MODELING THE 3S13P BATTERY SYSTEM WITH SOC ESTIMATION MODELS AND MACHINE LEARNING PREDICTION FOR BMS APPLICATIONS

Rina Latuconsina; Luwis Herman Laisina

doi:10.30598/barekengvol20iss3pp1937-1948

Rina Latuconsina D3 Electrical Engineering Study Program, Politeknik Negeri Ambon, Indonesia https://orcid.org/0009-0005-7516-5170
Luwis Herman Laisina D3 Electrical Engineering Study Program, Politeknik Negeri Ambon, Indonesia https://orcid.org/0009-0009-9095-7669

DOI: https://doi.org/10.30598/barekengvol20iss3pp1937-1948

Keywords: Battery management system (BMS), State of charge (SOC), Adaptive Kalman filter (AKF), 3S13P Lithium-Ion configuration, LSTM, Thermal modeling

Abstract

This study aims to enhance estimation accuracy and energy management by incorporating thermal aspects into Battery Management Systems (BMS). Through experimental evaluation and mathematical modeling, key parameters such as charging time, internal resistance, State of Charge (SOC), Depth of Discharge (DOD), and effective capacity are analyzed. The SOC profile follows a logistic curve, with system energy efficiency ranging from 93% to 97%, depending on internal resistance variations. An Adaptive Kalman Filter (AKF) is applied for real-time SOC estimation, achieving an accuracy of ±1.5%, while a Long Short-Term Memory (LSTM) neural network performs time-series SOC prediction with an RMSE of 0.95%. Furthermore, three-dimensional thermal modeling reveals a significant increase in resistance beyond 45 °C, emphasizing the effect of temperature on battery dynamics. These findings highlight the importance of integrating real-time estimation and AI-based prediction algorithms into adaptive BMS architectures, contributing to advancements in intelligent energy management for electric mobility and stationary storage systems. However, this study was conducted under controlled temperature and fixed charging conditions, which may limit generalization to dynamic real-world operations; future work will address these factors.

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MODELING THE 3S13P BATTERY SYSTEM WITH SOC ESTIMATION MODELS AND MACHINE LEARNING PREDICTION FOR BMS APPLICATIONS

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