Estimation of the State of Charge of Lithium Battery Based on the Combination of Unscented Kalman Filtering and Gross Error Detection and Compensation

The battery measurement data acquired by sensors can be disturbed by gross errors, which in turn affects the accurate estimation of the State of Charge (SOC) of the battery. To address the aforementioned issue, this paper proposes a lithium-ion battery SOC estimation method, UKF-GEDC, that combines Unscented Kalman Filtering (UKF) with Gross Error Detection and Compensation (GEDC). This method can detect errors in real-time within the measured data of lithium-ion battery, classify them precisely, and apply effective compensation, thereby significantly improving the accuracy of state estimation. Compared to the traditional UKF, UKF-GEDC can effectively handle different types of gross errors present in the measurement data and exhibits higher robustness. Experimental results demonstrate that the proposed UKF-GEDC can effectively suppress the influence of gross errors when estimating the SOC of lithium-ion battery, achieving more reliable and accurate SOC estimation.