Multiscale Diagnosis of Lithium‐Ion Battery Degradation under Extreme Operating Conditions with Integrated Data‐Driven and Post‐Mortem Validation

Comprehensive Summary Lithium‐ion batteries subjected to extreme operating conditions—such as high temperature, high C‐rates, and deep overdischarge— exhibit rapid and coupled aging behaviors that are challenging to disentangle using conventional diagnostics. While purely data‐driven models often lack interpretability ("black‐box"), physics‐based methods typically require measurements unavailable in practical applications. To bridge this gap, we propose the SIX‐ICA framework, an interpretable machine learning approach that integrates Incremental Capacity Analysis (ICA) features with an XGBoost regressor and SHAP analysis. By extracting mechanism‐informed ICA peak features from routine cycling data, the framework achieves robust State‐of‐Health (SOH) estimation. Crucially, SHAP analysis provides transparent feature attribution, linking statistical inputs directly to degradation pathways. Validated on LiFePO 4 /graphite pouch cells cycled at 65 °C and 3 C (comparing 2.5 V vs. 1.0 V cutoffs), the framework identifies Loss of Lithium Inventory (LLI) as the primary driver of capacity fade, noting its significant intensification under deep over‐discharge, while Loss of Active Material (LAM) plays a secondary role. These findings are corroborated by OCV fitting and post‐mortem characterization. This workflow advances interpretable SOH diagnostics under extreme conditions and offers a scalable route for other battery chemistries.