Diagnostic method for PEM fuel cell states using probability Distribution-Based loss component analysis for voltage loss decomposition

This study proposed a novel method referred to as the loss component analysis (LCA) to represent the current state of fuel cells. The LCA method was derived from an independent component analysis (ICA) and used probability density functions of activation, ohmic, and concentration losses. This method determined three weights related to each loss component reflecting the fuel cell states, and the fuel cell conditions were diagnosed using deviations in weight from the reference weight at the normal state. The maximum increase in weight allocated to each loss component was found to have the most significant impact on changes in the state of the fuel cell from its normal state. Moreover, LCA was applied to both the data obtained from empirical models and the data acquired through experiments that mimic the three faults that could occur during fuel cell operation. The results were compared to demonstrate the validity of the proposed method.

Automated summary

This study proposed a novel method, LCA, to represent the current state of fuel cells, and validated its effectiveness by using probability density functions and weights to diagnose the fuel cell states.