Sistem Cerdas untuk Menganalisis Parameter Pengisian Cepat Baterai Kendaraan Listrik menggunakan Raspberry PI 4

Advent Samuel Halomoan; Amperawan Amperawan; Sabilal Rasyad

doi:10.26905/jasiek.v7i2.15708

Authors

Advent Samuel Halomoan Program Studi D4 Teknik Elektro, Jurusan Teknik Elektro, Politeknik Negeri Sriwijaya https://orcid.org/0009-0000-9106-0196
Amperawan Amperawan Program Studi D4 Teknik Elektro, Jurusan Teknik Elektro, Politeknik Negeri Sriwijaya
Sabilal Rasyad Program Studi D4 Teknik Elektro, Jurusan Teknik Elektro, Politeknik Negeri Sriwijaya

DOI:

https://doi.org/10.26905/jasiek.v7i2.15708

Keywords:

Battery Management, Electric Vehicle, Fast Charging, LSTM-RNN, Raspberry Pi

Abstract

Advances in electric vehicle (EV) technology drive the need for fast and efficient battery charging. However, fast charging can cause problems such as overheating, cell degradation, and decreased battery performance. This research develops a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN)-based intelligent system running on Raspberry Pi to monitor and predict battery charging parameters in real-time. The system processes data from voltage, current, power, temperature, and State of Charge (SOC) sensors to detect critical conditions such as overcharging and overheating. Equipped with a Human-Machine Interface (HMI) for live data visualization, the system is able to predict SOC with high accuracy (MAE 1.97%, RMSE 2.84%) and respond to automatic control in less than 2 seconds. This integration improves the efficiency and safety of EV battery fast charging.

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