Electrolytic cell stack analysis method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of electrochemical analysis, in particular to an electrolytic cell stack analysis method and device, computer equipment and a storage medium, the method comprises the following steps: acquiring initial internal state data of a cell stack to be analyzed, the initial internal state data comprising an initial temperature field, an initial gas concentration field and an initial electrode damping coefficient; inputting the initial internal state data into a pre-constructed non-isothermal octyl kinetic model to obtain the current density, the updated temperature field and the updated concentration field of the cell stack; inputting the updated temperature field and the current density into the dynamic equivalent circuit model to obtain an overpotential parameter of the cell stack; performing multi-scale coupling time integration based on the current density by using the overpotential parameter to obtain thermal stress distribution of the cell stack; the analysis result of the cell stack is generated based on the current density, the updated temperature field, the updated concentration field and the thermal stress distribution, and the analysis reliability of the cell stack can be improved.

Description

Technical Field

[0001] The present application relates to the field of electrochemical analysis technology, and in particular to an electrolytic cell stack analysis method, device, computer equipment, and storage medium. Background Art

[0002] With the development of green energy, the performance of co-electrolysis cell stacks, as the core equipment for green hydrogen production, has attracted more and more attention. The performance of co-electrolysis cell stacks is usually significantly affected by the coupling of multiple physical fields such as electrolyte flow, electrochemical reaction, and heat conduction.

[0003] In related battery stack analysis technologies, in terms of modeling accuracy, the dynamic model of high-temperature co-electrolysis battery stacks generally adopts a step-by-step decoupling method, such as separating the finite element thermodynamic model and the electrochemical impedance spectrum; in terms of dynamic real-time performance, the traditional symplectic algorithm cannot be effectively applied under non-isothermal conditions; in terms of parameter calibration, the existing impedance spectrum feedback mechanism relies on the steady-state assumption and fails to combine real-time concentration field and temperature field data for dynamic correction.

[0004] Therefore, during implementation, the related technology still has at least the problem of low reliability in analyzing the electrolytic cell stack. Summary of the Invention

[0005] Based on this, the purpose of this application is to solve at least one of the above-mentioned technical defects, especially the technical defect of low reliability of battery stack analysis in the prior art. This application provides an electrolytic battery stack analysis method, device, computer equipment and storage medium.

[0006] In a first aspect, the present application provides an electrolytic cell stack analysis method, the method comprising:

[0007] Acquiring initial internal state data of the battery stack to be analyzed, wherein the initial internal state data of the battery stack includes an initial temperature field, an initial gas concentration field, and an initial electrode damping coefficient;

[0008] Input the initial internal state data into a pre-built non-isothermal symplectic kinetic model, and output the current density, updated temperature field, and updated concentration field of the battery stack. The non-isothermal symplectic kinetic model includes a non-isothermal Hamiltonian for heat exchange terms, a non-isothermal Hamiltonian for potential energy terms, and a non-isothermal Hamiltonian for kinetic energy terms.

[0009] The updated temperature field and current density are input into the pre-built dynamic equivalent circuit model, and the overpotential parameters of the battery stack are output;

[0010] Using overpotential parameters and multi-scale coupled time integration based on current density, the thermal stress distribution of the battery stack is obtained.

[0011] Generate analysis results of the battery stack based on current density, updated temperature field, updated concentration field, and thermal stress distribution.

[0012] In one embodiment, the method further comprises:

[0013] Obtain experimental observation data, initial activation energy data, and initial thermal conductivity data;

[0014] Input the updated temperature field, updated concentration field, and overpotential parameters into the pre-built observation data mapping model, and output the observed state prediction data;

[0015] The experimental observation data and the observation state prediction data are input into a pre-set loss value model, and the optimized activation energy and optimized thermal conductivity are output; wherein the loss value model includes an activation energy term optimized for the initial activation energy data and a thermal conductivity term optimized for the initial thermal conductivity data;

[0016] Among them, the optimized activation energy is used to feed back into the potential energy term, and the optimized thermal conductivity is used to feed back into the multiscale coupling time integral.

[0017] In one embodiment, the thermal stress distribution of the battery stack is obtained by using the overpotential parameter and performing multi-scale coupled time integration based on the current density, including:

[0018] Using the overpotential parameter, the current density is updated to obtain the updated current density;

[0019] Based on the updated current density and the preset fast-changing time step, the Joule thermal power parameter of the fast-changing time layer of the battery stack is obtained;

[0020] According to the Joule heat power parameter of the fast-varying time layer and the preset slow-varying time step, the global temperature of the slow-varying time layer is obtained;

[0021] The thermal stress distribution is obtained according to the global temperature, the preset reference temperature, and the preset Young's modulus.

[0022] In one embodiment, the updated temperature field and current density are input into a pre-built dynamic equivalent circuit model, and the overpotential parameters of the battery stack are output, including:

[0023] Obtaining the actual working potential applied externally to the battery stack;

[0024] The real-time thermodynamic potential is determined based on the updated concentration field, the updated temperature field, and the reference temperature. The concentrations of the gases corresponding to the updated concentration field are determined based on the current density in the dynamic equivalent circuit model.

[0025] The dynamic equivalent circuit model is used to iteratively adjust the error between the actual working potential and the real-time thermodynamic potential to obtain the overpotential parameter.

