Fault diagnosis of thermal management system in a polymer electrolyte membrane fuel cell

Abstract

One of the barriers for commercialization of polymer electrolyte membrane fuel cell system is its reliability. One good solution for reliability improvement is to develop fault diagnosis model. This study suggests a diagnostic method for polymer electrolyte membrane fuel cell thermal management system. With the increase of fuel cell stack power these days, waste heat from the stack also increased and thereby thermal management system is also receiving attention. In this study, thermal management system fault experiments are repeatedly performed under various loads and stack degradation conditions. With the degradation of stack, waste heat from the stack increases and fault response as well as its impact on the system varies. The diagnosis model developed in this study detects faults on component-level and diagnoses severity of the faulty components. Furthermore, the diagnosis model is applicable to fuel cell system with degraded stack. In the process of model development, single-task learning technique is applied to neural network diagnosis model for higher diagnostic accuracy and compared with conventional multi-task learning technique.