Impact of Electrical Parameter Measurement Error on Battery SOC Estimation Accuracy and Compensation Strategies

The estimation accuracy of the battery state of charge (SOC) directly affects the reliable and stable operation of the battery management system (BMS). In complex operating conditions such as vehicle-mounted applications and energy storage power stations, systematic biases and random noise are common in voltage and current sensing measurement. Most existing studies on SOC error focus on the independent effect of individual measurement error and lack systematic explanation on the propagation mechanism and quantitative influence of multi-source errors. In this paper, based on the second-order RC equivalent circuit model, the analytical transfer model of SOC estimation error is established under the framework of the extended Kalman filter (EKF), and the transfer path of measurement error in the algorithm is clarified. Then, a battery test bench with “reference-measurement” dual-channel parallel measurement is built. Under the constant-current discharge condition, the contribution weight quantification and sensitivity analysis of multi-source measurement error to SOC estimation error are completed, and it is revealed that the voltage measurement bias is the dominant error source of SOC estimation. On this basis, an augmented-state modified EKF algorithm (AM-EKF) with online identification of voltage bias is proposed. The voltage measurement bias is incorporated into the state vector to realize the joint estimation and online compensation of the operating state and measurement bias. The results show that under the constant-current discharge condition, the improved algorithm increases the proportion of SOC estimation error within the ±3% qualified interval to more than 85% in the whole discharge period. The maximum estimation error is significantly lower than that of the traditional EKF algorithm, and the accuracy is close to the unbiased ideal condition. Under the modified DST dynamic condition, the improved algorithm controls the SOC estimation error within 5%, which is 72% lower than the traditional EKF calculation error with bias input. The scheme has high robustness, no additional hardware cost, controllable computational complexity and excellent engineering application value.