Prasetiyo, Wahyu Dwi (2025) Estimasi State of Charge (SOC) dan State of Health (SOH) Baterai Lithium Menggunakan Metode Dual Extended Kalman Filter (DEKF) pada PLTS Komunal Pulau Panjang. Masters thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Estimasi State of Charge (SOC) dan State of Health (SOH) baterai merupakan komponen penting dalam sistem manajemen energi berbasis baterai. Penelitian ini bertujuan untuk mengembangkan metode estimasi simultan SOC dan SOH pada baterai lithium jenis LiFePO₄ menggunakan algoritma Dual Extended Kalman Filter (DEKF). Estimasi dilakukan berdasarkan data operasional aktual dari Pembangkit Listrik Tenaga Surya (PLTS) Komunal Pulau Panjang, tanpa penerapan langsung di sistem fisik.
Pemodelan baterai menggunakan pendekatan Equivalent Circuit Model (ECM) orde-3, yang terdiri atas tiga pasang elemen RC serta satu resistansi ohmik. Parameter internal (R₀, R₁–R₃, dan C₁–C₃) diestimasi menggunakan metode System Design Optimization (SDO) pada MATLAB/Simulink. Validasi model menunjukkan rata-rata error estimasi tegangan terminal sebesar ±2,1 mV.
Hasil estimasi menunjukkan bahwa metode DEKF mampu mengestimasi SOC dengan tingkat akurasi 99,9985% pada siklus ke-7000, lebih baik dibandingkan metode EKF yang mencapai 99,5216%. Sementara itu, hasil estimasi SOH pada siklus ke-4017 mencapai 85,04%, lebih tinggi dibandingkan nilai referensi pabrikan sebesar 67,12%. Perbedaan ini disebabkan oleh arus operasional aktual yang lebih rendah (maksimum 4,30 A untuk discharge dan 15,81 A untuk charge), yang cenderung memperlambat degradasi kapasitas baterai.
Secara keseluruhan, penelitian ini menunjukkan bahwa metode DEKF memiliki kemampuan estimasi yang andal berbasis data riil operasional, dan berpotensi diterapkan dalam sistem monitoring energi skala komunal.
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The estimation of State of Charge (SOC) and State of Health (SOH) is a critical component in battery-based energy management systems. This study aims to develop a simultaneous estimation method for SOC and SOH of LiFePO₄ lithium batteries using the Dual Extended Kalman Filter (DEKF) algorithm. The estimation is performed based on actual operational data from the Pulau Panjang Communal Solar Power Plant (PLTS), without direct implementation in a physical system.
The battery model employs a third-order Equivalent Circuit Model (ECM), consisting of three RC element pairs and one ohmic resistance. Internal parameters (R₀, R₁–R₃, and C₁–C₃) are estimated using the System Design Optimization (SDO) method in MATLAB/Simulink. Model validation indicates a mean error of terminal voltage estimation of ±2.1 mV.
The estimation results demonstrate that the DEKF method can estimate SOC with an accuracy of 99.9985% at cycle 7000, outperforming the Extended Kalman Filter (EKF), which achieved 99.5216%. Meanwhile, SOH estimation at cycle 4017 reaches 85.04%, higher than the manufacturer's reference value of 67.12%. This discrepancy is attributed to lower actual operating currents (maximum 4.30 A for discharge and 15.81 A for charge), which tend to slow down battery capacity degradation.
Overall, this study confirms that the DEKF method provides reliable estimation performance based on real operational data and holds potential for application in communal-scale energy monitoring systems.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | SOC, SOH, Dual Extended Kalman Filter (DEKF), ECM, System Design Optimization (SDO), baterai LiFePO₄, SOC, SOH, Dual Extended Kalman Filter (DEKF), ECM, System Design Optimization (SDO), LiFePO₄ battery |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK2941 Storage batteries |
| Divisions: | Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20101-(S2) Master Thesis |
| Depositing User: | Wahyu Dwi Prasetiyo |
| Date Deposited: | 27 Jul 2025 01:48 |
| Last Modified: | 27 Jul 2025 01:48 |
| URI: | http://repository.its.ac.id/id/eprint/121717 |
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