An Online SOC-SOTD Joint Estimation Algorithm for Pouch Li-Ion Batteries Based on Spatio-Temporal Coupling Correction Method

Abstract

An SOC (state of charge)-SOTD (state of temperature distribution) joint estimation algorithm is established for a pouch lithium-ion battery. This method integrates the first-order RC model, distributed heat generation model, thermal resistance network model, and spatio-temporal coupling correction based on spatial restoration algorithm and dual Kalman filter (DKF). Unlike the traditional 1-D SOT estimation model, the proposed algorithm can accurately estimate the temperature distribution in the battery online by restoring the battery surface temperature distribution, calculating the battery internal temperature distribution, and jointly correcting the SOC and average internal temperature. Then, the proposed method is used for the SOC-SOTD estimation of a 25-Ah pouch battery at the ambient temperatures of 10, 20, and 30 °C under the worldwide light-duty test cycle and new european driving cycle driving cycles, and the estimated values are verified by experiment and 3-D simulation. According to the verification results, calculation errors of SOC are no more than 2.12%, and the maximum average calculation errors are 0.16 °C for the surface temperature and 0.24 °C for the internal temperature. However, the DKF is needed for the SOC-SOTD joint estimation because the single KF can only correct SOC or average internal temperature but cannot handle the inconsistency in temperature distribution.