ESTIMASI STATE OF CHARGE BATERAI LITHIUM POLYMER MENGGUNAKAN BACK PROPAGATION NEURAL NETWORK

Mohammad Imron Dwi Prasetyo; Hasnira Hasnira; Novie Ayub Windarko; Anang Tjahjono

doi:10.30871/ji.v12i2.2163

Penulis

Mohammad Imron Dwi Prasetyo Politeknik Elektronika Negeri Surabaya
Hasnira Hasnira Politeknik Negeri Batam
Novie Ayub Windarko Politeknik Elektronika Negeri Surabaya
Anang Tjahjono Politeknik Elektronika Negeri Surabaya

DOI:

https://doi.org/10.30871/ji.v12i2.2163

Kata Kunci:

Back Propagation Neural Network, State Of Charge (SOC), Open Circuit Voltage (OCV)

Abstrak

Baterai merupakan salah satu komponen yang penting dalam konteks implementasi renewable energy. Jenis Baterai yang memiliki kepadatan dalam penyimpanan energy adalah lithium polymer. Parameter dalam baterai yang harus diperhatikan adalah estimasi State Of Charge (SOC). Pada umumnya estimasi SOC baterai menggunakan metode coloumb counting karena tingkat kesulitanya rendah. Namun terdapat kelemahan dari sisi ketergantungan terhadap utilitas sensor arus yang digunakan sebagai akumulasi dari integral arus yang masuk maupun arus yang keluar terhadap waktu. Dalam penelitian ini menyajikan Back Propagation Neural Network (BPNN) sebagai algoritma untuk estimasi SOC berdasarkan kurva karakteristik OCV "“ SOC. Kurva karakteristik OCV "“ SOC baterai didapatkan dari pengujian pulsa baterai. Tegangan, arus, dan waktu discharging baterai digunakan sebagai input layer BPNN pertama untuk estimasi Open Circuit Voltage (OCV). OCV akan dilearning sebagai input layer BPNN kedua untuk estimasi SOC baterai. Hasil dari simulasi estimasi SOC didapatkan galat rata-rata sebesar 0.479% terhadap SOC riil berdasarkan kurva karakteristik OCV "“ SOC.

Unduhan

Data unduhan belum tersedia.

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