Data-driven calculation method, device and equipment for hydrogen storage capacity of solid-state hydrogen storage device

Abstract

This application discloses a data-driven calculation method, apparatus, and equipment for the hydrogen storage capacity of a solid-state hydrogen storage device, belonging to the field of solid-state hydrogen storage technology. The method includes: obtaining the distributed parameters of the solid-state hydrogen storage device through a multiphysics fully coupled model, where the multiphysics fully coupled model is constructed based on the structural characteristics of the solid-state hydrogen storage device; acquiring a dynamic response database of the multiphysics fully coupled model, which characterizes the correspondence between the distributed parameters of the multiphysics fully coupled model and the hydrogen storage capacity of the solid-state hydrogen storage device; dynamically identifying the multiphysics fully coupled model through the dynamic response database to obtain an identification model of the solid-state hydrogen storage device; inputting the target distributed parameters of the solid-state hydrogen storage device into the identification model, and performing data-driven calculations on the target distributed parameters through the identification model to output the predicted hydrogen storage capacity of the solid-state hydrogen storage device. This method can reduce computational costs and solution time.

Description

Technical Field

[0001] This application belongs to the field of solid-state hydrogen storage technology, and in particular relates to a data-driven calculation method, apparatus and equipment for the hydrogen storage capacity of a solid-state hydrogen storage device. Background Technology

[0002] Compared to traditional high-pressure gas storage and liquefaction technologies, hydrogen storage in metal compounds shows greater potential for commercial and automotive applications due to its superior safety and reliability. The formation of metal hydrides is closely linked to gas pressure and temperature, requiring suitable high temperatures to catalyze the chemical reaction. The hydrogen pressure must exceed the equilibrium pressure for adsorption or desorption to initiate the reaction. Simultaneously, the adsorption or desorption of hydrogen by metals is typically accompanied by heat release or absorption, which can lead to uneven temperature distribution. Due to the complex interaction between these thermal effects and chemical reactions, optimizing the kinetics of metal hydrogen storage requires extensive experimental research and simulation analysis.

[0003] Current research primarily focuses on the effects of operating conditions and structural parameters on the performance of metal hydrogen storage, while the exploration of its dynamic behavior is relatively lacking. Rapid chemical reaction kinetics coupled with slow mass transfer processes can lead to instability in the dynamic response, thereby affecting the stability of hydrogen supply. Therefore, in-depth research into the dynamic processes of metal hydrogen storage is particularly crucial.

[0004] However, while multiphysics modeling techniques can provide internal distributed parameters, they suffer from high computational costs and long solution times. Summary of the Invention

[0005] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a data-driven calculation method, apparatus, and equipment for the hydrogen storage capacity of a solid-state hydrogen storage device, which can reduce computational costs and solution time.

[0006] In a first aspect, this application provides a data-driven calculation method for the hydrogen storage capacity of a solid-state hydrogen storage device, the method comprising:

[0007] The distributed parameters of the solid-state hydrogen storage device are obtained through a multi-physics fully coupled model, which is constructed based on the structural characteristics of the solid-state hydrogen storage device.

[0008] Obtain the dynamic response database of the multiphysics fully coupled model, which is used to characterize the correspondence between the distributed parameters of the multiphysics fully coupled model and the hydrogen storage capacity of the solid hydrogen storage device;

[0009] By using a dynamic response database, the multi-physics fully coupled model is dynamically identified to obtain the identification model of the solid hydrogen storage device.

[0010] The target distributed parameters of the solid-state hydrogen storage device are input into the identification model, and the identification model performs data-driven calculations on the target distributed parameters to output the predicted hydrogen storage capacity of the solid-state hydrogen storage device.

[0011] According to one embodiment of this application, the step of dynamically identifying the multiphysics fully coupled model through a dynamic response database to obtain the identification model of the solid-state hydrogen storage device includes:

[0012] Based on the dynamic response database, the multiphysics fully coupled model is identified to obtain a dynamic model, which is constructed based on the input-error model;

[0013] The nonlinearity of the input of the dynamic model is expanded by a polynomial chaos, and the nonlinearity of the output of the dynamic model is represented by a wavelet network to obtain the identification model.

[0014] According to one embodiment of this application, the step of performing a chaotic expansion of the input nonlinearity of the dynamic model using a polynomial includes:

[0015]

[0016] in, The input is nonlinear. Let be the space of the input parameter distribution, n be the dimension of the input parameters, b be the index of the n-dimensional space, and ψ be the index of the input parameters. b (x) is a multidimensional polynomial basis, given by the tensor product of univariate orthogonal shift Legendre polynomials, a b These are the polynomial coefficients, b i It is a single-variable polynomial The number of times.

[0017] According to one embodiment of this application, the output nonlinearity of the dynamic model is represented by a wavelet network, including:

[0018]

[0019] Where μ(y) is the output nonlinearity, l and m are node indices, h(i) is the output value of node i in the hidden layer, and ω ik These are the weights connecting the hidden layer and the output layer, where k and i are the number of nodes in the output layer and hidden layer, respectively, and h is the weight. i It is the mother wavelet function, ω i These are the weights connecting the input layer and the hidden layer, d i It is the translation factor, a i It is the stretching factor.

[0020] According to one embodiment of this application, after inputting the target distributed parameters of the solid-state hydrogen storage device into the identification model, and performing data-driven calculations on the target distributed parameters through the identification model to output the predicted hydrogen storage capacity of the solid-state hydrogen storage device, the method further includes:

[0021] Multiple test distributed parameters are sequentially input into the multiphysics fully coupled model to obtain the first hydrogen storage response process output by the multiphysics fully coupled model;

[0022] The multiple test distributed parameters are sequentially input into the identification model to obtain the second hydrogen storage capacity response process output by the identification model;

[0023] An updated identification model is constructed based on the error between the first hydrogen storage response process and the second hydrogen storage response process.

[0024] According to one embodiment of this application, after constructing the updated identification model, the method further includes:

[0025] The hydrogen storage capacity of the solid hydrogen storage device is predicted by the identification model to obtain a first prediction result.

[0026] The hydrogen storage capacity of the solid hydrogen storage device is predicted by the updated identification model to obtain a second prediction result.

[0027] If the error of the first prediction result is greater than the error of the second prediction result, or if the error of the first prediction result exceeds the error range, the identification model is replaced by updating the identification model.

