Challenges and Strategies for Implementation of Lead-Acid Battery Health Monitoring and Prediction in Off-Grid Solar Panel Systems

Yoga Prastiya Wibawa; I Putu Ananta Yogiswara; Bondan Suwandi; Andi Kurnianto; Budi Sulistya; Abhimata Ar-Rasyiid; Nashrullah Taufik; Arief Rufiyanto; I Made Astawa

doi:10.31436/iiumej.v26i3.3612

Authors

Yoga Prastiya Wibawa National Research and Innovation Agency Indonesia https://orcid.org/0000-0002-4735-6595
I Putu Ananta Yogiswara National Research and Innovation Agency
Bondan Suwandi National Research and Innovation Agency Indonesia https://orcid.org/0000-0001-8243-0706
Andi Kurnianto National Research and Innovation Agency Indonesia
Budi Sulistya National Research and Innovation Agency Indonesia https://orcid.org/0000-0002-2861-4536
Abhimata Ar-Rasyiid National Research and Innovation Agency Indonesia
Nashrullah Taufik National Research and Innovation Agency Indonesia https://orcid.org/0009-0005-3583-9652
Arief Rufiyanto National Research and Innovation Agency Indonesia https://orcid.org/0009-0000-6638-7242
I Made Astawa National Research and Innovation Agency Indonesia

DOI:

https://doi.org/10.31436/iiumej.v26i3.3612

Keywords:

Lead-acid battery, IoT monitoring, internal resistance, open-circuit voltage, state of health(SOH)

Abstract

This study presents an IoT-based real-time battery health monitoring system that integrates an Equivalent Circuit Model (ECM) with a linear regression approach to estimate internal resistance (IR) and open-circuit voltage (VOC). Unlike conventional electrochemical models or machine learning-based solutions, this method offers a computationally efficient yet accurate approach to predicting battery degradation. The findings demonstrate that increasing IR correlates strongly with declining battery health, reinforcing the necessity of continuous monitoring for predictive maintenance. Additionally, this research highlights the impact of environmental conditions, particularly temperature fluctuations, on battery efficiency, challenging the assumption that heavy rainfall significantly lowers ambient temperatures. Furthermore, this study addresses real-world implementation challenges, including data loss from network disruptions, by proposing a fault-tolerant IoT architecture. These advancements contribute to smart energy storage and provide a scalable solution for remote and off-grid applications, particularly in smart farming.

ABSTRAK: Kajian ini memperkenalkan sistem pemantauan kesihatan bateri masa nyata berasaskan IoT yang terkini, mengintegrasikan Model Litar Ekuivalen (ECM) dengan pendekatan regresi linear untuk menganggarkan rintangan dalaman (IR) dan voltan litar terbuka (VOC). Berbeza dengan model elektrokimia konvensional atau kaedah pembelajaran mesin yang memerlukan sumber pengiraan tinggi, pendekatan ini menawarkan keseimbangan antara ketepatan dan kecekapan pemprosesan dalam meramalkan degradasi bateri. Hasil kajian menunjukkan bahawa peningkatan IR berkait rapat dengan kemerosotan kesihatan bateri, menegaskan kepentingan pemantauan berterusan untuk penyelenggaraan prediktif. Selain itu, kajian ini meneliti kesan persekitaran terhadap prestasi bateri, terutamanya variasi suhu, dan mencabar tanggapan bahawa hujan lebat secara signifikan menurunkan suhu ambien. Kajian ini juga menangani cabaran pelaksanaan di dunia nyata, termasuk kehilangan data akibat gangguan rangkaian, dengan mencadangkan seni bina IoT yang tahan gangguan. Kemajuan ini menyumbang kepada pembangunan sistem storan tenaga pintar dan menawarkan penyelesaian yang berskala untuk aplikasi luar grid, terutamanya dalam bidang pertanian pintar.

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