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A temperature modeling method for pemfc system based on mutation particle swarm and differential evolution hybrid algorithm

A technology of mutated particle swarm and hybrid algorithm, applied in the field of proton exchange membrane fuel cell system temperature modeling, can solve the problems of increasing electrochemical reaction rate, model versatility and generality deterioration, membrane shrinkage and rupture, etc. Chemical properties, effect of reducing modeling difficulty

Active Publication Date: 2019-03-19
SOUTHEAST UNIV
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Problems solved by technology

[0003] Thermal management and water management are two important indicators to optimize the performance of the PEMFC system. In the case of high power density, the humidification water and the internal chemical reaction of the battery will produce excessive water, which will lead to the "water flooding" phenomenon of the electrode pores and limit the reaction. Gas diffusion rate increases concentration polarization and reduces the electrochemical performance of the battery
Generally, the operating temperature of the battery is in the range of 0-100 ° C. The increase in temperature is beneficial to increase the electrochemical reaction rate, reduce the ohmic polarization of the membrane, and improve the performance of the battery. However, if the temperature is too high, the loss of moisture in the membrane will be accelerated, resulting in The partial pressure of water vapor increases, which will cause the film to shrink and rupture in severe cases, resulting in a decrease in battery performance
On the contrary, too low temperature will cause the mass transfer in the stack to be limited and the electrochemical reaction speed will be reduced, which will also cause the performance of the battery to decline.
[0004] The steady-state and dynamic heat transfer models based on single cells and stacks are all based on multiphase flow process, mass transfer process in membrane electrodes, electrochemical reaction mass transfer process and heat transfer process, some of which are The variables are strongly coupled to each other and are easily disturbed by the external load (current density); there are still a large number of experimental parameters in the stack model, which makes the generality and generality of the model worse, the nonlinear characteristics are strong, and the system analytical modeling A large number of simplifications and assumptions in the process lead to a great drop in the accuracy of the model; at the same time, the established model expression is very complicated and difficult to be used in the design of system control

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  • A temperature modeling method for pemfc system based on mutation particle swarm and differential evolution hybrid algorithm
  • A temperature modeling method for pemfc system based on mutation particle swarm and differential evolution hybrid algorithm
  • A temperature modeling method for pemfc system based on mutation particle swarm and differential evolution hybrid algorithm

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Embodiment Construction

[0041] The present invention will be further described below in conjunction with the accompanying drawings.

[0042] Such as figure 1 Shown is a PEMFC temperature control algorithm flow chart based on mutant particle swarm and differential evolution, which includes the following steps:

[0043] (1) Define the temperature of each module of the PEMFC system: fuel gas temperature t 1 , oxidizing gas temperature t 2 , cooling water temperature t 3 , anode temperature t 4 , cathode temperature t 5 , proton membrane temperature t 6, anode side bipolar plate temperature t 7 , the cathode side bipolar plate temperature t 8 , define the temperature vector p=(t 1 ,t 2 ,t 3 ,t 4 ,t 5 ,t 6 ,t 7 ,t 8 );

[0044] (2) Establish a particle swarm containing m particles, set the population size m=8, n is the particle number, n∈[1~m] the maximum evolution algebra A max ;Individual n temperature vector t n =(t n1 ,t n2 ,...,t nD ,), d=1,2,...,D,t nd is the d-th dimension vec...

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Abstract

The invention discloses a temperature modeling method for a proton exchange membrane fuel cell (PEMFC) system based on a variation particle swarm and differential evolution hybrid algorithm. The PEMFC pile output performance is influenced by the temperature controlled by each module of an operation system, the method is combined with a PEMFC pile model based on the variation particle swarm and differential evolution hybrid algorithm, and the steps of resolving the optimal temperature operating parameter algorithm for each module of the pile when the electrochemical performance is optimal are proposed. The hybrid algorithm has good global and local searching and optimizing capacities, and the temperature control parameter for each module of the PEMFC system can be identified at high precision and controlled in real time, so that the electrochemical performance of the fuel cell system is improved.

Description

technical field [0001] The invention relates to a temperature modeling method of a proton exchange membrane fuel cell (PEMFC) system based on a hybrid algorithm of variation particle swarm and differential evolution. Background technique [0002] In today's era of energy crisis and environmental pollution problems, fuel cells, as a clean energy with high energy efficiency and zero pollution, are favored and widely studied. Among them, the proton exchange membrane fuel cell is a device that undergoes a chemical reaction between hydrogen and oxygen in the air under certain conditions, thereby directly converting chemical energy into electrical energy. Due to the advantages of no pollution, high energy conversion rate, and fast start-up, proton exchange membrane fuel cells have very good application prospects. [0003] Thermal management and water management are two important indicators to optimize the performance of the PEMFC system. In the case of high power density, the hum...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H01M8/04007H01M8/04298G06F17/50
CPCG06F30/367H01M8/04007H01M8/04298Y02E60/50
Inventor 赵立业沈翔李宏生黄丽斌刘锡祥李坤宇
Owner SOUTHEAST UNIV
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