Available online at www.ijournalse.org Emerging Science Journal (ISSN: 2610-9182) Vol. 7, No. 3, June, 2023 Page | 691 State of Charge Estimation of Lead Acid Battery using Neural Network for Advanced Renewable Energy Systems Ryo G. Widjaja 1 , Muhammad Asrol 1* , Iwan Agustono 1 , Endang Djuana 2 , Christian Harito 1 , G. N. Elwirehardja 3 , Bens Pardamean 3, 4 , Fergyanto E. Gunawan 1 , Tim Pasang 5 , Derrick Speaks 5 , Eklas Hossain 6 , Arief S. Budiman 1, 5, 6 1 Industrial Engineering Department, BINUS Graduate Program – Master of Industrial Engineering, Bina Nusantara University, Jakarta, Indonesia. 2 Electrical Engineering Department, Faculty of Industrial Technology, Universitas Trisakti, Jakarta, 11440, Indonesia. 3 Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia. 4 Computer Science Department, BINUS Graduate Program - Master of Computer Science Program, Bina Nusantara University, Jakarta, Indonesia. 5 Department of Mechanical and Manufacturing Engineering and Technology, Oregon Institute of Technology, Klamath Falls, OR 97601, United States. 6 Oregon Renewable Energy Center (OREC), Klamath Falls, OR 97601, United States. 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. Article History: Received: 09 August 2022 Revised: 16 January 2023 Accepted: 03 March 2023 Available online: 03 May 2023 1- Introduction Indonesia is the largest archipelagic country in the world, with five major islands and 30 smaller groups of islands separated by the ocean. This condition leads to uneven electricity distribution network infrastructure spread across remote areas [1, 2]. The problem also affects processes in the agriculture fields, especially farmers’ drying processes, which are very dependent on uncertain weather. The researchers have developed an independent energy system combined with Artificial Intelligence (AI) to tackle the issue [3], which is called the Solar Drying Dome (SDD). The SDD uses photovoltaics (PV) as the source of a power generator equipped with AI to assist decision-making by utilizing sensors [4–6]. The system adopts the concept of precision agriculture for monitoring and controlling drying operations. Decentralized areas in Indonesia, where most people work as farmers, do not have sufficient economic * CONTACT: muhammad.asrol@binus.ac.id DOI: http://dx.doi.org/10.28991/ESJ-2023-07-03-02 © 2023 by the authors. Licensee ESJ, Italy. This is an open access article under the terms and conditions of the Creative Commons Attribution (CC-BY) license (https://creativecommons.org/licenses/by/4.0/).