Hydrogen leakage diagnosis of fuel cell vehicles based on a dynamic-static recognition approach with dual-channel LeNet network

Abstract

The diagnosis of hydrogen leakage has attracted increasing attention, especially in fuel cell vehicles with high-pressure hydrogen systems within which the chance of deflagration is pretty high. Although online intelligent diagnosis can be achieved with the pressure signal of high-pressure hydrogen tank, the feature extraction process of machine learning is a time-consuming and laborious work and greatly impacts the final result. Motivated by this fact, a dynamic-static recognition approach and a dual-channel LeNet network based on transfer learning and D-S evidence theory are proposed to diagnose the hydrogen leakage of vehicles. The proposed method has the advantages of automatic feature extraction, high accuracy, and real-time recognition capability. The major contributions lie in two aspects. On the one hand, through the dynamic-static recognition approach, pressure signals of high-pressure hydrogen tank are converted into graphic characteristics: Markov transition field (MTF) and Gramian angular field (GAF). On the other hand, for the proposed dual-channel LeNet network, it uses transfer learning to reduce the training loss and improve the accuracy of the LeNet channel, while the D-S evidence theory is used to amalgamate the dual-channel LeNet outputs to obtain highly accurate hydrogen leak diagnosis results. In addition, after training, the diagnostic accuracy of the proposed method can reach 99.54%.