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The aim of the presented research is to propose and benchmark two brake models, namely the novel dynamic ILVO (Ilmenau-Volvo) model and a neural-network based regression. These can estimate the evolution of the brake friction between pad and disc under different load conditions, which are typically experienced in vehicle applications. The research also aims improving the knowledge of the underlying mechanism related to the evolution of the BLFC (boundary layer friction coefficient), the reliability of virtual environment simulations to speed up the product development time and reducing the amount of vehicle test in later phases and finally improving brake control functions. With the support of extensive brake dynamometer testing, the proposed models are benchmarked against State-of-the-Art. Both approaches are parametrized to render the friction coefficient dynamics with respect to the same input parameters.