The hydrogen absorption process prediction of AB2 hydrogen storage device based on data-driven approach.

The establishment of a prediction model for the hydrogen absorption state of a solid-state hydrogen storage device is crucial for its practical application. In this paper, a solid-state hydrogen storage device filled with AB 2 hydrogen storage alloy is investigated. The experimental data are obtained by controlling the hydrogen absorption temperature and hydrogen absorption rate respectively. The Broyden-Fletcher-Goldfarb-Shanno algorithm (BFGS) is used to fit the solid-state hydrogen storage device experimental data based on the Richards model, and Linear Regression, Gradient Descent and Artificial Neural Network algorithms are used to predict the parameters of the Richards model. It is confirmed that the Linear Regression model has the best prediction effect after verified by the test set, with coefficients of determination R2 ≥ 0.96. This study provides an effective way to predict the hydrogen absorption state of solid-state hydrogen storage devices quickly. • The effects of temperature and rate on hydrogen absorption process of solid-state hydrogen storage device are studied. • Richards model is used to fit the hydrogen absorption curve shaped as an S-type. • The prediction model of hydrogen absorption process of the device is established. • Three machine learning algorithms are applied to establish the prediction model. [ABSTRACT FROM AUTHOR]