Lifecycle SOC Estimation for Energy Storage Batteries Under Constant Power Discharge Conditions

In order to improve the operating performance of energy storage systems throughout their lifecycle, this paper focuses on calculating the state of cycle aging for energy storage batteries using a dynamic time warping (DTW) algorithm. This approach is based on the characteristics of batteries under constant power discharge conditions, which are identified utilizing full discharge voltage data. Initially, a second-order resistor-capacitance circuit (RC) model was established, and the least squares with a forgetting factor was adopted to identify the parameters. Subsequently, the governing equations were derived for the model, and the battery capacity was obtained from a combination with calculation data reflecting the state of cycle aging. Following this, the unscented Kalman filter (UKF) algorithm was used to estimate the state of charge (SOC) of batteries. Lastly, the accuracy of the proposed algorithms was verified under 20, 200, and 800-cycle conditions. The results revealed that at different aging stages, the maximum SOC estimation error of the DTW-UKF algorithm was below 4%, with an average estimation error below 2%. Comparisons with SOC calculation results from the Ah integration method and the UKF algorithm alone showed an average relative accuracy improvement of 67.6% and 47.8% respectively, validating that the DTW-UKF algorithm can achieve at higher precision in SOC estimations for energy storage systems throughout their lifecycle.