Method and device for predicting remaining service life of PEMFC

Abstract

The invention relates to the technical field of fuel cells, in particular to a method and a device for predicting the remaining service life of a PEMFC, and the method comprises the steps: collecting the current, current density, hydrogen air inlet and outlet pressure and output voltage data of the PEMFC at different moments, and carrying out the preprocessing of the collected data; by introducing a semi-empirical degradation model, selecting a health state SOH index capable of representing PEMFC degradation; performing random degradation modeling on the SOH index to obtain a performance degradation state space model; estimating SOH index data on line by adopting a UKF filtering algorithm according to the output voltage data; according to the output voltage data, updating parameters in a performance degradation state space model by adopting an EM algorithm; and training the initial GRU network model, and predicting the remaining service life according to the trained GRU network model. According to the invention, the service life prediction accuracy can be improved.

Description

technical field [0001] The invention relates to the technical field of fuel cells, in particular to a method and device for predicting the remaining service life of a PEMFC. Background technique [0002] Proton Exchange Membrane Fuel Cell (PEMFC, Proton Exchange Membrane Fuel Cell) is a new type of power supply device with broad application prospects. It is used in the fields of vehicle transportation, portable equipment and cogeneration, and has become a research hotspot of alternative energy devices. However, in the long-term operating environment of PEMFC (such as large load, start-up and shutdown, etc.), the internal key components such as catalyst, bipolar plate and membrane electrode will slowly age, resulting in the degradation of PEMFC performance. At the same time, the problems of short life and high maintenance cost also restrict the development of PEMFC. RUL (Remaining Useful Life), as the core technology of prediction and health management, can predict the rema...

AI Technical Summary

Problems solved by technology

[0006] 1) To solve the problem that it is difficult to establish an accurate degradation mechanism model for PEMFC and the data-driven method requires a large amount of data support, it is necessary to combine the advantages of these two methods to find a mixed model-based and data-driven method to predict the PEMFC system. remaining life

[0007] 2) Most of the existing research directly uses the output voltage as the performance degradation index for RUL prediction

However, existing studies have shown that two internal key parameters that actually cause system degradation: maximum current density and total resistance, not output voltage, so choosing output voltage as a degradation indicator is not enough to accurately describe the degradation of the system

[0008] 3) Affected by the comprehensive effect of external environment and internal factors, the degradation of the system is random, and the three sources of uncertainty, namely time uncertainty, individual difference and measurement uncertainty, generally exist in the degradation process of the system, while the existing Most studies on PEMFCs have not considered this issue

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