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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) TELKOMNIKA (Telecommunication Computing Electronics and Control)
Fauzi Dwi Setiawan
National Research and Innovation Agency
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Performance analysis of three-phase induction motor for railway propulsion system Syamsul Kamar; Meiyanne Lestari; Respatya Teguh Soewono; Sofwan Hidayat; Hilda Luthfiyah; Okghi Adam Qowiy; Fauzi Dwi Setiawan; Mulyadi Sinung Harjono
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 14, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v14.i3.pp1433-1441

Abstract

A three-phase induction motor absorbs the most electric power among other electrical loads. Therefore, three-phase induction motors are the primary electric motors used in industrial applications thanks to their simple construction and easy operation, as well as low cost and low maintenance costs. Efficiency is a critical parameter that characterizes an induction motor as a traction motor. The traction motor is defined as the engine's effectiveness in converting electrical power at its input into mechanical energy by rotating torque on its axis. One way to analyze the efficiency is to use test data obtained from laboratory tests in case-loaded and no-load tests. Calculations using several formulas on the efficiency of an induction motor as a traction motor produce the same result, namely the efficiency of 98.6% by applying variable frequency drive (VFD). The result of laboratory tests and their analysis can be used as a reference for designing three-phase induction motors for railway traction motors, especially traction motors for high-speed trains.
Application of artificial intelligence in emission prediction for hybrid electric vehicles: integrating ANN and GPR Heru Priyanto; Rizqon Fajar; Yaaro Telaumbanua; Ariyanto Ariyanto; Mohammad Mukhlas Af; Sigit Tri Atmaja; Muhammad Samsul Maarif; Kurnia Fajar Adhi Sukra; Fauzi Dwi Setiawan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 6: December 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i6.27128

Abstract

In recent years, hybrid electric vehicles (HEVs) have emerged as a promising solution to mitigate vehicular emissions and improve fuel efficiency. This study focuses on the Toyota Prius HEV, employing advanced artificial neural networks (ANN) and Gaussian process regression (GPR) to develop a predictive model for vehicle emissions. The model considers multiple pollutants, including carbon monoxide (CO), carbon dioxide (CO₂), hydrocarbons (HC), and nitrogen oxides (NOx), measured under diverse driving conditions. The ANN model predicts emission trends, while GPR estimates prediction uncertainty, enhancing the model’s robustness. The GPR models achieved uncertainty levels of ±0.829 ppm for CO, ±9.978 ppm for HC, ±0.144 ppm for NOx, and ±411.256 ppm for CO₂, respectively, underscoring the robustness of the integrated approach for emission prediction. This research aims to support the development of more sustainable vehicle technologies and inform policy making for environmental sustainability (e.g., Euro 6/Euro 7 standards). Overall, the study addresses how artificial intelligence (AI) can be utilized to achieve accurate multi-pollutant emission predictions in HEVs. The findings reveal that an integrated ANN-GPR approach yields superior predictive performance (R² values approaching 1.0) with quantifiable uncertainty, outperforming a stand-alone ANN model and providing a robust solution to the emission prediction challenge.
Co-Authors Ariyanto Ariyanto Heru Priyanto Hilda Luthfiyah Kurnia Fajar Adhi Sukra Meiyanne Lestari Mohammad Mukhlas Af Muhammad Samsul Maarif Mulyadi Sinung Harjono Okghi Adam Qowiy Respatya Teguh Soewono Rizqon Fajar Sigit Tri Atmaja Sofwan Hidayat Syamsul Kamar Yaaro Telaumbanua