Uncertainty Analysis of Piezoelectric Vibration Energy Harvesters Using a Finite Element Level-Based Maximum Entropy Approach

Abstract

Quantifying effects of system-wide uncertainties (i.e., affecting structural, piezoelectric, and/or electrical components) in the analysis and design of piezoelectric vibration energy harvesters has recently been emphasized. The present investigation proposes first a general methodology to model these uncertainties within a finite element model of the harvester obtained from an existing finite element software. Needed from this software are the matrices relating to the structural properties (mass, stiffness), the piezoelectric capacitance matrix, as well as the structural-piezoelectric coupling terms of the mean harvester. The thermal analogy linking piezoelectric and temperature effects is also extended to permit the use of finite element software that do not have piezoelectric elements but include thermal effects on structures. The approach is applied to a beam energy harvester. Both weak and strong coupling configurations are considered and various scenarios of load resistance tuning are considered, i.e., based on the mean model, for each harvester sample, or based on the entire set of harvesters. The uncertainty is shown to have significant effects in all cases even at a relatively low level and these effects are dominated by the uncertainty on the structure vs. the one on the piezoelectric component. The strongly coupled configuration is shown to be better as it is less sensitive to the uncertainty and its variability in power output can be significantly reduced by the adaptive optimization, and the harvested power can even be boosted if the target excitation frequency falls into the power saturation band of the system.

Recent advancements in piezoelectric energy harvesting for implantable medical devices

“…The results of the nanogenerator’s mechanical behaviour determined by the analytical models agreed well with those of the FEM models. The Nastran (Wang et al, 2021), MATLAB/Simulink (Abdelmageed et al, 2019; Gedeon and Rupitsch, 2018), Ansys (Benasciutti et al, 2010; Martinelli et al, 2023; Pourashraf et al, 2022; Qian et al, 2021; Safaei et al, 2018), Abaqus (Castagnetti, 2021; Castagnetti and Radi, 2018; Dagdeviren et al, 2014, 2017; Dong et al, 2019a, 2019b, 2019c; Sun et al, 2019) and COMSOL (He et al, 2021; Debnath and Kumar, 2022; Ghosh and Mandal, 2016); Hong et al, 2021; Hwang et al, 2014, 2015; İlik et al, 2018; Jiang et al, 2022; Mayekol Mayck et al, 2021; Park et al, 2014; Patel and Mukherjee, 2022; Shi et al, 2016a, 2016b, 2020; Sreeja et al, 2022; Wang et al, 2019a, 2019b; Xu et al, 2020; Xue et al, 2023; Zhang et al, 2022b; Zhu et al, 2023) is used for the FEM modelling of the various energy harvester.…”

Multistable vibration energy harvesters: Principle, progress, and perspectives

“…The dynamic characteristics and output voltages of harvesters will be influenced and become uncertain [127]. For linear energy harvesters, Wang et al [128] used a finite element level-based maximum entropy method to analyze the influence of random mass, stiffness, and electromechanical coupling coefficients on optimization design. For NEHs, Li et al [129,130] used an improved interval extension based on the second-order Taylor's series to reveal the nature of influence of excitation conditions, whose accuracy was verified by the Monte Carlo simulation.…”

Uncertainty analysis of galloping based piezoelectric energy harvester system using polynomial neural network

“…By selecting the proper electrical and mechanical parameters, power output can be improved along with the reduction of the vibration of the host structure (Rajarathinam and Ali, 2021). The effect of the randomness of the substrate and the piezoelectric properties on the power output was presented by Wang et al (2021).…”