## 1 Introduction  
PEMFCs are promising candidates for replacing internal combustion engines in mobile applications. However, fuel cell (FC) driven vehicles are still far from having a significant market share because of various challenges. One of the challenges is to control the FC during transient operations, especially regarding avoiding harmful operating conditions. Another one is to improve the FC system's efficiency via optimal control. Moreover, experimental expenditures during development are high but are reducible by using simulations instead. One of the first steps towards resolving the named and other challenges is obtaining a proper FC model. PEMFCs are promising candidates because they offer the rapid startup and low operating temperature required for automotive applications. However, it requires appropriate water management to address liquid water formation. Solid oxide FCs use non-noble metal catalysts, which results in low raw material costs, but the high operating temperature leads to thermal stress and precludes this FC for transient applications. Molten carbonate FCs can reform a wide variety of fuel sources, but the long startup time eliminates this FC for anything but continuous-power applications. Phosphoric acid FCs provide easy water management, but the low power density is not appropriate for portable applications. Alkaline FCs have the highest demonstrated operating efficiency of any FC system, but the intolerance to carbon dioxide forces the use of carbon dioxide removal equipment and pure oxygen and hydrogen [1]. In general, modeling approaches are distinguishable into three groups. First, black-box modeling means fitting an artificial model to replicate the measured input and output data correlation [2; 3; 4]. The model structure and parameters do not need to have any physical meaning, and extrapolation capabilities are limited. Black-box models, in general, do not replicate internal unmeasured physical states. Second, white-box models solely describe the FC with first principles known from theory. All model parameters are physical quantities, fundamental constants, and known values. Measurement data is not needed [5; 6; 7]. This approach is not feasible if not every parameter is known, which is usually the case for FC models. Third, grey-box models combine both approaches. They use the first principles known from theory and require measurement data to determine the model structure and parameters. This work does not consider black-box models because the internal states' knowledge is essential for the controller to avoid harmful operating conditions. White-box models are unsuitable because many parameters are unknown. Therefore the combination of both approaches, grey-box models, provides the needed structure and fidelity for FC control. Control-oriented grey-box PEMFC models aim to have a low spatial dimension to be numerically efficient for real-time applications [8; 9; 10; 11; 12]. Parametrizing grey-box PEMFC models is not straightforward because they are nonlinear, numerically stiff, and have numerous unknown parameters. This work proposes an efficient two-step parametrization method that drastically simplifies the optimization problem. The method subdivides the FC model into two submodels for parametrization, each yielding a lower-dimensional sub-solution space compared to the one of the entire model. The standard parametrization procedure determines as many parameters as possible from theory, datasheets, and expert knowledge. The remaining unknown parameters have to be obtainable by fitting measurement data. For example, [PERSON] et al. [13] developed a lumped parameter model, and they identified the tunable parameters using least squares. Unfortunately, this approach yields a high dimensional solution space for models with many unknown parameters. The proposed multiple-step method by [PERSON] et al. [14] estimates the nozzle coefficients first, and in the following step, a nonlinear least-squares algorithm identifies the electrochemical parameters for a steady-state voltage response.