Method for evaluating performance of hydrogen production electrolysis stack, electronic device and storage medium

Abstract

The application discloses a kind of water electrolysis hydrogen production electric pile performance evaluation method, electronic equipment and storage medium, belong to electric pile expansion size optimization technical field, the water electrolysis hydrogen production electric pile performance evaluation method includes: determining the two-phase flow model and electrochemical model of electric pile under current geometry structure: two-phase flow model includes: mass conservation equation, material balance equation, momentum conservation equation, assumption condition and boundary condition;Electrochemical model includes: current conservation equation, Ohm equation and boundary condition;Input decoupling parameter, solve two-phase flow model to obtain gas volume fraction, then solve electrochemical model, based on the obtained parameter evaluates the performance of current geometry structure electric pile.Through multi-model analysis and the decoupling of multiple models by introducing appropriate boundary conditions, the model is quickly solved and the key parameters are obtained, and the performance of the electric pile is accurately evaluated.

Description

Technical Field

[0001] This invention belongs to the field of fuel cell stack size optimization technology, and more specifically, relates to a method for evaluating the performance of a water electrolysis hydrogen production fuel cell stack, electronic equipment, and storage medium. Background Technology

[0002] Hydrogen is an essential element for net-zero emissions because its oxidation process eliminates greenhouse gas emissions and it has wide applications in industry. Hydrogen production through electrolysis using renewable energy sources, often referred to as green hydrogen, enables sustainable, zero-emission hydrogen production and provides a flexible means of integrating large-scale renewable energy systems into the grid as well as providing seasonal energy storage.

[0003] The increasing popularity of hydrogen electrolysis necessitates the expansion of hydrogen production capacity at electrolytic hydrogen production plants. For industrial fuel cell stacks composed of multiple compactly assembled electrolyzers, capacity can be increased by enlarging the area of ​​the electrolyzers or increasing the number of electrolyzers. However, increasing the electrolyzer area leads to the generation of more bubbles that are compressed around the electrode surface, reducing the contact area between the electrolyte and the catalyst—a phenomenon known as the bubble coverage effect. Increasing the number of electrolyzers also increases the bypass current, with bypass current channels formed by the KOH solution flowing between different stacks—a phenomenon known as the bypass current effect. Both bubble coverage and the bypass current effect contribute to a decline in stack performance. Therefore, when scaling up fuel cell stacks, it is essential to comprehensively consider these multiple factors, conduct performance evaluations of stacks with different geometries, and select the best-performing geometry to minimize performance degradation.

[0004] Currently, there are two methods for evaluating the performance of fuel cell stacks. The first method optimizes the stack performance at the individual electrolyzer level, using geometric similarity. For example, S. Toghyani et al. compared five different plate flow patterns in single-phase numerical analysis, including parallel flow patterns and four serpentine flow patterns, to determine the optimal pattern. However, these studies are mainly based on numerical methods that accurately reflect the multi-physics processes of the electrolyzer, not at the stack level, and are not applicable to large-scale stacks. The second method optimizes stack-level performance and validates solutions experimentally. Y. Sanath K. De Silva et al. enhanced zero-gap stacks by reducing electrode spacing and experimentally demonstrated that bipolar stacks outperformed monopolar designs. Efforts in this area mainly focus on experimentally comparing different stack designs; however, experimental validation is costly and time-consuming. Experimental methods require manufacturing stacks with different designs for testing. For large-scale stacks, the cost and time required for electrode manufacturing are extremely high, which will affect the stack development schedule.

[0005] Despite the progress made in current research, there are still limitations to improving the performance of fuel cell stacks. Summary of the Invention

[0006] In view of the above-mentioned defects or improvement needs of the existing technology, the present invention provides a method for evaluating the performance of water electrolysis hydrogen production stacks, electronic equipment and storage medium. The purpose is to evaluate the performance of the stack by constructing a suitable stack model, thereby improving the accuracy of the stack performance evaluation. The solution is simple, fast and low cost.

[0007] To achieve the above objectives, according to a first aspect of the present invention, a method for evaluating the performance of a water electrolysis hydrogen production stack is provided, comprising:

[0008] Determine the two-phase flow model and electrochemical model of the fuel cell stack under the current geometry:

[0009] The two-phase flow model includes: the mass conservation equation, the mass balance equation, the momentum conservation equation, and the assumptions u. g =u l =u m and boundary conditions In the formula, ρ g It is the density of the gas, u g u l u m These are the velocity vectors of a gas, a liquid, and a gas-liquid mixture, respectively. φ g It is the gas volume fraction, v g M is the rate of gas production. g It is the molar mass of the gas, F is the Faraday constant, and j r0 These are decoupling parameters;

[0010] The electrochemical model includes: the current conservation equation, Ohm's equation, and boundary conditions. In the formula, U ks0 E represents the electrolyte potential near the anode in electrolytic cell k. cell-i Let be the relative potentials of the anode and cathode of electrolytic cell i;

[0011] Input decoupling parameter j r0 Solving the two-phase flow model yields the gas volume fraction φ. g ;

[0012] According to equation j r0 =j r / (1-φ g Calculate the reaction current density j r The gas volume fraction φ g and reaction current density j r Substituting these values ​​into the Butler-Volmer equation, calculate the activation overpotential η for the electrochemical reactions at the anode and cathode of each electrolytic cell. A-i and η C-i ;

[0013] Based on the activation overpotential ηA-i and η C-i Calculate the relative potential E of the anode and cathode of electrolytic cell i. cell-i ;

[0014] The relative potential E cell-i Substituting into the electrochemical model, the bypass current density j is obtained. s ;

[0015] Based on bypass current density j s Calculate the bypass current i s Based on the reaction current density j r Calculate the reaction current i r The performance of the current geometry of the fuel cell stack is evaluated based on the obtained parameters.

