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

Renewable energy harvesting through solar photovoltaic with solar smart dome at rural area can help local farmers drying agricultural product such as coffee, spices, and dried fruit. To have a more viable and economical battery for the energy storage system, an accurate prediction battery State of Charge (SOC) is important to help control the battery charging and discharging, to extend the battery lifespan. This study explore correlation between SOC prediction with battery observable parameter such as voltage, current and temperature. Using Transformer Neural Network with comparison of Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU), prediction model constructed utilizing two different datasets of laboratory lithium battery LiFePO4 and actual lead acid battery OPzS to measure model accuracy and its training time with extreme condition. Result show that voltage having strong positive correlation with SOC prediction for both battery type, while temperature having strong positive correlation only on lead acid battery. Current didn’t have direct correlation to SOC but have strong positive correlation with voltage for both battery dataset. Best prediction result gained from GRU at 45 epochs with MAE 0.642%, RMSE 0.885 %, R2 99.88% and training time of 10.74s. Transformer Neural Network accuracy placed third after LSTM with MAE 1.175%, RMSE 1.634%, R2 99.69% but it has faster training time at 7.13 second. Generalization capability of neural network in SOC prediction to produce great accuracy is proven in this study on GRU model with highest MAE of 1.19% given its challenge of limited data quantity, quality, and different battery type. Keywords— Battery State Prediction, Transformer Neural Network, State of Charge, Machine Learning, Smart Solar Dryer

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