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Soelistiono , Soegianto
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Chemical Engineering, Chemistry & Bioengineering Electrical & Electronics Engineering Materials Science & Nanotechnology Physics

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Fuzzy-Based Adaptive Switching Time Determination for VRLA Batteries Based on Discharge–Recovery Characteristics Soelistiono , Soegianto; Rahmadani, Muhammad Azzam
Indonesian Applied Physics Letters Vol. 6 No. 2 (2025): Volume 6 No. 2 – December 2025
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v6i2.84886

Abstract

Valve Regulated Lead-Acid (VRLA) batteries are widely used in energy storage systems due to their reliability and low cost; however, their energy utilization is strongly affected by discharge patterns and recovery behavior. Recent studies have shown that dynamic battery switching can improve extractable energy compared to static configurations, yet the switching time is commonly treated as a fixed parameter, despite experimental evidence indicating that the optimal switching interval depends on battery capacity and operating conditions. This paper proposes a fuzzy-based framework for adaptive switching time determination in VRLA battery systems, where switching duration is treated as an explicit control variable inferred from discharge–recovery characteristics. Key indicators, including voltage drop rate, voltage recovery magnitude, and relative internal resistance, are incorporated as inputs to a Mamdani-type fuzzy inference system, while the switching time is defined as the fuzzy output. The proposed approach enables adaptive adjustment of switching duration without relying on detailed electrochemical models. Simulation-based analysis is conducted to qualitatively evaluate the behavior of the proposed method in comparison with fixed switching strategies. The results demonstrate that fuzzy-based adaptive switching produces smoother switching time evolution and more stable voltage trends, indicating improved utilization of discharge–recovery dynamics. This study establishes a conceptual foundation for adaptive switching time control and provides a basis for future experimental validation and real-time implementation in intelligent battery management systems.
Machine Learning-Based Prediction of Distance Coverage (DC) in Electric Motorcycle Under Full Throttle Usage Pattern Pambudi, Henri Setyo; Soelistiono , Soegianto
Indonesian Applied Physics Letters Vol. 6 No. 2 (2025): Volume 6 No. 2 – December 2025
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v6i2.84891

Abstract

The development of electric vehicles (EVs) in Indonesia is accelerating following government policies aimed at reducing greenhouse gas emissions. Despite their benefits, the adoption of electric motorcycles remains limited due to concerns about battery life and charging station availability. This study proposes a machine learning-based model to predict distance coverage (DC) based on the state of charge of the battery (SoC) for electric motorcycles, specifically under a full throttle dominant usage pattern. The research employs multiple regression and classification algorithms, including Linear Regression, Random Forest Regression, and Support Vector Regression (SVR) for prediction, along with Random Forest Classifier, Logistic Regression, and K-Nearest Neighbors (KNN) Classifier for travel classification. The results demonstrate that Linear Regression outperforms other models for DC prediction, achieving an R2 value of 0.9818, while the Random Forest Classifier achieves 98% accuracy in classifying travel distances. A graphics user interface (GUI)-based software was developed to integrate these models, enabling real-time prediction and travel classification for users. The findings indicate that ML-based DC prediction can enhance user confidence and optimize battery usage in electric motorcycles.
Co-Authors Pambudi, Henri Setyo Rahmadani, Muhammad Azzam