[0028] According to one embodiment of this application, the multiphysics fully coupled model includes a mass and momentum transfer module, a chemical reaction module, and a heat transfer module.

[0029] According to one embodiment of this application, the structural features of the solid hydrogen storage device include a reaction zone, an expansion volume, and a hydrogen inlet / outlet channel;

[0030] The mass and momentum transfer module is used to characterize the hydrogen flow process in the expansion volume.

[0031] The chemical reaction module is used to characterize the motion state of the metal hydride reaction bed in the reaction zone;

[0032] The heat transfer module is used to characterize the heat transfer state of the metal hydride reaction bed in the reaction zone.

[0033] Secondly, this application provides a data-driven calculation device for the hydrogen storage capacity of a solid-state hydrogen storage device, the device comprising:

[0034] The first processing module is used to obtain the distributed parameters of the solid-state hydrogen storage device through a multi-physics fully coupled model of the solid-state hydrogen storage device. The multi-physics fully coupled model is constructed based on the structural features of the solid-state hydrogen storage device.

[0035] The acquisition module is used to acquire the dynamic response database of the multiphysics fully coupled model, and the dynamic response database is used to characterize the correspondence between the distributed parameters of the multiphysics fully coupled model and the hydrogen storage capacity of the solid hydrogen storage device.

[0036] The second processing module is used to dynamically identify the multi-physics fully coupled model through a dynamic response database to obtain the identification model of the solid hydrogen storage device.

[0037] The third processing module is used to input the target distributed parameters of the solid-state hydrogen storage device into the identification model, and to perform data-driven calculations on the target distributed parameters through the identification model to output the predicted hydrogen storage capacity of the solid-state hydrogen storage device.

[0038] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the data-driven calculation method for the hydrogen storage capacity of the solid-state hydrogen storage device as described in the first aspect above.

[0039] Fourthly, this application provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the data-driven calculation method for the hydrogen storage capacity of the solid-state hydrogen storage device as described in the first aspect above.

[0040] Fifthly, this application provides a chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the data-driven calculation method for the hydrogen storage capacity of the solid-state hydrogen storage device as described in the first aspect.

[0041] In a sixth aspect, this application provides a computer program product, including a computer program that, when executed by a processor, implements the data-driven calculation method for the hydrogen storage capacity of a solid-state hydrogen storage device as described in the first aspect above.

[0042] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application.

[0043] The present invention provides a data-driven calculation method, apparatus, and equipment for the hydrogen storage capacity of a solid-state hydrogen storage device, which has the following advantages over the prior art:

[0044] (1) By dynamically identifying the multi-physics fully coupled model, the high-speed analysis of the multi-physics distribution inside the solid hydrogen storage device is realized. The simplified identification model can predict the hydrogen storage capacity of the solid hydrogen storage device with high accuracy, low cost and fast response. It can compress the calculation time of several hours required for finite element calculation to tens of seconds, solve the problems of high calculation cost and long solution time of multi-physics modeling technology, reduce calculation cost and guide experimental implementation, and has the potential to be extended to the dynamic research of other nonlinear systems.

[0045] (2) By using the dynamic response database, the multi-physics fully coupled model is identified, and the resulting dynamic model has lower complexity, which can more accurately predict the hydrogen storage capacity of the solid hydrogen storage device, enhance the understanding of the dynamic characteristics of the solid hydrogen storage device, and help to effectively control the solid hydrogen storage device.

[0046] (3) By using polynomial chaotic expansion on the input nonlinearity, nonlinear features can be effectively captured and chaotic regions and their boundaries can be identified, reducing computational complexity and improving the predictive ability of the identification model. By representing the output nonlinearity through wavelet network, efficient feature extraction can be achieved, enhancing the flexibility of the identification model, improving the prediction accuracy of the identification model, simplifying the model, improving computational efficiency, and having good interpretability. Attached Figure Description

[0047] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0048] Figure 1 This is one of the flowcharts illustrating the data-driven calculation method for the hydrogen storage capacity of a solid-state hydrogen storage device provided in this application embodiment;

[0049] Figure 2 This is a schematic diagram of the longitudinal section of the solid hydrogen storage device provided in the embodiments of this application;

[0050] Figure 3 This is the second flowchart illustrating the data-driven calculation method for the hydrogen storage capacity of the solid-state hydrogen storage device provided in the embodiments of this application;

[0051] Figure 4 This is a schematic diagram of the structure of the identification model provided in the embodiments of this application;

[0052] Figure 5 This is a schematic diagram of the hydrogen adsorption process prediction of the solid hydrogen storage device provided in the embodiments of this application;

[0053] Figure 6 This is a schematic diagram of the hydrogen desorption process prediction of the solid hydrogen storage device provided in the embodiments of this application;

[0054] Figure 7 This is a schematic diagram of the structure of the data-driven calculation device for the hydrogen storage capacity of the solid-state hydrogen storage device provided in the embodiments of this application;

[0055] Figure 8 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0056] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0057] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0058] The following description, in conjunction with the accompanying drawings, details the data-driven calculation method for hydrogen storage capacity of a solid-state hydrogen storage device, the data-driven calculation device for hydrogen storage capacity of a solid-state hydrogen storage device, the electronic equipment, and the readable storage medium provided in this application, through specific embodiments and application scenarios.

[0059] Among them, the data-driven calculation method for the hydrogen storage capacity of solid-state hydrogen storage devices can be applied to the terminal, specifically executed by the hardware or software in the terminal.

[0060] The terminal includes, but is not limited to, portable communication devices such as mobile phones or tablets with touch-sensitive surfaces (e.g., touchscreen displays and / or touchpads). It should also be understood that, in some embodiments, the terminal may not be a portable communication device, but rather a desktop computer with touch-sensitive surfaces (e.g., touchscreen displays and / or touchpads).

[0061] The following embodiments describe a terminal including a display and a touch-sensitive surface. However, it should be understood that the terminal may include one or more other physical user interface devices such as a physical keyboard, mouse, and joystick.

[0062] The data-driven calculation method for the hydrogen storage capacity of a solid-state hydrogen storage device provided in this application embodiment can be executed by an electronic device or a functional module or entity within an electronic device that can implement the data-driven calculation method for the hydrogen storage capacity of the solid-state hydrogen storage device. The electronic devices mentioned in this application embodiment include, but are not limited to, mobile phones, tablets, computers, cameras, and wearable devices. The following description uses an electronic device as the execution subject to illustrate the data-driven calculation method for the hydrogen storage capacity of a solid-state hydrogen storage device provided in this application embodiment.

