Research on Key Parameters Identification Method of Dummy Model in Vehicle Collision Simulation

It is necessary to accurately analyze the dynamic responses of the dummy model in the development process of the vehicle system collision dynamic simulation software project. This paper proposes a dynamic learning factors particle swarm optimization algorithm (DLPSO), which uses the improved inertia weight to control the learning factors. By enhancing the interaction between the weights and the learning factors, the global exploration and local development capabilities of the algorithm are balanced, and the particles converge to the global optimum more accurately. The proposed method is applied to the dynamic model of the dummy tree topology system, which based on the new transfer matrix method of the multibody system (MSTMM), and the identification of the key parameters of the dummy model can be effectively optimized. First, four test functions are used to confirm the optimal performance of the DLPSO algorithm. Additionally, the key parameters of the vehicle collision simulation dummy model are identified, and the identification results are substituted into ADAMS and the new transfer matrix method of the multibody system (MSTMM) respectively. The results show that compared with the traditional particle swarm optimization algorithm such as adaptive particle swarm optimization algorithm, the optimization algorithm in this paper has better optimization effect and convergence accuracy in the identification of the key parameters of the dummy model under the premise of ensuring the speed.