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All Journal ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika INTEK: Jurnal Penelitian Budimas : Jurnal Pengabdian Masyarakat JAREE (Journal on Advanced Research in Electrical Engineering)
Yanaratri, Diah Septi
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Religion Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Social Sciences Other

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Journal : ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi,

 1 
Estimasi SOC Saat Discharging pada Baterai VRLA Berbasis Elman Backpropagation YANARATRI, DIAH SEPTI; SUTEDJO, SUTEDJO; FIRMANSYAH, ACHMAD DICKY; IRIANTO, IRIANTO; RAKHMAWATI, RENNY; ADILA, AHMAD FIRYAL
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 12, No 4: Published October 2024
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v12i4.862

Abstract

ABSTRAKPenurunan performa baterai terjadi akibat siklus pengisian dan pengosongan berulang yang melebihi batas, mempercepat degradasi. Penelitian ini bertujuan untuk meningkatkan akurasi estimasi State of Charge (SOC) baterai menggunakan Artificial Neural Network (ANN) dengan algoritma Elman Backpropagation. Metode digunakan karena menambahkan lapisan context neuron yang mampu menangkap pola dinamis pada data baterai. Pengujian dilakukan dengan membandingkan hasil estimasi SOC dari metode ini dengan metode Coulomb Counting. SOC baterai diestimasi dari 100% hingga 60%, dan hasil menunjukkan bahwa meskipun Coulomb Counting awalnya memberikan SOC lebih tinggi, estimasi dari kedua metode menjadi lebih mirip seiring waktu. Error estimasi berkisar antara 0,1% hingga 14,7%. Algoritma Elman Backpropagation terbukti mampu memberikan estimasi SOC yang lebih akurat, namun masih memerlukan kalibrasi lebih lanjut untuk meningkatkan presisi.Kata kunci: Artificial Neural Network, Baterai, Coulumb Counting, Elman Backpropagation, State of Charge. ABSTRACTThe decline in battery performance occurs due to repeated charge and discharge cycles that exceed limits, accelerating degradation. This study aimed to improve the accuracy of State of Charge (SOC) estimation using an Artificial Neural Network (ANN) with the Elman Backpropagation algorithm. The method used was unique in adding a context neuron layer capable of capturing dynamic patterns in battery data. Testing was conducted by comparing SOC estimates from this method with the Coulomb Counting method. The battery's SOC was estimated from 100% to 60%, and the results showed that although Coulomb Counting initially provided higher SOC estimates, the estimates from both methods became more similar over time. Estimation errors ranged from 0.1% to 14.7%. The Elman Backpropagation algorithm proved to provide more accurate SOC estimates, although further calibration is needed to improve precision.Keywords: Artificial Neural Network, Battery, Coulumb Counting, Elman Backpropagation, State of Charge.
Estimasi State of Charge pada Baterai Lead Acid menggunakan Elman Recurrent Neural Network RAKHMAWATI, RENNY; SUTEDJO, SUTEDJO; OKTAVIANI, FITROTIN NAFISA; IRIANTO, IRIANTO; YANARATRI, DIAH SEPTI; ADILA, AHMAD FIRYAL
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 11, No 4: Published October 2023
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v11i4.864

Abstract

ABSTRAKPenggunaan panel surya sebagai sumber energi terbarukan membutuhkan baterai sebagai tempat penyimpanan energi. Penggunaan baterai secara terus menerus, dapat menyebabkan pengurangan kapasitas dan penurunan performa. Untuk mengatasi permasalahan tersebut, diperlukan sistem estimasi nilai State of Charge (SOC) pada baterai yang berfungsi untuk mengontrol kondisi charge, agar performa baterai tetap optimal. Pada penelitian dikembangan suatu sistem estimasi SOC pada baterai jenis lead acid, dengan metode algoritma Elman Recurrent Neural Network (ERNN). Keunggulan yang terkait dengan metode ERNN meliputi proses iterasi menjadi lebih cepat, peningkatan kecepatan pembaruan parameter, dan pencapaian konvergensi yang lebih cepat. Hasil dari penelitian estimasi SOC pada baterai lead acid 12V, 12Ah dengan menggunakan algoritma ERNN sebesar 0.101% sedangkan dengan algoritma Feedforward Backpropagation sebesar 0.767%. Sehingga dapat disimpulkan bahwa algoritma ERNN lebih efisien dalam mengestimasi nilai SOC pada baterai lead acid.Kata kunci: Baterai, Elman Recurrent Neural Network, Panel Surya, State of Charge; Lead Acid ABSTRACTUsing solar panels as a renewable energy source requires batteries as energy storage. Continuous use of batteries can result in reduced capacity and performance degradation. Based on these problems, a State of Charge (SOC) estimation system is needed for the battery to control charge conditions so that battery performance remains optimal. In this research, a SOC estimation system was developed for lead acid battery using the Elman Recurrent Neural Network (ERNN) algorithm. The advantage of the ERNN method is that the iteration process is faster, the parameter update speed is increased, and convergence is faster. The results of the SOC estimation for a 12V, 12Ah lead acid battery using the ERNN algorithm were 0.101%, while the Feedforward Backpropagation algorithm resulted in 0.767%. The ERNN algorithm is more efficient in estimating the SOC value of a lead acid battery.Keywords: Battery, Elman Recurrent Neural Network, Solar Panel, State of Charge, Lead Acid
Co-Authors Ahmad Firyal Adila FIRMANSYAH, ACHMAD DICKY Habibi, Muhammad Nizar Hartono, Helleina Rejeki Putri Indhana Sudiharto Irianto Irianto Lucky Pradigta Setiya Raharja Luki Septya Mahendra Nismayanti, Nila Nanda Nugroho, Mochammad Ari Bagus Oktaviani, Fitrotin Nafisa Putih, Bening Putri Permata Rachma Prilian Eviningsih, Rachma Prilian Rakhmawati, Renny Rusli, Muhammad Rizani Sutedjo Sutedjo