[0063] like Figure 1 As shown, the method includes steps 110 to 140.

[0064] Step 110: Obtain the distributed parameters of the solid-state hydrogen storage device through a multi-physics fully coupled model of the solid-state hydrogen storage device. The multi-physics fully coupled model is constructed based on the structural features of the solid-state hydrogen storage device.

[0065] The reaction rate is the speed at which reactants are converted into products during the formation or decomposition of metal hydrides.

[0066] Solid-state hydrogen storage devices are used to store hydrogen storage materials such as metal hydrides, etc. Figure 2 As shown, rotating this longitudinal section along the z-axis yields a solid-state hydrogen storage device. Hydrogen (H2) is injected through the upper hydrogen inlet / outlet channel. R in H represents the inlet radius. ex H represents the height of the expanded volume. mb The solid-state hydrogen storage device, constructed using COMSOL multiphysics simulation software, is a cylindrical structure, representing the height of the metal hydride reaction bed. Its structural features, from bottom to top, consist of a metal hydride reaction zone, an expansion volume, and a hydrogen inlet / outlet channel at the top. When hydrogen needs to be output, the metal oxide undergoes a chemical reaction in the reaction zone to generate hydrogen. The expansion volume accommodates the volume changes generated during the metal hydride reaction, and the generated hydrogen is output through the hydrogen inlet / outlet channel.

[0067] In this step, the multiphysics fully coupled model of the solid hydrogen storage device is meshed, and distributed parameters such as reaction rate, temperature and pressure of each mesh can be extracted.

[0068] In the multiphysics fully coupled model, distributed parameters are set as several key boundary conditions to configure the model. These distributed parameters include initial temperature, initial hydrogen flow rate, initial density of metal hydride, and initial hydrogen pressure. Once these distributed parameters are accurately input, the calculation process of the chemical reaction can be activated, thereby advancing the simulation.

[0069] Step 120: Obtain the dynamic response database of the multiphysics fully coupled model. The dynamic response database is used to characterize the correspondence between the distributed parameters of the multiphysics fully coupled model and the hydrogen storage capacity of the solid-state hydrogen storage device.

[0070] Among them, the distributed parameters are a very comprehensive set of data that can cover a variety of operating scenarios for solid-state hydrogen storage devices. The dynamic response database includes distributed parameters and the hydrogen storage capacity of the solid-state hydrogen storage device corresponding to the distributed parameters.

[0071] The hydrogen storage capacity of a solid-state hydrogen storage device is characterized by a hydrogen storage capacity response process. By changing the distributed parameters input to the multi-physics fully coupled model, the hydrogen storage capacity response process of the solid-state hydrogen storage device can be obtained, which is the dynamic process of the numerical change of the hydrogen storage capacity.

[0072] In this step, distributed parameters are input into a multiphysics fully coupled model on the COMSOL simulation platform. The damped Newton method is used to solve the multiphysics model of the solid-state hydrogen storage device, and the accuracy of the multiphysics fully coupled model is verified to confirm that the multiphysics fully coupled model is accurate. High damping is used at the beginning, and then the damping coefficient is gradually reduced to improve the convergence of the solution. The dynamic response process of the hydrogen storage capacity of the solid-state hydrogen storage device corresponding to each set of distributed parameters is obtained to construct a dynamic response database, which corresponds to the structure of the solid-state hydrogen storage device.

[0073] Step 130: Dynamically identify the multiphysics fully coupled model using a dynamic response database to obtain the identification model of the solid hydrogen storage device.

[0074] Among them, the identification model is used to characterize the dynamic kinetic relationships of solid hydrogen storage devices.

[0075] In this step, the three-dimensional field model is simplified by dynamically identifying the fully coupled multiphysics model. The resulting identification model can calculate the hydrogen storage capacity of the solid-state hydrogen storage device in a data-driven manner.

[0076] Step 140: Input the target distributed parameters of the solid-state hydrogen storage device into the identification model, and perform data-driven calculations on the target distributed parameters through the identification model to output the predicted hydrogen storage capacity of the solid-state hydrogen storage device.

[0077] Among them, the identification model can be a nonlinear model built based on data-driven approaches.

[0078] In this step, the identification model is configured by inputting the target distributed parameters of the solid hydrogen storage device to be predicted into the identification model. The identification model performs data dynamic calculation on the target distributed parameters to obtain the predicted hydrogen storage capacity of the solid hydrogen storage device and output it.

[0079] According to the data-driven calculation method for hydrogen storage capacity of solid-state hydrogen storage devices provided in the embodiments of this application, by dynamically identifying a fully coupled multiphysics model, a high-speed analysis of the distribution of multiphysics fields inside the solid-state hydrogen storage device is achieved. The simplified identification model obtained can predict the hydrogen storage capacity of solid-state hydrogen storage devices with high accuracy, low cost, and fast response. It can compress the calculation time of several hours required for finite element calculation to tens of seconds, solve the problems of high calculation cost and long solution time in multiphysics modeling technology, reduce calculation cost and guide experimental implementation, and has the potential to be extended to the dynamic research of other nonlinear systems.

[0080] In some embodiments, the multiphysics fully coupled model includes a mass and momentum transfer module, a chemical reaction module, and a heat transfer module.

[0081] In some embodiments, the structural features of the solid hydrogen storage device include a reaction zone, an expansion volume, and hydrogen inlet and outlet channels;

[0082] The mass and momentum transfer module is used to characterize the hydrogen flow process in the expansion volume.

[0083] The chemical reaction module is used to characterize the motion state of the metal hydride reaction bed in the reaction zone;

[0084] The heat transfer module is used to characterize the heat transfer state of the metal hydride reaction bed in the reaction zone.

[0085] It should be noted that the mass and momentum transfer module is represented by formulas (1)-(4) and (6)-(8), the chemical reaction module is represented by formulas (12)-(15), and the heat transfer module is represented by formulas (5) and (9)-(11).

[0086] In the mass and momentum transfer module, since only hydrogen gas flows in the expanding volume, it can be described by continuity and the Navier-Stokes equations:

[0087]

[0088] Where, ρ g Here, t is the gas density, and t is time. This is the gradient calculation symbol, p g It is gas pressure. It is the gas velocity vector, I is the identity tensor, and μ g V is the fluid viscosity, V is the gas velocity vector, and g is the gravitational acceleration of the fluid.

