The hydrogen absorption process prediction of AB2 hydrogen storage device based on data-driven approach
Jie Gao, Xiumei Guo, Yuanfang Wu, Wei Xiao, Lei Hao
Abstract
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 R 2 ≥ 0.96. This study provides an effective way to predict the hydrogen absorption state of solid-state hydrogen storage devices quickly.