Physically Motivated Water Modeling in Control-Oriented Polymer Electrolyte Membrane Fuel Cell Stack Models

Abstract

Polymer electrolyte membrane fuel cells (PEMFCs) are prone to membrane dehydration and liquid water flooding, negatively impacting their performance and lifetime. Therefore, PEMFCs require appropriate water management, which makes accurate water modeling indispensable. Unfortunately, available control-oriented models only replicate individual water-related aspects or use oversimplistic approximations. This paper resolves this challenge by proposing, for the first time, a control-oriented PEMFC stack model focusing on physically motivated water modeling, which covers phase change, liquid water removal, membrane water uptake, and water flooding effects on the electrochemical reaction. Parametrizing the resulting model with measurement data yielded the fitted model. The parameterized model delivers valuable insight into the water mechanisms, which were thoroughly analyzed. In summary, the proposed model enables the derivation of advanced control strategies for efficient water management and mitigation of the degradation phenomena of PEMFCs. Additionally, the model provides the required accuracy for control applications while maintaining the necessary computational efficiency.

Concentration Estimation for Fuel Cells: Design of Experiments, Nonlinear Identification, and Observer Design With Experimental Validation

“…The third and final step is the observer design, followed by validation with experimental data. The authors' study [16] describes a PEMFC model, which serves as the modeling basis because it can reproduce various effects of water, essential for degradation avoidance and diagnosis. The observer estimation algorithm used to demonstrate the design workflow is based on [4], a constrained extended Kalman filter (CEKF).…”

“…The modeling basis for our approach is a control-oriented lumped parameter PEMFC stack model as presented in [16] with the following assumptions: The respective manifolds are lumped into one volume with no spatial expansion. The gases are ideal and have homogeneous properties inside a manifold.…”

“…The membrane connects the cathode and the anode, and these submodels deliver the electrochemical model's prerequisites. As in [16], the state-space representation for this nonlinear model is given as…”

“…The model outputs are the stack voltage U , the cathode supply p ca,sm and exit manifold pressure p ca,em , and the anode inflowing mass ṁan,in and exit manifold pressure p an,em . The tunable parameters of the model are the respective volumes V , various mass flow and nozzle coefficients k, the characteristic membrane time constant τ m , the ohmic contact resistance R c , the intrinsic exchange current parameters K , and the combined diffusivities D. The derivation of model equations is beyond the scope of this paper, and the reader is referred to [16] in this regard.…”

“…Also, the anode exit manifold pressure p an,em has a slight offset (Fig. 6e) due to simplifying assumptions in the recirculation flow modeling [16]. The remaining output signals (Fig.…”