[0089] Fluid viscosity μ g and gas density ρ g It is a function of temperature and can be expressed as:

[0090]

[0091] Where T is temperature, M g It is the molecular mass, R g It is the universal gas constant.

[0092] The temperature field of the expanded volume can be expressed as:

[0093]

[0094] Among them, C p,g It is specific heat capacity, kJ / m³. g It is the thermal conductivity.

[0095] In the chemical reaction module, the momentum conservation of the metal hydride reaction bed section is expressed as:

[0096]

[0097] Where, ε b It is the porosity of the reaction bed, ρ g It is the density of hydrogen gas. It is the reaction source term. It is the gas velocity vector.

[0098] Gas velocity vector Calculated using Darcy's theorem:

[0099]

[0100] Where κ is permeability, μ g It is the fluid viscosity, p g It is gas pressure.

[0101] The mass conservation of the metal hydride reaction bed is expressed as follows:

[0102]

[0103] Where, ρ s It is the density of the solid, ε b It is porosity, S s t is the quality source term, and t is time.

[0104] The heat transfer module of the metal hydride reaction bed is represented by energy conservation:

[0105]

[0106] Among them, (ρc p ) eff It is the effective heat capacity, k eff It is the effective thermal conductivity, ΔH is the enthalpy change, and ρ is the effective thermal conductivity.g It is the density of the gas, C p,g It is the specific heat capacity of hydrogen, C s It is the specific heat capacity of a metal.

[0107] Effective heat capacity (ρc) p ) eff and effective thermal conductivity k eff It can be represented as:

[0108] (ρc p ) eff =ε b ρ g C p,g +(1-ε b )ρ s C s (10)

[0109] k eff =ε b k g +(1-ε b )k s (11)

[0110] Where, ε b It is porosity, C p,g It is the specific heat capacity of hydrogen, kJ / L. eff It is the effective thermal conductivity, k g It is the thermal conductivity of the gas, k s It is the thermal conductivity of solids, p g It is the gas density, ρ s It is the density of the solid, C s It is the specific heat capacity of a metal.

[0111] After meshing the multiphysics fully coupled model, the local reaction of the metal hydride in each mesh can be either a hydrogenation or dehydrogenation process, depending on whether the local gas pressure of the mesh is higher or lower than the equilibrium pressure. The reaction rate m of the local reaction in each mesh can be expressed as:

[0112]

[0113] m = 0, p eq,a >p g >p eq ,d (13)

[0114]

[0115] Among them, R g It is the universal gas constant, C a It is the absorption rate constant, E a It is the activation energy of the absorption process, R g It is the universal gas constant, T is temperature, and p is the gas constant.g It is gas pressure, p eq,a It is the equilibrium pressure during the absorption process, ρ sat It is the saturation density of the metal hydride, ρ s It is the density of the metal hydride, C d It is the desorption rate constant, E d It is the activation energy of the desorption process, p eq,d It is the equilibrium pressure during the absorption process, ρ emp It is the density at which the metal hydride is completely desorbed.

[0116] The equilibrium pressure is determined by the van der Hoff relation. In the case of a LaNi5-H2 metal hydrogen storage system, the effect of hydrogen composition on the equilibrium pressure change is negligible. The equilibrium pressure is a function of temperature and can be expressed as:

[0117]

[0118] Where, p eq It is the equilibrium pressure, p ref A is the reference pressure, and A and B are the equilibrium pressure coefficients.

[0119] The values ​​for hydrogen absorption and dehydrogenation are different under the reference pressure; at a reference pressure of p... ref =1 MPa, for the adsorption process A = 10.700, B = 3704.6, for the desorption process A = 10.570, B = 3704.6. The difference in A values ​​is due to the hysteresis phenomenon between absorption and desorption.

[0120] In this embodiment, by constructing a multi-physics fully coupled model that includes a mass and momentum transfer module, a chemical reaction module, and a heat transfer module, the hydrogen storage scenario of solid-state hydrogen storage devices can be fully covered, enabling the multi-physics fully coupled model to accurately predict the hydrogen storage capacity of solid-state hydrogen storage devices based on distributed parameters.

[0121] In some embodiments, to ensure that the distributed parameters in the dynamic response database can fully reflect the dynamic behavior characteristics of the solid-state hydrogen storage device, it is typically necessary to collect dynamic transition processes between random and aperiodic steady-state operating points. Furthermore, the time interval between adjacent dynamic transition processes should be set to allow the solid-state hydrogen storage device to reach a steady state during dynamic transitions, in order to obtain the time constant of the dynamic process.

[0122] The sampling rate is set as follows:

[0123] To accurately describe the strong coupling of multiphysics, a cooperative angle equation is constructed based on the gradient vectors in the multiphysics. The cooperative angle β between velocity and temperature can be expressed as follows:

[0124]

[0125] Where U is the velocity vector, It is the temperature gradient vector. A smaller cooperation angle indicates stronger coupling in the parameter region, while a larger cooperation angle indicates weaker coupling in the parameter region.

[0126] A smaller cooperation angle β indicates stronger coupling in the parameter region, while a larger cooperation angle β indicates weaker coupling. When sampling in the operating parameter region where the cooperation angle β is less than 45°, the sampling rate is... The sampling rate is [value] when the cooperative angle β is greater than 45° in the operating range.

[0127] Actual sampling frequency f re as follows:

[0128]

[0129] Among them, f min It is the minimum sampling frequency.

[0130] The combination of distributed parameters collected at the actual sampling frequency and the hydrogen storage capacity calculated by the multiphysics fully coupled model is used as the sampling data.

[0131] like Figure 3 As shown, based on the chemical reaction process, mass transfer process, momentum transfer process and heat transfer process of the solid hydrogen storage device, a multiphysics simulation is performed on the solid hydrogen storage device to obtain a fully coupled multiphysics model and generate a dynamic response database (dynamic characteristic database).

[0132] In the model validation phase, the dynamic model of the multiphysics fully coupled model is identified using the Hammerstein-Wiener model, resulting in an identified model. Data-driven dynamic prediction is then performed using the identified model.

