Parameter Identification of Magic Formula Tire Model Based on Fibonacci Tree Optimization Algorithm

The magic formula (MF) tire model is a semi-empirical tire model that can precisely simulate tire behavior. The heuristic optimization algorithm is typically used for parameter identification of the MF tire model. To avoid the defect of the traditional heuristic optimization algorithm that can easily fall into the local optimum, a parameter identification method based on the Fibonacci tree optimization (FTO) algorithm is proposed, which is used to identify the parameters of the MF tire model. The proposed method establishes the basic structure of the Fibonacci tree alternately through global and local searches and completes optimization accordingly. The global search rule in the original FTO was modified to improve its efficiency. The results of independent repeated experiments on two typical multimodal function optimizations and the parameter identification results showed that FTO was not sensitive to the initial values. In addition, it had a better global optimization performance than genetic algorithm (GA) and particle swarm optimization (PSO). The root mean square error values optimized with FTO were 5.09%, 10.22%, and 3.98% less than the GA, and 6.04%, 4.47%, and 16.42% less than the PSO in pure lateral and longitudinal forces, and pure aligning torque parameter identification. The parameter identification method based on FTO was found to be effective.