Dynamic Multi-Physics Modelling of a PEM Stack for a Digital Twin Application
Authors: Engel Franziska Katharina , Ulmer Sebastian , Zander Hans-Jörg , Rehfeldt Sebastian , Klein Harald 
Published: December 2024

Electrolysis plants producing green hydrogen often face fluctuating renewable power availability and prices. Proton-Exchange-Membrane (PEM) electrolysis technology offers a potential solution, as fast dynamic load changes are possible. With expeditious adjustments to the power availability, a PEM plant can contribute to grid stability while using economically attractive excess power. Alternatively, adapting to fluctuating product demands can reduce the required energy for intermediate storage of hydrogen in liquid form or in pressure tanks.

To consistently operate the plant at the optimal load point, precise monitoring of plant performance, integrity, and state is vital. A digital twin running concurrently with the plant increases the available information of the stack data, as well as it simultaneously offers a platform to engage in anomaly detection and diagnosis processes. The goal is to increase reliability, availability, and maintainability, which is made possible through data provided by virtual sensors (calculated sensor values from the digital twin).

With a digital twin, critical states of non-measurable conditions in a PEM cell, such as hydrogen crossover, temperature gradients, or the state of degradation, can be determined and addressed in real-time. With all additional information accessible, the digital twin can visualize plant operation and helps in determining efficient transitions between operating points, augmenting plant operation and optimally utilizing the PEM plant’s load flexibility.

To leverage these advantages, a digital twin is developed for PEM electrolysis plants. Its structure consists of a dynamic plant model, an interface to the plant, and a state estimation algorithm. The modelling focus lies on the PEM stacks, for which a detailed dynamic multi-physics model is developed. It is structured into the submodels of mass transport, voltage, temperature, and pressure, which combine into an equation-based model solved by a DAE solver. Main features are the membrane discretization for mass transport via the Stefan-Maxwell equations and the one-dimensionally resolved temperature profile across the cell. The voltage model is represented in form of an equivalent circuit, modelling the electric effects of each individual cell layer.

Validation and results of the dynamic model are shown, especially highlighting the interaction and dependencies between the sub-models. The mass transport model shows non-linear concentration profiles of crossover components inside the membrane. Consequently, an assessment of the different positions of additional recombination catalyst layers inside the membrane is presented, including the influence of different operating conditions and dynamic loads. Following, temperature profiles across the cell and dynamic voltage responses for operation are shown. All results are discussed in the context of a simulated PEM electrolysis plant in dynamic operation, which is especially expedient concerning automatic load change and automatic start-up strategies.