Robust Core Temperature Estimation of Lithium-Ion Batteries Based on Maximum Correntropy Enhanced Sage-Husa Adaptive Extended Kalman Filter in Thermal Management Systems

Excessive core temperature in lithium-ion batteries for new energy vehicles can accelerate electrochemical corrosion, thereby degrading battery lifespan. In extreme cases, it may even induce thermal runaway. However, the inherent complexity of internal electrochemical reactions and the battery’s sealed structure make direct measurement of its core temperature highly challenging. To address the challenge in precise core temperature measurement, this paper proposes a maximum correntropy criterion improved adaptive extended Kalman filter (MCC-AEKF) for accurate battery core temperature estimation. The method establishes a battery thermal model with parameters identified by adaptive forgetting factor recursive least squares, then enhances the traditional EKF by incorporating MCC and Sage-Husa adaptive criterion to mitigate the effects of non-Gaussian noise and initial parameter settings, thereby improving estimation accuracy. Validation through co-simulations in AMESim and MATLAB/Simulink under urban dynamometer driving schedule and highway fuel economy test conditions demonstrates that the proposed MCC-AEKF achieves at least 41.7% higher accuracy than conventional methods. This approach effectively resolves the low-accuracy issue in traditional algorithms for core temperature estimation of new energy vehicle power batteries, significantly enhancing battery safety.