[0026] In one embodiment, determining the real-time thermodynamic potential based on the updated concentration field, the updated temperature field, and the reference temperature includes:

[0027] Determine the thermodynamic equilibrium potential of the battery stack based on the updated concentration field and the updated temperature field;

[0028] According to the updated temperature field and the reference temperature, the thermodynamic temperature correction potential is obtained;

[0029] The potential is corrected according to the thermodynamic equilibrium potential and the thermodynamic temperature to obtain the real-time thermodynamic potential.

[0030] In one embodiment, after obtaining the global temperature of the slow-varying time layer according to the Joule heat power parameter of the fast-varying time layer and the preset slow-varying time step, the method further includes:

[0031] The global temperature is used to update the thermodynamic equilibrium potential and the thermodynamic temperature correction potential respectively.

[0032] In one embodiment, the non-isothermal symplectic kinetic model is constructed by a Hamiltonian, wherein the Hamiltonian is expressed as follows:

[0033]

[0034] in, is the kinetic energy term, is the momentum vector, is the mass matrix; is the potential energy term; is the hot exchange item, is the thermal conductivity coefficient, is the gas temperature, is the initial temperature field.

[0035] In a second aspect, the present application provides an electrolytic cell stack analysis device, the device comprising:

[0036] An initial state acquisition module, used to acquire initial internal state data of the battery stack to be analyzed, wherein the initial internal state data of the battery stack includes an initial temperature field, an initial gas concentration field, and an initial electrode damping coefficient;

[0037] An internal state update module is used to input the initial internal state data into a pre-built non-isothermal symplectic kinetic model and output the current density, updated temperature field, and updated concentration field of the battery stack. The non-isothermal symplectic kinetic model includes a non-isothermal Hamiltonian for heat exchange terms, a non-isothermal Hamiltonian for potential energy terms, and a non-isothermal Hamiltonian for kinetic energy terms.

[0038] An overpotential determination module is used to input the updated temperature field and current density into a pre-built dynamic equivalent circuit model and output the overpotential parameters of the battery stack;

[0039] The multi-scale coupling module is used to use the overpotential parameter to perform multi-scale coupling time integration based on the current density to obtain the thermal stress distribution of the battery stack;

[0040] The battery stack analysis module is used to generate analysis results of the battery stack based on current density, updated temperature field, updated concentration field, and thermal stress distribution.

[0041] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0042] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when the computer program is executed by a processor.

[0043] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0044] The electrolytic cell stack analysis method, device, computer equipment and storage medium provided in the present application can effectively process the internal state data of the cell stack in different dimensions through the non-isothermal symplectic kinetic model. For example, it can synchronously process the electric-thermal-chemical field coupling effects of different dimensions such as temperature field, gas concentration field, electrode damping, etc., and can combine the multi-scale coupling time integration method to solve the time scale difference between time layers of different dimensions, and can use the dynamic equivalent circuit model to correct the overpotential parameters in real time, thereby improving the modeling accuracy, dynamic real-time performance and parameter calibration accuracy of the cell stack analysis, and further improving the analysis reliability of the electrolytic cell stack. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0046] Figure 1 A schematic flow chart of an electrolytic cell stack analysis method provided in an embodiment of the present application;

[0047] Figure 2 A schematic flow chart of the steps following obtaining the thermal stress distribution of a battery stack provided in an embodiment of the present application;

[0048] Figure 3 A schematic diagram of a process for obtaining thermal stress distribution of a battery stack according to an embodiment of the present application;

[0049] Figure 4 A schematic flow chart of the steps for outputting overpotential parameters of a battery stack provided in an embodiment of the present application;

[0050] Figure 5 A schematic structural diagram of an electrolytic cell stack analysis device provided in an embodiment of the present application;

[0051] Figure 6 A schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0053] As the core equipment for green hydrogen production, the performance of the co-electrolysis cell stack is affected by the coupling of multiple physical fields. Traditional analysis methods use a step-by-step decoupling method to deal with the electro-thermal-chemical field, resulting in the inability to reflect the dynamic coupling effect in real time. Under high-temperature environments, the time scales of the fast-changing time layer and the slow-changing layer are significantly different. A single time step integration is difficult to synchronously process millisecond-level electrochemical reactions and second-level heat conduction processes, resulting in deviations in thermal stress calculations. Existing parameter calibration methods rely on steady-state assumptions and do not combine real-time concentration field and temperature field data, resulting in insufficient accuracy in the online correction of activation energy and charge transfer resistance.

[0054] Based on this, the present application provides an electrolytic cell stack analysis method, device, computer equipment and storage medium. Through the non-isothermal symplectic kinetic model, it is possible to effectively process the internal state data of the cell stack in different dimensions. For example, it is possible to synchronously process the electric-thermal-chemical field coupling effects of different dimensions such as temperature field, gas concentration field, electrode damping, etc., and it can be combined with the multi-scale coupling time integration method to solve the time scale difference between time layers of different dimensions, and the dynamic equivalent circuit model can be used to correct the overpotential parameters in real time, thereby improving the modeling accuracy, dynamic real-time performance and parameter calibration accuracy of the cell stack analysis, and thus improving the reliability of the analysis of the electrolytic cell stack.

[0055] In an exemplary embodiment, Figure 1 A schematic diagram of a flow chart of an electrolytic cell stack analysis method provided in an embodiment of the present application is shown as follows: Figure 1As shown, an electrolytic cell stack analysis method is provided, and the method is applied to a terminal for illustration. It is understandable that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. The terminal can be, but is not limited to, various personal computers, laptops, smart phones, and tablet computers. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. In this embodiment, the method includes the following S101 to S105. Among them:

[0056] S101 . Acquire initial internal state data of a battery stack to be analyzed, wherein the initial internal state data of the battery stack includes an initial temperature field, an initial gas concentration field, and an initial electrode damping coefficient.

