Proton exchange membrane fuel cell variable forgetting factor recursive subspace identification method

Abstract

The invention discloses a proton exchange membrane fuel cell variable forgetting factor recursion subspace identification method, which comprises the steps of selecting input and output variables of an identification model, and collecting experimental data on a PEMFC measurement and control platform; constructing a Hankel matrix of input and output data at the current moment, and solving a subspace predictor model at the current moment by utilizing a subspace identification method; constructing a Hankel matrix of input and output data at the next moment, and solving prediction output at the next moment by utilizing the subspace predictor model at the current moment; and determining actual output data at the next moment according to the control action added by the PEMFC, calculating outputprediction errors of actual output and prediction output, if the output prediction errors are greater than an allowable error, updating the solution subspace predictor model by utilizing a recursive subspace identification algorithm, and otherwise, not updating the model. According to the method, a subspace identification algorithm is adopted, pile model priori knowledge is not needed, model parameters are few, and the identification speed is high; and a recursive algorithm of a variable forgetting factor is added, so that model parameters can be corrected and updated on line in real time.

Description

technical field [0001] The invention belongs to the field of industrial control, and in particular relates to a proton exchange membrane fuel cell variable forgetting factor recursive subspace identification method. Background technique [0002] Proton exchange membrane fuel cell (PEMFC) is a power generation device that directly converts the chemical energy of hydrogen into electrical energy. Fields are playing an increasingly important role. PEMFC is a multivariable, strongly coupled, time-varying complex system. In order to improve the power generation performance of PEMFC, it is necessary to control the electrical characteristics of PEMFC during the power generation process. The first task is to accurately model the electrical characteristics of PEMFC in real time. The modeling method used should not only accurately describe its dynamic characteristics, but also update the model online in real time. [0003] The existing fuel cell modeling methods mainly focus on mecha...

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Problems solved by technology

[0005] 2) The modeling process is complex, and a large number of model parameters need to be determined

[0006] 3) Many conditional assumptions need to be made, and the results may be quite different from the actual operating status

[0007] 4) The model cannot be updated online in real time, and does not meet the time-varying characteristics of fuel cells

Yin Liangzhen, Li Qi, Hong Zhihu, et al. FFRLS online identification and real-time optimal temperature generalized predictive control method for PEMFC power generation system[J]. Chinese Journal of Electrical Engineering, 2017(11): 169-181+324. Study on the effect of PEMFC operating temperature on The influence of output voltage is based on the recursive least square method with forgetting factor to model and correct the temperature of PEMFC system online, but this model only considers the influence of operating temperature on output performance, ignoring many factors that affect its output performance. The coupling relationship between parameters is insufficient to consider the multivariate problem of PEMFC system

Buchholz M, Eswein M, Krebs V.Modelling PEMfuel cell stacks for FDI using linear subspace identification[C].IEEEInternational Conference on Control Applications.IEEE,2008:341-346. Subspace identification method based on canonical variable analysis (CVA) The fuel cell vehicle stack is modeled and applied to stack fault detection, but the model is identified offline and cannot be updated online, which ignores the time-varying characteristics of PEMFC

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