[0133] When model validation is required, a dynamic response database (dynamic characteristic database) is regenerated based on the dynamic behavior characteristics of the solid hydrogen storage device to obtain an updated identification model.

[0134] The original identification model and the updated identification model are compared, and the original identification model is updated in real time based on the error between the two.

[0135] In some embodiments, the step of dynamically identifying the multiphysics fully coupled model through a dynamic response database to obtain the identification model of the solid-state hydrogen storage device includes:

[0136] Based on the dynamic response database, the multiphysics fully coupled model is identified to obtain a dynamic model, which is constructed based on the input-error model;

[0137] The nonlinearity of the input of the dynamic model is expanded by a polynomial chaos, and the nonlinearity of the output of the dynamic model is represented by a wavelet network to obtain the identification model.

[0138] The dynamic response database includes the correspondence between distributed parameters and the hydrogen storage capacity of the solid-state hydrogen storage device. During the dynamic identification of the multiphysics fully coupled model, the distributed parameters are used as input data and the hydrogen storage capacity is used as output data.

[0139] In actual implementation, the Hammerstein-Wiener identification method is used to sort the data in the dynamic response database according to the time series and construct the Hammerstein model and the Wiener model. Both the Hammerstein model and the Wiener model include nonlinear and linear parts.

[0140] Polynomials, piecewise linear functions, neural networks, etc., are selected as the nonlinear part. Through regression analysis or neural network training, the input data is used to fit the nonlinear function as the nonlinear part.

[0141] After identifying the nonlinear component, linear regression, state-space model, or autoregressive model are used to model the nonlinear output linearly, and least squares or other optimization algorithms are used to identify the parameters of the linear component.

[0142] The combination of the obtained linear and nonlinear parts is used as a dynamic model to achieve the identification of the fully coupled multiphysics model.

[0143] The kinetic model of a solid-state hydrogen storage device is described as follows:

[0144]

[0145] Where u(t) is the temperature and inlet pressure of the solid-state hydrogen storage device, y(t) is the adsorption / desorption reaction rate of solid-state hydrogen storage, e(t) is the system disturbance, and n is the system delay. The input is nonlinear, μ(y) is the output nonlinearity, and F(z) and B(z) are the values ​​of z, respectively. -1 The polynomial of the operator, b i These are the numerator coefficients, i = 1, 2, ..., kb-1, where kb-1 is the number of terms in the numerator; f j is the denominator coefficient, and kf is the index of the denominator term.

[0146] Data-driven calculations are performed using sampled data (distributed parameters and hydrogen storage capacity) from the dynamic response database. The structure of the kinetic model is represented by an output-error model as follows:

[0147]

[0148] Where u(t) is the system input, y(t) is the system output, e(t) is the system disturbance, and n is the system delay. F(z) and B(z) are respectively z -1 Operator polynomial:

[0149]

[0150] Where u(t) is the temperature and inlet pressure of the solid-state hydrogen storage device, y(t) is the adsorption / desorption reaction rate of solid-state hydrogen storage, e(t) is the system disturbance, and n is the system delay. The input is nonlinear, μ(y) is the output nonlinearity, and F(z) and B(z) are the values ​​of z, respectively. -1 The polynomial of the operator.

[0151] Due to their inherent complexity, solid-state hydrogen storage devices are typical nonlinear systems. In order to accurately capture their dynamic behavior, nonlinear elements must be incorporated into traditional linear system models.

[0152] The dynamic model is based on the Hammerstein-Wiener model and is effective in describing the dynamic characteristics of nonlinear systems.

[0153] like Figure 4 As shown, the kinetic model consists of three key parts: a core linear kinetic model of the solid-state hydrogen storage device, and nonlinear components located at the input and output ends, respectively. The linear kinetic model is responsible for simulating the basic kinetic characteristics of the solid-state hydrogen storage device, while the nonlinear components at the input and output are specifically used to capture and express the nonlinear behavior of the system, thus providing a faster and easier simulation method for system analysis.

[0154] In some embodiments, the input nonlinearity of the kinetic model is represented by a polynomial chaotic expansion, that is, the input nonlinearity of the kinetic model of the solid hydrogen storage device is represented by a polynomial chaotic expansion:

[0155]

[0156] Among them, ψ b (x) is a multidimensional polynomial basis, given by the tensor product of univariate orthogonal shift Legendre polynomials, a b These are the polynomial coefficients, b i It is a single-variable polynomial The number of times.

[0157] Next, an ablation experiment was conducted to transform the polynomial into a sparse polynomial, while ensuring that the calculation accuracy was not less than 95%.

[0158] In some embodiments, the output nonlinearity of the kinetic model is represented by a wavelet network, that is, the output nonlinearity of the kinetic model of the solid hydrogen storage device is represented by a wavelet network:

[0159]

[0160] Where μ(y) is the output nonlinearity, l and m are node indices, h(i) is the output value of node i in the hidden layer, and ω ik These are the weights connecting the hidden layer and the output layer, where k and i are the number of nodes in the output layer and hidden layer, respectively, and h is the weight. i It is the mother wavelet function, ω i These are the weights connecting the input layer and the hidden layer, d i It is the translation factor, a i It is the stretching factor.

[0161] The identification model calculates the hydrogen adsorption / desorption capacity of the solid hydrogen storage device by calculating the distributed parameters such as input temperature and inlet pressure.

[0162] In the embodiments of this application, the identification model is built on a multiphysics fully coupled model, and the dynamic calculation process is simulated using COMSOL software. A relative tolerance is set in the identification model as a standard for calculation accuracy.

[0163] The relative tolerance is used as a criterion for judging the calculation results output by the identification model during the iterative calculation process of the identification model.

[0164] During the calculation, the identification model uses the finite element method to perform iterative calculations based on these input distributed parameters, obtaining iterative results. The iteration process terminates when the difference between two consecutive iteration results falls below the relative tolerance. Through this process, key information related to the distributed parameters (boundary conditions), such as hydrogen adsorption / desorption rates, temperature distribution of metal hydrides, and hydrogen flow rate distribution, can be obtained.

[0165] The predicted hydrogen storage capacity output by the identification model reflects the dynamic characteristics of metal hydrides.

[0166] The identification model consists of computational units and judgment units. The computational unit first uses the input distributed parameters as initial parameters to initiate the iterative process of the finite element analysis. In each iteration, based on the results of the previous iteration, the computational unit generates new parameter values, which are then used as the starting point for the next iteration. This continuous iterative process continues until the accuracy standard set by the identification model is reached, ensuring the accuracy and reliability of the calculation results.

