Proton exchange membrane fuel cell non-linear state space model identification method

Abstract

The invention discloses a proton exchange membrane fuel cell non-linear state space model identification method. According to the method, firstly, a hydrogen flow and a load current are selected as input variables, a voltage is selected as an output variable, and plenty of data is acquired; secondly, a Hankel matrix is constructed through the acquired data, and a dual matrix is solved; thirdly, amatrix projection is constructed through utilizing the result of a matrix equation; and lastly, inclined projection is decomposed through utilizing a singular value to acquire state sequence estimation of a system. The method is utilized repeatedly to solve the system matrix, and the low-rank approximation technology is utilized to acquire non-linear system characteristic estimation; actually-measured input data is taken as an abscissa, a non-linear identification characteristic is taken as an ordinate, and a MATLAB curve fitting tool is utilized to carry out polynomial fitting of the system non-linear characteristic. The method is advantaged in that the non-linear characteristic of a proton exchange membrane fuel cell can be quite accurately described, not only can solution schemes be provided for proton exchange membrane fuel cell modeling of actual engineering personnel and subsequent control system design, and relatively good reference values are for modeling of similar systems.

Description

technical field [0001] The invention belongs to the field of industrial control, in particular to a method for identifying a nonlinear state space model of a proton exchange membrane fuel cell. Background technique [0002] In the current situation of energy shortage and severe environmental pollution, the development of new clean and sustainable energy is the only way for any country in the world. Proton exchange membrane fuel cell is a new type of power generation device that can generate electricity only by feeding hydrogen and air. It has outstanding advantages such as high efficiency, cleanliness, and low noise, and is playing an increasingly important role in the field of new energy. The design and development of fuel cell stacks, as well as the design and simulation of power generation control systems require precise mathematical models. [0003] The existing commonly used fuel cell modeling methods mainly focus on mechanism modeling, and model the static / dynamic, el...

AI Technical Summary

Problems solved by technology

[0004] 1) It is necessary to have a clear understanding of the stack and a lot of prior knowledge

2) Moreover, the modeling process is cumbersome, and a large number of model parameters need to be determined (model parameters often have large discrepancies due to different stack models)

3) The built model is often unable to be directly used for control simulation due to structural problems

Xu Xiayin.PEMFC temperature subspace identification model[J].Industrial Control Computer,2015(9):77-78. Only using the fuel cell temperature data to identify a single-input single-output state-space model, but the model error is large , and this method is not good at describing the nonlinear characteristics

Buchholz M, Eswein M, Krebs V.Modelling PEM fuel cell stacks for FDI using linear subspace identification[J].2008:341-346. This paper uses the method of subspace identification to obtain the voltage model, but for the typical nonlinear The description accuracy of the characteristic link still needs to be improved

Images