Wirareswara, Rafif (2024) Optimasi Multi-Objektif Motor Penggerak Pada Multipurpose Electric Vehicle Its (MEVITS) Menggunakan Metode Genetic Algorithm Dan Backpropagation Neural Networka. Masters thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Penggunaan kendaraan listrik merupakan salah satu solusi efektif untuk menciptakan lingkungan yang bebas polusi karena beberapa keunggulan yaitu, bebas emisi dan efisiensi energi motor listrik yang lebih tinggi dari kendaraan internal combustion engine (ICE) sehingga konsumsi tenaga menjadi lebih rendah. Hal ini sejajar dengan target sustainable development goals (SDG) 2030 pada bidang energi, yaitu meningkatkan efisiensi energi pada skala global. Selain itu, untuk meningkatkan adhesi pada jalan dan meminiamlisir maintenance cost pada ban, maka perameter tire workload (TWL) menjadi hal yang penting untuk diminimalisir. Salah satu solusi untuk meningkatkan efisiensi energi, menurunkan TWL, dan konsumsi daya adalah dengan mengaplikasikan teknologi 4 wheel independent drive (4WID) pada multi purpose electric vehicle ITS (MEvITS). Teknologi tersebut diintegrasikan dengan sistem torque vectoring (TV), torque distribution control (TDC), model backpropagation neural network (BPNN), dan metode optimasi genetic algorithm (GA). Pada penelitian ini didapatkan perbandingan hasil optimasi metode genetic algorithm dengan metode konvensional pada fungsi multi-objektif dengan peningkatan nilai rata-rata efisiensi sebesar 1.15 %, penurunan tire work load sebesar 26.46 % dan konsumsi tenaga sebesar 8.51 %
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The use of electric vehicles is an effective solution for creating a pollution-free environment because of several advantages, namely, zero emissions and the energy efficiency of electric motors which is higher than internal combustion engine (ICE) vehicles so that power consumption is lower. This is in line with the 2030 sustainable development goals (SDG) target in the energy sector, namely increasing energy efficiency on a global scale. Apart from that, to increase adhesion to the road and minimize tire maintenance costs, it is important to minimize tire workload (TWL) parameters. One solution to increase energy efficiency, reduce TWL and power consumption is to apply 4 wheel independent drive (4WID) technology to the multi-purpose electric vehicle ITS (MEvITS). This technology is integrated with a torque vectoring (TV) system, torque distribution control (TDC), backpropagation neural network (BPNN) model and genetic algorithm (GA) optimization method. In this research, we found a comparison of the optimization results of the genetic algorithm method with conventional methods for multi-objective functions with an increase in the average efficiency value of 1.15%, a decrease in tire work load of 26.46% and energy consumption of 8.51%
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | Kendaraan Listrik, Efisiensi Energi, 4WID, Tire work load, Konsumsi Tenaga; Genetic Algorithm, Torque Distribution Control, BPNN |
| Subjects: | T Technology > T Technology (General) T Technology > T Technology (General) > T57.84 Heuristic algorithms. |
| Divisions: | Faculty of Industrial Technology and Systems Engineering (INDSYS) > Mechanical Engineering > 21101-(S2) Master Thesis |
| Depositing User: | RAFIF WIRARESWARA |
| Date Deposited: | 15 Feb 2024 01:06 |
| Last Modified: | 15 Feb 2024 01:06 |
| URI: | http://repository.its.ac.id/id/eprint/107254 |
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