基于COA-ASRCKF的单液流锌镍电池SOC估计

SOC estimation of single-flow zinc-nickel battery based on COA-ASRCKF

针对容积卡尔曼滤波(CKF)算法在迭代过程中存在诸多破坏协方差对称性和正定性的敏感操作,进而导致算法终止的现象,提出一种自适应平方根容积卡尔曼滤波(ASRCKF)算法。采用ASRCKF算法在估算单液流锌镍电池荷电状态(SOC)时,过程噪声协方差Q、量测噪声协方差初值R(0)和状态误差协方差初值P_(0)的设定,对估算精度和鲁棒性有重要影响。为此,应用郊狼优化算法(COA)对Q、R(0)和P_(0)进行参数寻优。实验结果表明,提出的COA-ASRCKF算法能较好地应用于单液流锌镍电池SOC估计。与CKF和ASRCKF算法相比,估算精度更高、鲁棒性更强,均方根误差小于1%。

Aiming at the phenomenon that the cubature Kalman filter(CKF)algorithm had many sensitive operations that destroyed the symmetry and positive definiteness of the covariance in the iterative process,leading to the termination of the algorithm,an adaptive square root cubature Kalman filter(ASRCKF)algorithm was proposed.Process noise covariance Q,the initial value R(0)of measurement noise covariance and the initial value P_(0) of state error covariance had important influence on estimation accuracy and robustness when ASRCKF algorithm was applied to SOC estimation of single-flow zinc-nickel battery.Then,the coyote optimization algorithm(COA)was used to optimize the parameters of Q,R(0)and P_(0).The test results showed that the proposed COA-ASRCKF algorithm could be applied to state of charge(SOC)estimation of single-flow zinc-nickel battery.Compared with CKF and ASRCKF algorithm,it had higher estimation accuracy and stronger robustness,the root mean square error was less than 1%.