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Sonki Prasetya
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 1 
Dynotest Design Analysis for Electrical Converted Vehicles Sumarsono, Danardono Agus; Zainuri , Fuad; Hidayat Tullah, Muhammad; Noval, Rahmat; Prasetya, Sonki; Subarkah, Rahmat; Rahmiati, Tia; widi, Widiyatmoko; Ridwan, Muhammad
Recent in Engineering Science and Technology Vol. 1 No. 01 (2023): RiESTech Volume 01 No. 01 Years 2023
Publisher : MBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59511/riestech.v1i01.3

Abstract

The study comprises dynotest design and analysis to measure torque and horsepower. Basically, a dynotest carried out by apply certain load to the axle of a combustion motor through the braking mechanism of its crankshaft. Due to the high price of a Dynotest unit in the market, it is relatively difficult for a developing institution to own it on their site. The study target to design a simple and good accurate Dynotest within a reasonable price. The study used a common standard method for design analysis which rely on function and structural approach. Functionally, Dynotest is designed to be used to an ouput of an electical motor. Loading on motor shaft was done by disc brake braking mechanism. Structurally, Dynotest was designed to use rollers. As a main component, its mounting construction is connected to a motor to generate electrical power. Power transmitted from the motor to Dynotest through a center joint shaft, torque measured by load cell while the rotation of shaft itself counted by a digital tachometer. Test result show that electricity was produced from the simple construction and Dynotest functioned well in measuring it. Measurement data of roller support shaft performance showed a motor torque performance curve which are similar with the typical of similar Dynotest. Construction Test done by applying Solid Work software analysis to some components partially on rollers and on the construction assembly as a whole unit
Electric Vehicle Conversion Study for Sustainable Transport Zainuri, Fuad; Hidayat Tullah, Muhammad; Prasetya, Sonki; Susanto, Iwan; Purnama, Dewin; Subarkah, Rahmat; Ramiati, Tia; Widiyatmoko; Noval, Rahmat
Recent in Engineering Science and Technology Vol. 1 No. 02 (2023): RiESTech Volume 01 No. 02 Years 2023
Publisher : MBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59511/riestech.v1i02.15

Abstract

The conversion of conventional motor vehicles to electric vehicles has become a popular choice in an effort to reduce greenhouse gas emissions and air pollution from transportation. Electric vehicle conversion involves replacing a gasoline or diesel engine with an electric motor and a reinstalled battery. In this paper, we cover the basics of electric vehicle conversion, conversion methods, and trial results of converted electric vehicles. We also discuss the benefits and challenges of converting to electric vehicles. Some keywords related to this topic include: electric vehicles, vehicle conversion, electric motors, batteries, sustainable transportation.
IoT-based Intelligent Monitoring & Control System Planning Using Project Management Method and Business Feasibility Analysis Prasetya, Sonki; Rahman, Muhammad Farid Aditya; Ridlwan, Hasvienda M
Recent in Engineering Science and Technology Vol. 2 No. 02 (2024): RiESTech Volume 02 No. 02 Years 2024
Publisher : MBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59511/riestech.v2i02.47

Abstract

Various PLTS systems have been installed in the Energy Laboratory of Politeknik Negeri Jakarta, ranging from on grid, off grid, and SHS systems, and various sources of PLTH, PLN, and Generator Set (Genset.) However, the problem that occurs there is that the hybrid system in the Lab. Solar System PNJ room cannot be monitored easily and controlled automatically. As a result of these problems, monitoring the performance of hybrid systems and learning in the PNJ Solar Systems Lab cannot be done optimally. The power source in the lab can be combined in a Hybrid PLTS system and generator with a switch method using the ATS switch control system and monitoring for student learning. The purpose of this research will be to analyze the economic value and use of ATS switches for various concepts in saving electricity in a certain period of time using project management analysis so that it can see the feasibility of this project to be implemented or not. The method in this study is to calculate the economic feasibility value, then find the value of Internal Rate of Return (IRR), NPV, Payback Period using Microsoft excel software and analyze project risks. The results obtained in this project are the IRR value> Interest rate, namely 6.51%> 5.75%. The NPV value obtained is Rp. Rp.572,252 with a payback period in year 12. From the results obtained, this project is declared feasible to continue.
Comparative Analysis of Regression Methods for Estimation of Remaining Useful Life of Lithium Ion Battery Assagaf, Idrus; Abdillah, Abdul Azis; Edistria, Ega; Sukandi, Agus; Prasetya, Sonki; Apriana, Asep; Nugroho; Kamil, Raihan
Recent in Engineering Science and Technology Vol. 3 No. 01 (2025): RiESTech Volume 03 No. 01 Years 2025
Publisher : MBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59511/riestech.v3i01.93

