In order to solve the problem of multi-parameter identification of Super capacitor model and the problems of poor convergence accuracy and slow convergence speed of traditional identification algorithm, a parameter identification method based on dynamic self-learning particle swarm optimization (pso) is proposed. According to the Super capacitor equivalent circuit model, the identification model is discretized by bilinear transformation, and the parameters of each branch are identified by dynamic self-learning particle swarm optimization.The simulation results show that, compared with the basic particle swarm and the adaptive inertial weighted particle swarm, the parameter identification method based on dynamic self-learning particle swarm algorithm has fast convergence speed, high convergence accuracy and strong global optimization ability, which can more accurately reflect the dynamic characteristics of supercapacitors.