State Estimation Method of Energy Storage Battery Based on Improved Dual Extended Kalman Filter

The state of charge (SOC) is a crucial indicator of energy storage batteries. When estimating the SOC of battery, the voltage measurement bias will cause the deviation of SOC estimation results. And in the case of different SOC, the deviation of SOC estimation caused by the voltage measurement bias is different. To address this issue, this paper proposes a method for estimating the state of energy storage batteries based on the improved dual extended Kalman filter. Firstly, the dual extended Kalman filter model is used to estimate the battery SOC and voltage measurement bias simultaneously, with the voltage measurement bias dynamically compensated during the SOC estimation process. Secondly, an adaptive error coefficient adjustment strategy is introduced in the dual extended Kalman filter model to enhance the accuracy of SOC estimation for energy storage batteries. The case study results demonstrate that the proposed method, based on the improved dual extended Kalman filter, can effectively reduce the impact of voltage measurement bias and enhance the accuracy and precision of energy storage batteries’ state estimation.