Advanced Model-Based Framework for State-of-X Diagnostics in Low- and High-Temperature Proton Exchange Membrane Fuel Cells
Andraž Kravos & Tomaž Katrašnik
University of Ljubljana
Faculty of Mechanical Engineering – Laboratory for Internal combustion engines and electromobility
Our partners from University of Ljubljana presented their latest findings during the ModVal 2025 conference! Their presentation is available below.
Presentation abstract:
Simultaneously achieving high efficiency and a long service life in proton exchange membrane fuel cells (PEMFCs) requires advanced and cost-effective diagnostic methods. Conventional monitoring strategies, which rely on multiple physical sensors to detect local phenomena, are often incompatible with strict economic constraints and do not provide a comprehensive insight into the complicated interplay between electrochemical reactions, transport processes and degradation phenomena. To overcome these challenges, this work presents a novel State-of-X (SoX) diagnostic methodology that combines multi-scale simulation, parameter identification and real-time observation methodology to elucidate both the State-of-Operation-Conditions (SoOC) and the State-of-Health (SoH) of PEMFCs. At the centre of this methodology is a computationally efficient multi-scale simulation framework [1,2] that is applicable to both low-temperature (LT) and high-temperature (HT) PEMFCs. This framework integrates advanced sub-models that resolve key physical processes such as liquid water dynamics, membrane water uptake and gas crossover effects leading to mixed potentials. Owing to an optimised numerical approach, the overall simulation can run faster than real-time, thus enabling sophisticated observer functionalities while keeping the computational effort manageable.
The SoX diagnostics concept consists of two main components, the online SoOC and the cloud-based SoH observer. The online SoOC observer uses an Unscented Kalman Filter (UKF) algorithm coupled with the multi-scale model to enable virtual real-time sensing of intra-FC states using only boundary conditions, lumped voltage and current measurements. By capturing spatio-temporal variations of critical variables such as reactant and product concentrations, membrane humidity, liquid water void fractions and current density, this observer facilitates advanced control and diagnostic protocols and ultimately enables optimisation of performance under dynamically changing operating conditions. On the other hand, the cloud-based SoH observer employs parameter identification techniques to define track parameter values change that is inherently linked to the specific component degradation. By focusing on parameter uniqueness and fidelity, the observer successfully pinpoints root causes of performance decay. This functionality may be invoked periodically during routine maintenance checks or on demand when SoH metrics fall below predefined thresholds.
Experimental validation and numerical analyses confirm the high accuracy of the proposed SoX diagnostics in deciphering the internal states of PEMFCs and tracking degradation phenomena. Crucially, the seamless integration of the SoOC and SoH observers enables continuous updating of model parameters, ensuring robust control strategies even when conditions in the FC change. This synergy provides unprecedented clarity on both short-term operational factors through observation of SoOC and long-term through observation of SoH, promoting greater reliability, service life, and cost savings. By consolidating these advances, the proposed SoX diagnostics methodology significantly advances PEMFC monitoring and management, paving the way for widespread adoption of cleaner, sustainable energy solutions.
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