SOC estimation method for power lithium batteries in energy storage system

Abstract The state of charge (SOC) of the battery is an index set to measure the leftover capacity of the battery. Its scientific estimation plays a vital role in ensuring the secure operation and efficient control of the battery. To enhance the certainty of battery SOC estimation, this study proposes a SOC estimation approach that blends the unscented Kalman filter (UKF) and the crisscross optimization algorithm (CSO) based on a second-order RC network equal battery model. Building upon hybrid pulse power characteristic (HPPC) testing and parameter estimation using the least squares method, the battery SOC is estimated using the urban dynamometer driving schedule (UDDS) test data and the UKF algorithm. Different from the extended Kalman filter (EKF), the UKF algorithm avoids the loss of multi-system nonlinearity during the linearization process through the Gaussian transformation based on sigma points. The CSO algorithm is utilized to calculate the noise in the battery model estimation by considering the battery charge-discharge voltage, current, and estimation results. By excluding the estimation error from the original estimation statistics, more accurate estimation statistics are obtained. Experimental analysis is conducted by comparing the proposed CSO-UKF algorithm with the UKF algorithm and the PSO-UKF algorithm, using battery charge-discharge data collected under the UDDS operating condition with a battery charge-discharge test instrument. The experimental statistics endorse the effectiveness and trustworthiness of the proposed algorithm.