[0167] The decision unit plays a crucial role in the identification model. It maintains the pre-defined relative tolerance and captures the result value after each iteration. The decision unit meticulously compares the result of the current iteration with that of the previous iteration to ensure computational accuracy. When the difference between the results of two consecutive iterations falls below the preset relative tolerance threshold, the decision unit issues a termination command to the calculation module. This mechanism ensures that the calculation process stops promptly after reaching the required accuracy, avoiding unnecessary resource waste. Ultimately, the result value obtained from the last iteration is identified as the key parameter value of the solid-state hydrogen storage device at that specific moment, providing reliable data support for system analysis and optimization.

[0168] In this embodiment, the multiphysics fully coupled model is identified through a dynamic response database. The resulting dynamic model has lower complexity and can more accurately predict the hydrogen storage capacity of the solid hydrogen storage device. This enhances the understanding of the dynamic characteristics of the solid hydrogen storage device and helps to effectively control it. It also solves the problems of high computational cost and long solution time in multiphysics modeling technology.

[0169] By performing a chaotic expansion of the input nonlinearity using a polynomial, we can effectively capture nonlinear features and identify chaotic regions and their boundaries, reducing computational complexity and improving the predictive ability of the identification model. By representing the output nonlinearity using a wavelet network, we can achieve efficient feature extraction, enhance the flexibility of the identification model, improve the prediction accuracy of the identification model, simplify the model, improve computational efficiency, and at the same time have good interpretability.

[0170] In addition, the identification model and the multiphysics fully coupled model can be compared and verified. The multiphysics fully coupled model can update the identification model to calculate the hydrogen storage capacity of the solid hydrogen storage device.

[0171] In some embodiments, after inputting the target distributed parameters of the solid-state hydrogen storage device into the identification model, and performing data-driven calculations on the target distributed parameters through the identification model to output the predicted hydrogen storage capacity of the solid-state hydrogen storage device, the method further includes:

[0172] Multiple test distributed parameters are sequentially input into the multiphysics fully coupled model to obtain the first hydrogen storage response process output by the multiphysics fully coupled model;

[0173] The multiple test distributed parameters are sequentially input into the identification model to obtain the second hydrogen storage capacity response process output by the identification model;

[0174] An updated identification model is constructed based on the error between the first hydrogen storage response process and the second hydrogen storage response process.

[0175] In actual execution, by inputting multiple test distributed parameters into the multiphysics fully coupled model and the identification model respectively, the first hydrogen storage response process and the second hydrogen storage response process are obtained.

[0176] The first hydrogen storage response process and the second hydrogen storage response process are compared. If the error between the two is too large, the dynamic response database of the multiphysics fully coupled model is re-acquired in order to identify the multiphysics fully coupled model and obtain an updated identification model.

[0177] In this embodiment, the computational accuracy of the identification model is verified, and an updated identification model is constructed when the error is too large, so as to control the computational accuracy of the identification model.

[0178] In some embodiments, after constructing the updated identification model, the method further includes:

[0179] The hydrogen storage capacity of the solid hydrogen storage device is predicted by the identification model to obtain a first prediction result.

[0180] The hydrogen storage capacity of the solid hydrogen storage device is predicted by the updated identification model to obtain a second prediction result.

[0181] If the error of the first prediction result is greater than the error of the second prediction result, or if the error of the first prediction result exceeds the error range, the identification model is replaced by updating the identification model.

[0182] In actual implementation, the dynamic update method for the identification model of solid-state hydrogen storage devices is as follows:

[0183] When using online updates, the identification model f will be updated. new And the original identification model f old Simultaneously perform predictions, if the identification model f is updated new The second prediction result has a smaller error over the cumulative time period than the original identification model f. old The error of the first prediction result, or the original identification model f oldThe error of the first prediction result is greater than the boundary value ε of the acceptable error range. i Then, the model is updated and replaced by updating the identification model f. new Replace the original recognition model f old .

[0184] Where, ε i This refers to the acceptable boundary value of the cumulative error under the physical fields of temperature, velocity, pressure, and the mass of hydrogen in the metal hydride. When any physical field error reaches this acceptable boundary value, a model update is performed. The specific rules are as follows:

[0185] (∑(|f old (I)-O r |>|f new (I)-O r |))>d t1 (twenty two)

[0186] d t2 <(∑(|f old (I)-O r |>|f new (I)-O r |)) <d t1 (twenty three)

[0187] ∑(|f old (I)-O r |-|f new (I)-O r |)>ε i (twenty four)

[0188] Where, ε i It is the acceptable boundary value of cumulative error under different physical fields, f old (I) is the original identification model, where I represents the distributed parameters (inlet temperature, pressure) input to the model, and f new (I) is the updated identification model, d t1 d t2 It is the cumulative duration boundary value of the model update, O r It is the actual value of hydrogen storage corresponding to the distributed parameters.

[0189] The accumulated error ε under different physical fields is set based on the parameter space of temperature, velocity, pressure, and the mass of hydrogen in the metal hydride. i The setting is represented as follows:

[0190] ε i =|z max -z min |×γ (25)

[0191] Among them, z maxIt represents the maximum value of the hydrogen mass in the metal hydride at the given temperature, pressure, flow rate, and z. min Temperature, pressure, flow rate, minimum hydrogen mass in metal hydrides, and γ is the error tolerance.

[0192] In this embodiment, the accuracy of the identification model is ensured by updating the identification model.

[0193] By dividing the multiphysics fully coupled model into a grid, the distribution of actual reactant concentrations along the flow direction is considered. The set of parameter values ​​within each grid block, obtained by using the reactivity concentration within that block, is used as a distributed parameter. This better reflects the local characteristics of solid-state hydrogen storage operation, making the multiphysics coupling process of the model more complete and improving its accuracy and application value. The combination of data-driven methods and multiphysics models facilitates the development of solid-state hydrogen storage models, promotes the development of practical applications, and enables the prediction of solid-state hydrogen storage operation.

