State of Charge Estimation for Battery Based on Improved Cubature Kalman Filter

This study conducted research on the SOC estimation of lithium-ion battery: Aiming at the parameter identification problem of battery model, an online identification method for model parameters based on the recursive least squares method of forgetting factor (FRLS) was proposed. The model parameters were identified online and updated in real time, avoiding the model error caused by the fixed model parameters. For the problem of noise sensitivity in cubature Kalman filtering, an adaptive cubature Kalman filter method based on random weighting (ARWCKF) was proposed, which restrained the disturbances of system noises on state estimation and avoided the error caused by the fixed weight value of the cubature point. The results indicate that the online parameter identification based on recursive least squares method and ARWCKF filtering has good estimation accuracy and fast convergence ability. The voltage estimation error does not exceed 40 mV, and the SOC estimation error does not exceed 1%.