Big Brother Is Watching You: Leveraging Artificial Intelligence for Automated Fuel Cell Monitoring (Technical Report)
Authors: Klass Lukas , Holz Laurin , Koenig Lukas , Kabza Alexander , Sehnke Frank , Strecker Katharina , Hölzle Markus 
Published: December 2024

Precise monitoring and anomaly detection play a critical role in the successful and continuous operation of fuel cells. Especially in durability testing, it is important to detect failures at an early stage and prevent external influences from affecting the test results or damaging the fuel cell stack. Because most external influencing factors are not directly sensed, they only exhibit in the currently unmonitored cell voltage. This emphasises the need for a sensitive cell voltage monitoring concept.

In [1] we presented a machine learning based approach for cell voltage monitoring using neural networks as digital twins. It was successfully shown that this approach enables the detection of anomalies in cell voltage measurements. However, the combination of both steady-state and degradation effects in a single digital twin model hindered the transfer of the model to new fuel cells.

Building on this approach, we present a second-generation of the fuel cell digital twin model. As shown in the Figure, the combined model of the previous approach is split into a steady-state fuel cell model and a corrective degradation model. The stationary fuel cell model predicts the stationary cell voltage based on the current operating conditions of the fuel cell. The degradation model accounts for fuel cell degradation and predicts a degradation correction based on historical fuel cell operating data. The combination of the predicted steady-state cell voltage and the degradation correction finally results in a prediction of the degraded cell voltage. This final prediction can be subsequently used for cell voltage monitoring. By decoupling stationary effects and degradation effects into two separate neural networks, each model can be adapted much more specifically to new data using transfer learning. This allows simulation data to be used to pre-train the steady-state model, significantly reducing the amount of real training data required to less than 100 data points. The degradation model on the other hand benefits from the reduced model complexity since only the few operating parameters that influence the fuel cell degradation remain as model inputs.

To demonstrate the accuracy and adaptability of this approach, the new digital twin model is applied to real fuel cell data. In particular, the model’s ability to precisely detect previously undetected anomalies in the test data is shown.

Finally, we present a framework for automated fuel cell monitoring. The framework provides a web-based graphical user interface to set up the monitoring configurations and to initiate the automated training of digital twins. Subsequently, the trained models are continuously applied to detect anomalies in new measured data. Upon detection of an anomaly, a notification will be sent to the test station operator.

[1] L. Klass, A. Kabza, F. Sehnke, K. Strecker and M. Hölzle, “Lifelong performance monitoring of PEM fuel cells using machine learning models,” Journal of Power Sources, vol. 580, 2023.

Figure 1