基于不确定性量化的鲁棒锂离子电池容量估计方法：应对时间序列数据掩蔽挑战的渐进学习框架

Abstract Accurate estimation of lithium-ion battery capacity is critical for reliability management but it faces challenges due to temporal data masking, a prevalent issue in real-world cloud applications causing time-series masking and data degradation. To address this, we propose a progressive learning framework that constructs a data-quality-aware learning pathway, enabling robust training solely on high-quality laboratory data by progressively generating and incorporating artificially masked low-quality samples. The framework integrates dynamic sampling and adaptive resampling strategies to enhance model robustness against data skewness. Additionally, uncertainty quantification with strong physical interpretability is efficiently achieved through implicit ensemble learning on homologous charging segments, avoiding the computational bottlenecks of Bayesian or ensemble-based methods. Validated on the LFP, NCA, and NCM datasets, our method achieves RMSEs of 0.2170 %, 0.1924 %, and 0.1326 % on clean data, respectively. When 50 % of the data is masked, the RMSEs increase only slightly, with the maximum absolute increase being just 0.0303 %, and the model maintains high accuracy even with masking ratios as high as 70 %. The framework also generalizes well across different deep learning architectures. This work bridges the gap between laboratory models and real-world deployment for battery management systems.