A Feature-Turned XGBoost Model For Real Time SOC Prediction

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K Dillibabu; Dr. K. VENKATARAMANA

A Feature-Turned XGBoost Model For Real Time SOC Prediction

Authors: K Dillibabu, Dr. K. VENKATARAMANA

Unique Paper ID: 175794
Volume: 11
Issue: 11
PageNo: 4679-4681

Keywords: No Keywords Found

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.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 11
PageNo: 4679-4681

A Feature-Turned XGBoost Model For Real Time SOC Prediction

Available:https://ijirt.org/article?manuscript=175794

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