[0016] In one embodiment, the mass conservation equation is:

[0017] The mass balance equation is:

[0018] The momentum conservation equation is:

[0019] In the formula, ρ l It is the density of the liquid, φ l It is the liquid volume fraction, ρ is the density of the mixture, ρ = ρ g φ g +ρ l φ l j is the average volumetric flux density vector of the mixture, j = φ g u g +φ l u l p is the pressure vector of the mixture, K is the viscous stress tensor vector of the mixture, and g is the gravitational acceleration;

[0020] The current conservation equation is:

[0021] The Ohm equation is:

[0022] In the formula, U s For the electrolyte potential distribution, σ l It is the conductivity of the electrolyte, j s This represents the bypass current density.

[0023] In one embodiment, the relative potential E between the anode and cathode of electrolytic cell i is... cell-i The calculation formula is:

[0024]

[0025] η ohm-i =i r (r p +r d )

[0026] In the formula, For reversible voltage, η ohm-i R is the ohmic overpotential of electrolytic cell i. d This corresponds to the resistance of the diaphragm within the electrolytic cell, r. p It corresponds to the resistance of the electrodes inside the electrolytic cell.

[0027] In one embodiment, the activation overpotential η is calculated according to the following Butler-Volmer equation. A-i and η C-i :

[0028]

[0029]

[0030] In the formula, i HER It is the exchange current density of the HER catalyst, i OER It is the exchange current density of the OER catalyst, α A It is the charge transfer coefficient of the cathode, α C is the charge transfer coefficient of the anode, z is the number of electrons participating in the electrode reaction, F is the Faraday constant, R is the gas constant, and T represents the thermodynamic temperature.

[0031] In one embodiment, the formula for calculating the viscous stress tensor K of the mixture is:

[0032]

[0033] μ=φ g μ g +φ l μ l

[0034] In the formula, μ is the viscosity coefficient of the mixture. g μ l are the viscosity coefficients of the gas and liquid, respectively, and I is a unit vector in the same direction as j.

[0035] In one embodiment, the fuel cell stack performance evaluation method includes obtaining the relationship between current efficiency and the average potential of the electrolyzer within the fuel cell stack, specifically including: inputting multiple decoupling parameters j r0 For each decoupling parameter, calculate the corresponding reaction current i. r Bypass current i s and the potential E of each electrolytic cell cell-i And calculate the current efficiency ηi and the average potential of the electrolytic cell

[0036]

[0037]

[0038] In the formula, n is the number of electrolytic cells in the fuel cell stack;

[0039] Through multiple groups By fitting the data, the current efficiency η is obtained. i With the average potential of the electrolytic cell The relationship curve.

[0040] In one embodiment, the fuel cell stack performance evaluation method includes obtaining the relationship between energy efficiency and average reaction current density, specifically including:

[0041] Input multiple decoupling parameters j r0 For each decoupling parameter, calculate the corresponding reaction current i. r Bypass current i s and the potential E of each electrolytic cell cell-i And calculate the energy efficiency η i and average reaction current density

[0042]

[0043]

[0044] In the formula, n is the number of electrolytic cells in the fuel cell stack. Where is the reversible voltage, and S is the cross-sectional area of ​​the fuel cell stack;

[0045] Through multiple groups By fitting the data, the energy efficiency η can be obtained. i With average reaction current density The relationship curve.

[0046] In one embodiment, the fuel cell stack performance evaluation method includes evaluating the relationship between the hydrogen production capacity of a fuel cell stack powered by renewable energy in off-grid mode and the power consumption P of the fuel cell stack, specifically including:

[0047] Input multiple decoupling parameters j r0 For each decoupling parameter, calculate the corresponding gas generation rate v. g , reaction current i r and the relative potential E of each electrolytic cell cell-i And calculate the hydrogen production rate H and the power consumption P of the fuel cell stack:

[0048] H = v gt

[0049] P = i r U

[0050]

[0051] In the formula, t is the time step, n is the number of electrolytic cells in the fuel cell stack, and U is the total voltage of the fuel cell stack.

[0052] By fitting multiple sets (H, P), the relationship curve between hydrogen production H and stack power consumption P was obtained.

[0053] According to a second aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0054] According to a third aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0055] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects:

[0056] This invention proposes a two-phase flow model and an electrochemical model for the fuel cell stack, both of which satisfy fundamental physicochemical laws. In conventional techniques, these two models are coupled, resulting in a highly complex solution process that may fail to converge. This invention introduces boundary conditions into the two-phase flow model. Bypass current density j r0 The intermediate parameter introduced to achieve decoupling is related to the reaction current density j. r The following methods exist: j r0 =j r / (1-φ g The above boundary conditions allow for the decoupling of the two-phase flow model and the electrochemical model, enabling separate solutions. The two-phase flow model is solved first to obtain the gas volume fraction φ. g Then, based on the solution results of the two-phase flow model, the relevant parameters in the electrochemical model are calculated and substituted into the electrochemical model for solution to obtain the bypass current. After solving the two models, the performance of the fuel cell stack under the current geometry can be evaluated based on the solved parameters. In this invention, by using multi-model analysis and introducing appropriate boundary conditions to decouple the multiple models, the model can be solved quickly and key parameters can be obtained. This allows for rapid analysis of fuel cell stack performance at a low cost. Experiments have shown that this method is relatively accurate in evaluating fuel cell stack performance and can be widely used.

