Proton exchange membrane hydrogen production electrolytic cell power prediction method and device, electronic equipment and storage medium

Abstract

The invention discloses a power prediction method and device for a proton exchange membrane hydrogen production electrolytic cell, electronic equipment and a storage medium. The method comprises the following steps: constructing an electrochemical model of the proton exchange membrane hydrogen production electrolytic cell; based on a multi-subswarm particle swarm optimization algorithm, optimizing parameters to be identified in the electrochemical model of the proton exchange membrane hydrogen production electrolytic cell to obtain optimal parameters of the model; and obtaining operation parameters of the proton exchange membrane hydrogen production electrolytic cell to be evaluated, and inputting the operation parameters of the proton exchange membrane hydrogen production electrolytic cell to be evaluated into the electrochemical model of the proton exchange membrane hydrogen production electrolytic cell based on the optimal model parameters to obtain the power of the proton exchange membrane hydrogen production electrolytic cell to be evaluated. According to the technical scheme, the to-be-identified parameters in the electrochemical model of the proton exchange membrane hydrogen production electrolytic cell are optimized through the multi-subswarm particle swarm optimization algorithm, high-precision identification of the model parameters is achieved, and therefore the power prediction accuracy of the electrochemical model is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electrolytic cell development, and particularly relates to a proton exchange membrane hydrogen production electrolytic cell power prediction method and device, electronic equipment and storage medium. BACKGROUND

[0002] Under the global carbon neutralization goal, the energy structure is accelerating the transformation to low carbonization and cleanization. Hydrogen energy, as a zero-carbon energy carrier, is regarded as a key technical path to achieve energy transformation.

[0003] By predicting the power of a proton exchange membrane (PEM) hydrogen production electrolytic cell, the operating parameters (such as current density, temperature, pressure, etc.) of the electrolytic cell can be dynamically adjusted according to the power, thereby reducing power consumption and improving energy conversion efficiency.

[0004] In the process of implementing the present application, the inventors have found that there are at least the following technical problems in the prior art: the existing proton exchange membrane hydrogen production electrolytic cell power prediction model has insufficient precision and the predicted power is inaccurate. SUMMARY

[0005] The present application provides a proton exchange membrane hydrogen production electrolytic cell power prediction method, device, electronic equipment and storage medium to achieve high-precision identification of model parameters, thereby improving the power prediction accuracy of the electrochemical model.

[0006] According to an aspect of the present application, a proton exchange membrane hydrogen production electrolytic cell power prediction method is provided, comprising:

[0007] constructing an electrochemical model of a proton exchange membrane hydrogen production electrolytic cell;

[0008] optimizing the to-be-identified parameters in the electrochemical model of the proton exchange membrane hydrogen production electrolytic cell based on a multi-subgroup particle swarm optimization algorithm to obtain optimal model parameters;

[0009] obtaining operating parameters of a proton exchange membrane hydrogen production electrolytic cell to be evaluated, inputting the operating parameters of the proton exchange membrane hydrogen production electrolytic cell to be evaluated into the electrochemical model of the proton exchange membrane hydrogen production electrolytic cell based on the optimal model parameters, and obtaining the power of the proton exchange membrane hydrogen production electrolytic cell to be evaluated.

[0010] According to another aspect of the present application, a proton exchange membrane hydrogen production electrolytic cell power prediction device is provided, comprising:

[0011] an electrochemical model construction module configured to construct an electrochemical model of a proton exchange membrane hydrogen production electrolytic cell;

[0012] An electrochemical model parameter identification module is configured to perform optimization on the to-be-identified parameters in the electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on a multi-swarm particle swarm optimization algorithm, so as to obtain model optimal parameters.

[0013] A to-be-evaluated electrolyzer power prediction module is configured to obtain operation parameters of a to-be-evaluated proton exchange membrane hydrogen production electrolyzer, and input the operation parameters of the to-be-evaluated proton exchange membrane hydrogen production electrolyzer into the electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on the model optimal parameters, so as to obtain power of the to-be-evaluated proton exchange membrane hydrogen production electrolyzer.

[0014] According to another aspect of the present application, an electronic device is provided, which comprises:

[0015] at least one processor;

[0016] and a memory connected in communication with the at least one processor;

[0017] wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the proton exchange membrane hydrogen production electrolyzer power prediction method according to any one of the embodiments of the present application.

[0018] According to another aspect of the present application, a computer readable storage medium is provided, which stores computer instructions for enabling a processor to perform the proton exchange membrane hydrogen production electrolyzer power prediction method according to any one of the embodiments of the present application.

