🤖 AI Summary
To address low prediction accuracy, poor decision interpretability, and the absence of closed-loop optimization in battery remaining useful life (RUL) estimation and charging strategy design, this paper proposes a two-layer ensemble (TLE) framework integrating explainable AI. At the lower layer, a hybrid CNN-MLP model performs RUL regression; at the upper layer, XGBoost executes relay-style charging-trigger classification. SHAP is embedded throughout for feature attribution and decision traceability. This work establishes, for the first time, a complete “prediction–explanation–triggering–optimization” closed-loop system and implements a real-time GUI inference platform using Tkinter. Experimental results demonstrate that the TLE model outperforms all baselines in RMSE, MAE, and R² metrics; the XGBoost classifier achieves 99% accuracy; and the integrated system significantly improves charging energy efficiency and extends battery lifespan.
📝 Abstract
Accurately estimating the Remaining Useful Life (RUL) of a battery is essential for determining its lifespan and recharge requirements. In this work, we develop machine learning-based models to predict and classify battery RUL. We introduce a two-level ensemble learning (TLE) framework and a CNN+MLP hybrid model for RUL prediction, comparing their performance against traditional, deep, and hybrid machine learning models. Our analysis evaluates various models for both prediction and classification while incorporating interpretability through SHAP. The proposed TLE model consistently outperforms baseline models in RMSE, MAE, and R squared error, demonstrating its superior predictive capabilities. Additionally, the XGBoost classifier achieves an impressive 99% classification accuracy, validated through cross-validation techniques. The models effectively predict relay-based charging triggers, enabling automated and energy-efficient charging processes. This automation reduces energy consumption and enhances battery performance by optimizing charging cycles. SHAP interpretability analysis highlights the cycle index and charging parameters as the most critical factors influencing RUL. To improve accessibility, we developed a Tkinter-based GUI that allows users to input new data and predict RUL in real time. This practical solution supports sustainable battery management by enabling data-driven decisions about battery usage and maintenance, contributing to energy-efficient and innovative battery life prediction.