Abdullah, Jeffry (2021) Implementasi Smart Solar Charging System Untuk Baterai Pada Electric Vehicle Berbasis Fuzzy Ant Colony Controller. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Pada penelitian ini dilakukan perancangan smart solar charging system dengan kontrol fuzzy logic yang di optimisasi dengan ant colony optimization pada electric vehicle. Konverter yang digunakan adalah buck converter dengan tegangan serta arus output yang diinginkan ≤14,4 V dan ≤7 A.Charging dilakukan dengan pembatasan tegangan baterai 11,52-13,04 V.Pada saat simulasi kontrol fuzzy logic pada Software Matlab, analisis respon pengendalian tegangan memiliki maximum overshoot sebesar 19,02 Volt (32,14%), settling time sebesar 0,92 ms, dan error steady state sebesar 0,83%. Sedangkan, pada simulasi kontrol fuzzy ACO, output tegangan memiliki maximum overshoot sebesar 16,36Volt (13.61%), settling time sebesar 0,89 ms, dan error steady state sebesar 0,5%. Hasil pengambilan data menunjukan saat dilakukan uji charging satu baterai, efisiensi pada kontrol fuzzy-ACO lebih besar 3,12% dibandingkan kontrol fuzzy, dan SOC meningkat meningkat 48,9% selama 12 jam. Sedangkan saat saat dilakukan uji charging empat baterai, SOC baterai pertama meningkat 2,36% selama 1,5 jam, baterai kedua meningkat 4,2% selama 2 jam, baterai ketiga meningkat 14,47% selama 4,5 jam dan baterai keempat meningkat 12,36% selama 3,5 jam.
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In this study, a smart solar charging system was designed with fuzzy logic control which was optimized with ant colony optimization on electric vehicles. The converter used is a buck converter with the desired output voltage and current 14.4 V and 7 A. Charging is done with a battery voltage limitation of 11.52-13.04 V. During the simulation of fuzzy logic control in Matlab Software, response analysis voltage control has a maximum overshoot is 19.02 Volts (32.14%), settling time is 0.92 ms, and steady-state error is 0.83%. Meanwhile, in the fuzzy ACO control simulation, the output voltage has a maximum overshoot is 16.36Volt (13.61%), settling time is 0.89 ms, and a steady-state error is 0.5%. The results of data collection showed that when a single battery charging test was conducted, the efficiency of the fuzzy-ACO control was 3.12% greater than that of the fuzzy control, and the SOC increased by 48.9% for 12 hours. Meanwhile, when testing four batteries charging, the first battery SOC increased 2.36% for 1.5 hours, the second battery increased 4.2% for 2 hours, the third battery increased 14.47% for 4.5 hours and the fourth battery increased 12.36% for 3.5 hours.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Uncontrolled Keywords: | Smart Solar Charging, Buck converter, Fuzzy Ant Colony Controller |
| Subjects: | Q Science > QA Mathematics > QA9.64 Fuzzy logic T Technology > TJ Mechanical engineering and machinery > TJ165 Energy storage. T Technology > TJ Mechanical engineering and machinery > TJ808 Renewable energy sources. Energy harvesting. T Technology > TJ Mechanical engineering and machinery > TJ810.5 Solar energy T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL220 Electric vehicles and their batteries, etc. |
| Divisions: | Faculty of Industrial Technology and Systems Engineering (INDSYS) > Physics Engineering > 30201-(S1) Undergraduate Thesis |
| Depositing User: | Jeffry Abdullah |
| Date Deposited: | 07 Sep 2021 08:35 |
| Last Modified: | 12 Nov 2024 04:01 |
| URI: | http://repository.its.ac.id/id/eprint/91798 |
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