Water logging fault diagnosis method for proton exchange membrane fuel cell

Abstract

The invention discloses a flooding fault diagnosis method for a proton exchange membrane fuel cell. The method is a fault diagnosis method based on the combination of a model and data driving (BP neural network). Firstly, an internal resistance characteristic model of the proton exchange membrane fuel cell is established by analyzing an internal mechanism of the proton exchange membrane fuel cell and combining a U-I characteristic curve; in combination with an internal resistance characteristic model, the principle and the performance of a pile flooding fault are analyzed, four variables in the model are selected as characteristic quantities of flooding fault diagnosis, an available BP neural network classifier is constructed by using R software through known classification of 10000 groups of training set data, and finally four characteristic data of a fuel cell in an unknown state are collected. And normalizing, substituting into a classifier, and judging whether the fuel stack is in a flooding fault state or not and judging the degree of the flooding fault if the fuel stack is in the flooding fault state. The method can effectively improve the efficiency and accuracy of fuel cell fault diagnosis, and has good engineering economy and application prospects.

Description

Technical field: [0001] The invention belongs to the field of fault diagnosis of proton exchange membrane fuel cells, and relates to a flood fault diagnosis method of proton exchange membrane fuel cells. Background technique: [0002] As a leader in fuel cells, PEMFC has the advantages of short cold start time, clean and pollution-free, and fits the needs of new energy vehicles. Two key issues currently constraining further commercial development of PEM fuel cells are durability and sustainability. It is the most direct and effective method to improve the durability and continuity of PEMFC from the manufacturing process, but it is also a very important part to carry out real-time fault diagnosis and repair of PEMFC in the subsequent use process. So far, there are many methods for fault diagnosis of fuel cells. The most common one is the model-based fault diagnosis method. The model-based fault diagnosis method establishes a PEMFC simulation model, calculates the parameter d...

AI Technical Summary

Problems solved by technology

Although this kind of diagnosis method can identify the type of fault from the mechanism and take corresponding measures, it is difficult to establish an accurate stack diagnosis model due to the difficulty in obtaining internal data in the system.

At the same time, residuals are always affected by measurement and calculation uncertainties, so fault diagnosis is often not so accurate

Therefore, only model-based fault diagnosis methods cannot meet the needs of real-time and accurate fault diagnosis

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