Data-Driven Predictive Diagnostics and Fault-Tolerant Control for Fuel-Cell Electric Vehicle Powertrains Under Uncertainty

Fault-tolerant control; Predictive diagnostics; Fuel-cell electric vehicles; Data-driven fault detection and isolation (FDI); Uncertainty-aware control reconfiguration

Authors

  • Adel Elgammal Professor, Utilities and Sustainable Engineering, The University of Trinidad & Tobago UTT
February 21, 2026
February 23, 2026

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Fuel-cell electric vehicles (FCEVs) are susceptible to sensor drift, actuator degradation and air path/water management faults that can quickly deteriorate efficiency, drivability and durability. This work introduces a control-oriented data-driven framework to integrate predictive diagnostics with FTC for FCEV powertrains subject to uncertainty. A multi-sensor feature pipeline comprising stack voltage/current, cathode pressure, compressor speed, hydrogen flow rate DC-link voltage and traction power is first established and the health indicators are generated via a probabilistic sequence model modeling temporal dependencies. The diagnostic module conducts an online detection and isolation of the faults in early stages with fault magnitude estimation for remaining useful time to predict constraint violation. Second, an active FTC layer allows real-time fault estimation–based control reconfiguration to update an uncertainty-aware powertrain controller by which fuel-cell ramp-rate limits, air-path constraints and energy-storage limits are enforced. The controller ensures that fast power transients are absorbed by the battery/supercapacitor, while keeping the fuel-cell system operational at high efficiencies and ensuring traction power tracking in addition to safe limits during faults. Uncertainty is dealt with by disturbance modeling and confidence-weighted adaptation to avoid overreacting to noisy diagnostic outputs. Simulation results for representative urban and aggressive drive cycles are presented, which incorporate introduced sensor bias, compressor efficiency loss and hydrogen starvation scenarios that show superior robustness compared with the non-tolerant 'baseline' in terms of increased fault detection lead time, no constraint or emissions coverage issues as well as efficient minimisation of hydrogen consumption while maintaining drivability. The considered combination of predictive diagnostics and FTC can thus offer a practical approach to reliable FCEV operation that will bring quantifiable safety and efficiency gains under real-world automotive scenarios.