[0019] The technical solution of the embodiments of the present application comprises the following steps: an electrochemical model of a proton exchange membrane hydrogen production electrolyzer is constructed, then to-be-identified parameters in the electrochemical model of the proton exchange membrane hydrogen production electrolyzer are optimized based on a multi-swarm particle swarm optimization algorithm, so as to obtain model optimal parameters, then operation parameters of a to-be-evaluated proton exchange membrane hydrogen production electrolyzer are obtained, and the operation parameters of the to-be-evaluated proton exchange membrane hydrogen production electrolyzer are input into the electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on the model optimal parameters, so as to obtain power of the to-be-evaluated proton exchange membrane hydrogen production electrolyzer. The above technical solution performs optimization on the to-be-identified parameters in the electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on the multi-swarm particle swarm optimization algorithm, so as to realize high-precision identification of model parameters, thereby improving the power prediction accuracy of the electrochemical model.

[0020] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiments description. Obviously, the drawings in the following description only show some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without any creative effort.

[0022] Figure 1 is a flow chart of a proton exchange membrane hydrogen production electrolyzer power prediction method according to an embodiment of the present application;

[0023] Figure 2 is a flow chart of a proton exchange membrane hydrogen production electrolyzer power prediction method according to an embodiment of the present application;

[0024] Figure 3 is a flow chart of a proton exchange membrane hydrogen production electrolyzer power prediction method according to an embodiment of the present application;

[0025] Figure 4 is a flow chart of a proton exchange membrane hydrogen production electrolyzer power prediction method according to an embodiment of the present application;

[0026] Figure 5 is a structural schematic diagram of a proton exchange membrane hydrogen production electrolyzer power prediction device according to an embodiment of the present application;

[0027] Figure 6 is a structural schematic diagram of an electronic device for implementing a proton exchange membrane hydrogen production electrolyzer power prediction method according to an embodiment of the present application. DETAILED DESCRIPTION

[0028] In order to make the technical personnel in the art better understand the present application, the following will combine the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without any creative effort should be within the scope of protection of the present application.

[0029] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and in the above drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices. The acquisition, storage, use, processing and other data in the technical solutions of the present application comply with the relevant provisions of national laws and regulations.

[0030] Embodiment one

[0031] Figure 1 A flowchart of a proton exchange membrane hydrogen production electrolytic cell power prediction method provided by the first embodiment of the present application, the present embodiment can be applicable to the case of predicting the power of the proton exchange membrane hydrogen production electrolytic cell, and the method can be executed by a proton exchange membrane hydrogen production electrolytic cell power prediction device. The proton exchange membrane hydrogen production electrolytic cell power prediction device can be realized in the form of hardware and / or software, and can be configured in a terminal, a server or other electronic device. As shown in the figure, the method comprises: Figure 1

[0032] S110, constructing an electrochemical model of the proton exchange membrane hydrogen production electrolytic cell.

[0033] The electrochemical model of the proton exchange membrane hydrogen production electrolytic cell is a physical model describing the internal electrochemical reaction process, energy conversion mechanism and dynamic characteristics of the electrolytic cell.

[0034] Specifically, a system of differential equations can be constructed based on physical laws such as the Nernst equation, the Arrhenius modified Butler-Volmer equation, etc., so as to obtain the electrochemical model of the proton exchange membrane hydrogen production electrolytic cell describing the electrochemical reaction, mass transfer and heat transfer process.

[0035] S120, based on the multi-subgroup particle swarm optimization algorithm, the to-be-identified parameters in the electrochemical model of the proton exchange membrane hydrogen production electrolytic cell are optimized to obtain the optimal model parameters.

[0036] ​Among them, the multi-swarm particle swarm optimization algorithm (MSPSO) is an extension of the standard particle swarm optimization algorithm (PSO), which enhances the global search ability and convergence stability of the algorithm by introducing a multi-swarm cooperation mechanism, while avoiding falling into a local optimal solution. The to-be-identified parameters in the electrochemical model of the proton exchange membrane hydrogen production electrolyzer can include one or more of the anode exchange current density (unit: A / cm 2 ), the cathode exchange current density (unit: A / cm 2 ), the anode charge transfer coefficient (dimensionless), the cathode charge transfer coefficient (dimensionless), the ohmic resistance (including non-film resistance components such as contact resistance and current collector resistance, unit: Ω·cm 2 ), the membrane hydration coefficient (dimensionless), the anode activation energy (unit: J / mol), the cathode activation energy (unit: J / mol), the anode effective diffusion coefficient (unit: cm 2 / s), the cathode effective diffusion coefficient (unit: cm 2 / s), the anode limiting exchange current density (unit: A / cm 2 ), and the cathode limiting exchange current density (unit: A / cm 2 ). The model optimal parameters are the results of optimization of the to-be-identified parameters.