[0194] This embodiment is based on COMSOL Multiphysics. TM 6.1 Implementation using the Matlab simulation platform, where relevant parameters of the solid-state hydrogen storage device during the simulation are shown in Table 1:

[0195] Table 1. Parameters related to solid-state hydrogen storage devices

[0196] parameter Numerical Hydrogen adsorption rate constant <![CDATA[59.187s -1 ]]> Hydrogen desorption rate constant <![CDATA[9.57s -1 ]]> Hydrogen specific heat capacity 14890 J / (mol K) Metal specific heat capacity 419 J / (mol K) Hydrogen adsorption activation energy 21179.6 J / mol Hydrogen desorption and adsorption activation energy 16473 J / mol Effective heat convection coefficient <![CDATA[1652W / (m 2 K)]]> Metal permeability <![CDATA[10 -8 m 2 ]]> Thermal conductivity of hydrogen 0.1815 W / (m K) Thermal conductivity of metals 1.087 W / (m K) Molecular mass of hydrogen <![CDATA[2.01588·10 -3 kg / mol Porosity of metals 0.5 Density of metal hydrides without hydrogen adsorption <![CDATA[7164kg / m 3 ]]> Density of hydrogen-saturated metal hydrides <![CDATA[7259kg / m 3 ]]> Reference pressure 1Mpa

[0197] like Figure 5 As shown, the horizontal axis represents time (in seconds), and the vertical axis represents the hydrogen mass in the metal hydride (in kg). The dynamic process of hydrogen adsorption in the metal hydride is predicted at a temperature of 293 K and an inlet pressure of 0.8 MPa.

[0198] like Figure 6 As shown, the horizontal axis represents time (in seconds), and the vertical axis represents the hydrogen mass in the metal hydride (in kg). The dynamic process of hydrogen desorption and adsorption of metal hydrides is predicted at a temperature of 313 K and an inlet pressure of 0.025 MPa.

[0199] The simulation period can be set to 4000s, and the dynamic response will begin at 0s. The purpose of the experiment is to predict the dynamic process of hydrogen adsorption / desorption in the metal hydride system under different operating conditions.

[0200] The data-driven calculation method for hydrogen storage capacity of a solid-state hydrogen storage device provided in this application embodiment can be executed by a data-driven calculation device for hydrogen storage capacity of a solid-state hydrogen storage device. This application embodiment uses the execution of the data-driven calculation method for hydrogen storage capacity of a solid-state hydrogen storage device by the data-driven calculation device as an example to illustrate the data-driven calculation device for hydrogen storage capacity of a solid-state hydrogen storage device provided in this application embodiment.

[0201] This application also provides a data-driven calculation device for the hydrogen storage capacity of a solid-state hydrogen storage device.

[0202] like Figure 7 As shown, the data-driven calculation device for the hydrogen storage capacity of the solid-state hydrogen storage device includes: a first processing module 710, an acquisition module 720, a second processing module 730, and a third processing module 740.

[0203] The first processing module 710 is used to obtain the distributed parameters of the solid hydrogen storage device through a multi-physics fully coupled model of the solid hydrogen storage device. The multi-physics fully coupled model is constructed based on the structural features of the solid hydrogen storage device.

[0204] The acquisition module 720 is used to acquire the dynamic response database of the multiphysics fully coupled model, and the dynamic response database is used to characterize the correspondence between the distributed parameters of the multiphysics fully coupled model and the hydrogen storage capacity of the solid hydrogen storage device.

[0205] The second processing module 730 is used to dynamically identify the multi-physics fully coupled model through a dynamic response database to obtain the identification model of the solid hydrogen storage device.

[0206] The third processing module 740 is used to input the target distributed parameters of the solid-state hydrogen storage device into the identification model, and perform data-driven calculations on the target distributed parameters through the identification model to output the predicted hydrogen storage capacity of the solid-state hydrogen storage device.

[0207] According to the data-driven computing device for hydrogen storage capacity of solid-state hydrogen storage device provided in the embodiments of this application, by dynamically identifying the multi-physics field fully coupled model, a high-speed analysis of the multi-physics field distribution inside the solid-state hydrogen storage device is achieved. The simplified identification model obtained can predict the hydrogen storage capacity of solid-state hydrogen storage device with high accuracy, low cost and fast response. It can compress the calculation time of several hours required for finite element calculation to tens of seconds, solve the problems of high calculation cost and long solution time of multi-physics field modeling technology, reduce calculation cost and guide experimental implementation, and has the potential to be extended to the dynamic research of other nonlinear systems.

[0208] The data-driven computing device for the hydrogen storage capacity of the solid-state hydrogen storage device in this application embodiment can be an electronic device or a component within an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal or other devices besides a terminal. For example, the electronic device can be a mobile phone, tablet computer, laptop computer, or personal computer (PC), etc., and this application embodiment does not specifically limit it.

[0209] The data-driven calculation device for the hydrogen storage capacity of the solid-state hydrogen storage device in this embodiment can be a device with an operating system. This operating system can be Android, Linux, or other possible operating systems; this embodiment does not specifically limit its use.

[0210] The data-driven calculation device for the hydrogen storage capacity of the solid hydrogen storage device provided in this application embodiment can realize the various processes implemented in the above-described data-driven calculation method embodiment for the hydrogen storage capacity of the solid hydrogen storage device. To avoid repetition, these processes will not be described again here.

[0211] In some embodiments, as Figure 8 As shown, this application embodiment also provides an electronic device 800, including a processor 801, a memory 802, and a computer program stored in the memory 802 and executable on the processor 801. When the program is executed by the processor 801, it implements the various processes of the above-described data-driven calculation method embodiment for the hydrogen storage capacity of the solid-state hydrogen storage device and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0212] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.

[0213] This application also provides a non-transitory computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the various processes of the above-described data-driven calculation method embodiment for the hydrogen storage capacity of the solid-state hydrogen storage device and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0214] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0215] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the data-driven calculation method for the hydrogen storage capacity of the solid-state hydrogen storage device described above.

[0216] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0217] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface and the processor are coupled. The processor is used to run programs or instructions to implement the various processes of the above-described data-driven calculation method embodiment for the hydrogen storage capacity of the solid-state hydrogen storage device, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0218] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0219] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0220] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the data-driven calculation method for the hydrogen storage capacity of the solid-state hydrogen storage device of the various embodiments of this application.

[0221] In the description of this application, "first feature" and "second feature" may include one or more of the features.

[0222] In the description of this application, "multiple" means two or more.

[0223] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

[0224] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0225] Although embodiments of this application have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of this application, the scope of which is defined by the claims and their equivalents.

