Online Internal Temperature Estimation Method for Prismatic Li-Ion Battery Using Embedded Physics-Informed Neural Networks

Accurate estimation of internal battery temperature is critical for the safety and state-of-health assessment of lithium-ion batteries, yet it remains challenging due to the trade-off between model accuracy and computational feasibility on resource-constrained edge hardware. This work targets stationary large-scale battery energy storage stations (BESS), where ambient temperatures are actively regulated within a narrow range (typically 15–35 °C), and is developed and validated on large-format prismatic LFP cells. We propose ThermaPhysLite, a lightweight physics-informed neural network (PINN) framework with three innovations: (i) a lightweight PINN architecture tailored for edge devices; (ii) integration of a simplified electro–thermal model—a lumped-parameter thermal circuit coupled with the Bernardi heat generation equation—into a multi-scale temporal convolutional network (MS-TCN) through the PINN paradigm; and (iii) real-time online deployment on the ESP32-S3 embedded platform. Ground-truth internal temperatures were obtained via side-drilled thermocouple embedding in disassembled cells. Offline validation under three operating conditions demonstrates RMSE values of 0.15–0.20 °C. Following INT8 quantization (compressed to 84.29 KB), online deployment yields RMSE values of 0.17–0.24 °C with single-cell inference latency of 120 ms, demonstrating practical viability for BMS in large-scale energy storage systems.