[0037] Specifically, the anode exchange current density, the cathode exchange current density, the anode charge transfer coefficient, the cathode charge transfer coefficient, the ohmic resistance, the membrane hydration coefficient, the anode activation energy, the cathode activation energy, the anode effective diffusion coefficient, the cathode effective diffusion coefficient, the anode limiting exchange current density, and the cathode limiting exchange current density in the electrochemical model of the proton exchange membrane hydrogen production electrolyzer can be optimized by the multi-swarm particle swarm optimization algorithm to obtain the optimal anode exchange current density, the cathode exchange current density, the anode charge transfer coefficient, the cathode charge transfer coefficient, the ohmic resistance, the membrane hydration coefficient, the anode activation energy, the cathode activation energy, the anode effective diffusion coefficient, the cathode effective diffusion coefficient, the anode limiting exchange current density, and the cathode limiting exchange current density.

[0038] S130, obtaining the operating parameters of the proton exchange membrane hydrogen production electrolyzer to be evaluated, inputting the operating parameters of the proton exchange membrane hydrogen production electrolyzer to be evaluated into the electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on the model optimal parameters, and obtaining the power of the proton exchange membrane hydrogen production electrolyzer to be evaluated.

[0039] Among them, the operating parameters of the proton exchange membrane hydrogen production electrolyzer to be evaluated can include current, temperature, pressure, and voltage.

[0040] Exemplarily, the current can be collected by a current sensor, the temperature can be collected by a temperature transmitter, the pressure can be collected by a pressure transmitter, and the voltage can be collected by a voltage collection module. Further, the current, the temperature, the pressure and the voltage of the proton exchange membrane hydrogen production electrolyzer to be evaluated are input into the electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on the model optimal parameters, so that the electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on the model optimal parameters can predict the power of the proton exchange membrane hydrogen production electrolyzer to be evaluated.

[0041] The technical scheme of the embodiment of the present application builds the electrochemical model of the proton exchange membrane hydrogen production electrolyzer, and then optimizes the to-be-recognized parameters in the electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on the multi-swarm particle swarm optimization algorithm to obtain the model optimal parameters, and then obtains the operating parameters of the proton exchange membrane hydrogen production electrolyzer to be evaluated, and inputs the operating parameters of the proton exchange membrane hydrogen production electrolyzer to be evaluated into the electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on the model optimal parameters to obtain the power of the proton exchange membrane hydrogen production electrolyzer to be evaluated. The above technical scheme optimizes the to-be-recognized parameters in the electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on the multi-swarm particle swarm optimization algorithm, realizes high-precision identification of model parameters, and thus improves the power prediction accuracy of the electrochemical model.

[0042] Embodiment Two

[0043] Figure 2 A flowchart of a proton exchange membrane hydrogen production electrolyzer power prediction method provided by the second embodiment of the present application is provided, and the method of the present embodiment can be combined with each optional scheme in the proton exchange membrane hydrogen production electrolyzer power prediction method provided in the above embodiments. The proton exchange membrane hydrogen production electrolyzer power prediction method provided by the present embodiment further limits the construction of the electrochemical model of the proton exchange membrane hydrogen production electrolyzer.

[0044] As Figure 2 shown, the method comprises:

[0045] S210, a multi-physical field coupling voltage prediction model is constructed by the Nernst voltage, the activation overpotential and the ohmic overpotential.

[0046] In the embodiment of the present application, the multi-physical field coupling voltage prediction model can be:

[0047] V cell = E rev + η act,anode + η act,cathode + η ohm ;

[0048]

[0049] ηohm = i x (R ohm,i + R ohm,e ) ;

[0050] wherein V cell represents the cell single-compartment voltage, E rev represents the Nernst voltage, η act,anode represents the anode activation overpotential, η act,cathode represents the cathode activation overpotential, η ohm represents the ohmic overpotential; T represents the thermodynamic temperature, R represents the gas constant, F represents the Faraday constant, P cathode,eff represents the cathode effective pressure, P anode,eff represents the anode effective pressure; T ref represents the reference temperature, i represents the current density, α anode represents the anode charge transfer coefficient, sinh -1 (·) represents the inverse hyperbolic sine function, i 0,anode represents the anode exchange current density, E a,anode represents the anode activation energy; α cathode represents the cathode charge transfer coefficient, i 0,cathode represents the cathode exchange current density, E a,cathode represents the cathode activation energy; R ohm,i represents the membrane resistance, R ohm,e represents the contact resistance.

[0051] In the embodiments of the present application, the Faraday constant can be 96485 C / mol or other values.

[0052] The activation overpotential is the activation overvoltage corrected by using the Arrhenius formula.

[0053]

[0054]

[0055] wherein λ represents the humidification degree of the membrane, δ m is the membrane thickness.

[0056] S220, constructing a diffusion overpotential dynamic supplement model by the diffusion overpotential.

