State-of-health diagnosis based on hamming neural network using output voltage pattern recognition for a PEM fuel cell

This work investigates a pattern recognition-based diagnosis approach as an application of the Hamming neural network to the identification of suitable fuel cell model parameters, which aim to diagnose state-of-health (SOH) for a polymer electrolyte membrane (PEM) fuel cell. The fuel cell output voltage (FCOV) patterns of the 20 PEM fuel cells were measured, together with the model parameters, as representative patterns. Through statistical analysis of the FCOV patterns for 20 single cells, the Hamming neural network is applied for identification of the representative FCOV pattern that matches most closely of the pattern of the arbitrary cell to be measured. Considering the equivalent circuit fuel cell model, the purpose is to select a representative loss ΔRd, defined as the sum of two losses (activation and concentration losses). Consequently, the selected cell’s ΔRd is properly applied to diagnose SOH of an arbitrary cell through the comparison with those of fully fresh and aged cells with the minimum and maximum of the ΔRd in experimental cell group, respectively. This avoids the need for repeated parameter measurement. Therefore, these results could lead to interesting perspectives for diagnostic fuel cell SOH.

► Precise state-of-health (SOH) diagnosis is critical in practical applications of a PEMFC.
► The Hamming neural network is designed explicitly for binary pattern recognition.
► The Fuel cell output voltage (FCOV) patterns were measured, as representative patterns.
► The FCOV pattern is used to discriminate among PEMFC with different characteristics.
► This research enables us to provide interesting perspectives for diagnostic fuel cell SOH.