[0057] An electrolysis cell stack refers to a large electrochemical device composed of multiple electrolysis cells (devices that perform electrochemical reactions) stacked in series or parallel. Examples include water electrolysis hydrogen production stacks, CO2 electrolysis stacks, and fuel cell stacks (operating in opposite modes). The stack structure allows for higher total voltage or current output within a compact space.

[0058] Initial internal state data refers to the set of thermodynamic state parameters of the electrolytic cell stack at the initial stage of operation. The initial temperature field can refer to the temperature distribution data at the initial moment. The initial gas concentration field can refer to the concentration distribution data of various reactant and product gases at locations such as the internal flow channels and porous electrode pores of the cell stack at the initial moment. The initial electrode damping coefficient can refer to a parameter that describes the inherent energy dissipation characteristics of the electrode material during the electrochemical reaction.

[0059] For example, the initial internal state data of the electrolytic cell stack can be transmitted to a terminal, allowing the terminal to further analyze and process the input initial internal state data. The initial temperature field can be acquired using a distributed temperature sensor array to form a three-dimensional spatial temperature distribution matrix. The gas concentration field can be monitored in real time using a multi-channel gas sampling system to monitor the hydrogen and oxygen concentration gradients in each region. The initial electrode damping coefficient can be obtained from Ni-GDC impedance test data.

[0060] S102. Input the initial internal state data into a pre-built non-isothermal symplectic kinetic model, and output the current density, updated temperature field, and updated concentration field of the battery stack. The non-isothermal symplectic kinetic model includes a non-isothermal Hamiltonian for heat exchange terms, a non-isothermal Hamiltonian for potential energy terms, and a non-isothermal Hamiltonian for kinetic energy terms.

[0061] The non-isothermal symplectic kinetic model refers to a Hamiltonian system consisting of heat exchange, potential, and kinetic terms. This can be implemented using a symplectic solver to numerically integrate the Hamiltonian equations. The kinetic energy term describes the electrolysis rate, the potential energy term represents the concentration gradient, and the heat exchange term reflects the heat conduction between the gas and the battery. Current density refers to the current intensity (A / m²) passing through a unit electrode area. Current density directly reflects the rate and intensity of the electrochemical reaction. The updated temperature field refers to the new temperature distribution after accounting for electrochemical reaction heat (activation heat, reversible heat), Joule heating, and heat exchange. The updated concentration field refers to the new gas concentration distribution after accounting for reaction consumption / generation and mass transfer.

[0062] For example, the terminal or server can input the acquired / received initial state parameters into a pre-built non-isothermal symplectic kinetic model to simulate the core electrochemical processes (reactions, mass transfer) occurring inside the battery stack and their coupled evolution with the temperature field, such as obtaining the current density distribution by solving the Hamiltonian canonical equation, while updating the initial temperature field and the initial concentration field.

[0063] S103 , inputting the updated temperature field and current density into a pre-built dynamic equivalent circuit model, and outputting the overpotential parameters of the battery stack.

[0064] The dynamic equivalent circuit model refers to a circuit network that reflects polarization losses. This can be achieved by using variable resistors to simulate charge transfer impedance and dynamically correcting the equilibrium potential in conjunction with a real-time temperature field. The overpotential parameter can refer to the overpotential (η), which can be the difference between the actual electrode potential and its thermodynamic equilibrium potential (e.g., as determined by the Nernst equation).

[0065] Exemplarily, the terminal can utilize a pre-built dynamic equivalent circuit model to process and update the temperature field and current density. The dynamic equivalent circuit model can calculate the thermodynamic equilibrium potential based on the updated temperature field and determine the overpotential parameters in combination with the externally applied working potential.

[0066] S104 , using the overpotential parameter, performing multi-scale coupled time integration based on the current density to obtain the thermal stress distribution of the battery stack.

[0067] Multiscale coupled time integration refers to a numerical calculation method that handles different time steps. It can be used to deal with systems involving coupling of different time scales and different physical fields (electrochemical, thermal, and mechanical). Specifically, this can be achieved using an explicit-implicit hybrid algorithm to separately process the Joule heating power of the fast-varying time layer and the global temperature of the slow-varying layer, with the fast-varying time step set to milliseconds and the slow-varying time step set to seconds.

[0068] Thermal stress distribution refers to the spatial distribution of mechanical stress (tensile, compressive, and shear stress) generated within the materials due to uneven temperature distribution within the battery stack and different thermal expansion coefficients between materials. Thermal stress is one of the main causes of deformation, cracking, and failure of battery stack components (such as bipolar plates, seals, and membrane electrode).

[0069] For example, when the terminal uses the fast-varying time layer to calculate the Joule thermal power, the explicit Euler method can be used for millisecond-level iteration; when the slow-varying time layer is used to calculate the global temperature, the implicit difference method can be used for second-level integration to obtain the thermal stress distribution results of the battery stack, thereby providing an analytical data basis for the analysis of the battery stack.

[0070] S105 . Generate analysis results of the battery stack based on the current density, updated temperature field, updated concentration field, and thermal stress distribution.

[0071] The analysis results can refer to the evaluation conclusions on the battery stack's performance, status, and reliability, integrating the key information output by all model calculations. For example, they can include overall or local performance (voltage, efficiency, current distribution uniformity), the location and size of temperature hotspots, the size and distribution of thermal stress, and the prediction of high-stress / high-risk areas.