[0057] In the embodiments of the present application, the diffusion overpotential dynamic supplement model can be:

[0058]

[0059] wherein i represents the current density, T represents the thermodynamic temperature, R represents the gas constant, F represents the Faraday constant, P cathode,eff represents the cathode effective pressure, P anode,effrepresents the anode effective pressure, δ represents the diffusion layer thickness, n represents the number of electron transfer, D eff,anode represents the anode effective diffusion coefficient, D eff,cathode represents the cathode effective diffusion coefficient, i L,anode represents the anode limiting exchange current density, i L,cathode represents the cathode limiting exchange current density, η diff,anode represents the anode diffusion overpotential, η diff,cathode represents the cathode diffusion overpotential. C anode represents the anode reactant concentration, C cathode represents the cathode reactant concentration.

[0060] S230, based on the multi-physical field coupling voltage prediction model and the diffusion overpotential dynamic supplement model, an electrochemical model of the proton exchange membrane hydrogen production electrolyzer is constructed.

[0061] Specifically, the multi-physical field coupling voltage prediction model and the diffusion overpotential dynamic supplement model can be collectively used as the electrochemical model of the proton exchange membrane hydrogen production electrolyzer.

[0062] S240, based on the multi-subgroup particle swarm optimization algorithm, the to-be-identified parameters in the electrochemical model of the proton exchange membrane hydrogen production electrolyzer are optimized to obtain model optimal parameters.

[0063] S250, the operation parameters of the to-be-evaluated proton exchange membrane hydrogen production electrolyzer are obtained, and the operation parameters of the to-be-evaluated proton exchange membrane hydrogen production electrolyzer are input into the electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on the model optimal parameters, to obtain the power of the to-be-evaluated proton exchange membrane hydrogen production electrolyzer.

[0064] Specifically, the formula for calculating the power using the electrochemical model can be:

[0065] P cell = n x V cell x I;

[0066] wherein, P cell represents the power of the to-be-evaluated proton exchange membrane hydrogen production electrolyzer, I represents the current, V cell represents the single cell voltage of the electrolyzer, and n represents the number of single cells of the electrolyzer.

[0067] The technical scheme of the embodiment of the present application constructs a multi-physical field coupling voltage prediction model through the Nernst voltage, the activation overpotential and the ohmic overpotential, constructs a diffusion overpotential dynamic supplement model through the diffusion overpotential, and constructs an electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on the multi-physical field coupling voltage prediction model and the diffusion overpotential dynamic supplement model, realizes the double dynamic compensation of the environmental variables to the reaction kinetics and the mass transfer process, and effectively eliminates the steady-state error of the traditional model under the temperature gradient condition.

[0068] Embodiment three

[0069] Figure 3 A flow chart of a method for predicting the power of a proton exchange membrane hydrogen production electrolyzer is provided in Embodiment three of the present application. The method of this embodiment can be combined with any of the optional solutions provided in the methods for predicting the power of a proton exchange membrane hydrogen production electrolyzer in the above embodiments. The method for predicting the power of a proton exchange membrane hydrogen production electrolyzer provided in this embodiment further optimizes the parameter identification process.

[0070] As shown in Figure 3 , the method comprises:

[0071] S310, constructing an electrochemical model of the proton exchange membrane hydrogen production electrolyzer.

[0072] S320, using a multi-swarm particle swarm optimization algorithm with three-stage adaptive inertia weight to optimize the to-be-identified parameters in the electrochemical model of the proton exchange membrane hydrogen production electrolyzer, to obtain the optimal model parameters.

[0073] The update strategy of the inertia weight of the multi-swarm particle swarm optimization algorithm with three-stage adaptive inertia weight is:

[0074]

[0075] wherein t represents the current iteration number, T max represents the maximum iteration number, w max represents the upper limit of the inertia weight, w min represents the lower limit of the inertia weight.

[0076] It should be noted that the dynamic adjustment of the inertia weight is realized by the update strategy of the three-stage adaptive inertia weight in the embodiments of the present application. Specifically, in the global exploration stage, a large range of parameter space is covered; in the transition stage, the potential optimal area is gradually focused by linear weight decay; and in the local development stage, fine search is performed in combination with gradient assistance, balancing the exploration and development needs.

[0077] On the basis of the above embodiments, optionally, the to-be-identified parameters in the electrochemical model of the proton exchange membrane hydrogen production electrolyzer are optimized based on a multi-swarm particle swarm optimization algorithm with three-stage adaptive inertia weight to obtain the optimal model parameters, including: the to-be-identified parameters in the electrochemical model of the proton exchange membrane hydrogen production electrolyzer are optimized based on a multi-swarm particle swarm optimization algorithm with three-stage adaptive inertia weight to obtain the optimal model parameters; wherein the multi-swarm particle swarm optimization algorithm with three-stage adaptive inertia weight integrates a cooperative strategy of dynamic mutation and gradient assistance.

