Data-Driven Predictive Modelling for Fuel Cell Performance and Degradation
Description
The Data-Driven Predictive Modelling service provides an AI-based framework for predicting the performance evolution and degradation of hydrogen fuel cell systems using time-series operational data. Based on LSTM networks with self-attention mechanisms, the service supports condition monitoring, predictive maintenance, and lifecycle optimisation by delivering reliable forecasts of stack voltage as the primary performance health indicator.
More about the service
Business Need
Hydrogen fuel cell systems experience progressive voltage degradation over their operational lifetime, but predicting when performance will fall below acceptable thresholds requires accurate long-term forecasting from operational data. System operators need condition monitoring and predictive maintenance capabilities that account for varying operating regimes and support evidence-based maintenance planning — particularly for safety-critical applications in transport and distributed energy.
Key Performance Indicators
Mean Squared Error (MSE), RMSE, MAE, and R² across forecast horizons
Accuracy across both minimal-sensing (voltage-only) and multi-sensing configurations
Reproducibility and traceability of predictions against documented model configurations
Data Provided
Time-indexed stack voltage predictions over predefined horizons (short-term and long-term degradation)
Inputs: fuel cell voltage time-series; optionally temperature, pressure, and reactant flow rates