An investigation into the multi-parameter identification and model optimization strategy of the PCT curves of hydrogen storage alloys by multiple intelligent algorithms

Abstract

A multi-parameter sensitivity analysis method was proposed based on Latin hypercube sampling and the rank correlation method. The general genetic algorithm was improved by incorporating a niche parallel algorithm, suspicious peak point judgment, and a local search strategy, and both the improved genetic algorithm and multi-parameter sensitivity analysis method were implemented through C++ programming. The hydrogen pressure-composition isotherm (PCT curve) was found to play an important role in the study of hydrogen storage properties of metallic materials, and its reaction enthalpy change term was widely replaced by a power series form of smoothing. Based on the improved multi-parameter sensitivity analysis method, the effects of the range of parameter values and the number of samples on sensitivity were investigated, where the sensitivity ranking of the 10 parameters in the classical model was obtained. In addition, the equilibrium hydrogen pressure expression models of the highest order i of the power series from 3 to 9 were developed. The influence of the highest order on the optimization of multi-parameter identification was compared using an improved genetic algorithm, and we concluded that the eighth order polynomial model had the optimal convergence and fitting effect. Based on the inspiration of the function trend shape, two innovative forms of the PCT model power series were proposed in this work. Combined with typical examples, three high-order models were fully evolved, and a better power series form was obtained compared to the traditional model.