A real-time hydrogen consumption estimation method for fuel cell vehicles

Abstract

ABSTRACT As an economic evaluation indicator for fuel cell vehicles (FCVs), real-time hydrogen consumption’s accuracy and immediateness are important. Real-time hydrogen consumption obtained through the fuel cell system (FC Power method) has strong immediateness but poor accuracy, while through the hydrogen mass (Mass method) has good accuracy but poor immediateness. Given the difficulty in obtaining sufficient training data on actual vehicles, this paper establishes a hydrogen storage system model whose input and output can be used to obtain data. Then, a new estimation method based on the prediction of the mass’s difference (PMD method) has been constructed based on the Neural basis expansion analysis with exogenous variables (NBEATSx) algorithm. After the NBEATSx algorithm is trained, the prediction accuracy of the PMD method is verified under urban and high-speed conditions. The result shows that the new method proposed in this article improves Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) by nearly 45% compared to the FC Power method, and the hydrogen consumption’s sampling point is advanced compared to the Mass method. The trained model can be input into Hydrogen Management System (HMS) which is installed on the hydrogen storage system and output data in real-time.