[0072] Exemplarily, the terminal may form a dynamically output optimized electrolytic cell stack parameter system based on the cell stack analysis data determined in steps S101-S104, so as to further output analysis results of the electrolytic cell stack through the parameter system.

[0073] In this embodiment, the use of a non-isothermal symplectic kinetic model enables real-time coupled calculations of multiple physical fields, including electro-thermal and chemical fields, overcoming the asynchronous updating of the temperature and concentration fields caused by traditional step-by-step decoupling methods. Furthermore, by associating a dynamic equivalent circuit model with real-time temperature field data, the accuracy of overpotential parameter calculations can be improved. Furthermore, a multi-scale coupled time integration strategy enables cross-scale transfer of Joule heat power and global temperature, making thermal stress calculations more accurate. This, in turn, improves the reliability of electrolytic cell stack analysis.

[0074] In an exemplary embodiment, the non-isothermal symplectic kinetic model is constructed by a Hamiltonian, wherein the Hamiltonian is expressed as follows:

[0075]

[0076] in, is the kinetic energy term, is the momentum vector, is the mass matrix; is the potential energy term; is the hot exchange item, is the thermal conductivity coefficient, is the gas temperature, is the initial temperature field.

[0077] As an example, the initial internal state data of the battery stack to be analyzed is obtained. Specifically, the initial temperature field , initial gas concentration field and , and the initial electrode damping coefficient γ. γ can be obtained from Ni-GDC (Nickel-Gadolinium Doped Ceria) impedance test data.

[0078] Alternatively, the non-isothermal symplectic kinetic model is as follows (1):

[0079] (1);

[0080] in, is the Hamiltonian, which can specifically include the following items:

[0081] is the kinetic energy term, which characterizes the electrolysis reaction rate, and the momentum vector With the mass matrix The inverse matrix product of reflects the energy distribution.

[0082] is the potential energy term, which can describe the gas concentration gradient ( ) interacts with the electric potential field, reflecting the free energy in the thermodynamic equilibrium state.

[0083] is the heat exchange term, which represents the temperature difference driving force between the gas and the battery stack, is the thermal conductivity coefficient, is the gas temperature, is the real-time battery temperature; it can be calculated by the Joule heat increment renew.

[0084] The current density can be calculated by the model as shown in expression (1): , update the temperature field and update the concentration field At the same time, it can also output the updated momentum field , in the symplectic algorithm, momentum is the endogenous variable of the system, and its initial value can be determined by the generalized coordinates The initial state and physical constraints of are derived naturally without additional input.

[0085] In practical applications, the variables in expression (1) can be described by the following definitions:

[0086] It can describe the evolution of concentration field / potential field over time. is the momentum-driven diffusion rate.

[0087] In the expression:

[0088] The concentration diffusion force is driven by the potential energy gradient; is the gradient of Joule heat power in the concentration / potential field, characterizing the local heat source distribution; Joule heat power density; is the electrode damping loss (can be derived from Ni-GDC impedance data); is the damping coefficient, which characterizes the inhibitory effect of the electrode material on the reaction rate; is the time step. To update the momentum field.

[0089] Specifically, the updated temperature field can also be determined by the following expression (2):

[0090] (2);

[0091] in, ; ; is the temperature field of the previous step (can be the initial temperature field), is the Joule heat increment, which can be obtained from the momentum equation calculate. The ohmic resistance may come from the impedance characteristics of the electrode material. is the heat capacity of the battery stack, which is related to the material specific heat capacity and mass.

[0092] The current density can be determined by the following expression (3):

[0093] (3);

[0094] Expression (3) can be derived from the kinetic energy term of the Hamiltonian H;

[0095] Therefore, the concentration field can be updated by outputting the non-isothermal symplectic kinetic model through the specific implementation of the above practical application. , update the momentum field , update the temperature field , current density .

[0096] Furthermore, the updated temperature field can be and current density Input into the dynamic equivalent circuit model to calculate the overpotential parameter η of the battery stack. Using the overpotential parameter η, based on the current density Perform multi-scale coupled time integration. As an example, the Joule heating power can be calculated in the fast-changing time layer , and the global temperature can be calculated in the slow-varying time layer , and the thermal stress distribution is obtained .

[0097] Based on the current density, updated temperature field, updated concentration field and thermal stress distribution, analysis results of the battery stack can be generated. The analysis results may include key performance indicators such as electrolysis efficiency and thermo-mechanical stress.

[0098] In an exemplary embodiment, Figure 2 A schematic flow chart of the steps after obtaining the thermal stress distribution of the battery stack provided in an embodiment of the present application is shown as follows: Figure 2 As shown, it can be Figure 1 Based on the above, the steps after obtaining the thermal stress distribution of the battery stack are exemplarily described. The method may further include S201 to S203, wherein:

[0099] S201. Acquire experimental observation data, initial activation energy data, and initial thermal conductivity data.

[0100] S202 , inputting the updated temperature field, updated concentration field, and overpotential parameters into a pre-built observation data mapping model, and outputting observation state prediction data.

[0101] S203. Input the experimental observation data and the observation state prediction data into a pre-set loss value model, and output the optimized activation energy and the optimized thermal conductivity; wherein the loss value model includes an activation energy term optimized for the initial activation energy data and a thermal conductivity term optimized for the initial thermal conductivity data.

