CN117154127A - Solid oxide fuel cell flow channel structure and multi-objective optimization method thereof - Google Patents

Solid oxide fuel cell flow channel structure and multi-objective optimization method thereof Download PDF

Info

Publication number
CN117154127A
CN117154127A CN202311222785.2A CN202311222785A CN117154127A CN 117154127 A CN117154127 A CN 117154127A CN 202311222785 A CN202311222785 A CN 202311222785A CN 117154127 A CN117154127 A CN 117154127A
Authority
CN
China
Prior art keywords
flow channel
fuel cell
solid oxide
oxide fuel
channel structure
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202311222785.2A
Other languages
Chinese (zh)
Inventor
贺振宗
赵薇薇
毛军逵
梁凤丽
左敏
蒋新宇
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nanjing University of Aeronautics and Astronautics
Original Assignee
Nanjing University of Aeronautics and Astronautics
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nanjing University of Aeronautics and Astronautics filed Critical Nanjing University of Aeronautics and Astronautics
Priority to CN202311222785.2A priority Critical patent/CN117154127A/en
Publication of CN117154127A publication Critical patent/CN117154127A/en
Pending legal-status Critical Current

Links

Classifications

    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M8/00—Fuel cells; Manufacture thereof
    • H01M8/02—Details
    • H01M8/0202—Collectors; Separators, e.g. bipolar separators; Interconnectors
    • H01M8/0258—Collectors; Separators, e.g. bipolar separators; Interconnectors characterised by the configuration of channels, e.g. by the flow field of the reactant or coolant
    • H01M8/026—Collectors; Separators, e.g. bipolar separators; Interconnectors characterised by the configuration of channels, e.g. by the flow field of the reactant or coolant characterised by grooves, e.g. their pitch or depth
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M8/00—Fuel cells; Manufacture thereof
    • H01M8/04—Auxiliary arrangements, e.g. for control of pressure or for circulation of fluids
    • H01M8/04298—Processes for controlling fuel cells or fuel cell systems
    • H01M8/04305—Modeling, demonstration models of fuel cells, e.g. for training purposes
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M8/00—Fuel cells; Manufacture thereof
    • H01M8/10—Fuel cells with solid electrolytes
    • H01M8/12—Fuel cells with solid electrolytes operating at high temperature, e.g. with stabilised ZrO2 electrolyte
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M8/00—Fuel cells; Manufacture thereof
    • H01M8/10—Fuel cells with solid electrolytes
    • H01M8/12—Fuel cells with solid electrolytes operating at high temperature, e.g. with stabilised ZrO2 electrolyte
    • H01M2008/1293—Fuel cells with solid oxide electrolytes
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/30—Hydrogen technology
    • Y02E60/50—Fuel cells

Landscapes

  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Manufacturing & Machinery (AREA)
  • Sustainable Development (AREA)
  • Sustainable Energy (AREA)
  • Chemical & Material Sciences (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Electrochemistry (AREA)
  • General Chemical & Material Sciences (AREA)
  • Fuel Cell (AREA)

Abstract

The invention discloses a solid oxide fuel cell flow channel structure and a multi-objective optimization method thereof. Firstly, based on a Latin hypercube sampling method, a database of the influence rule of convex sphere block configuration parameters on the power generation and pump power requirements of the solid oxide fuel cell is established, and based on a neural network learning method, a correlation mechanism between input parameters and output results of the database is established, so that a proxy model capable of predicting the power generation and pump power requirements characteristics of the solid oxide fuel cell with high precision is obtained; and then, based on the obtained agent model, developing a pareto multi-objective optimization technology which gives consideration to the requirements of high fuel cell power generation efficiency and low pump power, and realizing the optimization design of convex spherical block configuration parameters.

