An improved equivalent capacitance model of the triboelectric nanogenerator incorporating its surface roughness

Abstract

Triboelectric nanogenerator (TENG) is a kind of device that generates electric energy in the external circuit through contact electrification and electrostatic induction. Recently, although the development of TENG applications is accelerating, the development of TENG fundamental theoretical model is relatively slow. At present, the latest TENG model has considered the distance-dependent and load-dependent and can better predict the open-circuit voltage. However, the TENG model considering the effect of surface roughness on capacitance has not been introduced yet, which may underestimate the equivalent capacitance of TENG, resulting in the underestimate of outputs such as short-circuit current. Here, A TENG model considering the effect of surface roughness on capacitance is established for the first time. Based on the established load-dependent model, the effect of surface roughness on TENG capacitance is analyzed by the Greenwood-Williamson model to determine a more accurate TENG equivalent capacitance. Compared with the load-dependent model without considering effect of surface roughness on capacitance, our model can better predict the outputs such as short-circuit current and transferred charge. The experimental results show that after considering the effect of surface roughness on capacitance, the average relative error between the calculated and measured results of the equivalent capacitance decreases from 47.47% to 11.41%, which is about 1/4 of the original error. Whether it is distance-dependent or load-dependent, the model can better predict the performance of TENG. The model in this paper provides a more comprehensive understanding of the working principle of TENG and more accurate output trend prediction, which can help to design a more efficient TENG device.