Wavelets-based approach for online Fuel Cells Remaining Useful lifetime Prediction

Abstract

Prognostics and health management (PHM) techniques for proton exchange membrane fuel cell (PEMFC) systems are of great importance for increasing their reliability and sustainability. PEMFC systems suffer from relatively poor long-term performance and durability, and prediction and prognosis can give early indications about when components should be fixed or replaced. Prognostics modeling needs to take account of a number of phenomena, including degradation mechanisms that are not easily measured. A number of works are currently investigating PHM in fuel cell systems, as well as the problem of estimating remaining useful lifetime. Any reduction in the volume of data required for making predictions is clearly advantageous. In this paper, a univariate prognostic approach based on signal processing, namely discrete wavelet transform (DWT), is proposed. The proposed approach aims at achieving an online prognostic for PEMFC systems. DWT is first introduced, and then, the predictions are built using the power signals of two different PEMFC stacks in two different scenarios, namely static and dynamic operating conditions. Results show that the method is reliable for online prediction of power, with prediction errors less than 3%.