Fuzzy physics-informed thermal diagnostics via non-invasive frequency-domain signature distillation in high-energy-density batteries

Abstract

Accurate real-time monitoring of internal temperature in lithium-ion batteries remains critical for preventing thermal runaway, as conventional approaches sacrifice either computational efficiency or cross-scenario robustness. We present a generalized fuzzy physics-informed framework that distills thermally sensitive electrochemical processes while circumventing redundant physical constraints, thereby establishing an explicit mechanism-constrained mapping between frequency-domain signals and internal temperature. This framework facilitates online thermal estimation, with dynamic validations in LiFePO₄/graphite 18650-type cells confirming real-time capability with near-instantaneous acquisition (∼6 s per measurement), exceptional accuracy (±0.5 °C) within the operational temperature range (30–50 °C), and operational resilience across 20 %–80 % state-of-charge. The framework maintains predictive fidelity (±1.0 °C at 30 °C and ±4.0 °C at 60 °C, 95 % prediction intervals) across 80 %–100 % state-of-health while demonstrating adaptability to cathode materials and structural architectures. This strategy resolves the competing imperatives of physical interpretability, computational efficiency, and cross-scenario generalizability, offering a universal paradigm for embedded thermal management in safety-critical applications.