Abstract: To address the problem of the high cost of the energy storage system when hybrid energy storage is used to smooth out the wind power fluctuations, this paper proposes a control strategy based on the Kalman filtering and the model predictive control. The method is based on the wind-storage co-generation system, and on the basis of meeting the wind power smoothing demand, the optimal storage target power is obtained by presetting the cut-off frequency to minimise the change of storage capacity with the lowest power fluctuation as the multi-objective, and using genetic algorithm to solve the Kalman filter adaptive parameters. In order to improve the coordinated operation of the hybrid energy storage systems, the state of charge (SOC) is considered to regulate the dynamic power allocation taking into account the battery operating life and the changes of the SOC of the supercapacitor through model predictive controlling. Finally, the simulation is validated with the actual wind power data. The results show that the proposed strategy is able to effectively improve the battery SOC, reduce the supercapacitor capacity, meet the demands of the energy storage to level off the wind power, and improve the rationality of the power allocation considering the differences of the characteristics of the two energy storage devices to improve the economics of energy storage system.