Artificial intelligence-based optimization of a bimorph-segmented tapered piezoelectric mems energy harvester for multimode operation

This paper presents a study on the design and multiobjective optimization of a bimorph-segmented linearly tapered piezoelectric harvester for low-frequency and multimode vibration energy harvesting. The procedure starts with a significant number of FEM simulations of the structure with different geometric dimensions—length, width, and tapering ratio. The datasets train the artificial neural network (ANN) that provides the fitting function to be modified and used in algorithms for optimization, aiming to achieve minimal resonant frequency and maximal generated power. Levenberg–Marquardt (LM) and scaled conjugate gradient (SCG) methods were used to train the ANN, then the goal attainment method (GAM) and genetic algorithm (GA) were used for optimi-zation. The dominant solution resulted from optimization by the genetic algorithm integrated with the ANN fitting function obtained by the SCG training method. The optimal piezoelectric harvester is 121.3 mm long and 71.56 mm wide and has a taper ratio of 0.7682. It ensures over five times greater output power at frequencies below 200 Hz, which benefits the low frequency of the vibration spectrum. The optimized design can harness the power of higher-resonance modes for multimode applications

Investigation of Nonlinear Piezoelectric Energy Harvester for Low-Frequency and Wideband Applications

This paper proposes a monostable nonlinear Piezoelectric Energy Harvester (PEH). The harvester is based on an unconventional exsect-tapered fixed-guided spring design, which introduces nonlinearity into the system due to the bending and stretching of the spring. The physical–mathematical model and finite element simulations were performed to analyze the effects of the stretching-induced nonlinearity on the performance of the energy harvester. The proposed exsect-tapered nonlinear PEH shows a bandwidth and power enhancement of 15.38 and 44.4%, respectively, compared to conventional rectangular nonlinear PEHs. It shows a bandwidth and power enhancement of 11.11 and 26.83%, respectively, compared to a simple, linearly tapered and nonlinear PEH. The exsect-tapered nonlinear PEH improves the power output and operational bandwidth for harvesting low-frequency ambient vibrations

AI Assisted Optimization of Unimorph Tapered Cantilever for Piezoelectric Energy Harvesting

This paper presents the results of the deploying machine learning models in the design and optimization of a unimorph tapered cantilever with proof mass, aimed for piezoelectric energy harvesting. Multiobjective optimization as described in the paper was performed in order to find the optimal dimensions of the structure, its length, its width at the anchor and the ratio between widths at the anchor and at the tip, with respect to the salient parameters for the energy harvesting applications, namely low frequency and high power generated by the structure. The method is applicable for the optimization of the design of more complex MEMS structures aimed for energy harvesting applications