Penentuan Nilai EBS Berdasarkan Energi Maksimum Yang Dihasilkan Sistem Regenerative Braking Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS) Pada Sepeda Motor Listrik Konversi

Aysah, Isroqol (2024) Penentuan Nilai EBS Berdasarkan Energi Maksimum Yang Dihasilkan Sistem Regenerative Braking Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS) Pada Sepeda Motor Listrik Konversi. Diploma thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

PT. Braja Elektrik Motor merupakan perusahaan yang telah diverifikasi oleh pemerintah untuk melakukan konversi kendaraan, dengan fokus utama pada pengembangan kendaraan yang ramah lingkungan dan efisien. Salah satu masalah yang dihadapi adalah keterbatasan daya baterai dan jangkauan pada sepeda motor listrik hasil konversi. Untuk mengatasi masalah ini, perusahaan mengimplementasikan sistem regenerative braking dan mencari solusi untuk meningkatkan kinerja energi regeneratif dari proses pengereman. Penelitian ini bertujuan untuk mengestimasi nilai Electric Brake System (EBS) yang efektif menggunakan metode Adaptive Neuro Fuzzy Inference System (ANFIS). ANFIS dipilih karena kemampuannya dalam menangani hubungan non-linier dan kompleks antara variabel input dan output, yang cocok untuk mengoptimalkan proses pengereman regeneratif. Metode ANFIS diimplementasikan dengan training data input berupa massa, kecepatan (RPM), dan EBS serta data output berupa energi menggunakan sebanyak 8 membership function berbentuk triangular (trimf) untuk setiap variabel input, dengan target mencapai Error sebesar 3,5542 dan 100 epoch. Hasil penelitian menunjukkan bahwa model ANFIS yang diusulkan mampu memberikan estimasi nilai EBS yang efektif dengan tingkat akurasi yang tinggi, dimana nilai Mean Absolute Percentage Error (MAPE) terbaik yang diperoleh adalah sebesar 10%. Hasil analisis menyarankan bahwa nilai EBS efektif yang optimal untuk mencapai energi regeneratif maksimum adalah EBS 20. Selisih energi regenerative braking yang dihasilkan sebelum menggunakan metode ANFIS adalah 130,01wh dengan EBS awal 12 dengan kondisi yang sama hanya menghasilkan energi 30,9wh dan setelah ANFIS dengan EBS 20 dan kondisi yang sama menghasilkan energi 161wh.
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PT. Braja Elektrik Motor is a company verified by the government for vehicle conversion, with a primary focus on developing environmentally friendly and efficient vehicles. One of the challenges faced is the limited battery power and range of the converted electric motorcycles. To address this issue, the company has implemented a regenerative braking system and is seeking solutions to enhance the regenerative energy performance of the braking process. This research aims to estimate the effective Electric Brake System (EBS) value using the Adaptive Neuro-Fuzzy Inference System (ANFIS) method. ANFIS was chosen due to its capability to handle non-linear and complex relationships between input and output variables, which is suitable for optimizing the regenerative braking process. The ANFIS method was implemented with training input data including mass, speed (RPM), and EBS, and output data representing energy, using 8 triangular membership functions (trimf) for each input variable, with a target error of 3.5542 and 100 epochs. The results show that the proposed ANFIS model can provide effective EBS value estimations with high accuracy, with the best Mean Absolute Percentage Error (MAPE) obtained being 10%. The analysis suggests that the optimal effective EBS value to achieve maximum regenerative energy is an EBS of 20. The regenerative braking energy generated before using the ANFIS method was 130.01 Wh with an initial EBS of 12, yielding 30.9 Wh under the same conditions. After applying ANFIS with an EBS of 20, the energy generated was 161 Wh under the same conditions.

Item Type:	Thesis (Diploma)
Uncontrolled Keywords:	Adaptive Neuro Fuzzy Inference System (ANFIS), Konversi kendaraan listrik, Regenerative braking, Electric Brake System (EBS), Efisiensi energi, electric vehicle conversion, energy efficiency
Subjects:	T Technology > T Technology (General) T Technology > T Technology (General) > T58.8 Productivity. Efficiency T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1001 Production of electric energy or power T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK2681.O85 Electric motors, Brushless. T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK2791 D.C. to A.C. transforming machinery. T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK2797 Motor-generator sets. Cascade T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK2851 Voltage regulators. T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK2941 Storage batteries T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK2943 Battery chargers.
Divisions:	Faculty of Vocational > 36304-Automation Electronic Engineering
Depositing User:	ISROQOL AYSAH
Date Deposited:	05 Sep 2024 08:47
Last Modified:	05 Sep 2024 08:47
URI:	http://repository.its.ac.id/id/eprint/115604

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