State of Charge Estimation for Lithium-Ion Battery Based on Improved Cubature Kalman Filter Algorithm

An improved cubature Kalman filter (CKF) algorithm for estimating the state of charge of lithium-ion batteries is proposed. This improved algorithm implements the diagonalization decomposition of the covariance matrix and a strong tracking filter. First, a first-order RC equivalent circuit model is first established and verified, whose voltage estimation error is within 1.5%; this confirms that the model can be used to describe the characteristics of a battery. Then the calculation processes of the traditional and proposed CKF algorithms are compared. Subsequently, the improved CKF algorithm is applied to the state of charge estimation under the constant-current discharge and dynamic stress test conditions. The average errors for these two conditions are 0.76% and 1.2%, respectively, and the maximum absolute error is only 3.25%. The results indicate that the proposed method has higher filter stability and estimation accuracy than the extended Kalman filter (EKF), unscented Kalman filter (UKF) and traditional CKF algorithms. Finally, the convergence rates of the above four algorithms are compared, among which the proposed algorithm track the referenced values at the highest speed.