Fuel cell output prediction method and system based on mixing model

Abstract

The invention discloses a fuel cell output prediction method and system based on a mixing model, the method and the system enable the mixing dynamic model to have higher accuracy in both statical and dynamic aspects. In the present method, a statical mechanism model of fuel cell output voltage, a dynamic mechanism model of a fuel cell anodic and cathode gas flow dynamic mechanism models of fuel cell anodic gas flow and fuel cell cathode gas flow, a statical neural network and a variable structure neural network are established. The output of two dynamic mechanism models are used as part input parameters of the statical mechanism model. Vout is acquired after using the statical neural network to complement the output of the statical mechanism model. The variable structure neural network is used to approach the derivative of the error Ve between the fuel cell actual output and the Vout, then integration is performed on the output of the variable structure neural network to acquire the error estimation value, and the fuel cell output voltage estimation value will be acquired by superposition of the error estimation value and the Vout.

Description

technical field [0001] The invention belongs to the technical field of proton exchange membrane fuel cells, and in particular relates to a proton exchange membrane fuel cell output prediction method and system based on a mixed model. Background technique [0002] In engineering applications, an accurate fuel cell dynamic model is the premise and basis for fuel cell system control system design, fault diagnosis and power distribution. At present, there are two main types of proton exchange membrane fuel cell modeling methods: one is the mechanism model, and the other is the black box model. [0003] Literature (Amphlett J C, Baumert R M, Peppley B A, et al. Performance modeling of the ballard mark iv solid polymer electrolyte fuel cell, i. mechanical model development [J]. Journal of Electrochemical Society, 1995, 142 (1): 1- 8) The mechanism model of the proton exchange membrane fuel cell was analyzed, and a static empirical model based on the mechanism model was establishe...

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Problems solved by technology

However, this method needs to optimize the parameters of the mechanism model, which is time-consuming and laborious; at the same time, the hybrid dynamic model based on the static neural network has poor adaptability, and the prediction accuracy of the dynamic performance of the fuel cell is not high.

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