An artificial neural network‐based approach for the impedance modeling of piezoelectric energy harvesting devices.

Abstract: In this paper, the equivalent series resistance (ESR) and capacitance (ESC) of piezoelectric energy harvesting devices have been modeled accurately over a wide frequency range for the first time. It is shown that the impedance modeling of the ESR and ESC is demonstrated as an important factor for the design of the Bias Flip (or the parallel synchronized switching harvesting on an inductor) interface circuit, which affects the performance of energy harvesting seriously. The conventional Butterworth‐Van Dyke (BVD) model and the modified BVD model are analyzed to state their imprecise modeling performance. To address this problem, an artificial neural network (ANN) technique with prior knowledge input method is employed to improve the model accuracy of the ESR and ESC. Also, a least‐squares curve fitting method is presented to compare with the ANN model. A good matching has been obtained between the measured and ANN‐predicted ESR and ESC in the frequency range of 2600 to 3600 Hz. The excellent performance on the accuracy of the proposed method is further illustrated through the comparison on the average relative error and maximum relative error with the conventional BVD, modified BVD model, and the curve fitting method. [ABSTRACT FROM AUTHOR]