Degradation prediction of PEM fuel cell based on artificial intelligence

In the last years, Proton Exchange Membrane Fuel Cells (PEMFC) became a promising energy converter for both transportation and stationary applications. However, durability of fuel cells still needs to be improved to achieve a widespread deployment. Degradation mechanisms and aging laws are not yet fully understood. Therefore, long-term durability tests are necessary to get more information. Moreover, degradation models are requested to estimate the remaining useful life of the system and take adequate corrective actions to optimize durability and availability. This paper presents in a first part the results of a long-term durability test performed on an open cathode fuel cell system operated during 5000 h under specific operating conditions including start/stop and variable ambient temperature. Performance evolution and degradation mechanisms are then analyzed to understand influence of operating conditions and how to extend the durability. In a second part of the paper, the results are used to build a degradation model based on echo state neural network in order to predict the performance evolution. Results of the degradation prediction are very promising as the normalized root mean square error remains very low with a prediction time over 2000 h.