Temperature‐Modulated MEMS Gas Sensor Array Enabled by Physics‐Informed Deep Learning for Thermal‐Runaway Characteristic Gas Discrimination

ABSTRACT Gas monitoring is a promising method for early warning of thermal runaway, yet existing sensor systems suffer from insufficient miniaturization, inadequate discrimination ability, and severe drift‐induced performance degradation. This work presents a 2 × 2 integrated single‐chip MEMS gas sensor array with temperature modulation and deep learning for discriminating thermal‐runaway characteristic gases. The array comprises four distinct temperature zones achieved by varying the line width of the heating electrodes, with the entire array measuring 1.8 × 1.8 × 0.5 mm 3 . Furthermore, it is capable of detecting four critical thermal‐runaway characteristic gases (H 2 , CO, CH 4 , and CO 2 ) and exhibits distinct temperature‐modulated response patterns. To achieve reliable multi‐gas discrimination, we develop a Hierarchical Kolmogorov‐Arnold Mixture‐of‐Experts (HK‐MoE) model. Under single‐gas conditions, the model achieved initial F1‐scores of 99.53% for target gas presence detection, 99.88% for gas classification, and 98.09% for concentration determination. While performance experienced some degradation following a 30‐day continuous operational aging, the model consistently demonstrated satisfactory resilience and significantly outperformed conventional baselines and state‐of‐the‐art Transformer‐based time‐series models. Further validation using multi‐component gas mixtures yielded overall F1‐scores above 99.83% for all three tasks, demonstrating reliable mixed‐gas discrimination capability. This integrated framework offers a compact solution to drift‐induced instability, demonstrating its potential for reliable and early‐stage battery safety monitoring.