Article Per Year (5 Year)
p-Index From 2020 - 2025
1.325
P-Index
This Author published in this journals
All Journal ComEngApp : Computer Engineering and Applications Journal Teknika Jurnal Teliska Jurnal Ampere Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Teknologi Informasi dan Pendidikan TECHNOLOGIC
Muslimin, Selamat
Politeknik Negeri Sriwijaya
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 11 Documents
Search

 1   2 
The Application of Adaptive Neuro Fuzzy Inference System (ANFIS) Method in Estimating State of Charge (SOC) and State of Health (SOH) of Lithium-Ion Batteries Muslimin, Selamat; Prihatini, Ekawati; Husni, Nyayu Latifah; Caesandra, Wahyu
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 4, November 2025 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i4.2357

Abstract

The increasing reliance on lithium-ion batteries (LIBs) for electric vehicles and portable electronics demands accurate monitoring of battery performance, particularly the State of Charge (SOC) and State of Health (SOH). Conventional estimation methods—such as Coulomb counting, Kalman filtering, and equivalent circuit modeling—face challenges under dynamic conditions due to drift and limited adaptability. Recent studies have explored machine learning and neuro-fuzzy approaches to enhance prediction accuracy, yet many lack integration of real-time hybrid learning or struggle with high estimation error in noisy data environments. This research aims to apply the Adaptive Neuro-Fuzzy Inference System (ANFIS) to estimate SOC and SOH using experimental data from a 48V lithium-ion battery. The novelty lies in combining voltage, current, and capacity data within a MATLAB-based ANFIS framework that employs a hybrid learning algorithm integrating backpropagation and Recursive Least Squares Estimation (RLSE). Training data for SOC estimation used charging voltage and current, while SOH estimation incorporated discharging data and capacity. Results show that ANFIS achieved high accuracy with RMSE of 0.1466 and MAE of 0.021 for SOC, and RMSE of 0.012 and MAE of 0.0017 for SOH. The estimated SOH was 33.61%, closely aligned with actual values. These findings confirm ANFIS as a robust and adaptive method for real-time battery diagnostics. Future work will explore multi-input hybrid models, the integration of IoT-based BMS telemetry, and testing across diverse battery chemistries to generalize the model's performance and extend its application in smart energy systems
Co-Authors Amperawan Amperawan, Amperawan Andika Pratama, Destra Bibrosi, Nur Alif Zaki Caesandra, Wahyu Dewi Permata Sari Dira, Arbi Pratam Faisal Damsi, Faisal Iskandar Iskandar Marniati, Yessi Nyayu Latifah Husni, Nyayu Latifah Pola Risma Prihatini, Ekawati Ramadhan, M. Daffa Rasyad, Sabilal Savitri, Nurul Thoriq, Noval Al Yaksyah, Rafi Yudi Wijanarko, Yudi