[0078] The formula of the dynamic mutation is:

[0079]

[0080] wherein, p mutate denotes the dynamic variation probability;

[0081] The gradient assisted formula is:

[0082]

[0083] wherein, denotes the gradient of the loss function to the activation energy, used to guide the parameter update direction;△ denotes the perturbation step, used for numerical differentiation;E a denotes the activation energy, denotes the loss function.

[0084] It should be noted that the embodiment of the application solves the problems of easy falling into local optimum and insufficient prediction accuracy of the traditional method by integrating the synergistic strategy of dynamic variation and gradient assistance.

[0085] S330, obtain the operation parameter of the proton exchange membrane hydrogen production electrolyzer to be evaluated, input the operation parameter of the proton exchange membrane hydrogen production electrolyzer to be evaluated into the electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on the optimal parameter of the model, and obtain the power of the proton exchange membrane hydrogen production electrolyzer to be evaluated.

[0086] The technical scheme of the embodiment of the application, at the level of optimization algorithm, designs a distributed multi-subgroup collaborative optimization architecture, realizes intelligent transition from global exploration to local development through parallel search and adaptive information interaction mechanism of multiple subgroups, and cooperates with a three-stage adaptive inertia weight adjustment strategy, innovatively integrates the synergistic strategy of dynamic variation and gradient assistance, and forms a multi-level premature convergence prevention system.

[0087] Figure 4 A flowchart of a proton exchange membrane hydrogen production electrolyzer power prediction method provided by the embodiment of the application is provided.

[0088] 1. Construct an electrochemical model of a proton exchange membrane hydrogen production electrolyzer.

[0089] 2. Collect operation parameters through a hardware data acquisition layer.

[0090] Specifically, the current is collected through a current sensor, the temperature is collected through a temperature transmitter, the pressure is collected through a pressure transmitter, and the voltage is collected through a voltage acquisition module.

[0091] 3. Perform real-time data cleaning, engineering feature extraction and data standardization on the operation parameters through a data preprocessing layer.

[0092] 4. Using the pre-processed operation parameters, the to-be-identified parameters in the electrochemical model of the proton exchange membrane hydrogen production electrolyzer are optimized based on the improved multi-swarm particle swarm optimization algorithm, and optimal parameters of the model are obtained.

[0093] The multi-swarm particle swarm optimization algorithm adopts a 10-swarm parallel PSO architecture (each swarm contains 100 particles). Specifically, the inertia weight is initialized, and the dynamic adjustment of the inertia weight is realized through a three-stage adaptive inertia weight updating strategy. Specifically, in the global exploration stage, the weight inertia is set to 0.9; in the transition stage, the weight inertia is linearly decayed from 0.9 to 0.4; in the local development stage, the dynamic adjustment of the weight inertia is 0.4 ± 0.08 sin(t) by combining gradient assistance (step size h = 1e-8) for local search and quasi-Newton method local optimization. Further, premature convergence detection is performed, which includes stagnation algebra detection, mutation increase, population reset, and diversity recovery. Further, the swarm information exchange is performed, and when the mean absolute error (MAE) is less than or equal to 0.005V, the optimal parameters of the model are output.

[0094] 5. The current, temperature, pressure, and voltage of the proton exchange membrane hydrogen production electrolyzer to be evaluated are input to the electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on the optimal parameters of the model through the model verification unit and the digital twin interface, so that the electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on the optimal parameters of the model can predict the output power of the proton exchange membrane hydrogen production electrolyzer to be evaluated. The model verification unit can also be used to provide polarization curve generation, error distribution statistics, and voltage component contribution analysis functions, and the digital twin interface can also interact with the industrial control system in real time through the OPC UA protocol.

[0095] The technical scheme of the embodiment of the present application optimizes the to-be-identified parameters in the electrochemical model of the proton exchange membrane hydrogen production electrolyzer through the multi-swarm particle swarm optimization algorithm, realizes high-precision identification of the model parameters, and thereby improves the power prediction accuracy of the electrochemical model.

[0096] Embodiment Four

[0097] Figure 5 A structure schematic diagram of a proton exchange membrane hydrogen production electrolyzer power prediction device provided by Embodiment Four of the present application is shown in FIG. 4. Figure 5 As shown in FIG. 4, the device includes:

[0098] The electrochemical model construction module 410 is configured to construct an electrochemical model of a proton exchange membrane hydrogen production electrolyzer.

[0099] The electrochemical model parameter identification module 420 is configured to optimize to-be-identified parameters in the electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on a multi-swarm particle swarm optimization algorithm, and obtain optimal parameters of the model.