Description

Solid oxide fuel cell flow channel structure and multi-objective optimization method thereof
Technical Field
The invention relates to the technical field of solid oxide fuel cells, in particular to a novel multi-objective optimization method for a flow channel structure of a solid oxide fuel cell.
Background
Hydrogen energy is an ideal energy carrier, and is outstanding in a plurality of new energy sources due to the advantages of high heat value, good combustion performance, rich reserves, cleanness, environmental protection and the like. The hydrogen fuel cell is used as one of core technologies for utilizing hydrogen energy, can directly convert chemical energy into electric energy without being limited by Carnot cycle, and has high energy conversion efficiency, thus becoming one of important research directions in the field of energy.
Solid oxide fuel cells are among the most widely used fuel cells today. The reactant gas enters the cell and is mainly transmitted by convection and diffusion, and the quality of the reactant transmission performance is critical to the output performance of the cell. In order to effectively improve the output performance of the solid oxide fuel cell and realize higher energy conversion efficiency, the fuel cell generally has reasonable structure and high-efficiency materials, can fully utilize the reaction gas and improve the gas utilization rate. Therefore, there is a need for improved fuel cell output performance by reasonably optimizing the design of solid oxide fuel cells.
The existing solid oxide fuel cell performance optimization method is mainly divided into material optimization and structural design optimization. The material optimization is an optimization method for improving the output performance of the battery while reducing the production cost and manufacturing difficulty by improving the material performance or searching new materials for various components of the battery, including electrodes, electrolytes, catalysts and the like. Structural design optimization is generally performed according to specific application scenes, so that an optimal configuration is obtained, and the output performance of the battery is improved.
In the prior solid oxide fuel cell output performance optimization, main optimization variables comprise electrode, electrolyte material type and porosity, reaction gas inlet speed and flow, runner specific structure, system temperature and pressure and the like. It is often considered that different types of baffles are arranged in the flow channel, and the battery has higher output power under the structure, but larger pressure drop is generated, so that larger additional power consumption is caused. Therefore, designing a flow channel structure, reducing the flow pressure drop and avoiding generating larger additional power consumption under the condition of improving the output power, is a problem to be solved.
Disclosure of Invention
The invention aims to provide a novel multi-objective optimization method for a solid oxide fuel cell flow channel structure. The invention shortens the optimization period of the solid oxide fuel cell with the novel flow passage structure and improves the performance of the cell.
The technical scheme of the invention is as follows:
the utility model provides a solid oxide fuel cell runner structure, includes positive pole runner, negative pole runner, positive pole electrode, negative pole electrode, electrolyte, its characterized in that: the anode flow channel and the cathode flow channel are rectangular straight flow channels, and a group of convex spherical blocking blocks are respectively arranged on the anode flow channel and the cathode flow channel.
Further, the convex ball structures in the anode flow channel and the cathode flow channel are distributed at equal intervals.
A multi-objective optimization method for a flow channel structure of a solid oxide fuel cell is characterized by comprising the following steps of: the flow channel structural parameter is determined by the adjacent center distance d of the convex ball structures in the anode flow channel and the cathode flow channel and the height R of the convex ball structures in the flow channel; the optimization structure parameters of the flow channel are obtained based on a proxy model and a pareto multi-target optimization technology, the optimization targets are maximum output power and minimum pump power consumption of the solid oxide fuel cell, and the proxy model is obtained by training a neural network and is used for establishing a mapping relation between design variables and target functions of multi-target optimization.
The method comprises the following specific steps:
s1, establishing a three-dimensional numerical model of a solid oxide fuel cell with a novel convex ball flow channel structure in COMSOL, and verifying the model;
s2, determining design variables and objective functions, generating a plurality of groups of parameters by using a Latin hypercube sampling method, simulating, and establishing a database;
s3, establishing a proxy model of the mapping relation between the design variable and the objective function by using a neural network, wherein the input of the neural network is a plurality of groups of parameters, and the output of the neural network is the objective function;
and S4, based on the obtained agent model, performing parameter optimization by using an NSGA-II algorithm to obtain a plurality of groups of pareto optimal solutions, and selecting the satisfactory optimal solution in the pareto optimal solution set as a final scheme of the solid oxide fuel cell.
