Q1 Scopus Paper: State of Charge Estimation of Lead Acid Battery using Neural Network for Advanced Renewable Energy Systems

Dosen dan Mahasiswa Magister Teknik Industri telah berhasil mempublikasikan artikel di Emerging Science Journal. Topik yang dituliskan pada paper ini adalah materi yang juga dibahas pada perkuliahan di Magister Teknik Industri. Berikut adalah artikel tersebut:

State of Charge Estimation of Lead Acid Battery using Neural Network for Advanced Renewable Energy Systems

Written by: Ryo G. Widjaja, Muhammad Asrol, Iwan Agustono, Endang Djuana, Christian Harito, G. N. Elwirehardja, Bens Pardamean, Fergyanto E. Gunawan, Tim Pasang, Derrick Speaks, Eklas Hossain, Arief S. Budiman

Abstract
The Solar Dryer Dome (SDD), an independent energy system equipped with Artificial Intelligence to support the drying process, has been developed. However, inaccurate state-of-charge (SOC) predictions in each battery cell resulted in the vulnerability of the battery to over-charging and over-discharging, which accelerated the battery performance degradation. This research aims to develop an accurate neural network model for predicting the SOC of battery-cell level. The model aims to maintain the battery cell balance under dynamic load applications. It is accompanied by a developed dashboard to monitor and provide crucial information for early maintenance of the battery in the SDD. The results show that the neural network estimates the SOC with the lowest MAE of 0.175, followed by the Random Forest and support vector machine methods with MAE of 0.223 and 0.259, respectively. A dashboard was developed to help farmers monitor batteries efficiently. This research contributes to battery-cell level SOC prediction and the dashboard for battery status monitoring.

Keywords
Dashboard; State-of-Charge. Lead Acid Battery; Neural Network; Solar Dryer Dome.

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