Lithium battery and its application have gradually become a research hotspot in recent years. In order to improve the estimation accuracy of Battery Management System(BMS) for battery State Of Charge(SOC) and State Of Health(SOH), an improved extended Kalman filter algorithm for online collaborative estimation of battery state of charge and health is proposed based on the establishment of the second-order Thevenin equivalent circuit model. The model parameters are obtained by the least squares method through a staged pulse discharge experiment. The performance of improved extended Kalman algorithm in SOC and SOH estimation accuracy, convergence of algorithm and complexity of algorithm under Dynamic Stress Test(DST) conditions are compared and analyzed by Matlab. Experiments show that the improved extended Kalman filter algorithm can accurately estimate SOC and SOH at each sampling point, with an error about 1%. Moreover, when the initial value is not accurate, the algorithm can rapidly converge to the true value with good robustness, and the accuracy of the algorithm is almost unaffected by the fine-tuning of model parameters.