Allocation Optimization of Multi-Axis Suspension Dynamic Parameter for Tracked Vehicle

Abstract

The dynamic parameter allocation of the suspension system has an important influence on the comprehensive driving performance of the tracked vehicle. Usually, the allocation of suspension parameters is based on a single performance index, which has the disadvantage of not being able to achieve multi-performance optimization. Therefore, a novel optimization method using multi-performance index-oriented is presented. Firstly, considering the vertical vibration excitation caused by road roughness, the input (excitation) model of road roughness is embedded to establish the parametric dynamic model of the tracked vehicle. Then, the evaluation index and its quantitative algorithm, which reflect the multi-aspect performance of the suspension system, are proposed. Moreover, the parameter allocation objective function based on multi-index information fusion is designed. Finally, two allocation optimization methods are presented to solve the parameter allocation, i.e., equal weight allocation and expert knowledge-based weight allocation. By comparing the results obtained by the two methods, it is found that the performance of the suspension system can be improved effectively by optimizing the parameters of suspension stiffness and damping. Furthermore, the optimization of weight allocation based on expert knowledge is more effective. These provide a better knowledge reference for suspension system design.