Abstract: Accurate Accurate temperature monitoring is crucial for ensuring the thermal safety and performance of lithium-ion batteries (LIBs), which are extensively used in electric vehicles, robotics, and energy storage systems. The optimal operating temperature for LIBs is strictly confined between 20°C and 40°C. Temperatures outside this range can lead to performance degradation, capacity loss, accelerated aging, or even thermal runaway. Conventional methods, such as sensor-based measurements and electrothermal modeling, face challenges in providing rapid and comprehensive temperature distribution data for large-format batteries due to spatial and cost limitations, which hinder sensor deployment. To address these challenges, this study introduces a novel temperature field modeling and reconstruction approach for ternary lithium batteries using Physics-Informed Neural Networks (PINNs). This method integrates battery thermal modeling with deep learning techniques, enabling real-time, sensor-free monitoring of spatiotemporal temperature distributions within the battery system.