[0102] Among them, the optimized activation energy is used to feed back into the potential energy term, and the optimized thermal conductivity is used to feed back into the multiscale coupling time integral.

[0103] Experimental observation data can refer to key performance indicators measured during battery stack operation (e.g., CO2 conversion rate, temperature gradient distribution). Initial activation energy can refer to the initial estimate of the reaction energy barrier, which determines the electrochemical reaction rate. Initial thermal conductivity can refer to the initial value of the material's equivalent thermal conductivity, which affects the uniformity of the temperature field.

[0104] The observation data mapping model can be a neural network or polynomial regression model, and can be a model that maps the state parameters (concentration field, temperature field, overpotential) output by the algorithm to the observation space. For example, the overpotential parameter can be associated with the gas diffusion rate by constructing a linear mapping relationship between the temperature field and the concentration field.

[0105] A loss value model can be used to minimize the error between the predicted observed state data and the experimental observed data. The activation energy term can be designed as the mean squared error between the initial activation energy and the measured activation energy, and the thermal conductivity term can be used to introduce regularization constraints. The parameter optimization process can use a gradient descent algorithm to adjust the activation energy and thermal conductivity parameters through backpropagation to minimize the error between the predicted observed state data and the experimental observed data.

[0106] For example, during stack operation, gas concentration distribution data and infrared thermal imaging data collected in real time are integrated into an experimental observation dataset and transmitted to a terminal for stack analysis. The terminal then inputs the updated temperature and concentration field data into an observation data mapping model to generate observation state prediction data including current density distribution and heat flux density. By comparing the predicted data with the experimental data, the chemical kinetic error in the activation energy parameter and the thermal conduction error in the thermal conductivity parameter are simultaneously considered in the loss function.

[0107] Optionally, the optimized activation energy parameters can be fed back into the potential energy term of the non-isothermal symplectic kinetic model to correct the calculated activation energy barrier for the electrode reaction. Furthermore, the optimized thermal conductivity parameters can be synchronized with the multiscale coupled integration module to adjust the thermal conductivity between the fast- and slow-varying time layers. This dynamic closed-loop correction mechanism can reduce the error in the model's thermodynamic potential calculations under high-temperature conditions, significantly improving the real-time and accuracy of thermal stress distribution predictions.

[0108] In this example, a direct correlation between model predictions and actual experimental data is established through an observational data mapping model. A loss function incorporating initial activation energy and thermal conductivity terms allows for dynamic optimization of activation energy and thermal conductivity parameters, thereby calibrating the theoretical model's potential energy term and heat conduction process. The optimized parameters are fed back to the corresponding calculation modules, improving the accuracy of electrochemical reaction potential energy calculations and thermal stress distribution calculations, thereby enhancing the reliability of battery stack analysis results.

[0109] In an exemplary embodiment, Figure 3 A schematic flow chart of the steps for obtaining the thermal stress distribution of a battery stack provided in an embodiment of the present application is shown as follows: Figure 3 As shown, it can be Figure 1On the basis of the above, the steps included in the electrolytic cell stack analysis method are exemplarily described. The method may further include S301 to S303. In step S104, the overpotential parameter is used to perform multi-scale coupled time integration based on the current density to obtain the thermal stress distribution of the cell stack. The method may further include S301 to S304, wherein:

[0110] S301, using the overpotential parameter, updating the current density to obtain an updated current density;

[0111] S302, obtaining a Joule heat power parameter of a fast-changing time layer of the battery stack based on the updated current density and a preset fast-changing time step;

[0112] S303, obtaining the global temperature of the slow-varying time layer according to the Joule heat power parameter of the fast-varying time layer and the preset slow-varying time step;

[0113] S304 , obtaining thermal stress distribution according to the global temperature, the preset reference temperature, and the preset Young's modulus.

[0114] The fast-varying time step may refer to the time step of the fast-varying time layer. The Joule heat power parameter may refer to the heat power generated when current passes through the internal resistance of the battery. The slow-varying time step may refer to the time step of the slow-varying time layer. The global temperature may refer to the overall temperature distribution of the battery stack (such as the temperature value at each location) or the average temperature, which can reflect the thermal state of the battery stack. The reference temperature may refer to a pre-set reference temperature. Young's modulus may refer to an inherent mechanical parameter of a material, which may refer to the ratio of stress to strain within the elastic deformation range of the material.

[0115] For example, the dynamic update of current density can be achieved by real-time acquisition of electrode interface polarization data. The fast-changing time step is set to milliseconds to capture the Joule heat transient response, for example, a 0.1-10 millisecond step is used for local thermal power integration. The slow-changing time step can be set to seconds to adapt to the temperature diffusion characteristics, for example, a 1-30 second step is used for global temperature field iterative calculation. During the temperature gradient and thermal stress conversion process, the Young's modulus value can be dynamically adjusted based on the phase change characteristics of the material, for example, piecewise linear interpolation is used in the electrolyte solid-liquid phase change temperature range.

[0116] Optionally, in the Joule heat power calculation stage, the current density can be sampled at high frequency using a millisecond time step, which can effectively capture the current density mutation phenomenon caused by bubble generation and bursting on the electrode surface. In the global temperature calculation stage, the Joule heat power can be time-integrated using a second time step to avoid numerical oscillations in the temperature field caused by high-frequency fluctuations. This scale processing method allows the fast-changing time layer data to be input into the slow-changing layer after filtering and frequency reduction, that is, the instantaneous Joule heat accumulation of the fast-changing time layer can be averaged to the slow-changing layer to achieve thermodynamic stability constraints and avoid interference from short-time scale fluctuations on long-term temperature predictions. Finally, by comparing the spatial gradient distribution of the real-time temperature field and the reference temperature field, combined with the spatial variation characteristics of the Young's modulus parameters, the thermal stress concentration area at the electrolyte / electrode interface can be accurately calculated.

