Fault tolerance control of SOFC systems based on nonlinear model predictive control

Heat management and load tracking are two crucial tasks for solid oxide fuel cell (SOFC) systems development. In literature, plenty of temperature controllers and load tracking controllers have been successfully designed for SOFC systems. However, previous researches are limited to control design in the case of SOFC normal conditions. For a SOFC system, faults can occur on any parts at any time, thus a controller with a specific design must be used. In this work a new control strategy that tolerates the SOFC system faults is proposed, which includes a fault diagnosis module, a decision-making part and four backup controllers. The fault diagnosis part is used to classify the SOFC system current faults (normal, fuel leakage fault, air compressor fault, or both fuel leakage and air compressor faults). Based on the diagnosis results, the decision-making part is designed to select the appropriate backup controller. Four nonlinear model predictive controllers based on back propagation (BP) neural networks are respectively built to follow loads and maintain appropriate temperatures in the case of fuel leakage fault, air compressor fault, both fuel leakage and air compressor faults, and SOFC normal condition. The results show that the proposed fault tolerance control strategy can track the voltage and make the SOFC temperature steady near the enactment value in working in faulty conditions, which may result in both lifetime and performance improvement for the SOFC systems.