Parameter identification is an important part of tire model development. The prediction performance of a tire model highly depends on the identified parameter values of the tire model. Different optimization algorithms may yield different tire parameters with different computational accuracy. It is essential to find out which optimization algorithm is most likely to generate a set of parameters with the best prediction performance. In this study, four different MATLAB® optimization algorithms, including fminsearchcon, patternsearch, genetic algorithm (GA), and particleswarm, are used to identify the parameters of a newly proposed in-plane flexible ring tire model. The reference data used for parameter identification are obtained through a ADAMS FTire® virtual cleat test. After parameters are identified based on above four algorithms, their performances are compared in terms of effectiveness, efficiency, reliability, and robustness. Once the best optimization algorithm for the proposed tire model is determined, this optimization algorithm is used to test different types of cost functions to determine which cost function is the best choice for tire model parameter identification. The study in this article provides some important insights for the tire model parameter identification.
