The analysis of retired electric vehicle battery packs is a critical enabler for sustainable circular economy strategies. However, real-world second-life battery data exhibit mixed chemistries, diverse topologies, and heterogeneous battery management system designs, which complicate reliable and scalable diagnostics. This paper introduces a physics-informed, ensemble-based semi-supervised machine learning framework to address these challenges. The proposed approach leverages multi-condition pulse testing to extract informative feature representations and integrates a simplified voltage-drop consistency constraint to enhance anomaly detection without reliance on manufacturer-specific models or proprietary parameters. Experimental results demonstrate strong generalization across battery chemistries and operating conditions while maintaining robust detection performance under a fixed false positive rate calibration.