Further, the fuel cell gas flows in a concurrent flow mode, and the reaction gas enters the cell through a flow channel and then enters a porous electrode, so that electrochemical reaction occurs at a three-phase boundary to generate water and simultaneously release electrons, thereby forming current and supplying power to a load; the fuel cell product water flows out of the porous electrode as water vapor with the fuel gas.
The anode reaction gas is wet hydrogen and the cathode reaction gas is air.
The electrochemical reaction equations of the anode and the cathode of the battery are respectively as follows:
H 2 +O 2- →H 2 O+2e - (1)
conservation equations describing the transfer phenomenon during the chemical reaction of the battery are as follows:
conservation of mass:
conservation of momentum:
conservation of components:
conservation of energy:
where ρ is the density, ε is the porosity, u is the velocity, ζ is the shear stress tensor, μ is the dynamic viscosity, x i Is the mass fraction of the components in the mixture,is the effective diffusion coefficient, S T Is an overvoltage, k eff Is an effective thermal conductivity.
Further, the parameters include electrode porosity, electrolyte porosity, temperature, pressure, fuel intake velocity, air intake velocity, adjacent stud ball structure center distance, and stud ball structure height within the flow channel; the objective function includes solid oxide fuel cell output power and auxiliary system pumping power for driving the reactant gases; wherein the auxiliary system for driving the reaction gas pumps power W p The expression is as follows:
in the method, in the process of the invention,is the mass flow rate, ρ is the mixed gas density, Δp is the pressure loss in the flow channel.
Randomly dividing the obtained data set, wherein the data for training, testing and verifying respectively account for 80%,10% and 10%; and training the neural network by adopting a Bayesian regularization algorithm.
The invention gives consideration to the output power of the solid oxide fuel cell and the pressure drop of the flow channel, comprehensively considers the net output power of the fuel cell, sets a novel convex ball structure block in the flow channel, improves the mass transfer capability of the reaction gas, effectively improves the output power of the fuel cell and simultaneously realizes the improvement of the net output power of the cell at the cost of lower power consumption of an auxiliary system. In addition, an agent model capable of rapidly and accurately evaluating the performance of the solid oxide fuel cell is established, and is combined with a multi-objective optimization algorithm to find the optimal structural parameters or operation conditions of the solid oxide fuel cell, so that the optimization design efficiency is greatly improved, and the optimization difficulty is reduced.
Compared with the structural design optimization of the flow channel of the traditional solid oxide fuel cell, the invention has the outstanding advantages that:
the output performance of the fuel cell is better improved, and meanwhile, the larger power consumption of an auxiliary system can be avoided; the performance of the solid oxide fuel cell can be rapidly and accurately evaluated through the proxy model, and the optimal solid oxide fuel cell structural parameters and operating conditions are found.
Drawings
Fig. 1 is a cross-sectional view of a flow channel structure of a solid oxide fuel cell and a schematic diagram of structural parameters thereof. Wherein, (11) is a sectional view and (12) is a side view.
Fig. 2 is a schematic diagram of the flow channel structure according to the present invention compared with other flow channel structures, wherein (21) is an unobstructed flow channel, (22) is a flow channel with rectangular baffles, and (23) is a flow channel with convex ball structure.
Fig. 3 is a schematic diagram of a solid oxide fuel cell unit having a novel flow channel structure. Wherein, (31) is a cross-sectional view, and (32) is a three-dimensional view.
Fig. 4 is a comparison of solid oxide fuel cell output performance for three different flow channels.
Fig. 5 is a flow chart of the present invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings, and it should be noted that, while the present embodiment provides a detailed implementation and a specific operation process on the premise of the present technical solution, the protection scope of the present invention is not limited to the present embodiment.
Example 1
Referring to fig. 1, a novel solid oxide fuel cell flow channel structure is provided with a plurality of convex spherical blocking blocks on an anode flow channel and a cathode flow channel respectively according to the requirement of mass transfer enhancement.
The structural parameters of the novel flow channel are determined by the adjacent center distance d of the convex ball structures in the anode flow channel and the cathode flow channel and the height R of the convex ball structures in the flow channel.
The convex ball structures in the flow channel are distributed at equal intervals. Fig. 2 is a schematic diagram of a runner with a novel convex ball structure and a common runner.
The anode and cathode flow channels are rectangular straight flow channels.
The optimization structure parameters of the flow channel are obtained based on a proxy model and a pareto multi-target optimization technology, the optimization targets are maximum output power and minimum pump power consumption of the solid oxide fuel cell, and the proxy model is obtained by training a neural network and is used for establishing a mapping relation between design variables and target functions of multi-target optimization.
