Polymer electrolyte membrane fuel cell fault diagnosis based on empirical mode decomposition

Abstract

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

Durable and Efficient Hollow Porous Oxide Spinel Microspheres for Oxygen Reduction

“…Due to high cost of noble-metal catalysts and the declining oxygen reduction activity on the cathode of proton exchange membrane (PEM) fuel cells and metal-air batteries, recent research has witnessed intense investigation into the promising and versatile non-precious catalysts for the oxygen reduction reaction (ORR). [1][2][3][4][5] To this end, several low-cost non-noble-metal alternatives have emerged, including carbonbased materials, [6][7][8][9] non-precious transition metals and metal alloys (e.g., Fe, Co, Ni-Cu alloy), [10][11][12] and transition metal oxides (e.g., Fe 3 O 4 , Co 3 O 4 ). 13,14 However, transition metal oxides often possess high overpotentials.…”

Membrane‐hydration‐state detection in proton exchange membrane fuel cells using improved ambient‐condition‐based dynamic model

“…For example, a non-model-based method 58 used various sensors and proved that the current distribution among the cells could be responsible for faults in the PEMFC stack. Damoura et al proposed a fault diagnostic technique, which is based on signal processing, 63 and which involves empirical mode decomposition. The magnetic field sensing method requires a number of sensors and expensive equipment.…”

“…Other researchers proposed the use of artificial intelligence for fault diagnosis 46,[60][61][62] ; however, this technique may only be applicable to one type of PEMFC system and requires a huge amount of training data before it could be applied to other types of PEMFC system. Damoura et al proposed a fault diagnostic technique, which is based on signal processing, 63 and which involves empirical mode decomposition. This is an intuitive, direct, and empirical method based on signal processing (adaptive), without pre-determined basis functions.…”

Model-based diagnosis through Structural Analysis and Causal Computation for automotive Polymer Electrolyte Membrane Fuel Cell systems

“…On the one hand, model-based diagnosis uses a mathematical model to simulate system variables during normal operation and to generate residuals by comparing the simulated variables with those measured on the system [5,7,8,9,12,13,14]. On the other hand, signal-based approaches directly treat measured signals to extract information and define different patterns representative of variable behaviour during both normal and faulty conditions [15,16,17,18,19].…”