[0100] The electrolyzer power prediction module 430 is configured to obtain the operation parameter of the to-be-evaluated proton exchange membrane hydrogen production electrolyzer, input the operation parameter of the to-be-evaluated proton exchange membrane hydrogen production electrolyzer into the electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on the model optimal parameter, and obtain the power of the to-be-evaluated proton exchange membrane hydrogen production electrolyzer.

[0101] The technical scheme of the embodiment of the present application comprises the following steps: constructing an electrochemical model of a proton exchange membrane hydrogen production electrolyzer, and then performing optimization on to-be-recognized parameters in the electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on a multi-swarm particle swarm optimization algorithm to obtain model optimal parameters, and then obtaining the operation parameter of a to-be-evaluated proton exchange membrane hydrogen production electrolyzer, inputting the operation parameter of the to-be-evaluated proton exchange membrane hydrogen production electrolyzer into the electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on the model optimal parameter, and obtaining the power of the to-be-evaluated proton exchange membrane hydrogen production electrolyzer. The above technical scheme performs optimization on to-be-recognized parameters in the electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on the multi-swarm particle swarm optimization algorithm, realizes high-precision recognition of model parameters, and thus improves the power prediction accuracy of the electrochemical model.

[0102] In some optional embodiments, the electrochemical model construction module 410 comprises:

[0103] The multi-physical field coupling voltage prediction model construction unit is configured to construct a multi-physical field coupling voltage prediction model through the Nernst voltage, the activation overpotential, and the ohmic overpotential.

[0104] The diffusion overpotential dynamic supplement model construction unit is configured to construct a diffusion overpotential dynamic supplement model through the diffusion overpotential.

[0105] The electrochemical model construction unit of the proton exchange membrane hydrogen production electrolyzer is configured to construct an electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on the multi-physical field coupling voltage prediction model and the diffusion overpotential dynamic supplement model.

[0106] In some optional embodiments, the multi-physical field coupling voltage prediction model is:

[0107] V cell =E rev +η act,anode +η act,cathode +η ohm ;

[0108]

[0109] η ohm =i×(R ohm,i +R ohm,e );

[0110] wherein V cell represents the cell voltage, E rev represents the Nernst voltage, η act,anode represents the anode activation overpotential, η act,cathode represents the cathode activation overpotential, η ohm represents the ohmic overpotential; T represents the thermodynamic temperature, R represents the gas constant, F represents the Faraday constant, P cathode,eff represents the cathode effective pressure, P anode,eff represents the anode effective pressure; T ref represents the reference temperature, i represents the current density, α anode represents the anode charge transfer coefficient, sinh -1 (·) represents the inverse hyperbolic sine function, i 0,anode represents the anode exchange current density, E a,anode represents the anode activation energy; α cathode represents the cathode charge transfer coefficient, i 0,cathode represents the cathode exchange current density, E a,cathode represents the cathode activation energy; R ohm,i represents the membrane resistance, R ohm,e represents the contact resistance.

[0111] In some alternative embodiments, the diffusion overpotential dynamic compensation model is:

[0112]

[0113] wherein i represents the current density, T represents the thermodynamic temperature, R represents the gas constant, F represents the Faraday constant, P cathode,eff represents the cathode effective pressure, P anode,eff represents the anode effective pressure, δ represents the diffusion layer thickness, n represents the number of electron transfers, D eff,anode represents the anode effective diffusion coefficient, D eff,cathode represents the cathode effective diffusion coefficient, i L,anode represents the anode limiting exchange current density, i L,cathode represents the cathode limiting exchange current density, η diff,anode represents the anode diffusion overpotential, η diff,cathode represents the cathode diffusion overpotential.

[0114] In some alternative embodiments, the electrochemical model parameter identification module 420 comprises:

[0115] a three-stage adaptive inertia weight updating unit, configured to perform optimization on the to-be-identified parameters in the electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on a three-stage adaptive inertia weight multi-swarm particle swarm optimization algorithm, to obtain model optimal parameters;

[0116] The updating strategy of the inertia weight of the three-stage adaptive inertia weight multi-swarm particle swarm optimization algorithm is:

[0117]

[0118] wherein t represents the current iteration number, T max represents the maximum iteration number, w max represents the upper limit of the inertia weight, w min represents the lower limit of the inertia weight.

[0119] In some optional embodiments, the electrochemical model parameter identification module 420 comprises:

[0120] The dynamic mutation and gradient assistance collaborative strategy unit is configured to perform optimization on the to-be-identified parameters in the electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on the three-stage adaptive inertia weight multi-swarm particle swarm optimization algorithm, to obtain the optimal model parameters.

