A Study on Estimation of SOC of Emus Batteries

The on-board batteries of the Emus have extremely high accuracy requirements for real-time estimation of state of charge (SOC). The ordinary Ah integration method and the open-circuit voltage method cannot meet the actual requirements of the Emus. This paper estimates the battery SOC based on the extended Kalman filter (EKF) algorithm. By building a first-order Thevenin equivalent circuit model to combining the car battery with the EKF algorithm, ultimately improve SOC estimation accuracy. In order to improve the estimation accuracy of the model, the variation law of the polarization voltage and polarization capacitance of the battery pack under different SOC values were obtained, and the functional relationship was fitted by the least square method. Finally, the simulation was performed in MATLAB / Simulink environment. The experimental results show that the EKF algorithm overcomes the shortcoming that the AH integration method cannot eliminate the accumulated error, and can implement the estimated battery SOC value, in addition the estimated error is controlled within 4%.