The interest of this work is to describe a framework to propagate uncertainties in piezoelectric energy harvesters (PEHs). The uncertainties are related to the random error associated to the mathematical model adopted, incomplete knowledge of the model parameters and the randomness nature of the excitation. The framework presented could be employed to conduct Prior and Posterior Robust Stochastic predictions. The prior analysis assumes a known Probability Density Function (PDF) for the uncertain variables while the posterior analysis calculates this PDF by adopting a Bayesian updating technique. The framework is particularized to evaluate the behavior of the Frequency Response Functions (FRFs) in PEHs while its implementation is illustrated by the use of a unimorph PEH. Results reveal the importance to include the model parameters uncertainties in the estimation of the FRFs. In that sense, the present framework constitutes a powerful tool in the robust design and prediction of PEH’s performance.

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