Probabilistic modelling of a rotational energy harvester

Abstract

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 rackand-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.

Bayesian system identification of dynamical systems using large sets of training data: A MCMC solution

“…Originally tested at the University of Southampton's Institute of Sound and Vibration Research, only a very brief description of the device and experimental procedure is given here -more information can be found in the references [19,20]. It should be noted that the data from this experiment can be found in the electronic supplementary material of the paper [22].…”

“…The parameters of SDA were the same as in the previous example while, again, Gaussian priors truncated at zero were utilised (see Table 2). The prior was selected based on several static tests which had already been conducted -see [20] for more details.…”

Bayesian and Markov chain Monte Carlo methods for identifying nonlinear systems in the presence of uncertainty

“…The case study shown here was originally conducted as part of a collaborative project with the University of Southampton (full findings are published in [ 62 ]); it is included here as it clearly demonstrates how using MCMC methods within a Bayesian framework can be used to quantify and propagate the uncertainties involved in modelling nonlinear dynamical systems.…”

“…Figure 4 a shows a rotational energy harvester—a device which, via a ball–screw mechanism, is designed to convert low-frequency translational motion into high-frequency rotational motion (which can then be transformed into electrical energy). The device is mounted on a electro-hydraulic shaker while accelerometers are attached to the shaker and the oscillating mass (for a more detailed description of the experiment, see [ 62 , 63 ]). With the measured inputs and outputs ( x and y ) being the acceleration of the base and mass, respectively, the aim was to use a set of experimentally obtained data to infer a robust model of the device.…”

Electrically rectified piezoelectric energy harvesting induced by rotary magnetic plucking

“…But the speed of rotating host is typically not high enough to resonate a relatively small-scaled energy harvester. Therefore, harvesting energy from rotation requires special designs of harvesters suitable for distinct rotational environments, as in the cases of rotating shafts/machines [23][24][25], rolling wheels for tire pressure monitoring sensors [26][27][28], miniature turbines [29], and the case of converting translational movement into rotational motion [30][31][32].…”