Example 2
Referring to fig. 3, a solid oxide fuel cell mainly includes an anode flow channel, a cathode flow channel, an anode electrode, a cathode electrode, and an electrolyte, and the anode flow channel and the cathode flow channel adopt the solid oxide fuel cell flow channel structure described in the embodiment 1.
The fuel cell gas flow mode is downstream, and the reaction gas enters the cell through a flow channel and then enters a porous electrode, and electrochemical reaction occurs at a three-phase boundary to generate water and simultaneously release electrons, so that current is formed and power is supplied to a load; the fuel cell product water flows out of the porous electrode as water vapor with the fuel gas.
The anode reaction gas is wet hydrogen and the cathode reaction gas is air.
The electrochemical reaction equations of the anode and the cathode of the battery are respectively as follows:
H 2 +O 2- →H 2 O+2e - (1)
conservation equations describing the transfer phenomenon during the chemical reaction of the battery are as follows:
conservation of mass:
conservation of momentum:
conservation of components:
conservation of energy:
where ρ is the density, ε is the porosity, u is the velocity, ζ is the shear stress tensor, μ is the dynamic viscosity, x i Is the mass fraction of the components in the mixture,is the effective diffusion coefficient, S T Is an overvoltage, k eff Is an effective thermal conductivity.
Referring to fig. 4, the output performance of the solid oxide fuel cell having the convex-sphere structured flow channel is better than the other type of flow channel described in fig. 2 under the other conditions without change, considering the influence of pump power consumption.
Example 3
In order to obtain the optimal parameters, a multi-objective optimization method of the solid oxide fuel cell flow channel structure, see fig. 5, comprises the following steps:
s1, establishing a three-dimensional numerical model of a solid oxide fuel cell with a novel convex ball flow channel structure in COMSOL, and verifying the model;
s2, determining design variables and objective functions, generating a plurality of groups of parameters by using a Latin hypercube sampling method, simulating, and establishing a database;
s3, establishing a proxy model of the mapping relation between the design variable and the objective function by using a neural network, wherein the input of the neural network is a plurality of groups of parameters, and the output of the neural network is the objective function;
and S4, based on the obtained agent model, performing parameter optimization by using an NSGA-II algorithm to obtain a plurality of groups of pareto optimal solutions, and selecting the satisfactory optimal solution in the pareto optimal solution set as a final scheme of the solid oxide fuel cell.
The variable parameters comprise electrode porosity, electrolyte porosity, temperature, pressure, fuel inlet speed, air inlet speed, center distance of adjacent convex ball structures and height of the convex ball structures in the flow channel; the solid oxide fuel cell performance values include output power and auxiliary system pumping power for driving the reactant gases; wherein the auxiliary system for driving the reaction gas pumps power W p The expression is as follows:
in the method, in the process of the invention,is the mass flow rate, ρ is the mixed gas density, Δp is the pressure loss in the flow channel.
In this embodiment, the obtained data set is randomly divided, the data for training, testing and verifying respectively account for 80%,10% and 10%, the verification set is set to prevent the model from being over-fitted, and an early termination strategy is adopted to further improve the generalization performance of the model. Based on the advantages of strong mapping capability, strong self-adaptability, fault tolerance capability and the like of the BP neural network, the BP neural network is utilized to establish an approximate function relation between input parameters and output targets, and a Bayesian regularization algorithm is adopted to train the neural network in the neural network.
The invention respectively arranges a plurality of convex-sphere-shaped blocking blocks in the anode flow channel and the cathode flow channel, and optimizes the configuration parameters of the convex-sphere-shaped blocking blocks by adopting a neural network learning and multi-objective optimization technology so as to achieve the purposes of enhancing the interaction between the gas introduced into the flow channel and the catalytic layer, strengthening the gas transmission in the catalytic layer, improving the power generation of the fuel cell and reducing the pump power required by driving the gas flow of the anode flow channel and the cathode flow channel. The invention can shorten the optimal design period of the solid oxide fuel cell stack with the novel flow passage structure and can obtain the flow passage configuration of the high-performance solid oxide fuel cell.
In conclusion, the invention can avoid generating larger flow passage pressure drop while improving the output power of the battery, optimizes parameters based on a proxy model and a multi-objective optimization method, obtains the flow passage configuration of the solid oxide fuel battery with high performance, and shortens the optimization period of the solid oxide fuel battery with a novel flow passage structure.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (10)

