Estimasi State of Charge pada Baterai Lead Acid menggunakan Elman Recurrent Neural Network

RENNY RAKHMAWATI, SUTEDJO SUTEDJO, FITROTIN NAFISA OKTAVIANI, IRIANTO IRIANTO, DIAH SEPTI YANARATRI, AHMAD FIRYAL ADILA

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ABSTRAK

Penggunaan 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

 

ABSTRACT

Using 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


Kata Kunci


Baterai; Elman Recurrent Neural Network; Panel Surya; State of Charge; Lead Acid

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Referensi


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DOI: https://doi.org/10.26760/elkomika.v11i4.864

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