[0117] In this example, by using different time steps to process the Joule heat power and temperature field evolution, the transient changes of local thermal effects are effectively captured while ensuring the stability of the global temperature field calculation. The multi-scale coupling method improves the accuracy of the thermal stress distribution calculation, thereby enhancing the reliability of the battery stack analysis results.

[0118] In a specific embodiment, steps S301-S304 can be implemented by the following expression:

[0119] (4);

[0120] Represents a single rapidly varying time step (like, ) Joule heat power generated within. is the ohmic resistance.

[0121] (5); Expression (5) represents the time step of the fast-changing time layer Joule heat accumulation Passed as input to the slowly changing layer (like, ), ensuring thermodynamic stability constraints.

[0122] is the Joule thermal power density of the fast-changing time layer (which can be output from the momentum equation of expression (1)), unit: W / m³; is a slow-varying time step (e.g. 1 second) and a fast-varying time step (e.g. 1 millisecond) to form multi-scale coupling; is the time step number of the fast-changing time layer ( ).

[0123] In this way, the instantaneous Joule heat accumulation of the fast-changing time layer can be averaged to the slow-changing layer, realizing thermodynamic stability constraints and avoiding the interference of short-time scale fluctuations on long-term temperature predictions.

[0124] In practical applications, the global temperature of the slow-varying time layer can also be obtained through the above expressions (4) and (5): And the thermal stress distribution, where the thermal stress distribution can be expressed as follows (6):

[0125] (6);

[0126] in, is Young's modulus; is the reference temperature (e.g., initial temperature field); is the coefficient of thermal expansion.

[0127] In an exemplary embodiment, Figure 4 A flow chart of the steps for outputting the overpotential parameters of a battery stack provided in an embodiment of the present application is shown as follows: Figure 4 As shown, it can be Figure 1 Based on the above, the steps included in the electrolytic cell stack analysis method are exemplarily described. In step S103, the updated temperature field and current density are input into the pre-built dynamic equivalent circuit model, and the overpotential parameters of the cell stack are output, including S401 to S403, wherein:

[0128] S401, obtaining an actual working potential applied externally to the battery stack;

[0129] S402, determining a real-time thermodynamic potential based on the updated concentration field, the updated temperature field, and the reference temperature; the concentrations of each gas corresponding to the updated concentration field are determined based on the current density in the dynamic equivalent circuit model;

[0130] S403. Using a dynamic equivalent circuit model, iteratively adjust the error between the actual working potential and the real-time thermodynamic potential to obtain an overpotential parameter.

[0131] The actual working potential may refer to a measurable voltage directly applied across the battery stack by an external power source.

[0132] For example, the actual working potential can be collected in real time by a voltage sensor or an external circuit monitoring module, for example, by setting a high-precision voltage probe between the positive and negative electrodes of the battery stack.

[0133] In the dynamic equivalent circuit model, the terminal can capture the actual operating potential of the battery stack in real time through an external measurement device. It can also calculate the thermodynamic equilibrium potential based on the updated temperature and concentration field data output by the non-isothermal symplectic kinetic model. Simultaneously, the terminal can generate a temperature-corrected potential based on the difference between the current temperature field and the reference temperature. Furthermore, the thermodynamic equilibrium potential and the thermodynamic temperature-corrected potential can be superimposed to obtain the real-time thermodynamic potential. Furthermore, the overpotential parameter can be obtained from the difference between the actual operating potential and the real-time thermodynamic potential.

[0134] Therefore, steps S401 to S403 can reduce the error of the overpotential parameter by dynamically integrating the influence of temperature field changes on the electrochemical potential, thereby improving the calculation accuracy of Joule thermal power and thermal stress distribution in subsequent multi-scale coupled time integration, and further improving the reliability of the battery stack analysis results.

[0135] In this example, the accuracy of overpotential parameter calculations is improved by leveraging the influence of dynamic temperature field changes on the thermodynamic potential. Furthermore, by obtaining the actual operating potential and combining it with the real-time thermodynamic potential that accounts for temperature effects, the true electrochemical state of the battery stack can be accurately reflected. This provides highly accurate input parameters for subsequent multiscale coupled time integration, further improving the reliability of battery stack analysis.

[0136] In an exemplary embodiment, in step S402, determining the real-time thermodynamic potential based on the updated concentration field, the updated temperature field, and the reference temperature may include:

[0137] Determine the thermodynamic equilibrium potential of the battery stack based on the updated concentration field and the updated temperature field;

[0138] According to the updated temperature field and the reference temperature, the thermodynamic temperature correction potential is obtained;

[0139] The potential is corrected according to the thermodynamic equilibrium potential and the thermodynamic temperature to obtain the real-time thermodynamic potential.

[0140] The thermodynamic equilibrium potential may refer to the theoretical potential when the electrochemical reaction is in equilibrium at a specific temperature and concentration. The thermodynamic temperature correction potential may refer to the potential offset caused by the operating temperature deviating from the reference temperature. The real-time thermodynamic potential may refer to the theoretical potential reference value under actual operating conditions. The determination of the thermodynamic equilibrium potential can be based on updated concentration and temperature field data, specifically by calculating the Nernst equation in combination with the local gas concentration and temperature.

