Method and system for estimating state of health of proton exchange membrane fuel cell

Abstract

The invention relates to a method and a system for estimating the state of health of a proton exchange membrane fuel cell. The method comprises the steps of determining a polarization curve of a fuel cell; determining the relation among the output voltage of the fuel cell, the current density and the film thickness according to the control parameters in a set control range and the basic parameters of the fuel cell; adopting a reverse fitting method to determine the mapping relation between the film thickness and the operation time according to the relation between the output voltage and the current density and the film thickness and the polarization curve; fitting the mapping relation by using a Fourier fitting method, and determining a function relational expression between the film thickness and the operation time; according to the function relational expression and the operation time of the fuel cell, determining the current membrane thickness, and substituting the current membrane thickness back into the relational expression of the output voltage, the current density and the membrane thickness, so as to determine a real-time quantitative polarization curve according to the current membrane thickness; and determining the corresponding state of health according to the decline degree of the fuel cell determined by the real-time quantitative polarization curve. According to the method, the real-time performance of state-of-health estimation of the proton exchange membrane fuel cell and the quantification certainty can be improved.

Description

technical field [0001] The invention relates to the field of proton exchange membrane fuel cells, in particular to a method and system for estimating the state of health of a proton exchange membrane fuel cell. Background technique [0002] Proton exchange membrane fuel cells are widely used in transportation and other fields represented by fuel cell vehicles due to their many advantages such as no pollution, high energy conversion rate, low operating temperature, and low noise. As a typical multi-scale, multi-physics, multi-component, multi-factor coupled complex nonlinear system, the state of health estimation and life prediction of proton exchange membrane fuel cells are full of many unknown challenges. [0003] Currently, state-of-health estimation or lifetime prediction for PEMFCs is generally divided into three categories: ① model-based methods; ② data-driven methods; ③ hybrid methods of the two. The model-based method has high requirements on the accuracy of the mode...

AI Technical Summary

Problems solved by technology

The model-based method has high requirements on the accuracy of the model, the parameter calibration in the model is complicated, and it is difficult to apply in real time; the data-driven method needs to rely on a large amount of data, data training is time-consuming, takes up a lot of computing resources, and the cost of parameter adjustment is high. Poor migration ability; although the mixed method of the two can avoid the obvious shortcomings of the two types of methods to a certain extent, it often has shortcomings such as complicated adjustment of empirical parameters and time-consuming data training in practical applications, especially in the case of fuel cell vehicles. The real-time quantification of the battery health status represented by the transportation field is full of many uncertainties

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