Optimization of Inlet Hydrogen Temperature during the Fast-Filling Process Based on a Back Propagation Neural Network Model

Abstract

A reasonable inflating strategy must be developed for filling an onboard hydrogen storage tank with hydrogen gas. The inflow hydrogen temperature has always been a constant value in filling cases. However, in our opinion, the optimal inflow hydrogen temperature is not supposed to be a fixed value but a value that constantly changes and adjusts with filling time, i.e., the inflow hydrogen temperature is a function of the filling time. How to determine this functional relationship is a critical problem to be addressed. Herein, an approach is introduced. A dual-zone model is presented to research the thermal effect during the process of charging hydrogen storage tanks. Based on the numerical results of the dual-zone model, the charging process was divided into three stages, allowing us to obtain data for 1331 filling cases. Then, a back propagation (BP) neural network model was built to analyze the data, and the implicit relationship between the inflow hydrogen temperatures and maximum hydrogen temperature pressure could be deduced. With this implicit relationship, the critical values of the inflow hydrogen temperatures can be obtained from the critical situation. Suppose the inflow hydrogen temperatures in a practical case are higher than the critical values. In that case, the highest hydrogen temperature in the tank will exceed the limited safety value of 358 K. In contrast, if the inflow hydrogen temperatures are lower than the critical values, then more energy will be needed to precool the inlet hydrogen temperature. Thus, theoretically, the critical inflow hydrogen temperatures should be at their optimal values.

Keywords

• hydrogen filling
• hydrogen safety
• BP neural network model
• prediction
• optimization