[0121] The three-stage adaptive inertia weight multi-swarm particle swarm optimization algorithm integrates the dynamic mutation and gradient assistance collaborative strategy.

[0122] The formula of the dynamic mutation is:

[0123]

[0124] wherein p mutate represents the dynamic mutation probability;

[0125] The formula of the gradient assistance is:

[0126]

[0127] wherein represents the gradient of the loss function with respect to the activation energy, for guiding the parameter updating direction; △ represents the perturbation step, for numerical differentiation; E a represents the activation energy, represents the loss function.

[0128] In some optional embodiments, the to-be-identified parameters in the electrochemical model of the proton exchange membrane hydrogen production electrolyzer comprise:

[0129] One or more of the anode exchange current density, the cathode exchange current density, the anode charge transfer coefficient, the cathode charge transfer coefficient, the ohmic resistance, the membrane hydration coefficient, the anode activation energy, the cathode activation energy, the anode effective diffusion coefficient, the cathode effective diffusion coefficient, the anode limiting exchange current density, and the cathode limiting exchange current density.

[0130] The proton exchange membrane hydrogen production electrolyzer power prediction device provided by the embodiment of the present application can execute the proton exchange membrane hydrogen production electrolyzer power prediction method provided by any embodiment of the present application, has the function modules and beneficial effects corresponding to the execution method.

[0131] Embodiment five

[0132] Figure 6 A structural schematic diagram of an electronic device 10 that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application described and / or claimed in this document.

[0133] As shown in Figure 6 The electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11, wherein the memory stores a computer program that can be executed by the at least one processor. The processor 11 can execute various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An I / O interface 15 is also connected to the bus 14.

[0134] A plurality of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunications networks.

[0135] The processor 11 can be various general and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the proton exchange membrane hydrogen production electrolyzer power prediction method, which comprises:

[0136] constructing an electrochemical model of a proton exchange membrane hydrogen production electrolyzer;

[0137] performing optimization on the to-be-identified parameters in the electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on a multi-subgroup particle swarm optimization algorithm to obtain model optimal parameters;

[0138] obtaining operating parameters of a proton exchange membrane hydrogen production electrolyzer to be evaluated, inputting the operating parameters of the proton exchange membrane hydrogen production electrolyzer to be evaluated into the electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on the model optimal parameters to obtain power of the proton exchange membrane hydrogen production electrolyzer to be evaluated.

[0139] In some embodiments, the proton exchange membrane hydrogen production electrolyzer power prediction method can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the proton exchange membrane hydrogen production electrolyzer power prediction method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the proton exchange membrane hydrogen production electrolyzer power prediction method by any other appropriate means, such as by means of firmware.

[0140] The various embodiments of the systems and techniques described above can be implemented in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a complex programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0141] Computer programs used to implement the processes of the application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer program, when executed, can cause instructions defined in the flow charts and / or block diagrams to be implemented on the computer or other programmable apparatus. The computer programs can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0142] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store computer programs for use by or in connection with an instruction execution system, apparatus, or device. Computer-readable storage media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0143] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0144] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0145] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. A server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0146] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present disclosure. For example, the steps recited in the present disclosure can be executed in parallel, executed in sequence, or executed in a different order, as long as the desired results of the present disclosure are achieved, and the present disclosure is not limited herein.

[0147] The specific embodiments described above are not intended to be limiting, and persons skilled in the art will appreciate that various modifications, combinations, sub-combinations and alternatives can be made to the specific embodiments without departing from the spirit and principles of the disclosure. Accordingly, the disclosure is not limited to the specific embodiments described above, but only by the scope of the appended claims.

Claims

1. A method for power prediction of a proton exchange membrane hydrogen production electrolyzer, characterized in that, The method comprises the following steps: constructing an electrochemical model of a proton exchange membrane hydrogen production electrolyzer; optimizing to-be-identified parameters in the electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on a multi-swarm particle swarm optimization algorithm to obtain optimal parameters of the model; obtaining operating parameters of a proton exchange membrane hydrogen production electrolyzer to be evaluated, inputting the operating parameters of the proton exchange membrane hydrogen production electrolyzer to be evaluated into the electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on the optimal parameters of the model, and obtaining power of the proton exchange membrane hydrogen production electrolyzer to be evaluated.

2. The method of claim 1, wherein, The method of constructing the electrochemical model of the proton exchange membrane hydrogen production electrolyzer comprises the following steps: constructing a multi-physics field coupling voltage prediction model by using Nernst voltage, activation overvoltage and ohmic overvoltage; constructing a diffusion overvoltage dynamic supplement model by using diffusion overvoltage; constructing the electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on the multi-physics field coupling voltage prediction model and the diffusion overvoltage dynamic supplement model.

