IMPLEMENTASI PEMANTAUAN MANAJEMEN BATERAI BERBASIS IOT PADA PROTOTYPE HOME CHARGING SYSTEM

MUHAMMAD ARAFIAN PUADI, . (2026) IMPLEMENTASI PEMANTAUAN MANAJEMEN BATERAI BERBASIS IOT PADA PROTOTYPE HOME CHARGING SYSTEM. Sarjana thesis, UNIVERSITAS NEGERI JAKARTA.

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Abstract

Perkembangan kendaraan listrik dan sistem penyimpanan energi meningkatkan kebutuhan akan sistem monitoring baterai yang mampu memberikan informasi kondisi baterai secara akurat dan real-time. Monitoring yang masih dilakukan secara manual memiliki keterbatasan dalam mengetahui kondisi baterai selama proses pengisian, sehingga berpotensi menurunkan efisiensi dan umur pakai baterai. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem Internet of Things (IoT) untuk monitoring dan manajemen baterai pada prototipe Home Charging System menggunakan metode Kalman Filter. Sistem dibangun menggunakan mikrokontroler ESP32, sensor PZEM-017 untuk mengukur tegangan, daya, dan energi listrik, sensor ACS712 untuk mengukur arus pengisian baterai VRLA 12 V 5 Ah, serta Firebase sebagai media penyimpanan data dan aplikasi Android berbasis Kodular sebagai antarmuka pengguna. Pengambilan data dilakukan dengan interval sampling selama 5 detik. Hasil penelitian menunjukkan bahwa sistem berhasil melakukan monitoring parameter tegangan, arus, daya, energi, State of Charge (SoC), dan State of Health (SoH) secara real-time melalui aplikasi Android. Penerapan metode Kalman Filter mampu menghasilkan estimasi SoC dan SoH yang lebih stabil dibandingkan hasil pembacaan sensor secara langsung karena dapat mengurangi pengaruh noise dan kesalahan pengukuran. Selain itu, integrasi ESP32 dengan Firebase berjalan dengan baik sehingga data hasil monitoring dapat tersimpan dan ditampilkan secara otomatis. Dengan demikian, sistem yang dikembangkan mampu meningkatkan efektivitas monitoring dan manajemen baterai serta dapat dijadikan sebagai solusi pendukung dalam pemantauan kondisi baterai pada prototipe Home Charging System.*****The rapid development of electric vehicles and energy storage systems has increased the need for battery monitoring systems capable of providing accurate and real-time information on battery conditions. Conventional battery monitoring is still performed manually, making it difficult to obtain continuous information during the charging process, which may reduce charging efficiency and battery service life. Therefore, this study aims to design and implement an Internet of Things (IoT)-based battery monitoring and management system for a Home Charging System prototype using the Kalman Filter method. The proposed system employs an ESP32 microcontroller, a PZEM-017 sensor to measure voltage, power, and electrical energy, an ACS712 sensor to measure charging current of a 12 V 5 Ah VRLA battery, Firebase as the cloud database, and an Android application developed with Kodular as the user interface. Data acquisition was carried out at a sampling interval of 5 seconds. The results show that the developed system successfully performs real-time monitoring of battery voltage, current, power, energy, State of Charge (SoC), and State of Health (SoH) through the Android application. The implementation of the Kalman Filter provides more stable SoC and SoH estimation than direct sensor measurements by reducing the effects of measurement noise and sensor errors. Furthermore, the integration between the ESP32 and Firebase operates properly, enabling automatic data storage and visualization. Therefore, the proposed system improves the effectiveness of battery monitoring and management and can serve as an alternative solution for monitoring battery conditions in a Home Charging System prototype.

Item Type:	Thesis (Sarjana)
Additional Information:	1). Syufrijal, S.T., M.T. ; 2). Drs. Rimulyo Wicaksono, M.M.
Subjects:	Teknologi dan Ilmu Terapan > Teknik Elektronika Teknologi dan Ilmu Terapan > Teknik Energi Teknologi dan Ilmu Terapan > Teknik Komputer
Divisions:	FT > D IV Teknologi Rekayasa Otomasi
Depositing User:	Muhammad Arafian Puadi .
Date Deposited:	06 Aug 2026 07:04
Last Modified:	06 Aug 2026 07:04
URI:	http://repository.unj.ac.id/id/eprint/68759

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