[0141] For example, the thermodynamic equilibrium potential can be determined by collecting real-time gas concentration and temperature distribution data and calculating it in conjunction with the Nernst equation for the electrochemical reaction. For example, the gas concentration and temperature terms in the Nernst equation can be dynamically corrected, where the concentration term can be determined based on the local concentration ratio of hydrogen to oxygen in the updated concentration field, and the temperature term can be determined based on the current temperature value of the updated temperature field.

[0142] The generation of the thermodynamic temperature correction potential can introduce a preset reference temperature. For example, the reference temperature can be the initial temperature of the battery stack or the standard operating temperature. By calculating the difference between the updated temperature field and the reference temperature, linear or nonlinear compensation is performed in combination with the temperature coefficient, as shown in Expressions (9) to (11).

[0143] During the execution of the dynamic equivalent circuit model, the calculation of the thermodynamic equilibrium potential utilizes the latest concentration and temperature field data at each iteration. For example, during the operation of an electrolytic cell stack, the gas concentration field changes in real time with electrolyte flow and reaction progress, and the temperature field fluctuates dynamically due to Joule heating and heat conduction. The current thermodynamic equilibrium potential can be obtained by substituting the hydrogen and oxygen concentrations in the concentration field into the Nernst equation and superimposing the temperature effect of the temperature field on the equilibrium potential. Furthermore, the calculation of the temperature-corrected potential requires comparing the current temperature field with a reference temperature. For example, when the temperature field shifts due to external thermal disturbances, a baseline compensation is established using the reference temperature to eliminate the potential interference from transient temperature changes. For example, under high-temperature conditions, if a local increase in the temperature field causes the thermodynamic potential to deviate from the standard value, a negative correction can be applied using the reference potential corresponding to the reference temperature, ensuring that the real-time thermodynamic potential calculation remains stable. The data processing of the two aforementioned steps can be performed in parallel. The calculation frequency of the thermodynamic equilibrium potential is consistent with the iteration step size of the dynamic equivalent circuit model, and the calibration period of the temperature-corrected potential can be dynamically adjusted based on the rate of change of the temperature field. Furthermore, the sum of the thermodynamic equilibrium potential and the thermodynamic temperature-corrected potential can be calculated to obtain the real-time thermodynamic potential. By coupling the dynamic data of the concentration and temperature fields in real time and implementing a temperature compensation benchmark calibration based on a reference temperature, the calculated overpotential parameters are ultimately ensured to meet the requirements of dynamic operating conditions.

[0144] In this embodiment, the thermodynamic equilibrium potential reflects the true state of the current concentration and temperature fields, avoiding the hysteresis errors caused by traditional methods that rely solely on initial state data. Furthermore, by introducing a reference temperature to calibrate the temperature-corrected potential, thermodynamic potential drift caused by transient changes in the temperature field can be eliminated. This improves the accuracy of the thermodynamic potential parameters under dynamic operating conditions, thereby enhancing the overall reliability of the battery stack analysis model.

[0145] In some exemplary specific examples, the dynamic equivalent circuit model is as follows:

[0146] (7);

[0147] in, The ohmic resistance is the sum of the electrode material, electrolyte and contact resistance, and is linearly related to the current density. It is the charge transfer resistance, which reflects the kinetic resistance of electrochemical reactions (such as the activation energy of CO2 reduction and H2O electrolysis). is the double-layer capacitance, which describes the charge storage effect at the electrode / electrolyte interface and is related to the electrode surface area and dielectric constant. is the diffusion impedance, which is caused by gas diffusion (such as the transmission of CO2 in the electrode pores). The source can be the diffusion coefficient of q in expression (1) (can come from molecular dynamics simulation data); is the angular frequency, which can be the excitation signal frequency of the impedance spectrum test.

[0148] Combined with the parameters output by the non-isothermal symplectic kinetic model in S102, the impedance spectrum feedback can be adjusted in real time. Compensate for the error caused by concentration polarization in expression (1).

[0149] Furthermore, the following expression can be expressed:

[0150] (8);

[0151] (9);

[0152] (10);

[0153] (11);

[0154] in, is the charge transfer resistance before correction, is the corrected charge transfer resistance, For adaptive learning rate, dynamically adjust The update amplitude is adjusted to avoid overshoot or slow convergence. is the partial derivative of current density with respect to overpotential, reflecting the sensitivity of reaction rate. is the voltage perturbation used to stimulate the impedance spectrum response. The voltage applied by the external power supply to both ends of the high-temperature co-electrolysis cell stack, that is, the actual working potential, can be directly measured. is the real-time thermodynamic potential, is the thermodynamic equilibrium potential, Correct the potential for the thermodynamic temperature.

[0155] is the standard electrode potential, R is the gas constant, It can be determined by the temperature field output in expression (1), where n is the number of electron transfers, F is the Faraday constant, and the gas concentration is , [CO] and [H2] can be determined by the generalized coordinate vector q. The concentration field q in expression (1) contains 、 ], CO and H2 are generated through high-temperature co-electrolysis reaction. According to Faraday's law of electrolysis and the electrochemical reaction rate equation, the electrolysis reaction of CO2 and H2O generates CO and H2, as shown in expression (12):

[0156] (12);

[0157] The generation rate of expression (12) is related to the current density Related, that is , .

[0158] The thermal correction term can be determined by expression (13): :

[0159] (13);

[0160] in, is the temperature coefficient, is the reference temperature (such as 25°C).