Abstract

Lithium batteries play a critical role in modern technological applications, including electric vehicles and portable electronic devices. Ensuring accurate estimation of their remaining useful life is essential to improve system efficiency and reliability. This study focuses on predicting the remaining useful life of lithium batteries using advanced regression methods. Data were collected from lithium battery charge-discharge cycles, encompassing key operational parameters such as voltage, current, and temperature. The analysis employed several regression models, including linear regression, lasso regression, and Ridge regression, to identify relationships between these parameters and battery life. The models were evaluated based on estimation accuracy, with Root Mean Square Error (RMSE) as the primary performance metric. The findings demonstrate that regression methods can effectively capture non-linear relationships between input variables and the remaining useful life, with lasso and Ridge regression showing superior performance in reducing prediction errors. These results underscore the potential of regression-based approaches in providing robust and reliable estimations of battery life. The conclusions highlight the importance of these models for developing predictive battery management systems, which can optimize battery performance and extend their operational lifespan across various applications. This research establishes a solid foundation for future studies on intelligent battery health monitoring and management.
Comparing MLP and 1D-CNN Architectures for Accurate RUL Forecasting in Lithium Batteries Assagaf, Idrus; Sukandi, Agus; Jannus, Parulian; Prasetya, Sonki; Apriana, Asep; Edistria, Ega; Abdillah, Abdul Azis
Recent in Engineering Science and Technology Vol. 3 No. 04 (2025): RiESTech Volume 03 No. 04 Years 2025
Publisher : MBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59511/riestech.v3i04.127

Abstract

Accurately forecasting the Remaining Useful Life (RUL) of lithium-ion batteries is critical for optimizing battery management and ensuring operational reliability. This study compares the performance of two deep learning architectures—a Multilayer Perceptron (MLP) and a one-dimensional Convolutional Neural Network (1D-CNN)—in predicting RUL using datasets from CALCE batteries B35, B36, and B37. Data preprocessing involved outlier removal, missing value handling, and feature normalization, with key features extracted including Resistance, Constant Voltage Charging Time (CVCT), and Constant Current Charging Time (CCCT). Correlation analyses confirmed strong relationships between these features and RUL. Both models were trained and validated on preprocessed data, and their predictive accuracies were assessed using Root Mean Square Error (RMSE) and coefficient of determination (R2). Results indicated that while both architectures effectively captured battery degradation patterns, the MLP consistently outperformed the 1D-CNN, achieving on average 5% lower RMSE and 1.5% higher R2 across all tested batteries. These findings suggest that simpler fully connected networks may suffice for this forecasting task under the given feature set and preprocessing conditions. This work provides valuable insights into neural network model selection for battery health prognostics, guiding the development of efficient and accurate predictive maintenance strategies.
Co-Authors Abdillah, Abdul Azis Abdul Rozaq Achmad, Zacky Maulana Adhitya, M. Afriyani, Aulia Dyah Agus Sukandi Ainun Nidhar, Ainun Alfarezi, Fitratama Ali Djamhuri Amatullah Fatin, Shafa Aprianto, Hibatullah Anis Arbiyanti, Arsya Amarlaily Asep Apriana, Asep Asep Yana Assagaf, Idrus Avianto, Tiyo Belyamin Belyamin Belyamin Danardono A.S, Danardono A.S Darmawan, Awang Dewin Purnama Edistria, Ega Filzi, Rahman Filzi, Rahman Fuad Zainuri Fuad Zainuri, Fuad Garjati, Vina Nanda Hardiansyah, Muhamad Firman Hermanu, Adhi Indra Hidayat Tullah, Muhammad Ilyas, Dede Muhamad Ilyas Isnanda Nuriskasari Iswanto, Andri Iwan Susanto Jannus, Parulian Jusafwar, Jusafwar Kamal, Dianta Mustofa Kamil, Raihan Khairunnisa, Ratna M. Ridwan M.Pd S.T. S.Pd. I Gde Wawan Sudatha . MARIA BINTANG Maulana, Irham Muhammad Hidayat Tullah Muhammad Hidayat Tullah, Muhammad Hidayat Muhammad Ridwan Muhammad Todaro MumpuniAdhi, Pribadi Muslimin Muslimin Mustofa Kamal, Dianta Nova, Rahmat Noval, Rahmat Nugroho Nugroho, M Wisda Nugroho, Pinky Andi Nusyirwan Nusyirwan Pratama, Erlangga Yudha Rahman, Muhammad Farid Aditya Rahmat Noval Rahmat Subarkah Rahmiati, Tia Ramiati, Tia Rante, Hestiasari Ridlwan, Hasvienda M Ridlwan, Hasvienda M. Ridlwan, Hasvienda Mohammad Romadhoni, M Rizky Sagita, Ainova Ellis Saryanto, Widhi Yoga Sausanina, Aisyah Setiawan, Nuril Deny Suaib, Norhaida Mohd Subarkah, R. Sugeng Mulyono Sumarsono, Danardono Agus Tirta Lianda, Handri Todaro, Muhammad Todaru, M. Tullah, M. Hidayat Utama, Praditya Khrisna Utomo, Dimas Rianto Wahyono, Agus Eko widi, Widiyatmoko Widiyatmoko Wijaya, Ray Yusra Yogatama, Muhammad Tatag Yuli Mafendro Dedet Eka Saputra Zainuri , Fuad