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All Journal Journal of Applied Smart Electrical Network and System (JASENS)
Muhammad Irsyad, Sholahuddin
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Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Analisis Kinerja Sistem Kontrol Hybrid Electric Vehicle (HEV) Menggunakan Metode Neuro-fuzzy Rahma Annisa, Aulia; Andika, Yudi; Muhammad Irsyad, Sholahuddin; Sasmita Aji Pambudi, Dwi
Journal of Applied Smart Electrical Network and Systems Vol. 6 No. 2 (2025): Vol. 6 No. 02 (2025): Vol 06, No. 02 Desember 2025
Publisher : Indonesian Society of Applied Science (ISAS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/f5y40s25

Abstract

Electric cars are an environmentally friendly vehicle alternative developed to reduce exhaust gas emissions and air pollution. One example is the Hybrid Electric Vehicle (HEV), which combines an Internal Combustion Engine (ICE) and an electric motor (DC motor) to improve efficiency and torque performance. HEVs generally have a smaller capacity compared to conventional vehicles, making them more fuel-efficient and energy-efficient. The system in an HEV is complex and nonlinear, requiring dynamic model approaches and appropriate control methods to maintain optimal performance. This research aims to analyze the performance of the inverse model neuro-fuzzy control system predictor implemented on an HEV. The test results show that applying a neuro-fuzzy controller can significantly improve the system's ability to achieve a response that matches the reference model. The performance of the DC motor is able to help reduce the speed error difference by up to 50 rpm with a Root Mean Square Error (RMSE) value of 0.582%. Additionally, there is a 1.411% decrease in error value compared to when the ICE is operating without using a neuro-fuzzy controller. Based on these results, the neuro-fuzzy method has proven effective in improving the accuracy and stability of the control system in HEVs.
Co-Authors Rahma Annisa, Aulia Sasmita Aji Pambudi, Dwi Yudi Andika