1. The utility model provides a solid oxide fuel cell runner structure, includes positive pole runner, negative pole runner, positive pole electrode, negative pole electrode, electrolyte, its characterized in that: the anode flow channel and the cathode flow channel are rectangular straight flow channels, and a group of convex spherical blocking blocks are respectively arranged on the anode flow channel and the cathode flow channel.
2. The solid oxide fuel cell flow channel structure according to claim 1, characterized in that: the convex ball structures in the anode flow channel and the cathode flow channel are distributed at equal intervals.
3. A multi-objective optimization method for a flow channel structure of a solid oxide fuel cell is characterized by comprising the following steps of: the flow channel structural parameter is determined by the adjacent center distance d of the convex ball structures in the anode flow channel and the cathode flow channel and the height R of the convex ball structures in the flow channel; the optimization structure parameters of the flow channel are obtained based on a proxy model and a pareto multi-target optimization technology, the optimization targets are maximum output power and minimum pump power consumption of the solid oxide fuel cell, and the proxy model is obtained by training a neural network and is used for establishing a mapping relation between design variables and target functions of multi-target optimization.
4. The method for optimizing the flow channel structure of the solid oxide fuel cell according to claim 3, which is characterized by comprising the following specific steps:
s1, establishing a three-dimensional numerical model of a solid oxide fuel cell with a novel convex ball flow channel structure in COMSOL, and verifying the model;
s2, determining design variables and objective functions, generating a plurality of groups of parameters by using a Latin hypercube sampling method, simulating, and establishing a database;
s3, establishing a proxy model of the mapping relation between the design variable and the objective function by using a neural network, wherein the input of the neural network is a plurality of groups of parameters, and the output of the neural network is the objective function;
and S4, based on the obtained agent model, performing parameter optimization by using an NSGA-II algorithm to obtain a plurality of groups of pareto optimal solutions, and selecting the satisfactory optimal solution in the pareto optimal solution set as a final scheme of the solid oxide fuel cell.
5. The method for optimizing the flow channel structure of a solid oxide fuel cell according to claim 4, wherein: in the step S1, the gas flowing mode of the fuel cell is concurrent, and the reaction gas enters the cell through a flow channel and then enters a porous electrode, so that electrochemical reaction occurs at a three-phase boundary to generate water and simultaneously release electrons, thereby forming current and supplying power to a load; the fuel cell product water flows out of the porous electrode as water vapor with the fuel gas.
6. The method for optimizing the flow channel structure of a solid oxide fuel cell according to claim 5, wherein: the anode reaction gas is wet hydrogen and the cathode reaction gas is air.
7. The method for multi-objective optimization of a solid oxide fuel cell flow channel structure according to claim 6, wherein: the electrochemical reaction equations of the anode and the cathode of the battery are respectively as follows:
H 2 +O 2- →H 2 O+2e - (1)
1/2O 2 +2e - →O 2- (2)
8. the method for optimizing the flow channel structure of a solid oxide fuel cell according to claim 5, wherein: conservation equations describing the transfer phenomenon during the chemical reaction of the battery are as follows:
conservation of mass:
conservation of momentum:
conservation of components:
conservation of energy:
where ρ is the density, ε is the porosity, u is the velocity, ζ is the shear stress tensor, μ is the dynamic viscosity, x i Is the mass fraction of the components in the mixture,is the effective diffusion coefficient, S T Is an overvoltage, k eff Is an effective thermal conductivity.
9. The method for optimizing the flow channel structure of a solid oxide fuel cell according to claim 4, wherein: in step S2, the parameters include electrode porosity, electrolyte porosity, temperature, pressure, fuel intake velocity, air intake velocity, center distance of adjacent convex ball structures, and height of the convex ball structures in the flow channel; the objective function includes solid oxide fuel cell output power and auxiliary system pumping power for driving the reactant gases; wherein the auxiliary system for driving the reaction gas pumps power W p The expression is as follows:
in the method, in the process of the invention,is the mass flow rate, ρ is the mixed gas density, Δp is the pressure loss in the flow channel.
10. The method for optimizing the flow channel structure of a solid oxide fuel cell according to claim 4, wherein: the data sets in the obtained database are randomly divided, and the data for training, testing and verification respectively account for 80%,10% and 10%; and training the neural network by adopting a Bayesian regularization algorithm.
CN202311222785.2A 2023-09-21 2023-09-21 Solid oxide fuel cell flow channel structure and multi-objective optimization method thereof Pending CN117154127A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202311222785.2A CN117154127A (en) 2023-09-21 2023-09-21 Solid oxide fuel cell flow channel structure and multi-objective optimization method thereof

