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@article{175794,
        author = {K Dillibabu and Dr. K. VENKATARAMANA},
        title = {A Feature-Turned XGBoost Model For Real Time SOC Prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {4679-4681},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175794},
        abstract = {The evolution of cloud-based lithium-ion battery management systems has revolutionized state-of-charge (SOC) estimation. Traditional estimation methods, such as Extended Kalman Filter (EKF), struggle with accuracy and computational efficiency. This paper presents a comparative analysis of deep-learning-based SOC estimation algorithms, including Feedforward Neural Networks (FNN), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM). The extension introduces XGBoost for feature optimization, reducing RMSE and MAE errors. The integration of cloud computing enhances computational capabilities, allowing real-time estimation. Results indicate that EKF and XGBoost outperform conventional techniques, providing faster and more precise SOC predictions, ensuring efficient battery management and prolonging battery life for electric vehicles.},
        keywords = {},
        month = {April},
        }