## 1 Introduction
Additionally, a fitted neural network describes the dependency of the electrochemical parameters on the operating conditions. Using a neural network is suboptimal because the applicability is highly limited to the range of the available measurement data. [PERSON] et al. [15] conducted an approximated sensitivity analysis by varying the signals and parameters. The sensitivity analysis's sole purpose was to show the importance of accurate sensors for parametrization. They used specific measurements to identify parameters one by one. Curve fitting leads to the remaining parameters. Individually estimating the parameters is not always feasible because it is strongly dependent on the available measurement data and model structure. Moreover, incorporating the sensitivity analysis into the parametrization process will increase its efficiency, especially for large-scale problems. [PERSON] et al. [16] approximated the parameter sensitivities via difference quotients. In [17], the sensitivity analysis determines the most sensitive parameters for identification, and a global optimizer identifies this subset of parameters altogether. This approach's drawback is that a large subset still leads to a computationally-intensive problem. In [18], the authors used the parameter sensitivity analysis to divide the parameters into three groups. The groups contain the identifiable parameters in the low, medium, and high current regions, respectively. Unfortunately, this approach needs appropriately designed experiments. [PERSON] et al. [9] developed a PEMFC model focusing on its analytical differentiability. Existing models are usually not analytical differentiable but are transformable to a model with this property. Analytic differentiability enables an accurate and efficient calculation of the derivatives compared to a numerical approximation and is advantageous for various control applications. They locally linearized the nonlinear model in multiple operating points. Their proposed parameterization method optimizes each analytically linearized model for each operating point dependent on the same parameter set simultaneously. In [10], they additionally conducted a parameter sensitivity analysis based on the analytically linearized models. This approach's disadvantage is that it is limited to data with small excitations because of the linearization. Therefore it requires specially designed experiments, which is mostly not the case for given data. In this work, the two-step parameterization method is demonstrated with the model developed by [PERSON] et al. [10] because of its advantageous property of analytical differentiability. The model is adapted to reduce its stiffness for more efficient numerical evaluations. Subdividing the model into a thermodynamic and an electrochemical submodel leads to a parameterization method with two consecutive steps. In the first step, the thermodynamic submodel, which consists of a system of first-order ordinary differential equations (ODEs), is parametrized. The electrochemical submodel, which does not have any ODEs, is parametrized in the following second step. The subdivision reduces the solution space's dimension for each submodel, and an analytic parameter sensitivity analysis further reduces each parameter subset by excluding non-significant parameters. A global optimizer identifies the parameter subsets by minimizing the difference between simulated and measured output signals [19]. An existing FC test bench delivers the measurement data. Compared to the available approaches, this efficient method simplifies the parametrization problem, it does not require appropriately designed experiments, and the parameter sensitivity analysis is analytically evaluable. This paper is subsequently structured as follows: Section 2 describes the PEMFC model and its reduction. Section 3 presents the two-step parametrization method, explains the parameter sensitivity analysis, and shows the proposed method's validation. Section 4 depicts the experimental setup, and finally, Section 5 discusses the identification results.