Physics-Informed 20-Tensor Grey-Box Super-Learner with Monotonic Thermodynamic Constraints, Conformal Uncertainty, and Merkle-Tree Cryptographic Proofs for Non-Invasive EV Battery Flash Diagnostics

Protected under National Invention Patent Application No. NC2026/0015789 (Ref. RN-VOLTCHECK-IIC-2026, Superintendency of Industry and Commerce of Colombia - SIC) and Software Copyright Registration No. 1-2026-211850 (National Directorate of Copyright - DNDA). Official Researcher Curriculum at the Ministry of Science, Technology and Innovation of Colombia (MinCiencias SNCTeI CvLAC ID: 0002548157). Abstract: Accurate, rapid, and tamper-evident estimation of the State of Health (SoH) of high-voltage lithium-ion traction battery packs (LiFePO4 Blade and NMC/NCA) is critical for the secondary electric vehicle (EV) market, insurance underwriting, and warranty certification. This paper presents a cyber-physical diagnostic architecture (RN VoltCheck AI) that transforms an 8-dimensional static On-Board Diagnostics (OBD-II / UDS ISO 14229) or OAuth 2.0 telemetric snapshot into a 20-dimensional physics-informed tensor space. These 20 tensors feed a 12-model stacked Super-Learner enforcing directional thermodynamic monotonicity constraints paired with Split Conformal Prediction, internal degradation mode decoupling (LLI, LAM_NE, LAM_PE), forensic BMS-reset detection, and a 4-leaf Merkle-Tree cryptographic accumulator compliant with EU Battery Regulation 2023/1542. In blind testing (N = 2,400 across 26 EV/PHEV models), the proposed ensemble achieves MAE = +/-0.125% SoH, RMSE = 0.156%, and R^2 = 0.9997 in under 180 seconds.