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In this work, an inverse problem approach is employed to estimate the suspension parameters of a light vehicle based on field tests. The modeling process of a rear-wheel drive (RWD) vehicle is depicted. The model considers only the vertical dynamics of the vehicle. The experimental data were measured by sensors installed on the vehicle during specific road tests in a proving ground. The inverse problem is solved by using the Particle Swarm Optimization (PSO), minimizing the quadratic error between experimental data and numerical results of the vehicle simulation. Accuracy, computational time, efficiency and efficacy of the model were compared regarding the behavior of the performance responses of the vehicle measured on the road tests. Throughout this process, the vehicle model was validated to be used in future studies of vehicle dynamics.

This paper uses an inverse problem approach to estimate parameters of a tire model based on Julien’s Theory (JT). The modeling process of an all-wheel drive (AWD) vehicle is presented in this work, as well as JT and Pacejka’s Magic Formula (MF) tire models. Numerical simulations of the longitudinal vehicle dynamics, considering MF, provide pseudo-experimental data to the inverse problem. Particle Swarm Optimization (PSO), Random Restricted Window (R2W) and Differential Evolution (DE) are used to estimate the parameters of the JT tire model. Accuracy, computational time, efficiency and efficacy of the models are compared regarding the behavior of the performance responses of the vehicle. Throughout this process, Julien’s Theory is validated for use in future studies of vehicle dynamics.