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202311222785.2A CN117154127A (en) 2023-09-21 2023-09-21 Solid oxide fuel cell flow channel structure and multi-objective optimization method thereof

Publications (1)

Publication Number Publication Date
CN117154127A true CN117154127A (en) 2023-12-01

Family

ID=88909997

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202311222785.2A Pending CN117154127A (en) 2023-09-21 2023-09-21 Solid oxide fuel cell flow channel structure and multi-objective optimization method thereof

Country Status (1)

Country Link
CN (1) CN117154127A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117744438A (en) * 2023-12-21 2024-03-22 浙江大学 Fuel cell stack variable-scale modeling simulation method and system based on data driving

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117744438A (en) * 2023-12-21 2024-03-22 浙江大学 Fuel cell stack variable-scale modeling simulation method and system based on data driving

Similar Documents

Publication Publication Date Title
CN111625929B (en) SOFC numerical simulation method under multi-physical-field coupling effect
CN106654319B (en) A kind of PEMFC system temperature modeling method based on mutation particle swarm and differential evolution hybrid algorithm
CN117154127A (en) Solid oxide fuel cell flow channel structure and multi-objective optimization method thereof
CN109873186A (en) The quasi- two dimensional model method for building up of Proton Exchange Membrane Fuel Cells
CN1949571A (en) Proton exchange membrane fuel cell interdigited parallel combined flow field
CN115312815A (en) Electrochemical performance calculation method of air-cooled proton exchange membrane fuel cell stack
Sedighizadeh et al. Parameter optimization for a PEMFC model with particle swarm optimization
CN110688746A (en) Method for determining optimal operation point of SOFC system
CN114300713A (en) Cross-flow SOEC galvanic pile two-dimensional dynamic model construction method and application thereof
Maghsoudy et al. Investigation on various types of ion-exchange membranes in vanadium redox flow batteries: Experiment and modeling
Sarjuni et al. A review: Fluid dynamic and mass transport behaviour in a proton exchange membrane fuel cell stack
CN114530618B (en) Random optimization algorithm-based fuel cell and air compressor matching modeling method
CN1909276A (en) Management method for battery anode liquid of proton exchange film fuel battery
He et al. Analysis of proton exchange membrane fuel cell reactant gas dynamic response and distribution quality
CN108682885A (en) A kind of design method of microfluid fuel cell porous electrode geometric dimension
CN114970192A (en) Multi-target intelligent control method for fuel cell system
Zhang et al. Full-scale three-dimensional simulation of air cooling metal bipolar plate proton exchange membrane fuel cell stack considering a non-isothermal multiphase model
Quan et al. A hierarchical predictive strategy-based hydrogen stoichiometry control for automotive fuel cell power system
Sreenivasulu et al. Performance study of a PEM fuel cell with 4-Serpentine flow fields-experimental study
Zhou et al. A Study on Optimization Design of Hydrogen Supply Integrated Subsystem for Multi-Stack Fuel Cells
Zhang et al. An Electrochemical Performance Model Considering of Non-Uniform Gas Distribution Based on Porous Media Method in PEMFC Stack
CN101290998A (en) Self-humidifying electrochemical device
Hamzah Simulation and Analysis of PEM Fuel Cell System using MATLAB
Hou et al. Numerical simulation of electrical performance and distribution of SOFC stacks with different manifold arrangement
CN220086090U (en) Bipolar plate flow field structure and fuel cell

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination