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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Science and Technology Indonesian Journal of Cancer Scholaria: Jurnal Pendidikan dan Kebudayaan Emerging Science Journal Civil Engineering Journal ComTech: Computer, Mathematics and Engineering Applications Engineering, Mathematics and Computer Science Journal (EMACS) Indonesian Journal of Cancer
Bens Pardamean
Universitas Bina Nusantara Jakarta
Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Mathematics Medicine & Pharmacology Public Health Social Sciences

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Journal : Emerging Science Journal

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State of Charge Estimation of Lead Acid Battery using Neural Network for Advanced Renewable Energy Systems 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
Emerging Science Journal Vol 7, No 3 (2023): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-03-02

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. Doi: 10.28991/ESJ-2023-07-03-02 Full Text: PDF
Co-Authors Andika Rizky Andri Anugrahana Anjela Greizaria Djoeang Arief S. Budiman Bahtiar S. Abbas Christian Harito Derrick Speaks Digdo Sudigyo DJIAUW DJUNAIDI GODJALI DJIAUW DJUNAIDI GODJALI, DJIAUW DJUNAIDI Djoeang, Anjela Greizaria Eklas Hossain Elwirehardja, Gregorius Natanael Endang Djuana EVLINA SUZANNA EVLINA SUZANNA, EVLINA Febrian Valentino Fergyanto E. Gunawan G. N. Elwirehardja Gregorius N. Elwirehardja Gregorius Natanael Elwirehardja Harjanto Prabowo Hasbi Yasin Indri Anugraheni Iwan Agustono JONATHAN ADITYA MULYONO JONATHAN ADITYA MULYONO, JONATHAN ADITYA Karli Eka Setiawan Makalew, Brilly Andro Muhammad Asrol Nana Rosanna Tobing Nanggala, Kenjovan Rezzy Eko Caraka Rung Ching Chen Ryo G. Widjaja Suhartono Suhartono Teddy Suparyanto Tim Pasang Tjeng Wawan Cenggoro Tobing, Nana Rosanna Youngjo Lee