Sinaga, Daniel Anugerah Raja Bonor (2025) Optimasi Adaptive PID Controller Berbasis Logic-Based Switching Control (LSAC) untuk Peningkatan Kinerja Fast Charging Station (Level 3) Kendaraan Listrik. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Peralihan menuju kendaraan listrik (EV) sebagai solusi dekarbonisasi transportasi seringkali terhambat oleh lamanya waktu pengisian daya. Fast Charging Station Level 3 menjawab tantangan ini, di mana DC-to-DC Converter berperan krusial dalam mengatur aliran daya ke baterai. Namun, PID Controller konvensional dengan parameter tetap kesulitan beradaptasi dengan dinamika baterai yang non-linear selama proses pengisian Constant Current-Constant Voltage (CC-CV). Walaupun pendekatan adaptif canggih seperti Fuzzy Logic atau Reinforcement Learning telah ada, implementasinya seringkali menuntut komputasi yang kompleks. Penelitian ini mengusulkan optimasi kinerja melalui Adaptive PID Controller berbasis Logic-Based Switching Control (LSAC). Dengan menggunakan simulasi MATLAB/Simulink pada topologi Buck Converter, dirancang sebuah arsitektur kontrol cerdas yang beralih di antara dua PI Controller berbeda berdasarkan kondisi sistem. Analisis kinerja kuantitatif, yang menggunakan berbagai metrik seperti Mean Absolute Error (MAE), Integral of Absolute Error (IAE), dan Integral of Squared Error (ISE), dilakukan untuk memvalidasi pendekatan ini. Hasilnya menunjukkan keunggulan signifikan dari metode LSAC, yang secara khusus berhasil menekan Integral of Squared Error (ISE) dari 44358 menjadi 8779. Pendekatan LSAC terbukti superior dalam menjaga stabilitas dan akurasi, menjadikannya solusi yang lebih efisien dan andal untuk mendukung penggunaan kendaraan listrik secara luas.
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The widespread use of electric vehicles (EVs) as a solution for transport decarbonization is often hampered by long charging times. Level 3 Fast Charging Station infrastructure addresses this challenge, where the DC-to-DC Converter plays a crucial role in regulating power flow to the battery. However, a conventional PID Controller with fixed parameters struggles to adapt to the battery's non-linear dynamics during the Constant Current-Constant Voltage (CC-CV) charging process. While other adaptive approaches like Fuzzy Logic and Reinforcement Learning exist, their implementation often demands significant computational complexity. This research proposes performance optimization through an Adaptive PID Controller based on Logic-Based Switching Control (LSAC). Using MATLAB/Simulink simulations on a Buck Converter topology, an intelligent control architecture was designed to switch between two distinct PI Controllers based on system conditions. A quantitative performance analysis, utilizing various metrics such as Mean Absolute Error (MAE), Integral of Absolute Error (IAE), and Integral of Squared Error (ISE), was conducted to validate this approach. The results reveal the significant superiority of the LSAC method, which notably reduced the Integral of Squared Error (ISE) from 44358 to just 8779. The adaptive approach proves superior in maintaining stability and accuracy, presenting a more efficient and reliable solution for fast charging applications.
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | Fast Charging, DC-to-DC Converter, Buck Converter, PID Controller, Adaptive PID Controller, Logic-Based Switching Control (LSAC), Fast Charging, DC-to-DC Converter, Buck Converter, PID Controller, Adaptive PID Controller, Logic-Based Switching Control (LSAC) |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK3070 Automatic control |
| Divisions: | Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20201-(S1) Undergraduate Thesis |
| Depositing User: | Daniel Anugerah Raja Bonor Sinaga |
| Date Deposited: | 29 Jul 2025 07:01 |
| Last Modified: | 29 Jul 2025 07:01 |
| URI: | http://repository.its.ac.id/id/eprint/122864 |
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