Parameter estimation algorithms integrated in automated platforms for kinetic model identification are required to solve two optimization problems: i) a parameter estimation problem given the available samples; ii) a model-based design of experiments problem to select the conditions for collecting future samples. These problems may be ill-posed, leading to numerical failures when optimization routines are applied. In this work, an approach of online reparametrization is introduced to enhance the robustness of model identification algorithms towards ill-posed parameter estimation problems.