In model-based fault diagnosis, a mathematical model representing the real process runs alongside the PEFC during operation. Consisting of theoretical differential and/or algebraic equations, the process model takes the same input conditions received by the operating PEFC to generate an output. The model output is then compared with the output data of the PEFC and the difference between the two or 'residual' is calculated; a residual of zero indicates normal operation, while a non-zero residual signifies the presence of a fault. , . The faults were implemented into the model via the sensitivities of the residuals, allowing the SoH to be identified based on the relative values of the model residuals. The fault regions could then be displayed in the three-dimensional residual space, with the health of the PEFC being predicted based on the Euclidian distance between the position of the system itself and the six different faults. While this demonstrates how a whitebox model can successfully identify faults in real-time, it is too computationally demanding to be applied to a real system due to the large number of model equations involved. Moreover, since the model is a representation of the physical system, a perfectly accurate model simply cannot exist; hence, the model uncertainty must also be considered, further increasing its overall complexity . In the future, advances in onboard computing power may increase the viability of white-box models as a means for fault identification; however, as they stand, they are too complex to implement on-line.