[0057] Furthermore, based on the performance evaluation method for water electrolysis hydrogen production stacks proposed in this invention, the relationship between the current efficiency of stacks with different geometric structures and the average potential of the electrolyzers inside the stack can be analyzed. This allows us to understand the trend of current efficiency variation with the average potential of the electrolyzers inside the stack and the degree of difference in the variation of stacks with different geometric structures, providing a reference for stack structure optimization.

[0058] Furthermore, based on the performance evaluation method of the water electrolysis hydrogen production stack proposed in this invention, the relationship between energy efficiency and average reaction current density is analyzed. This allows us to understand the mutual constraint between energy efficiency and average reaction current density, as well as the degree of variation in stacks with different geometric structures, providing a reference for stack structure optimization.

[0059] Furthermore, based on the performance evaluation method for hydrogen production stacks via water electrolysis proposed in this invention, the relationship between the hydrogen production capacity and the power consumption P of the stack powered by renewable energy in off-grid mode can be analyzed. The hydrogen production capacity of stacks with different geometric structures powered by renewable energy in off-grid mode can be calculated, thereby allowing the selection of the geometric structure design with the highest hydrogen production capacity. Attached Figure Description

[0060] Figure 1 This is a schematic diagram of the structure of an AWE fuel cell stack according to one embodiment;

[0061] Figure 2 A flowchart illustrating the steps of a water electrolysis hydrogen production stack performance evaluation method according to one embodiment;

[0062] Figure 3 This is a comparison graph of the IV curve of an actual fuel cell stack and the simulated IV curve of one embodiment.

[0063] Figure 4 The reaction current density-voltage curve of CN-3 in one embodiment and the percentage of CN-3 reaction current density in other designed fuel cells;

[0064] Figure 5 This is a comparison chart of the current efficiency of different fuel cells in one embodiment at multiple average voltages in the range of 1.7V to 2.3V.

[0065] Figure 6 Energy efficiency η of different fuel cell stacks in one embodiment i With average reaction current density Relationship curve;

[0066] Figure 7 This is a comparison chart of the daily hydrogen production of different fuel cell stacks in one embodiment. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0068] The capacity expansion of industrial-grade AWE (Alkaline Electrolysis for Hydrogen) stacks is achieved by increasing the number of electrolyzers and the area of ​​each individual cell. Essentially, the structure of an electrolyzer consists of an anode electrode, an anode catalyst, a cathode electrode, a cathode catalyst, and a membrane. These basic electrolyzer modules are linearly scaled up to appropriate dimensions and then compactly assembled into an industrial-grade AWE stack, such as... Figure 1 As shown. However, both increasing the number of electrolytic cells and increasing their area will have a negative impact on performance. If the area of ​​the electrolytic cell is increased, the bubble coverage effect becomes more pronounced, and the bubbles generated by the reaction have a more serious impact on the contact between the electrolyte and the catalyst, thus reducing voltage efficiency. If the number of electrolytic cells is increased, more bypass current channels are formed in the manifold by the KOH solution, and the bypass current effect becomes more pronounced, thus reducing current efficiency.

[0069] Therefore, when expanding the capacity of a fuel cell stack, it is not always better to have more electrolytic cells or a larger cell area. The appropriate geometry, including the area of ​​a single cell and the number of electrolytic cells, needs to be selected based on actual requirements. Thus, performance evaluations of fuel cell stacks with different geometries are necessary to select the most suitable design from a range of options.

[0070] Typically, before structural design, the hydrogen production capacity after the fuel cell stack is expanded is determined. Once the capacity is determined, the total reaction area S of the fuel cell stack can be preliminarily determined based on Faraday's law. t :

[0071]

[0072] Among them, C e It is the hydrogen production capacity of the fuel cell stack. F is the density of hydrogen gas, and F = 96485.33 C / mol is Faraday's constant. It is the molecular mass of hydrogen, j 0c This is the rated current density, n is the number of batteries, and S is the rated current density. c It is the reaction area of ​​each single tank.

[0073] After determining the total reaction area, the geometry of the fuel cell stack needs further optimization. The key parameter of the geometry is the single-cell area S. c And the quantity n, the area of ​​a single tank S cThe quantity n satisfies the following relationship.

[0074] S t =n·S c

[0075] Considering factors such as mechanical processes, transportation convenience, and site limitations, the area S of a single tank... c It will be limited to an appropriate range:

[0076] S c ∈[S cmin ,S cmax ]

[0077] At this point, the number of slots n is subject to the following constraints:

[0078] n is an integer

[0079] Within the constraints mentioned above, various geometric structures can be initially determined. Then, the performance of the fuel cell stack under each geometric structure can be compared, and the geometric structure with the best performance can be selected as the final design parameter.

