The lithium battery SOC estimation on square root unscented Kalman filter

Accurate state of charge (SOC) estimation is still one of the most important academic and application problems in the field of new energy so far. In order to improve SOC accuracy of lithium cobalt oxide battery, the authors obtained the SOC data with battery open circuit voltage (OCV) by charge/discharge experiments. Besides, the parameters of second-order RC equivalent circuit, including polarization capacitors and resistances, were identified through the rebound voltage test under different SOC condition. The model simulation data was carried out with MATLAB/Simulink by applying user-defined current. The simulation results whose error value was less than 25 mV fit the experimental data quite well and it well verified the correctness of the battery model and the accuracy of the parameters identifications. In this paper, a square root UKF algorithm was constructed Systematically on the lithium cobalt oxide battery model to estimate the SOC under complex conditions and environment. By comparing the SOC estimation results between traditional Kalman filter with the SR-UKF, the data indicated that SR-UKF algorithm our proposed can increase the robust of the filter and the estimation error can be reduced to less than 1.5%. (C) 2022 The Author(s). Published by Elsevier Ltd.

Automated summary

Accurate state of charge (SOC) estimation is an important problem in the field of new energy. In this study, a method based on second-order RC equivalent circuit and square root unscented Kalman filter (SR-UKF) algorithm is proposed to improve the SOC estimation accuracy under complex conditions.