Data-driven modeling and fault diagnosis for fuel cell vehicles using deep learning
Yangeng Chen, Jingjing Zhang, Shuang Zhai, Zhe Hu
Abstract
The reliability and safety of fuel cell vehicle are crucial for the daily operation. Insulation resistance serves as a crucial index of vehicle reliability, especially when fuel cells operate at high voltages. Low insulation resistance can lead to vehicle malfunctions, exposing the operator to the risk of electric shock. In this study, long-term insulation resistance data from thirteen vehicles equipped with three different types of fuel cell systems are analyzed to diagnose possible low insulation resistance issues. For this purpose, a robust locally weighted scatterplot smoothing method is utilized to filter the original data. In this research, an insulation variation model is developed using a data-driven long short-term memory neural network to identify insulation resistance value anomalies caused by deionizer failure. The results indicate that the coefficient of determination of the failure model is 99.78%. Moreover, current model efficiently identifies insulation faults resulting from reliability issues, such as conductivity issues of cooling pipes and erosion of vehicle wiring harnesses.