Probabilistic modelling of a rotational energy harvester

Relatively recently, many researchers in the field of energy harvesting have focused on the concept of harvesting electrical energy from relatively large-amplitude, low-frequency vibrations (such as the movement caused by walking motion or ocean waves). This has led to the development of 'rotational energy harvesters' which, through the use of a rack-and-pinion or a ball-screw, are able to convert low-frequency translational motion into high-frequency rotational motion. A disadvantage of many rotational energy harvesters is that, as a result of friction effects in the motion transfer mechanism, they can exhibit large parasitic losses. This results in nonlinear behaviour, which can be difficult to predict using physical-law-based models. In the current article a rotational energy harvester is built and, through using experimental data in combination with a Bayesian approach to system identification, is modelled in a probabilistic manner. It is then shown that the model can be used to make predictions which are both accurate and robust against modelling uncertainties.