A Dual-Driven Interpretable Ensemble Model for Efficient and Reliable SOH Estimation of Lithium-Ion Batteries

A dual-driven ensemble framework for battery state of health (SOH) prediction is proposed to address the challenges of weak interpretability and insufficient physical mechanism correlation of existing methods. First, two key health features (HFs) containing physical information are extracted based on the first-order resistance-capacitance (RC) equivalent circuit model and the incremental capacity (IC) curve. Secondly, combined with traditional statistical features, a multivariate degradation representation collection is constructed to enhance the ability to characterize HFs. Then, by means of ensemble strategies, three basic models - kernel ridge regression (KRR), random forest (RF) and explainable boosting machine (EBM) are integrated, thereby significantly improving prediction efficiency, robustness and applicability capabilities. Finally, the feature ablation experiment is employed to verify the effectiveness of the proposed multivariate HFs system, and the interpretability of the framework is enhanced with the assistance of SHAP analysis. Experimental results on two public data sets reveal that, in comparison with several conventional deep learning models, the proposed framework maintains or even improves the prediction accuracy at the model inference level while increasing the processing speed by at least 60%.