Claims

1. A data-driven calculation method for the hydrogen storage capacity of a solid-state hydrogen storage device, characterized in that, include: The distributed parameters of the solid-state hydrogen storage device are obtained through a multi-physics fully coupled model, which is constructed based on the structural characteristics of the solid-state hydrogen storage device. Obtain the dynamic response database of the multiphysics fully coupled model, which is used to characterize the correspondence between the distributed parameters of the multiphysics fully coupled model and the hydrogen storage capacity of the solid hydrogen storage device; By using a dynamic response database, the multi-physics fully coupled model is dynamically identified to obtain the identification model of the solid hydrogen storage device. The target distributed parameters of the solid-state hydrogen storage device are input into the identification model, and the identification model performs data-driven calculations on the target distributed parameters to output the predicted hydrogen storage capacity of the solid-state hydrogen storage device. After inputting the target distributed parameters of the solid-state hydrogen storage device into the identification model, and performing data-driven calculations on the target distributed parameters through the identification model to output the predicted hydrogen storage capacity of the solid-state hydrogen storage device, the method further includes: Multiple test distributed parameters are sequentially input into the multiphysics fully coupled model to obtain the first hydrogen storage response process output by the multiphysics fully coupled model; The multiple test distributed parameters are sequentially input into the identification model to obtain the second hydrogen storage capacity response process output by the identification model; An updated identification model is constructed based on the error between the first hydrogen storage response process and the second hydrogen storage response process; The multiphysics fully coupled model includes a mass and momentum transfer module, a chemical reaction module, and a heat transfer module. The structural features of the solid hydrogen storage device include a reaction zone, an expansion volume, and hydrogen inlet and outlet channels. The mass and momentum transfer module is used to characterize the hydrogen flow process in the expansion volume. The chemical reaction module is used to characterize the motion state of the metal hydride reaction bed in the reaction zone; The heat transfer module is used to characterize the heat transfer state of the metal hydride reaction bed in the reaction zone.

2. The data-driven calculation method for the hydrogen storage capacity of a solid-state hydrogen storage device according to claim 1, characterized in that, The step of dynamically identifying the multiphysics fully coupled model through a dynamic response database to obtain the identification model of the solid-state hydrogen storage device includes: Based on the dynamic response database, the multiphysics fully coupled model is identified to obtain a dynamic model, which is constructed based on the input-error model; The nonlinear input of the dynamic model is represented by a chaotic expansion of a polynomial, and the nonlinear output of the dynamic model is represented by a wavelet network, thus obtaining the identification model.

3. The data-driven calculation method for the hydrogen storage capacity of a solid-state hydrogen storage device according to claim 2, characterized in that, The nonlinear inputs of the dynamic model are represented by a chaotic expansion of polynomials, including: ； ； in, It is the nonlinear input, It is the space of the input parameter distribution, where n is the dimension of the input parameters. It is an n-dimensional index. It is a multidimensional polynomial basis, given by the tensor product of univariate orthogonal shift Legendre polynomials. These are polynomial coefficients. It is a single-variable polynomial The number of times.

4. The data-driven calculation method for the hydrogen storage capacity of a solid-state hydrogen storage device according to claim 2, characterized in that, The nonlinear output of the dynamic model is represented by a wavelet network, including: ； ； in, It is a non-linear output. l and m It is a node index. It is a node in the hidden layer The output value, These are the weights that connect the hidden layer and the output layer. and These are the number of nodes in the output layer and the hidden layer, respectively. It is the mother wavelet function. These are the weights connecting the input layer and the hidden layer. It is the translation factor. It is the stretching factor.

5. The data-driven calculation method for the hydrogen storage capacity of a solid-state hydrogen storage device according to claim 1, characterized in that, After constructing the updated identification model, the method further includes: The hydrogen storage capacity of the solid hydrogen storage device is predicted by the identification model to obtain a first prediction result. The hydrogen storage capacity of the solid hydrogen storage device is predicted by the updated identification model to obtain a second prediction result. If the error of the first prediction result is greater than the error of the second prediction result, or if the error of the first prediction result exceeds the error range, the identification model is replaced by updating the identification model.

6. A data-driven calculation device for the hydrogen storage capacity of a solid-state hydrogen storage device, characterized in that, include: The first processing module is used to obtain the distributed parameters of the solid-state hydrogen storage device through a multi-physics fully coupled model of the solid-state hydrogen storage device. The multi-physics fully coupled model is constructed based on the structural features of the solid-state hydrogen storage device. The acquisition module is used to acquire the dynamic response database of the multiphysics fully coupled model, and the dynamic response database is used to characterize the correspondence between the distributed parameters of the multiphysics fully coupled model and the hydrogen storage capacity of the solid hydrogen storage device. The second processing module is used to dynamically identify the multi-physics fully coupled model through a dynamic response database to obtain the identification model of the solid hydrogen storage device. The third processing module is used to input the target distributed parameters of the solid hydrogen storage device into the identification model, and to perform data-driven calculations on the target distributed parameters through the identification model to output the predicted hydrogen storage capacity of the solid hydrogen storage device. After inputting the target distributed parameters of the solid-state hydrogen storage device into the identification model, and performing data-driven calculations on the target distributed parameters through the identification model to output the predicted hydrogen storage capacity of the solid-state hydrogen storage device, the method further includes: Multiple test distributed parameters are sequentially input into the multiphysics fully coupled model to obtain the first hydrogen storage response process output by the multiphysics fully coupled model; The multiple test distributed parameters are sequentially input into the identification model to obtain the second hydrogen storage capacity response process output by the identification model; An updated identification model is constructed based on the error between the first hydrogen storage response process and the second hydrogen storage response process; The multiphysics fully coupled model includes a mass and momentum transfer module, a chemical reaction module, and a heat transfer module. The structural features of the solid hydrogen storage device include a reaction zone, an expansion volume, and hydrogen inlet and outlet channels. The mass and momentum transfer module is used to characterize the hydrogen flow process in the expansion volume. The chemical reaction module is used to characterize the motion state of the metal hydride reaction bed in the reaction zone; The heat transfer module is used to characterize the heat transfer state of the metal hydride reaction bed in the reaction zone.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements a data-driven calculation method for the hydrogen storage capacity of the solid-state hydrogen storage device as described in any one of claims 1-5.

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