Metamodel-based robust collaborative optimization for the suspension parameters of rail vehicles

This work deals with multi-objective optimization for the suspension parameters of railcars and proposes an optimization strategy combining the collaborative optimization method and the metamodel method to optimize the suspension parameters of railcars. In this strategy, the system-level optimization and subsystem-level optimization problems are efficiently calculated by the collaborative optimization method, and the metamodel replaces the actual model in the subsystem-level to improve the convergence and robustness of the optimization algorithm. Finally, an optimal combination of suspension parameters is determined by the effective implementation of the proposed optimization method, and compared with the original design in terms of performance and robustness. Here, the lateral running stability and vertical running smoothness of railcars were increased by 10.96% and 10.1%, respectively, and the derailment coefficient and the reduction ratio of wheel weight of railcars were increased by 6.14% and 6.36%, respectively. The optimization results show that the dynamic performance of railcars is improved remarkably with the robust collaborative optimization of the suspension parameters, and the reliability and effectiveness of the proposed robust collaborative optimization method for the suspension parameters' optimization of the railcars have also been verified.