International audienceFuel cells are powerful systems for power generation. They have a good efficiency and do not generate greenhouse gases. This technology involves a lot of scientific fields, which leads to the appearance of strongly inter dependent parameters. This makes the system particularly hard to control and increases fault's occurrence frequency. These two issues call for the necessity to maintain the system performance at the expected level, even in faulty operating conditions. It is called " fault tolerant control " (FTC). The present paper aims to give the state of the art of FTC applied to the proton exchange membrane fuel cell (PEMFC). The FTC approach is composed of two parts. First, a diagnosis part allows the identification and the isolation of a fault; it requires a good a priori knowledge of all the possible faults. Then, a control part allows an optimal control strategy to find the best operating point to recover/ mitigate the fault; it re quires the knowledge of the degradation phenomena and their mitigation strategies

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International audienceIn this paper, a Fault Tolerant Control Strategy (FTCS) dedicated to PEMFC (Polymer Electrolyte Membrane Fuel Cell) water management is implemented and validated online on a real PEMFC system. Thanks to coupling a Fault Detection and Isolation (FDI), an adjustable controller and a reconfiguration mechanism, FTCS allows addressing the important challenge of Fuel Cell (FC) reliability improvement. Only few works have already been conducted on FTCS applied to FC actuators faults, and none of them on FC water management faults. In this work, a neural-based diagnosis tool is computed online as FDI component and is coupled to a self-tuning PID controller. This diagnosis tool shows low computational time and high detection performance. The self-tuning PID controller shows robustness against noise measurements and model uncertainties. Its low computational cost makes it a suitable control method for real-time FTCS. Performed on a PEMFC system, the FTCS shows promising results on fault diagnosis and performance recovery

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International audienceDiagnosis tool for water management is relevant to improve the reliability and lifetime of polymer electrolyte membrane fuel cells (PEMFCs). This paper presents a novel signal-based diagnosis approach, based on Empirical Mode Decomposition (EMD), dedicated to PEMFCs. EMD is an empirical, intuitive, direct and adaptive signal processing method, without pre-determined basis functions. The proposed diagnosis approach relies on the decomposition of FC output voltage to detect and isolate flooding and drying faults. The low computational cost of EMD, the reduced number of required measurements, and the high diagnosis accuracy of flooding and drying faults diagnosis make this approach a promising online diagnosis tool for PEMFC degraded modes management

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