[0080] Based on this, this invention proposes a method for evaluating the performance of a water electrolysis hydrogen production stack. First, a two-phase flow model is used to describe the two-phase flow process considering the influence of bubble coverage on the gas generation rate. The distribution of bubble volume fraction within the electrolyzer is calculated, and then the electrolyzer voltage can be determined. Based on the electrolyzer voltage, the electrochemical model is solved to calculate the distribution of bypass current within the electrolyzer, thus quantitatively describing the influence of the bypass current effect. Subsequently, the performance of the stack under the current geometry is evaluated based on the obtained parameters.

[0081] Example 1

[0082] like Figure 2 The diagram shows a flowchart of the performance evaluation method for a water electrolysis hydrogen production stack in one embodiment, which mainly includes the following steps:

[0083] Step S100: Determine the two-phase flow model and electrochemical model of the fuel cell stack under the current geometry.

[0084] In the hydrogen production process of a fuel cell stack, liquid and gas are present, forming a mixture. Therefore, a two-phase flow model needs to be constructed for the gas-liquid mixture. The two-phase flow model specifically includes:

[0085] a. Mass conservation equation:

[0086]

[0087] φ g +φ l =1

[0088] In the formula, ρg It is the gas density, ρ l It is the density of the liquid, u g It is the gas flow rate, u l It is the liquid flow velocity, φ g It is the gas volume fraction, φ l It is the liquid volume fraction.

[0089] b. Momentum conservation equation:

[0090]

[0091] j = φ g u g +φ l u l

[0092] ρ=ρ g φ g +ρ l φ l

[0093] In the formula, ρ is the density of the mixture, j is the volume-average flux density of the mixture, p is the pressure of the mixture, K is the viscous stress tensor of the mixture, and g is the gravitational acceleration.

[0094] The momentum conservation equation described above is a simplified equation based on the homogeneous flow assumption, which assumes that the velocity of the bubbles is the same as the velocity of the electrolyte, and that the two phases are in thermodynamic equilibrium, i.e., the slip velocity u between the gas and the liquid. s =0. This can greatly improve the convergence of the model and is beneficial for solving the numerical calculation model of two-phase flow at the fuel cell stack level.

[0095] In one embodiment, the formula for calculating the viscous stress tensor K of the mixture is:

[0096]

[0097] μ=φ g μ g +φ l μ l

[0098] In the formula, μ is the viscosity coefficient of the mixture. g μ l are the viscosity coefficients of the gas and liquid, respectively, and I is a unit vector in the same direction as j.

[0099] c. Material balance equation:

[0100]

[0101] In the formula, u m The flow rate of the mixture is denoted as .

[0102] d. Assumptions:

[0103] u g =u l =u m

[0104] This assumption reflects the above-mentioned homogeneous flow assumption, which assumes that the velocity of the bubbles is the same as the velocity of the electrolyte.

[0105] e. Boundary conditions:

[0106]

[0107] In the formula, v g The bubble generation rate is the rate at which hydrogen is produced; the faster the bubble generation rate, the faster the hydrogen production rate. r0 M is an intermediate decoupling parameter that is related to the reaction current density (explained in detail later). g is the molar mass of the gas, and F is the Faraday constant.

[0108] In the above two-phase flow model, ρ g ρ l M g F and g are known quantities, j r0 The value is input from the outside, u g u l φ g φ l p, v g Let j be the unknown quantity to be solved. Input any decoupling parameter j. r0 This allows us to solve the two-phase flow model described above, and obtain the decoupling parameters j of the current geometry of the fuel cell stack. r0 The values ​​of various parameters are as follows. Among them, the gas volume fraction φ g In conjunction with the electrochemical model described below, the gas volume fraction φ can be solved. g This can then be used to solve electrochemical models.

[0109] The electrochemical model reflects the relationship between voltage and current within the fuel cell stack, and includes:

[0110] d. Current conservation equation:

[0111]

[0112] In the formula, j s This represents the bypass current density.

[0113] f. Ohm's equation:

[0114]

[0115] In the formula, σl U is the conductivity of the electrolyte, a known quantity. s This represents the potential distribution of the electrolyte.

[0116] g. Boundary conditions:

[0117]

[0118] In the formula, U ks0 Let E be the electrolyte potential near the anode in the k-th electrolytic cell arranged from the negative to the positive electrode of the fuel cell stack. cell-i The relative potentials of the anode and cathode of electrolytic cell i can be calculated based on the solution results of the two-phase flow model.

[0119] After determining the above model, execute:

[0120] Step S200: Input decoupling parameter j r0 Solving the two-phase flow model yields the gas volume fraction φ. g .

[0121] Decoupling parameter j r0 The input is a known quantity, and the input decoupling parameter j is... r0 Afterwards, the mesh generation and discretization process is performed, which allows the two-phase flow model to be solved, and the solution is obtained at the current decoupling parameter j. r0 Other parameter values ​​are as follows.

[0122] Step S300: According to equation j r0 =j r / (1-φ g Calculate the reaction current density j r The gas volume fraction φ g and reaction current density j r Substituting these values ​​into the Butler-Volmer equation, calculate the activation overpotential η for the electrochemical reactions at the anode and cathode of each electrolytic cell. A-i and η C-i .

[0123] Decoupling parameter j r0 With reaction current density j r The following relationship exists:

[0124] j r0 =j r / (1-φ g )

[0125] Based on a given j r0 Solving the two-phase flow model yields the gas volume fraction φ. g Then, the corresponding reaction current density j can be calculated using the above relationship. r j r0These are merely intermediate variables used for decoupling; the parameters obtained from solving the fuel cell stack correspond to the reaction current density j. r The parameters below.

