TY - JOUR
T1 - State of Charge Estimation of Lead Acid Battery using Neural Network for Advanced Renewable Energy Systems
AU - Widjaja, Ryo G.
AU - Asrol, Muhammad
AU - Agustono, Iwan
AU - Djuana, Endang
AU - Harito, Christian
AU - Elwirehardja, G. N.
AU - Pardamean, Bens
AU - Gunawan, Fergyanto E.
AU - Pasang, Tim
AU - Speaks, Derrick
AU - Hossain, Eklas
AU - Budiman, Arief S.
N1 - Publisher Copyright:
© 2023 by the authors. Licensee ESJ, Italy.
PY - 2023/6
Y1 - 2023/6
N2 - 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.
AB - 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.
KW - Dashboard
KW - Neural Network
KW - Solar Dryer Dome
KW - State-of-Charge. Lead Acid Battery
UR - http://www.scopus.com/inward/record.url?scp=85160565847&partnerID=8YFLogxK
U2 - 10.28991/ESJ-2023-07-03-02
DO - 10.28991/ESJ-2023-07-03-02
M3 - Article
AN - SCOPUS:85160565847
VL - 7
SP - 691
EP - 703
JO - Emerging Science Journal
JF - Emerging Science Journal
IS - 3
ER -
