Optimal estimation of MC parameter in SAE J2601 hydrogen refuelling protocol based on modified formula and artificial neural networks

Abstract

The MC method was officially released in SAE J2601 protocol in 2016 to refuel light-duty hydrogen fuel cell vehicles quickly and safely. For the non-communication MC method, the pressure target for controlling the end of filling is calculated based on the formula of the final hydrogen temperature, which contains a key parameter, MC. SAE has proposed two formulas to estimate the MC parameter. This article proposes a modified formula and artificial neural networks (ANN) model for MC parameter. To generate simulated data for fitting the modified formula and training the ANN model, a hydrogen filling model (0D1D) with a zero-dimensional gas and a one-dimensional tank wall is established and thoroughly verified. In establishing the ANN model, Latin hypercube sampling (LHS) is used to sample independent variables randomly, and genetic algorithms (GA) are used to optimize the initial weights and biases of the ANN. The Sobol sensitivity analysis is used to determine the importance of various initial conditions on the MC parameter. Research shows that the final fueling time and initial pressure have a greater impact on the MC parameter, while the ambient and precooling temperatures have little impact. The modified formula of the MC parameter improves R2 from the original 0.76073 to 0.98132 when fitting the MC simulated data. In situations of short filling time, the modified formula and ANN models of the MC parameter reduce the relative error between the 0D1D model by 13.8 % − 58.8 % and 16.6 % − 82.3 %, respectively, thus improving the calculation accuracy of the final hydrogen temperature and ending pressure target. Moreover, the ANN model can predict the MC parameter during the whole fueling process, while the MC formula utilizes the fixed value when the fueling time is less than 30 s. This work has positive significance for improving refuelling protocol.