[0126] Then, based on the gas volume fraction φ g and reaction current density j r Furthermore, the activation overpotential η of the electrochemical reactions at the anode and cathode of each electrolytic cell can be calculated using the Butler-Volmer equation. A-i and η C-i The specific relationship is as follows:

[0127]

[0128]

[0129] In the formula, i HER It is the exchange current density of the HER catalyst, i OER It is the exchange current density of the OER catalyst, α A It is the charge transfer coefficient of the cathode, α C is the charge transfer coefficient of the anode, z is the number of electrons participating in the electrode reaction, F is the Faraday constant, R is the gas constant, and T represents the thermodynamic temperature. All these parameters are known quantities.

[0130] Step S400: Based on the activation overpotential η A-i and η C-i Calculate the relative potential E of the anode and cathode of electrolytic cell i. cell-i .

[0131] The relative potential E of the anode and cathode of electrolytic cell i cell-i The calculation formula is:

[0132]

[0133] η ohm-i =i r (r p +r d )

[0134] In the formula, The reversible voltage is 1.23V, η ohm-i R is the ohmic overpotential of electrolytic cell i, representing the voltage drop caused by the resistance between the two electrodes. d This corresponds to the resistance of the diaphragm within the electrolytic cell, r. p It corresponds to the resistance of the electrodes inside the electrolytic cell.

[0135] Step S500: Set the relative potential E cell-i Substituting into the electrochemical model, the bypass current density j is obtained. s .

[0136] Based on relative electric potential E cell-i The boundary conditions can be calculated, and then the bypass current density j can be determined based on the determined boundary conditions. s .

[0137] Step S600: Based on bypass current density j s Calculate the bypass current i s Based on the reaction current density j r Calculate the reaction current i r The performance of the current geometry of the fuel cell stack is evaluated based on the obtained parameters.

[0138] In the above model, bold text indicates vectors, while unbold text indicates scalars.

[0139] Finally, key parameters of the fuel cell stack can be output, including the bypass current i. s , reaction current i r And the relative potential E of each electrolytic cell cell-i Based on these key parameters, the performance of the current geometry of the fuel cell stack can be evaluated. Specific evaluation methods and reference indicators can be flexibly selected according to actual conditions. It should be noted that each input decoupling parameter j... r0 This yields a set of corresponding fuel cell stack parameters, allowing for the selection of multiple decoupling parameters j. r0 Multiple sets of corresponding fuel cell stack parameters were obtained for comprehensive evaluation.

[0140] The size of the geometric entities affects the calculation results of the governing equations. For example, as the number of individual cells (n) in the electrolytic cell stack decreases, the area (Sc) of each cell increases. On one hand, the fluid channels in the two-phase flow are relatively long, with more discrete computational grids along the flow path. Since bubbles inside the electrolyzer only increase and never decrease, and always move from upstream to downstream of the streamline, as n decreases, with the governing equations remaining unchanged, the grid at the end of the streamline will inherit a larger bubble volume fraction from the grid at the front, resulting in a larger average bubble volume fraction. When the reaction current density is equal, due to the bubble coverage effect, the electrolyzer will generate a larger overpotential, consuming more electrical energy and causing a decrease in energy efficiency. On the other hand, as the number of individual cells (n) in the electrolytic cell stack decreases, the number of bypass current branches between cells also decreases. Similarly, the losses caused by bypass currents also decrease, reducing electrical energy consumption. In other words, decreasing the number of cells (n) in a fuel cell stack enhances the bubble coverage effect but weakens the bypass effect; similarly, increasing the number of cells (n) weakens the bubble coverage effect but enhances the bypass effect. Therefore, it's difficult to directly analyze how the final fuel cell stack efficiency changes. Thus, quantitative calculations using a model are needed to reflect the efficiency changes of the fuel cell stack under different size parameters, compare the performance of fuel cell stacks at each size, and ultimately select the design with the best performance.

[0141] It should be noted that the above order of steps is for illustrative purposes only and is not a limitation. As long as each step can be executed smoothly, there is no restriction on the order of execution.

[0142] In this invention, boundary conditions are introduced into the two-phase flow model. Bypass current density j r0 The intermediate parameter introduced to achieve decoupling is related to the reaction current density j. r The following methods exist: j r0 =j r / (1-φ g The above boundary conditions allow for the decoupling of the two-phase flow model and the electrochemical model, enabling separate solutions. The two-phase flow model is solved first to obtain the gas volume fraction φ. g and liquid volume fraction φ l Then, based on the solution results of the two-phase flow model, the relevant parameters in the electrochemical model are calculated and substituted into the electrochemical model for solution to obtain the bypass current. After solving the two models, the performance of the fuel cell stack under the current geometry can be evaluated based on the solved parameters. In this invention, by using multi-model analysis and introducing appropriate boundary conditions to decouple the multiple models, the model can be solved quickly and key parameters can be obtained. This allows for rapid analysis of fuel cell stack performance at a low cost. Experiments have shown that this method is relatively accurate in evaluating fuel cell stack performance and can be widely used.

