Robust state of charge estimation for lithium-ion batteries in variable temperatures using truncated singular value decomposition Cubature Kalman Filter algorithm

Abstract

Advanced battery management systems play a significant role in the safe and efficient use of battery energy storage systems. While the accurate and reliable estimation of state of charge (SOC) is essential for improving the utilization efficiency of batteries and extending their service life. However, the working environment of battery energy storage systems is highly complex, and temperature changes can often affect the accuracy of modeling and estimation. The traditional methods of SOC estimation often ignore the changes in temperature of the whole system, thus resulting in the cumulative errors in SOC estimation. To address this problem, an improved method is proposed in this paper to estimate the SOC of lithium-ion batteries. It requires the use of Cubature Kalman Filter (CKF) algorithm that is based on Truncated Singular Value Decomposition (TSVD). Additionally, the Forgetting Factor Recursive Least Squares (FFRLS) method is used to determine the parameters of the Thevenin equivalent model for lithium batteries. This algorithm is then validated through simulation analysis and comparison with the experimental data obtained under cyclic operating conditions at a temperature of 5 ℃, 25 ℃, and 40 ℃, respectively. The results demonstrate that the proposed TSVD-CKF algorithm is accurate in reflecting the impact of ambient temperature on the model parameters and is applicable to SOC estimation in a wide range of temperatures, showing a strong robustness.