## 5 Results and Discussion
### Discussion  
The simulated outputs fit very well. However, the simulated pressures deviate more from the measurements than the voltage. The simulated anode exit manifold pressure \(p_{\text{an,em}}\) (Figure 4c,g) has deviations, which seem to be load-dependent. During low currents, the measured pressure is higher and vice versa. A reason could be the recirculation flow. The recirculation pump runs at a constant speed and should provide a constant flow during steady-state. The flow is not measured and is assumed constant in the model. However, steady-state conditions are not always given, especially during purging. Measuring the recirculation flow and adding it as an input to the model could minimize the deviations. The simulated cathode pressures deviate more from the validation data (Figure 4e,f) than from the parametrization data (Figure 4a,b). The maximum deviation is 5%, and a reason may be the inflow air temperature, which differs up to 10 \({}^{\circ}\)C between the two data sets. Incorporating the inflow air temperature into the model may improve the agreement between the simulated and measured cathode pressures. The model's simulated voltage response (Figure 4h) replicates the undershooting only in a moderate way for the validation data. One reason could be the underrepresented voltage undershooting in the parametrization data (Figure 4d). Using a data set with more often recurring voltage undershooting may improve the model's behavior in this regard. Figure 2b depicts the total information of the electrochemical parameters \(\sigma_{\theta_{\text{d,i}}}\). The combined diffusion coefficients, \(CD_{\text{Ca}}\) and \(CD_{\text{an}}\), have relatively low information. The reason is that the experiments only cover the safe operating region. The named coefficients determine the limit current and are only well identifiable in the unsafe limit current region. According to Figure 2a, the supply and exit manifold volumes (\(V_{\text{ca,sm}}\), \(V_{\text{ca,em}}\), \(V_{\text{an,sm}}\), and \(V_{\text{an,em}}\)) are not identifiable. The reason is that only the pressure states needed them, which do not exist anymore. Thus the named volumes are not used in the reduced model at all. The named aspects only concern the model and the experiments, but not the parametrization method itself. A drawback of the two-step method is that by conducting separate parameter sensitivity analyses for the two submodels, slightly different conclusions may follow compared to the complete model analysis. The reason is that the electrochemical model's output contributes additional information on the thermodynamic parameters. However, the advantage of the reduced solution space dimension compensates for this drawback. The stated conclusions derived from the FIM only hold in a region near the initial guess of the parameters \(\theta_{0}\). Hence the FIM is only a local parameter sensitivity analysis. With another parameter vector, the conclusions from the newly derived FIM may be different. In general, parameters could be nonsignificant if the excitation is ill-suited (e.g., \(CD_{\text{Ca}}\)), it is not relevant in the model (e.g., \(V_{\text{ca,sm}}\)), or both. High significancies are interpretable in a vice versa manner. As discussed, the parameter identifiability and the derived conclusionsare strongly dependent on the available measurement data and the model structure. With this knowledge, remodeling or a more targeted design of the experiment is possible to improve identifiability. From the engineering point of view, this information is utilizable to distribute engineering resources efficiently. Focusing on improving the significant parameter's real-world counterparts affects the system outputs substantially more than optimizing the less significant ones. Optimizing the latter will lead to hardly any changes in the output.