Lithium-Ion Battery Temperature Estimation Based on Electrochemical Impedance Spectroscopy

The electrification of commercial vehicles demands precise battery thermal management, but direct measurement of the cell core temperature is challenging. This paper presents an electrochemical impedance spectroscopy (EIS)-based approach for rapid indirect estimation of the mean internal temperature in 2170 NMC lithium-ion cells. Three measurement approaches and various fitting methods, including Steinhart–Hart, least-squares polynomials, and nonlinear Arrhenius-based fits, are compared using experimental data. The results indicate that estimation accuracy is more strongly influenced by the selection of measurement frequency than by the choice of fitting approach. The optimal method combines a single optimized frequency with a nonlinear polynomial incorporating an Arrhenius term, achieving a maximum deviation of 0.23K and a mean deviation of 0.14K. This framework enables indirect EIS-based estimation of the cell core temperature and can be further refined through measurements on multiple cells or by combining different fitting methods. Future work should extend the proposed approach to dynamic EIS measurements, battery ageing, and integration with thermal models for early overheating warning and identification of the first thermal runaway warning stage in high-power commercial vehicles.