanuandri Putrasari Yuwono, Taufik