双自适应衰减卡尔曼滤波锂电池荷电状态估计

A dual adaptive fading extended Kalman filter(DAFEKF) algorithm is proposed for the problem of low accuracy and convergent speed of state-of-charge (SOC) estimation. The algorithm designs an observer of state-of-charge for the power battery, and the measured current and voltage are taken as input and observation values of the observer, respectively. Then the state of charge of a battery is estimated by the DAFEKF. The DAFEKF bases on the Kalman algorithm, and adds the time-varying fading factor to reduce the influence of past data on current filtering values and to adaptively adjust the covariances of the process noise and measurement noise. SOC results of a lithium battery obtained using the proposed DAFEKF are compared with those obtained using the extended Kalman filter (EKF) and the adaptive extended Kalman filter (AEKF), and the comparison shows that the DAFEKF method provides better accuracy, robustness and convergence, and the SOC error of the proposed method is less than 2%.