Modeling capacitance of carbon-based supercapacitors by artificial neural networks

Abstract

Carbon-based electrodes effectively promote the specific capacitance of the supercapacitors. The Specific capacitance of carbon-based electrodes has been modeled using an artificial neural network (ANN) with the backpropagation learning algorithm. This paper describes the creation of an ANN model to interpret how voltage window (V), ID/IG, N/O-dopings (at. %), pore size (nm), and specific surface area (m2/g) parameters influence the specific capacitance (F/g). The experimentation has been carried out with several ANN architectures to achieve the best fit between the inputs and output. The model predictions (adj.R2 = 0.99) and estimation of the isolated effect of independent variables, such as voltage window, cannot be varied independently in practice. The results from the ANN model were consistent with the existing theory and reasonable in estimating the specific capacitance beyond the scope of the experimental data. The model successfully expresses the specific capacitance of carbon-based supercapacitors as a function of physiochemical and electrochemical process variables and can be used to design electrical storage devices.