Analisis Kinerja Sistem Kontrol Hybrid Electric Vehicle (HEV) Menggunakan Metode Neuro-fuzzy
DOI:
https://doi.org/10.52158/f5y40s25Keywords:
Hybrid Electric Vehicle (HEV), Predictor Model, Neuro-fuzzyAbstract
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.
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Copyright (c) 2025 Aulia Rahma Annisa, Yudi Andika, Sholahuddin Muhammad Irsyad, Dwi Sasmita Aji Pambudi
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