Mohammad, Luthfansyah (2021) Peningkatan Kinerja Pengisian Baterai Panel Surya Pada Electric Vehicle Charging Station System Berbasis Kombinasi Algoritma Kontrol Mppt-Avpso Dan Proportional Integral. Masters thesis, Institut Teknologi Sepuluh Nopember.
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02311950025001-Master_Thesis.pdf - Accepted Version Restricted to Repository staff only until 1 October 2023. Download (7MB) | Request a copy |
Abstract
Kendaraan listrik atau EV adalah bukti konkrit tumbuhnya kesadaran manusia dalam menjaga iklim bumi. Peningkatan penggunaan kendaraan listrik turut membuat stasiun pengisian daya atau charging station menjadi permasalahan baru bagi jaringan listrik PLN. Melihat permasalahan tersebut, diperlukan sebuah sistem charging yang tepat dengan memanfaatkan sumber energi terbarukan. Pembangkit Listrik Tenaga Surya (PLTS) adalah sumber energi listrik yang hijau dan terbarukan yang belum dapat dioptimalkan dengan baik dan masih memiliki banyak kendala ketika diterapkan pada sistem charging station. Terdapat beberapa hal yang dapat mepengaruhi tingkat kinerja pada PLTS charging station, salah satunya adalah metode kontrol yang digunakan. Metode algoritma AVPSO dan kontrol PI digunakan pada sistem PLTS untuk mendapatkan posisi keluaran daya yang paling maksimal maupun untuk mengatur keluaran arus dan tegangan secara konstan. Metode kontrol memegang peranan penting pada charging station khususnya untuk meningkatkan efisiensi, kesehatan, dan kualitas charging pada baterai secara proporsional. Berbagai penelitian telah dilakukan seperti membatasi energi yang diproduksi, memberi pengaman LVD dan OVD, hingga mengombinasi atau memadukan beberapa metode kontrol. Melihat Pentingnya fungsi kontrol, penelitian ini berfokus memadukan algoritma AVPSO dengan metode kontrol PI. Beberapa algoritma kontrol akan aktif secara bergantian melalui perhitungan coulomb counting dan bekerja secara adaptif menyesuaikan kondisi baterai. Berdasarkan uji lapangan, disimpulkan jika kombinasi algoritma AVPSO dan kontrol PI telah bekerja sesuai target yang ditentukan khususnya ketika baterai telah mencapai SOC 85%. Algoritma AVPSO mampu menaikkan keluaran daya panel surya hingga 13.64% lebih tinggi dibandingkan keluaran daya panel surya yang dihubungkan langsung ke baterai. Kontrol PI yang digunakan juga mampu menjaga nilai tegangan charging baterai hingga 74.12% lebih akurat dibanding sistem tanpa kontrol. Oleh karena itu, dapat disimpulkan jika rancang bangun sistem yang dibuat telah berhasil menyelesaikan rumusan permasalahan yaitu meningkatkan kinerja sistem pengisian baterai panel surya pada electric vehicle charging station.
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EVs are concrete evidence of the growing human awareness in maintaining the earth's climate. The increase in the use of electric vehicles has also made charging stations a new problem for the PLN electricity network. Seeing these problems, we need an appropriate charging system by utilizing renewable energy sources. Solar Power Plants (PLTS) are green and renewable sources of electrical energy that cannot be optimized properly and still have many obstacles when applied to the charging station system. There are several things that can affect the performance level of the PLTS charging station, one of which is the control method used. The AVPSO algorithm method and PI control are used in the PV mini-grid system to get the maximum power output position as well as to adjust the current and voltage output constantly. The control method plays an important role in the charging station, especially to increase the efficiency, health, and quality of charging the battery proportionally. Various studies have been carried out such as limiting the energy produced, providing LVD and OVD protection, to combining or combining several control methods. Seeing the importance of the control function, this research focuses on combining the AVPSO algorithm with the PI control method. Several control algorithms will be active alternately through coulomb counting and work adaptively to adjust battery conditions. Based on field tests, it is concluded that the combination of AVPSO algorithm and PI control has worked according to the specified target, especially when the battery has reached 85% SOC. The AVPSO algorithm is able to increase the power output of solar panels up to 13.64% higher than the power output of solar panels that are connected directly to the battery. The PI control used is also able to keep the battery charging voltage value up to 74.12% more accurate than the system without control. Therefore, it can be concluded that the system design has succeeded in solving the problem formulation, namely improving the performance of the solar panel battery charging system at the electric vehicle charging station.
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