Noninvasive Internal Temperature Prediction of Lithium‐Ion Batteries Under Elevated Ambient Temperatures

Accurate, real‐time, and noninvasive monitoring of internal battery temperature is essential for understanding thermal behavior and enabling early thermal runaway warning in lithium‐ion batteries (LIBs). Although simplified thermal prediction models have been widely investigated for internal temperature estimation, their validation is predominantly conducted under room‐temperature conditions, and their reliability under elevated ambient temperatures remains insufficiently explored. In this study, the robustness and generalization capability of a simplified thermal prediction model are systematically validated under different ambient temperatures using fiber Bragg grating (FBG) sensing technology. To obtain operando internal temperature data, FBG sensors are embedded into the central void of commercial 18650 LIBs, while additional sensors simultaneously monitor the battery surface and ambient temperatures. The prediction model is evaluated under different ambient temperatures and charging/discharging rates. The predicted internal temperatures show good agreement with the experimentally measured values across all operating conditions, demonstrating high reliability and robustness under complex thermal boundaries and elevated‐temperature environments. The proposed validation framework provides experimental support for the applicability of surface temperature‐based internal temperature prediction models under realistic operating conditions.