The cycle life prediction of Mg-based hydrogen storage alloys by artificial neural network

Abstract

Mg-based hydrogen storage alloys are a type of promising cathode material of Nickel-Metal Hydride (Ni-MH) batteries. But inferior cycle life is their major shortcoming. Many methods, such as element substitution, have been attempted to enhance its life. However, these methods usually require time-consuming charge–discharge cycle experiments to obtain a result. In this work, we suggested a cycle life prediction method of Mg-based hydrogen storage alloys based on artificial neural network, which can be used to predict its cycle life rapidly with high precision. As a result, the network can accurately estimate the normalized discharge capacities vs. cycles (after the fifth cycle) for Mg 0.8Ti 0.1M 0.1Ni (M = Ti, Al, Cr, etc.) and Mg 0.9 − x Ti 0.1Pd x Ni ( x = 0.04–0.1) alloys in the training and test process, respectively. The applicability of the model was further validated by estimating the cycle life of Mg 0.9Al 0.08Ce 0.02Ni alloys and Nd 5Mg 41–Ni composites. The predicted results agreed well with experimental values, which verified the applicability of the network model in the estimation of discharge cycle life of Mg-based hydrogen storage alloys.