Lithium-Ion Battery Module Internal Temperature Estimation Based on Rauch-Tung-Striebel Smoothing Technique

The temperature monitoring of the lithium-ion battery is crucial for the advanced battery thermal management systems (BTMS) to improve performance and ensure operational safety and reliability of the battery system. In real applications, the core temperature of the battery is unfortunately unmeasurable due to the impracticality of placing a sensor inside the core, and has to be estimated online in real-time. Meanwhile, only the measurement of battery surface temperature can not meet the need for advanced BTMS due to the impact of the large temperature gradient between the surface and internal in high power applications. The battery core temperature estimation will become challenging when encountering sensor bias and noise. In order to improve the accuracy and stability of battery core temperature estimation, the method based on the Rauch-Tung-Striebel smoothing technique and unscented Kalman filter is applied to reconstruct the core temperature for application in battery management systems. The smoothing process can effectively further reduce its estimation error. The unmeasurable parameters in the nonlinear thermal model are identified by the optimization. The electric-thermal coupling model is validated through experiments and CFD simulations. The simulation results show that the average errors of different battery core temperatures are less than 1K.