[0143] Example 2

[0144] The model proposed in this invention was verified as follows:

[0145] A three-dimensional numerical model was built in COMSOL Multiphysics 5.0 to simulate the AWE fuel cell stack. The model's geometry is identical to that of an industrial AWE fuel cell stack, employing a bipolar connection. The computation time for each solution varies depending on the hydrogen production capacity and the number of cells per stack, approximately 30 to 50 minutes per case. An AMD Ryzen 9 5950X 16-core 3.40GHz processor was used. The hydrogen production capacity was 30 L / h, and the rated reaction current density was set to 4000 A / m³. 2 To determine the total reaction area S of the fuel cell stack t In this embodiment, six single tanks are cascaded, each with an effective catalytic area of ​​20 cm². 2 In the experiment, the reaction temperature was set to 60℃ and the feed flow rate was set to 1 m / s. The IV curve of the actual fuel cell stack was measured and compared with the simulated IV curve. The results are as follows: Figure 3As shown, the horizontal axis represents the average voltage of a single cell, and the vertical axis represents the reaction current density. Comparison reveals that the relative error between the simulation results and experimental measurements of the stack voltage is within 4%, confirming the effectiveness of the proposed multiphysics model. This multiphysics model enables quantitative analysis of the energy efficiency of stacks with different designs.

[0146] Example 3

[0147] Hydrogen production rate is a crucial performance indicator for fuel cell stacks. Increasing the hydrogen production rate typically leads to a reduction in average hydrogen production costs, which is vital for hydrogen production plants. Based on the various parameters solved by the model in Example 1, the variation in hydrogen production rate under different stack geometries can be analyzed. According to Faraday's law, the hydrogen production rate is directly proportional to the reaction current. This example compares the hydrogen production rates of different designs by comparing reaction current densities.

[0148] First, this embodiment simulates and compares the hydrogen production rates under seven geometric structures with n = 3, 4, 5, 6, 7, 8, and 9. When simulating a stack with any geometric structure, multiple decoupling parameters j are input. r0 For each decoupling parameter, the corresponding reaction current density j is calculated. r and the average potential of the electrolytic cell in,

[0149]

[0150] In the formula, n is the number of electrolytic cells in the fuel cell stack.

[0151] Through multiple groups By fitting the data, the reaction current density j is obtained. r With the average potential of the electrolytic cell The relationship curve. For example... Figure 4 As shown, the red curve represents the reaction current density j obtained by fitting for n = 3 (CN-3). r With the average potential of the electrolytic cell The relationship curve is shown. In CN-i, i (i = 3, 4, 5, ..., 9) represents the number of cells in the fuel cell stack. Using CN-3 as a baseline, a bar chart is used to represent the percentage of the reaction current density of other fuel cell stack geometries compared to CN-3 at multiple average voltages within the range of 1.7V to 2.3V. The horizontal axis represents the average voltage per cell, the right vertical axis represents the reaction current density value of the corresponding curve, and the left vertical axis represents the percentage value of the corresponding bar chart. The graph shows that when the average voltage per cell is low, the reaction current density of each size design is almost identical, indicating that the hydrogen production rate is nearly the same. As the average voltage per cell increases, the difference in reaction current density under different designs gradually increases, and the more cells there are, the greater the reaction current density, indicating a faster hydrogen production rate.

[0152] Example 4

[0153] Improving efficiency is a crucial way to reduce energy consumption and further lower operating costs. The efficiency of the fuel cell stack is a key parameter for evaluating the performance of a hydrogen production system, and stack efficiency largely depends on current efficiency. In this embodiment, based on the various parameters solved by the model in Example 1, the key factors affecting current efficiency are compared and analyzed under different voltage conditions.

[0154] First, this embodiment simulates and compares the current efficiency under seven geometric structures with n = 3, 4, 5, 6, 7, 8, and 9. When simulating a fuel cell stack with any geometric structure, multiple decoupling parameters j are input. r0 For each decoupling parameter, calculate the corresponding reaction current i. r Bypass current i s and the potential E of each electrolytic cell cell-i And calculate the current efficiency η i and the average potential of the electrolytic cell

[0155]

[0156]

[0157] In the formula, n is the number of electrolytic cells in the fuel cell stack;

[0158] Through multiple groups By fitting the data, the current efficiency η is obtained. i With the average potential of the electrolytic cell The relationship curve.

[0159] like Figure 5 The figure shows a comparison of the current efficiency of different fuel cell stacks at multiple average voltages within the range of 1.7V to 2.3V. As can be seen from the figure, at 1.7V, the current efficiency ranges from 70.1% to 89.8% across seven different designs. At 2.3V, the current efficiency significantly improves, increasing from 89.8% to 99.6%. This indicates that higher voltage leads to higher current efficiency, but the difference in current efficiency between different fuel cell stack designs gradually decreases as the voltage increases.

[0160] Example 5

[0161] Examples 3 and 4 respectively analyze the comparison of hydrogen production rate and current efficiency of different fuel cell stack designs based on the method proposed in this invention. If only the hydrogen production rate or current efficiency is of concern, the analysis results of the aforementioned examples can be used to select a design that meets the requirements. However, in general, both hydrogen production rate and fuel cell stack efficiency need to be considered simultaneously. Therefore, in this example, based on the various parameters solved by the model in Example 1, the relationship between energy efficiency and average reaction current density is analyzed and obtained.

