The Voltage Oracle: A Foundation Model for Probabilistic PEM Fuel Cell Voltage Prediction

Long-term operation of proton exchange membrane (PEM) fuel cells requires precise monitoring to detect and prevent harmful anomalies. Cell voltage is a sensitive indicator for the fuel cell’s state of health. However, its dependency on fuel cell operating conditions impedes the detection of anomalies. To overcome this limitation, we present a machine learning driven foundation model for probabilistic fuel cell voltage prediction. The foundation model can be fitted to new fuel cell designs using transfer learning for anomaly detection. Since the availability and variance in real fuel cell test data sets is very limited, a physics-based fuel cell simulation model is used to generate 30,000 data points to train the machine learning model. To improve the prediction accuracy of the foundation model, additional steps such as feature engineering and target balancing are introduced and their individual effects are analyzed. The performance of the pre-trained foundation model is then tested by transfer learning the foundation model using real measurement data from two different automotive fuel cell designs. It is shown that with sufficient training data, the machine learning model outperforms the physics-based simulation model by an RMSE improvement of up to 37% and respectively by 61% on the two test data sets.