In-Operando EIS Monitoring of a PEM Fuel Cell Stack to Understand the Influence of Different Operating Conditions on Stack Performance
Bibliographic record
Abstract
Proton Exchange Membrane Fuel Cells (PEMFCs) are a popular alternative to replace combustion engines, providing a clean pathway for green mobility and green energy. With their ability to deliver high power density at relatively low operating temperatures, PEMFCs are ideally suited for applications ranging from heavy-duty vehicles to grid balancing. However, optimizing their performance while extending stack lifetime requires a deep understanding of the complex electrochemical and transport phenomena that govern their operation. This study presents a comprehensive investigation of an industrial-scale PEMFC stack from Plug Power operating at high current densities—up to 1 A/cm²—across 27 different operating conditions. The operational condition design of experiments varied key parameters including current density, temperature, fuel ratio, cathode and anode humidity, and cathode and anode pressure, measuring each of the 20 cells within the stack simultaneously. The goal was to map how these variables interact and influence electrochemical behavior under realistic operating conditions. To achieve this, we employed in-operando electrochemical impedance spectroscopy (EIS)— using specialized instruments developed by Pulsenics—to continuously monitor internal electrochemical processes during fuel cell operation. Unlike conventional EIS, which is performed post-operation or at rest, in-operando EIS captures real-time impedance signatures, allowing direct observation of performance-limiting mechanisms as they occur. Time-resolved data revealed strong correlations between operating conditions and changes in cell impedance. These insights link measurable electrochemical parameters to overall stack performance, enabling the development of operational strategies to optimize efficiency and durability. This work demonstrates the value of in-operando EIS as a high-resolution tool for fuel cell diagnostics, enabling more intelligent design, operation, and management of PEMFC systems in industrial applications. By bridging the gap between lab-based measurements and real-world operation, this technique offers a powerful pathway to accelerate the commercialization and reliability of fuel cell technology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".