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"Energi listrik adalah hasil dari metode konversi, seperti dari konversi energi panas, konversi energi kinetik, konversi energi angin, dll, dan telah memungkinkan untuk mengkonversi energi matahari menjadi energi listrik dengan bantuan modul fotovoltaik. Tapi karena matahari tidak bersinar sepanjang waktu atau dengan intensitas yang sama, perlu kita untuk menyimpan energi listrik yang lebih untuk digunakan nanti. Untuk menyimpan energi listrik kelebihan ini, kita dapat membuat sebuah sistem yang menyimpan kelebihan energi ini dengan menyimpannya dalam Electrical Storage System (ESS). Dalam tesis ini, EES terdiri dari baterai yang dapat menyimpan kelebihan energi tetapi pada saat yang sama juga dapat menarik energi tergantung pada kebutuhan daya beban.Target dari skripsi ini adalah untuk menciptakan sebuah sistem penyimpanan energi listrik yang dapat dihubungkan dalam sebuah pembangkit listrik PV yang sudah ada. Sistem tersebut harus mencakup kontrol pengisian dan pemakaian baterai. Analisis State of Charge (SOC), tegangan operasi baterai, dan state control baterai akan ditampilkan dan dibahas.Hasil simulasi menunjukkan bahwa baterai mampu di charge dan discharge pada radiasi tertentu dengan tegangan sistem yang diperlukan. Baterai mampu memberikan 200kW saat pemakaian.

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Electrical energy is the result of many conversion methods, such from heat energy conversion, kinetic energy conversion, wind energy conversion, etc., and it has been possible to convert solar energy directly to electrical energy with the help of photovoltaic modules. But since sun does not shine all the time or with equal intensity it is necessary to store any excess electrical energy produced during the day for later use. To store this excess electrical energy, we can create a system that saves the excess energy by storing it in an Electrical Storage System (ESS). In this thesis, the EES includes a battery that can store excess energy but at the same time be able to draw energy at any time of the day, depending on the load power requirement.

The target of this thesis is to create an electrical storage system that can be connected in an existing PV power generation. The system should include the battery's charging and discharging control. An analysis of the battery's state of charge (SOC), battery?s operating voltage, and battery state control will be discussed.

The result of the simulation shows that the battery is able to charge and discharge at a certain irradiance with the required system voltage. Battery is able to supply 200 kW when discharging."

"An active Radio-Frequency Identification (RFID) tag is a low-power device, which is often used as a tracking device, where operation of this tag will be in remote areas from an electrical power source. Therefore, this device requires an independent power source. To meet these needs, solar power may be used, which can be accessed anywhere there is sunlight. Supercapacitors (SC) are used as an energy source to support a solar power system. The advantage of supercapacitors, as an energy storage device, is their long life cycle that means more charging and discharging processes compared to a conventional battery. The design and fabrication of a solar power system for an active RFID tag with supercapacitors as the energy storage will be covered in this paper."

"An active Radio-Frequency Identification (RFID) tag is a low-power device, which is often used as a tracking device, where operation of this tag will be in remote areas from an electrical power source. Therefore, this device requires an independent power source. To meet these needs, solar power may be used, which can be accessed anywhere there is sunlight. Supercapacitors (SC) are used as an energy source to support a solar power system. The advantage of supercapacitors, as an energy storage device, is their long life cycle that means more charging and discharging processes compared to a conventional battery. The design and fabrication of a solar power system for an active RFID tag with supercapacitors as the energy storage will be covered in this paper."

"Sistem Manajemen Baterai (BMS), yang bertanggung jawab untuk memantau baterai isi ulang, memainkan peran penting dalam melindungi kendaraan dan instrumen listrik. Dua indikator utama yang perlu dipertimbangkan adalah State of Charge (SoC) dan State of Health (SoH). Memperkirakan SoC secara akurat penting untuk mencegah potensi masalah. Selain itu, ruang, waktu komputasi, dan biaya merupakan faktor penting dalam pengembangan perangkat keras. Untuk mengatasi pertimbangan ini, model Extended Kalman Filter (EKF) orde pertama dan Adaptive Extended Kalman Filter (AEKF) dipilih karena pra-pemrosesan datanya lebih sederhana dan akurasinya lebih baik. Estimasi ini didasarkan pada metode matematika. Studi ini merekomendasikan penggunaan metode First-Order Equivalent Circuit Model (ECM) bersama dengan algoritma EKF dan AEKF karena pengaturannya yang mudah dan proses komputasi yang efisien. Melalui penelitian yang melibatkan beberapa siklus pengisian-pengosongan, ditemukan bahwa metode AEKF secara konsisten mengungguli metode EKF dalam hal akurasi SoC. Hal ini semakin diperkuat dengan melakukan pengujian reliabilitas pada metode AEKF, yang menunjukkan akurasi estimasi SoC yang lebih unggul dibandingkan metode EKF ketika diberikan nilai SoC awal yang berbeda. Selain itu, waktu komputasi yang lebih singkat dari metode EKF menjadi pertimbangan untuk penerapan praktis di dunia nyata. Lebih lanjut, percobaan yang dilakukan selama 500 siklus mengungkapkan bahwa estimasi SoH menurun dari 99,97% menjadi 76,1947%, menunjukkan bahwa baterai telah mencapai tahap End of Life (EOL), seperti yang dilaporkan di berbagai jurnal.

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The Battery Management System (BMS), responsible for monitoring rechargeable batteries, plays an essential role in safeguarding electric vehicles and instruments. Two key indicators to consider are State of Charge (SoC) and State of Health (SoH). Accurately estimating SoC is important to prevent potential issues. Additionally, space, computing time, and cost are important factors in hardware development. To address these considerations the first-order Extended Kalman Filter (EKF) and Adaptive Extended Kalman Filter (AEKF) models were selected due to their simpler data pre-processing and better accuracy. These estimations are based on mathematical methods. The study recommends using the First-Order Equivalent Circuit Model (ECM) method in conjunction with the EKF and AEKF algorithms due to their straightforward setup and efficient computational process. Through research involving multiple charge-discharge cycles, it was found that the AEKF method consistently outperformed the EKF method in terms of SoC accuracy. This was further confirmed by subjecting the AEKF method to reliability testing, where it displayed superior SoC estimation accuracy compared to the EKF method when given different initial SoC values. Additionally, the shorter computing time of the EKF method is a consideration for practical real-world implementation. Furthermore, experiments conducted over 500 cycles revealed that SoH estimation declined from 99.97% to 76.1947%, suggesting that the battery has reached the End of Life (EOL) stage, as reported in various journals."