[0162] First, this embodiment simulates and compares the energy efficiency and average reactive current density under seven geometric structures (n=3, 4, 5, 6, 7, 8, and 9). When simulating a fuel cell stack with any geometric structure, multiple decoupling parameters j are input. r0 Input multiple decoupling parameters j r0 For each decoupling parameter, calculate the corresponding reaction current i. r Bypass current i s and the potential E of each electrolytic cell cell-i And calculate the energy efficiency η i and average reaction current density

[0163]

[0164]

[0165] In the formula, n is the number of electrolytic cells in the fuel cell stack. Where is the reversible voltage, and S is the cross-sectional area of ​​the fuel cell stack;

[0166] Through multiple groups By fitting the data, the energy efficiency η can be obtained. i With average reaction current density The relationship curve.

[0167] like Figure 6 As shown, the horizontal axis represents the average reaction current density, which is positively correlated with the hydrogen production rate, and the vertical axis represents the energy efficiency. The intersection point Pij represents the intersection of the CN-i and CN-j curves. At the same hydrogen production rate, a stack design with a higher trajectory has better energy efficiency than a stack design with a lower trajectory. At the same energy efficiency, a stack design with a farther trajectory has a better hydrogen production rate than a stack design with a closer trajectory. Therefore, if the average reaction current density is limited, the design with the maximum energy efficiency can be selected; if the energy efficiency is limited, the design with the maximum average reaction current density can be selected.

[0168] Example 6

[0169] AWE (Autonomous Wetland Engine) systems powered by renewable energy can produce low-carbon or zero-carbon hydrogen. Hydrogen-producing stacks can be coupled with renewable energy sources in either on-grid or off-grid modes. Off-grid AWE systems may experience unstable hydrogen production due to the intermittency of renewable energy sources, but may have lower electricity and hydrogen production costs. Conversely, on-grid systems with large-capacity renewable energy can provide more stable power and efficient hydrogen production, but incur high costs due to the use of electricity from the grid, thus hindering the adoption of green hydrogen. In this embodiment, the optimal stack design strategy for hydrogen production via AWE powered by renewable energy is further discussed, including both on-grid and off-grid modes.

[0170] If the grid-connected renewable energy power supply mode is selected, the power supply under this mode is stable, that is, the average reactive current density is relatively stable. In this case, the energy efficiency η obtained in Example 5 can be used as a reference. i With average reaction current density The relationship curve can be used to select the design that has the highest energy efficiency at a specific average reaction current density.

[0171] If off-grid renewable energy power supply is chosen, unlike optimization under constant current density, the power supply under this mode is unstable, meaning the average reactive current density is unstable, making it difficult to base energy efficiency η. i With average reaction current density The relationship curve directly determines the optimal design. Since off-grid renewable energy power supply directly utilizes renewable energy sources such as wind power, there are no electricity purchase costs. Therefore, energy efficiency can be disregarded, and hydrogen production can be directly used as the optimization indicator; the more hydrogen produced, the better. In this case, the optimal design can be determined using the following method:

[0172] Step 1: Analyze the relationship between hydrogen production and power supply for fuel cell stacks with different geometries based on the method in Example 1. Specifically, this includes the following steps:

[0173] Input multiple decoupling parameters j r0 For each decoupling parameter, calculate the corresponding gas generation rate v. g , reaction current i r and the relative potential E of each electrolytic cell cell-i And calculate the hydrogen production rate H and the power consumption P of the fuel cell stack:

[0174] H = v g t

[0175] P = i r U

[0176]

[0177] In the formula, t is the time step, n is the number of electrolytic cells in the fuel cell stack, and U is the total voltage of the fuel cell stack.

[0178] By fitting multiple sets (H, P), the relationship curve between hydrogen production H and stack power consumption P was obtained.

[0179] Step 2: Predict the changes in the power generation of renewable energy at various times over a period of time (e.g., one day), with a step size of t (e.g., 5 minutes) between each time point.

[0180] Step 3: Based on the relationship curve between hydrogen production H and stack power consumption P, obtain the hydrogen production at each time point and sum them up to obtain the total hydrogen production during that period.

[0181] Step 4: Compare the total hydrogen production of fuel cell stacks with different geometries, and select the geometries with the highest total hydrogen production as the preferred design.

[0182] like Figure 7 The graph shows a comparison of daily hydrogen production from different fuel cell stacks. This embodiment simulates seven geometric structures with n = 3, 4, 5, 6, 7, 8, and 9. The curves represent the daily renewable energy power generation, with the horizontal axis representing the time axis, the right vertical axis representing the normalized power generation, and the left axis representing the percentage of hydrogen production from each geometric structure relative to CN-3, based on CN-3's hydrogen production. In this embodiment, the graph shows that CN-5 has the highest hydrogen production, and therefore it is considered the optimal design.

[0183] Example 7

[0184] The present invention also relates to an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0185] The electronic device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The memory can be used to store computer programs and / or modules. The processor performs various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory.