3. The method of claim 2, wherein, The multi-physics field coupling voltage prediction model is: V cell = E rev + η act,anode + η act,cathode + η ohm ; η ohm = i x (R ohm,i + R ohm,e ); where V cell represents the cell single-compartment voltage, E rev represents the Nernst voltage, η act,anode represents the anode activation overpotential, η act,cathode represents the cathode activation overpotential, η ohm represents the ohmic overpotential; T represents the thermodynamic temperature, R represents the gas constant, F represents the Faraday constant, P cathode,eff represents the cathode effective pressure, P anode,eff represents the anode effective pressure; T ref represents the reference temperature, i represents the current density, α anode represents the anode charge transfer coefficient, sinh -1 (·) represents the inverse hyperbolic sine function, i 0,anode represents the anode exchange current density, E a,anode represents the anode activation energy; α cathode represents the cathode charge transfer coefficient, i 0,cathode represents the cathode exchange current density, E a,cathode represents the cathode activation energy; R ohm,i represents the membrane resistance, R ohm,e represents the contact resistance.

4. The method of claim 2, wherein, The diffusion overvoltage dynamic supplement model is: where i represents the current density, T represents the thermodynamic temperature, R represents the gas constant, F represents the Faraday constant, P cathode,eff represents the cathode effective pressure, P anode,eff represents the anode effective pressure, δ represents the diffusion layer thickness, n represents the number of electron transfer, D eff,anode represents the anode effective diffusion coefficient, D eff,cathode represents the cathode effective diffusion coefficient, i L,anode represents the anode limiting current density, i L,cathode represents the cathode limiting current density, η diff,anode represents the anode diffusion overpotential, η diff,cathode represents the cathode diffusion overpotential.

5. The method of claim 1, wherein, The method of optimizing to-be-identified parameters in the electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on the multi-swarm particle swarm optimization algorithm to obtain optimal parameters of the model comprises the following steps: optimizing to-be-identified parameters in the electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on a multi-swarm particle swarm optimization algorithm with three-stage adaptive inertia weight to obtain optimal parameters of the model; The inertia weight updating strategy of the multi-swarm particle swarm optimization algorithm with three-stage adaptive inertia weight is: where t denotes the current iteration number, T max denotes the maximum number of iterations, w max denotes the upper limit of the inertia weight, w min denotes the lower limit of the inertia weight.

6. The method of claim 5, wherein, The method of optimizing to-be-identified parameters in the electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on the multi-swarm particle swarm optimization algorithm with three-stage adaptive inertia weight to obtain optimal parameters of the model comprises the following steps: optimizing to-be-identified parameters in the electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on a multi-swarm particle swarm optimization algorithm with three-stage adaptive inertia weight to obtain optimal parameters of the model; The multi-swarm particle swarm optimization algorithm with three-stage adaptive inertia weight integrates a dynamic mutation and a gradient assistance cooperative strategy. The formula of the dynamic mutation is: where p mutate represents the dynamic variation probability; The formula of the gradient assistance is: wherein, denotes the gradient of the loss function with respect to the activation energy, which is used to guide the direction of parameter update; denotes the perturbation step, which is used for numerical differentiation; E a denotes the activation energy, denotes the loss function.

7. The method according to any one of claims 1 to 6, characterized in that, The to-be-identified parameters in the electrochemical model of the proton exchange membrane hydrogen production electrolyzer comprise one or more of the following parameters: anode exchange current density, cathode exchange current density, anode charge transfer coefficient, cathode charge transfer coefficient, ohmic resistance, membrane hydration coefficient, anode activation energy, cathode activation energy, anode effective diffusion coefficient, cathode effective diffusion coefficient, anode limiting exchange current density and cathode limiting exchange current density.

8. A proton exchange membrane hydrogen production electrolyzer power prediction device, characterized by, The method comprises the following steps: an electrochemical model construction module is configured to construct an electrochemical model of a proton exchange membrane hydrogen production electrolyzer; an electrochemical model parameter identification module is configured to optimize to-be-identified parameters in the electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on a multi-swarm particle swarm optimization algorithm to obtain optimal parameters of the model; The electrolyzer power prediction module to be evaluated is configured to obtain operation parameters of a proton exchange membrane hydrogen production electrolyzer to be evaluated, input the operation parameters of the proton exchange membrane hydrogen production electrolyzer to be evaluated into an electrochemical model of the proton exchange membrane hydrogen production electrolyzer based on optimal parameters of the model, and obtain power of the proton exchange membrane hydrogen production electrolyzer to be evaluated.

9. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected in communication with the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the proton exchange membrane hydrogen production electrolyzer power prediction method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for causing the processor to implement the proton exchange membrane hydrogen production electrolyzer power prediction method of any one of claims 1-7 when executed.

Images