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

doi:10.28991/ESJ-2023-07-03-02

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Ryo G. Widjaja Industrial Engineering Department, BINUS Graduate Program – Master of Industrial Engineering, Bina Nusantara University, Jakarta,, Indonesia
Muhammad Asrol
muhammad.asrol@binus.edu
Industrial Engineering Department, BINUS Graduate Program – Master of Industrial Engineering, Bina Nusantara University, Jakarta,, Indonesia
Iwan Agustono Industrial Engineering Department, BINUS Graduate Program – Master of Industrial Engineering, Bina Nusantara University, Jakarta,, Indonesia
Endang Djuana Electrical Engineering Department, Faculty of Industrial Technology, Universitas Trisakti, Jakarta, 11440,, Indonesia
Christian Harito Industrial Engineering Department, BINUS Graduate Program – Master of Industrial Engineering, Bina Nusantara University, Jakarta,, Indonesia
G. N. Elwirehardja Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta,, Indonesia
Bens Pardamean 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
Fergyanto E. Gunawan Industrial Engineering Department, BINUS Graduate Program – Master of Industrial Engineering, Bina Nusantara University, Jakarta,, Indonesia
Tim Pasang Department of Mechanical and Manufacturing Engineering and Technology, Oregon Institute of Technology, Klamath Falls, OR 97601,, United States
Derrick Speaks Department of Mechanical and Manufacturing Engineering and Technology, Oregon Institute of Technology, Klamath Falls, OR 97601,, United States
Eklas Hossain Oregon Renewable Energy Center (OREC), Klamath Falls, OR 97601,, United States
Arief S. Budiman 1) Industrial Engineering Department, BINUS Graduate Program – Master of Industrial Engineering, 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

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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

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Widjaja, R. G., Asrol, M., Agustono, I., Djuana, E., Harito, C., Elwirehardja, G. N., … Budiman, A. S. (2023). State of Charge Estimation of Lead Acid Battery using Neural Network for Advanced Renewable Energy Systems. Emerging Science Journal, 7(3), 691–703. https://doi.org/10.28991/ESJ-2023-07-03-02

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

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