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Fuel cell optimization modeling approach with adaptive genetic strategy rna-ga

An RNA-GA and fuel cell technology, applied in genetic rules, design optimization/simulation, gene models, etc., can solve problems such as premature convergence, poor local search ability, etc., to avoid premature convergence, good application prospects, and increase diversity Effect

Active Publication Date: 2020-04-17
ZHEJIANG UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

However, the traditional genetic algorithm is prone to shortcomings such as premature convergence and poor local search ability.

Method used

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  • Fuel cell optimization modeling approach with adaptive genetic strategy rna-ga
  • Fuel cell optimization modeling approach with adaptive genetic strategy rna-ga
  • Fuel cell optimization modeling approach with adaptive genetic strategy rna-ga

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0036] A proton exchange membrane fuel cell is an electrochemical device that uses the reverse reaction of electrolyzing water to generate electricity.

[0037] The electrochemical reaction equation of the two electrodes is as follows:

[0038] Anode: H 2 →2H + +2e -

[0039] cathode:

[0040] Total battery response:

[0041] The reaction products are DC electric energy, liquid water and heat of reaction.

[0042] The application object model of this example adopts the cell voltage V-I model proposed by J.C.Amphlett, and the basic expression of the output voltage of the cell is:

[0043] V cell =E Nernst -V act -V ohmic -V con (1)

[0044] where V cell is the road terminal voltage of the battery (V), E nernst ,V act ,V ohmic and V con They are battery thermodynamic voltage, activation polarization electromotive force, ohmic voltage drop and concentration overvoltage (V).

[0045]

[0046]

[0047] V ohmic =i(R M +R C ) (4)

[0048]

[0049] ...

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Abstract

The invention discloses a fuel cell optimization modeling method with an adaptive genetic policy RNA-GA. The method comprises the steps of 1) obtaining sampling data of an input current and an output voltage of a proton exchange membrane fuel cell through field operation or experiment; 2) taking an error square sum of sampling data of an estimated output and an actual output of a fuel cell model as an objective function during RNA-GA optimization search; 3) setting algorithm running parameters; and 4) running the RNA-GA to estimate unknown parameters in the fuel cell model, obtaining estimated values of the unknown parameters in the model by minimizing the objective function, and substituting the estimated values of the unknown parameters into the fuel cell model, thereby forming a mathematic model. According to the method, a decision is made to execute crossover or mutation operation by applying the adaptive genetic policy, so that the population diversity is effectively kept and the convergence speed of an algorithm to a global optimal solution is increased; the obtained fuel cell model parameters are reliable; and the method is also suitable for optimization modeling of other complex chemical reaction processes.

Description

technical field [0001] The invention relates to an intelligent optimization modeling method, in particular to a fuel cell optimization modeling method with an adaptive genetic strategy RNA-GA. Background technique [0002] Fuel cells are one of the high-tech technologies that have a great impact on human society in the 21st century. Among them, proton exchange membrane fuel cells (PEMFC) have attracted widespread attention because of their environmental protection, high efficiency and low emission. PEMFC is a nonlinear, multi-input, and strongly coupled complex system, and the study of its modeling issues is of great significance to the theoretical research and engineering applications of fuel cells. To build a reliable model, the first thing to solve is the parameter estimation problem in the model. The parameter estimation problem is essentially an optimization problem, and researchers have adopted some traditional optimization methods, such as Levenberg-Marquardt (L-M), ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N3/12G06F30/20
CPCG06F30/20G06N3/126
Inventor 张丽王宁
Owner ZHEJIANG UNIV
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