Hydrostatic pressure adaptive dual-polarized model for state of charge estimation of lithium-ion batteries

Pressure-tolerated lithium-ion batteries have been used in autonomous underwater vehicle (AUV), due to their ability to withstand pressure directly and save structural parts that protect itself. As an important component of the battery, state of charge (SOC) is critical for evaluating the reliable operation of AUV batteries. The influence of hydrostatic pressure is not considered in typical SOC estimation process, which leads to a large deviation of terminal voltage estimation, and finally the accurate estimation of SOC cannot be realized. In this paper, a dual-polarized equivalent circuit model dependent on hydrostatic pressure is established, which fully considers the influence of pressure on the internal characteristic parameters of the battery. The characteristic parameters in the equivalent circuit model are measured at hydrostatic pressure and identified by forgetting factor recursive least squares (FFRLS). Compared with the blank model without considering hydrostatic pressure, the proposed model can capture voltage and SOC, which can contribute to the estimation accuracy of terminal voltage in a wide pressure range. The typical algorithms of unscented Kalman filter (UKF), cubature Kalman filter (CKF) and adaptive cubature Kalman filter (ACKF) are used to estimate the SOC based on the two models (OCV-SOC-Pressure and OCV-SOC) at different hydrostatic pressures. The results show that the accuracy of the SOC estimation algorithm under different hydrostatic pressure environments is improved, which is attributed to the compensation correction of the original OCV-SOC model. The root mean square error (RMSE) value of the ACKF algorithm based on the hydrostatic pressure adaptive multivariate regression model is 0.94 %, which significantly improves the SOC estimation effectiveness of the OCV-SOC model without considering the pressure factor (RMSE is 7.36 %) at the hydrostatic pressure of 60 MPa. At the preset different SOC initial values, the three algorithms based on the hydrostatic pressure adaptive multivariate regression model can achieve fast convergence, and ACKF shows the best environmental adaptability.