Abstract: The internal temperature of a battery has a greater impact on its performance compared to the surface temperature, as it better characterizes the battery's operational state. However, directly obtaining the internal temperature is challenging. In this paper, we propose a novel algorithm for predicting the internal temperature of a battery based on the Electrochemical Impedance Spectroscopy (EIS) method. This approach utilizes the battery's surface temperature and the ambient temperature to select the frequency for the primary estimation of the internal temperature. Subsequently, a secondary estimation of the internal temperature is conducted using EIS, which offers higher accuracy and a wider measurable range compared to the single-frequency measurement method. This paper investigates the effects of internal temperature and state of charge (SOC) on the battery's impedance. It establishes a relationship between the internal temperature of the battery and the real part of the impedance, disregarding the effect of SOC. Then, we proceed with temperature estimation and compare the results with experimental data from the literature. The maximum error between the estimated temperature and the actual temperature is found to be 8.77%, which is 3% lower than the prediction error using a single frequency. The proposed method demonstrates improved accuracy in predicting the internal temperature of the battery, providing valuable guidance for temperature estimation strategies.