SR-CKF algorithm-based state-of-charge estimation of lithium-ion batteries for energy storage over a wide temperature range

Accurate estimation of the State of Charge (SOC) of lithium-ion batteries is essential for ensuring the safety, reliability, and longevity of energy storage systems. However, certain parameters of battery models can vary with environmental conditions, such as operating temperature, which can introduce errors in battery modeling and SOC estimation. To address these issues, this paper presents a technique for lithium-ion battery modeling and SOC estimation that accounts for the effects of operating temperature and current multiplication. Firstly, three compensation models are developed: a real capacity model of LIBs with temperature and current multiplier compensation, an open-circuit voltage model as a function of SOC with temperature compensation, and a resistive-capacitive parameter model with temperature compensation. These models are used to optimize the equivalent circuit model. Moreover, the optimized second-order RC equivalent circuit model is integrated with the SR-CKF algorithm to enhance the accuracy of SOC estimation. The proposed model, combined with the SR-CKF algorithm, is employed to estimate SOC under pulsed discharge experiments and has been rigorously validated in laboratory settings. The results indicate that the SOC estimation error of the proposed technique is significantly reduced by up to 78.61 % across different temperatures, with a mean absolute deviation (MAD) of only 1.51 % and mean absolute error (MAE) and root mean square error (RMSE) both below 1 %, even in the presence of disturbances.