[0186] Example 8

[0187] The present invention also relates to a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0188] Specifically, the memory may include high-speed random access memory, as well as non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital cards (SD), flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0189] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for evaluating the performance of a water electrolysis hydrogen production stack, characterized in that, include: Determine the two-phase flow model and electrochemical model of the fuel cell stack under the current geometry: The two-phase flow model includes: the mass conservation equation, the mass balance equation, the momentum conservation equation, and the assumptions u. g =u l =u m and boundary conditions In the formula, ρ g It is the density of the gas, u g u l u m These are the velocity vectors of a gas, a liquid, and a gas-liquid mixture, respectively. g It is the gas volume fraction, v g M is the rate of gas production. g It is the molar mass of the gas, F is the Faraday constant, and j r0 These are decoupling parameters; The electrochemical model includes: the current conservation equation, Ohm's equation, and boundary conditions. In the formula, U ks0 E represents the electrolyte potential near the anode in electrolytic cell k. cell-i Let be the relative potentials of the anode and cathode of electrolytic cell i; Input decoupling parameter j r0 Solving the two-phase flow model yields the gas volume fraction φ. g ; According to equation j r0 =j r / (1-φ g Calculate the reaction current density j r The gas volume fraction φ g and reaction current density j r Substituting these values ​​into the Butler-Volmer equation, calculate the activation overpotential η for the electrochemical reactions at the anode and cathode of each electrolytic cell. A-i and η C-i ; Based on the activation overpotential η A-i and η C-i Calculate the relative potential E of the anode and cathode of electrolytic cell i. cell-i ; The relative potential E cell-i Substituting into the electrochemical model, the bypass current density j is obtained. s ; Based on bypass current density j s Calculate the bypass current i s Based on the reaction current density j r Calculate the reaction current i r The performance of the current geometry of the fuel cell stack is evaluated based on the obtained parameters.

2. The method for evaluating the performance of a water electrolysis hydrogen production stack as described in claim 1, characterized in that, The mass conservation equation is: The mass balance equation is: The momentum conservation equation is: In the formula, ρ l It is the density of the liquid, φ l It is the liquid volume fraction, ρ is the density of the mixture, ρ = ρ g φ g +ρ l φ l j is the average volumetric flux density vector of the mixture, j = φ g u g +φ l u l p is the pressure vector of the mixture, K is the viscous stress tensor vector of the mixture, and g is the gravitational acceleration; The current conservation equation is as follows: The Ohm equation is: In the formula, U s For the electrolyte potential distribution, σ l It is the conductivity of the electrolyte, j s This represents the bypass current density.

3. The method for evaluating the performance of a water electrolysis hydrogen production stack as described in claim 1, characterized in that, The relative potential E of the anode and cathode of electrolytic cell i cell-i The calculation formula is: η ohm-i =i r (r p +r d ) In the formula, For reversible voltage, η ohm-i R is the ohmic overpotential of electrolytic cell i. d This corresponds to the resistance of the diaphragm within the electrolytic cell, r. p It corresponds to the resistance of the electrodes inside the electrolytic cell.

4. The method for evaluating the performance of a water electrolysis hydrogen production stack as described in claim 1, characterized in that, Calculate the activation overpotential η according to the following Butler-Volmer equation. A-i and η C-i : In the formula, i HER It is the exchange current density of the HER catalyst, i OER It is the exchange current density of the OER catalyst, α A It is the charge transfer coefficient of the cathode, α C is the charge transfer coefficient of the anode, z is the number of electrons participating in the electrode reaction, F is the Faraday constant, R is the gas constant, and f represents the thermodynamic temperature.

5. The method for evaluating the performance of a water electrolysis hydrogen production stack as described in claim 2, characterized in that, The formula for calculating the viscous stress tensor K of a mixture is: μ=φ g m g +φ l m l In the formula, μ is the viscosity coefficient of the mixture. g μ l are the viscosity coefficients of the gas and liquid, respectively, and I is a unit vector in the same direction as j.

6. The method for evaluating the performance of a water electrolysis hydrogen production stack as described in claim 1, characterized in that, The fuel cell stack performance evaluation method includes obtaining the relationship between current efficiency and the average potential of the electrolyzer within the fuel cell stack, specifically including: inputting multiple decoupling parameters j r0 For each decoupling parameter, calculate the corresponding reaction current i. r Bypass current i s and the potential E of each electrolytic cell cell-i And calculate the current efficiency η i and the average potential of the electrolytic cell In the formula, n is the number of electrolytic cells in the fuel cell stack; Through multiple groups By fitting the data, the current efficiency η is obtained. i With the average potential of the electrolytic cell The relationship curve.

7. The method for evaluating the performance of a water electrolysis hydrogen production stack as described in claim 1, characterized in that, The method for evaluating the performance of the fuel cell stack includes obtaining the relationship between energy efficiency and average reaction current density, specifically including: Input multiple decoupling parameters j r0 For each decoupling parameter, calculate the corresponding reaction current i. r Bypass current i s and the potential E of each electrolytic cell cell-i And calculate the energy efficiency η i and average reaction current density In the formula, n is the number of electrolytic cells in the fuel cell stack. Where is the reversible voltage, and S is the cross-sectional area of ​​the fuel cell stack; Through multiple groups By fitting the data, the energy efficiency η can be obtained. i With average reaction current density The relationship curve.

8. The method for evaluating the performance of a water electrolysis hydrogen production stack as described in claim 1, characterized in that, The fuel cell stack performance evaluation method includes evaluating the relationship between the hydrogen production capacity and the power consumption P of a fuel cell stack powered by renewable energy in off-grid mode, specifically including: Input multiple decoupling parameters j r0 For each decoupling parameter, calculate the corresponding gas generation rate v. g , reaction current i r and the relative potential E of each electrolytic cell cell-i And calculate the hydrogen production rate H and the power consumption P of the fuel cell stack: H=v g t P=i r IN In the formula, t is the time step, n is the number of electrolytic cells in the fuel cell stack, and U is the total voltage of the fuel cell stack. By fitting multiple sets (H, P), the relationship curve between hydrogen production H and stack power consumption P was obtained.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 8.

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