Core Temperature Estimation in Lithium-Ion Batteries Using EKF and Offline-Based Thermal Parameters

In Battery Management Systems (BMS), accurate estimation of the core temperature of Lithium-Ion Batteries (LIBs) is crucial for ensuring operational safety, enhancing performance, and preventing thermal runaway. Direct measurement of core temperature is impractical due to sensor placement limitations and reliability concerns. Therefore, model-based estimation techniques are employed. This study proposes a method that integrates an Extended Kalman Filter (EKF) with a twostate lumped thermal model to estimate core temperature. The Bernardi model is used to characterize heat generation, accounting for electrochemical over potential and entropy changes. This method uses offline parameter calculations based on cell material properties or datasheet values for parameter identification rather than Particle Swarm Optimization (PSO). This greatly lessens the need for lengthy experiments and computation time. The corresponding root mean square errors (RMSE) between estimated and measured core and surface temperatures are approximately 0.052° C and 0.155° C, respectively.