LT-PEM Fuel Cells diagnosis based on EIS, clustering, and automatic parameter selection

Abstract

In the field of fuel cells, early detection of faulty conditions can significantly improve the lifetime. Then, signal analysis techniques such as electrochemical impedance spectroscopy combined with machine learning algorithms can generate a representation of the system state of health space using data in known conditions. Although the onboard measurement of EIS can be done by controlling the harmonic content of the power converter at the output of the fuel cell, the implementation of in-vehicle diagnostic algorithms is still limited by the absence of a large database listing the evolution of performance throughout the life cycle. This paper presents a fast-diagnostic method able to consider the occurrence of new data to adapt the dimensional space representing the health state and compensate for the lack of data. Available measurements come from two low-temperature proton exchange membrane fuel cell technologies characterized by two laboratories. The results presented in the paper show that the automatic parameter selection provides performances as good as the ones obtained by an expert. The feasibility of the approach has also been demonstrated on a low-cost embedded platform.