A Review on The Application of Machine Learning To Predict The Battery State That Enables A Smart, Low-Cost, Self-Sufficient Drying And Storage System for Agricultural Purposes

Redi, Anak Agung Ngurah Perwira; Widjaja, Ryo Geoffrey; Agustono, Iwan; Asrol, Muhammad; Budiman, Arief Suriadi; Gunawan, Fergyanto E.

doi:10.1145/3557738.3557846

Cited by 1 publication

(1 citation statement)

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“…The lower attributes also have several advantages for building the model [38]. Redi et al [39] also described the most applied parameter in the battery application as being directed into these four attributes.…”

Section: -1-data Collectionmentioning

confidence: 99%

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

et al. 2023

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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 Full Text: PDF

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Section: -1-data Collectionmentioning

confidence: 99%