A Robust Lithium-Ion Battery Capacity Prediction Framework Using Multi-Point Voltage Temporal Features and an OOF-Trained Adaptive Gating Mechanism

Accurate capacity prediction is paramount for ensuring the operational safety and reliability of lithium-ion battery management systems (BMS). Nevertheless, contemporary data-driven approaches often grapple with limited feature representation—frequently relying solely on aggregate charging duration or noise measures—which compromises the robustness of these approaches. To address these limitations, this study proposes a robust framework integrating multi-point voltage temporal sampling (MVTS) with an adaptive gated hybrid ensemble learning strategy. The MVTS method is first used to extract high-dimensional geometric features from the constant-current (CC) charging phase (3.9 V–4.15 V), effectively capturing subtle degradation patterns. Subsequently, an unsupervised isolation forest algorithm is incorporated for automated anomaly detection and rectification, thereby augmenting data stability prior to training. In the fusion stage, a heterogeneous hybrid model comprising eXtreme gradient boosting (XGBoost) and long short-term memory (LSTM) is constructed. An adaptive gating mechanism based on random forest (RF) is added to dynamically weight the base learners. To mitigate data leakage during the stacking process, this study employs an out-of-fold (OOF) training strategy based on leave-one-battery-out (LOBO) cross-validation to generate unbiased meta-features for the gating model. This mechanism dynamically modulates fusion weights contingent upon the multi-point voltage features and model discrepancies, thereby accommodating diverse aging stages and capacity degradation patterns. Experimental results from the NASA battery aging dataset demonstrate that the proposed framework significantly outperforms single-model baselines in terms of RMSE and R2, exhibiting superior adaptability and predictive precision.