Abstract: On the practical issues such as accumulative state estimation errors caused by the inaccurate battery model， and time-varying and unknown noise statistical characteristics， a state co-estimation algorithm for Li-ion power batteries based on adaptive unscented Kalman filter (UKF) framework is proposed. During the iteration process， the measurement equation called by unscented transform (UT) each time is quasi-linearized， which could greatly reduce the computational complexity. The statistical characteristic parameters of unknown noises can be estimated by introducing Sage-Husa adaptive estimation method with forgetting factor， which could improve the adaptive fault tolerance of UKF estimation algorithm. If the divergence trend occurs in the filtering process， the error covariance will be penalized by employing adaptive attenuation factor to prevent the divergence of the filter， accordingly guaranteeing the numerical stability of filtering process. By employing co-estimation strategy， the inner ohmic resistance of the 1st-order Thevenin battery model， which could characterize the state of health (SOH) indirectly， can be estimated and adjusted in real time， and therefore a high-precision estimation of the state of charge (SOC) is able to be obtained owing to the accuracy of the battery model during the process of dynamic charge-discharge cycling tests. The comparative experimental results verify the feasibility and effectiveness of the adaptive co-estimation algorithm.

Key words: state of charge(SOC), state of health(SOH), unscented Kalman filter(UKF), adaptive filter, Li-ion power batter