Advanced Techniques for Estimating State of Charge (SoC) in Lithium-Ion Batteries | ||||
| Advanced Sciences and Technology Journal | ||||
| Articles in Press, Accepted Manuscript, Available Online from 22 August 2025 | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/astj.2025.391150.1065 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Ehab Ahmed Nossier  1; Donya Hamed2; Saif Farahat2; Mohab abdelraouf2; Ganna Deghady2; Engy Farouk2; Mostafa Mostafa2; Hassan Hanafy2; Youssef Zaki2; Mohamed Ismail Ahmed 3
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| 1Automotive Engineering Dept., Faculty of Engineering Ain Shams University | ||||
| 2Mechatronics Engineering Department, Faculty of Engineering and Technology, Egyptian Chinese University, Cairo, Egypt | ||||
| 3Egypt | ||||
| Abstract | ||||
| Accurate estimation of the State of Charge (SoC) of batteries is critical for the optimal performance and safety of electric vehicles and portable electronic devices. In this study, several machine learning regression models were investigated for state-of-charge (SoC) estimation, including Linear Regression, Support Vector Machines (SVM), Regression Trees, and Neural Networks. The publicly available LG 18650HG2 dataset [1] was utilized, comprising over 670,000 data points collected during charging and discharging cycles. Input features included voltage, current, temperature, average voltage, and average current. The models were implemented using MATLAB, and their performance was evaluated using standard metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The best-performing model—a wide neural network—achieved an RMSE of 0.0211, MAE of 0.0109, and an R² of 0.9959. These results indicate that Neural Networks outperform traditional regression techniques in terms of prediction accuracy and robustness. | ||||
| Keywords | ||||
| State of Charge (SoC); Lithium-Ion Batteries; Battery Management System; Electric Vehicles (EVs) | ||||
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