[0161] Thus, based on Expressions (8) to (13), the corrected charge transfer resistance can be output: ( The initial value of the exchange current density can be measured by steady-state polarization experiments , according to the formula Calculate initial value is the exchange current density), double layer capacitance (The initial value is obtained by fitting the equivalent circuit model through electrochemical impedance spectroscopy (EIS) experiment. The capacitance characteristic of the corresponding electrode / electrolyte interface. Its value is updated in real time through frequency domain fitting), the adjusted overpotential parameter .

[0162] In an exemplary embodiment, after obtaining the global temperature of the slow-varying time layer according to the Joule heat power parameter of the fast-varying time layer and the preset slow-varying time step, the method further includes:

[0163] The global temperature is used to update the thermodynamic equilibrium potential and the thermodynamic temperature correction potential respectively.

[0164] For example, the global temperature can be obtained by calculating a slow-varying time step. The update of the thermodynamic equilibrium potential can be achieved through the nonlinear relationship between the concentration field and the temperature field. The update of the thermodynamic temperature correction potential can be achieved by the difference between the temperature gradient and the reference temperature. The updated thermodynamic equilibrium potential is used to adjust the driving force of the electrode reaction, and its variation amplitude can be positively correlated with the temperature change. The updated thermodynamic temperature correction potential is used to compensate for the potential error caused by uneven temperature distribution, and the compensation amount is dynamically adjusted according to the difference between the local temperature and the global temperature.

[0165] After obtaining the global temperature of the slow-varying time layer, the global temperature can be synchronously input into the calculation module of the thermodynamic equilibrium potential. The update of the thermodynamic equilibrium potential can be achieved by correcting the temperature term in the model, for example, replacing the initial temperature field in the model with the current global temperature value. At the same time, the thermodynamic temperature correction potential can be updated by comparing the difference between the current global temperature and the reference temperature. The two updated potential parameters can be fed back to the dynamic equivalent circuit model in real time for recalculating the overpotential parameters. In this way, the current density calculation can include the dynamic correction of the influence of the temperature field on the thermodynamic equilibrium state and the temperature gradient, thereby eliminating the potential calculation deviation caused by the lag of the temperature parameter. Through the dual potential update within each slow-varying time step, the synchronous coupling of the temperature field and the electrochemical parameters on the millisecond time scale is achieved.

[0166] In this embodiment, the battery stack model more accurately reflects the impact of temperature changes on electrochemical reactions, improving the dynamic consistency of thermal and electrical parameters during multiscale coupling. Furthermore, it avoids the cumulative errors caused by the decoupled updating of temperature and potential parameters in traditional models, thereby enhancing the accuracy and reliability of battery stack performance predictions.

[0167] In a specific embodiment, the specific implementation of S201 to S203 may include:

[0168] According to expressions (1) to (13), the above output can be 、 、 ; Experimental observation data (CO2 conversion rate, temperature gradient), the loss value is determined by the following expression (14): (14);

[0169] in, As the model prediction function, the state parameters output by expressions (1) to (13) can be ) is mapped to the observation space, Can be a mapping input to a model. is the observation error covariance matrix, which quantifies the noise level of the experimental data. are the initial model state parameters (such as activation energy , thermal conductivity ), which needs to be optimized and adjusted. are background field parameters (a priori estimates), which can come from historical data or physical models. is the background error covariance matrix, which represents the prior uncertainty of the parameters.

[0170] The first term in expression (14) minimizes the difference (residual) between the model prediction and the experimental data to ensure the physical consistency of the model. The second term constrains the parameter adjustment amplitude to prevent excessive deviation from prior knowledge and ensure numerical stability.

[0171] Furthermore, the optimized activation energy can be determined by the following expression (15):

[0172] (15);

[0173] in, is the activation energy before optimization, is the learning rate (step size), which controls the parameter adjustment rate and is usually adjusted adaptively. is the gradient of the objective function with respect to the activation energy, reflecting the sensitivity of the parameter to the model error.

[0174] In practical applications, the gradient descent method can be used to backpropagate errors and optimize key physical parameters (such as activation energy and thermal conductivity ), making the model closer to the real system behavior.

[0175] Optimized activation energy It can directly affect the potential energy function in non-isothermal symplectic kinetic modeling in expression (1) , adjust the reaction kinetics path. Corrected thermal conductivity Data from high-temperature thermal property tests can be input into multi-scale coupled integration processing to optimize the simulation accuracy of the global temperature field and reduce thermal stress errors.

[0176] We can also use the variational objective function Fusion model prediction and measured data, back propagation to the potential energy gradient of expression (1) and in expression (8) parameter.

[0177] In some specific embodiments, by linking non-isothermal kinetic modeling, impedance spectrum feedback, multi-scale thermodynamic integration, and data assimilation, a dynamically optimized electrolytic cell stack parameter system (including concentration field, temperature field, impedance characteristics, and material performance parameters) is ultimately output, achieving the following core functions:

[0178] Accurate dynamic characteristic analysis: Real-time tracking of the multi-physics coupling effects of the electric-thermal-chemical fields during high-temperature co-electrolysis to improve the accuracy of transient response simulations. Enhanced stability: Compensation for model errors through impedance feedback and data assimilation to suppress performance degradation caused by concentration polarization or temperature gradients. Life and efficiency optimization: Optimized activation energy parameters reduce the reaction energy barrier and improve CO2 conversion efficiency; corrected thermal conductivity improves the uniformity of heat distribution and reduces thermal stress damage to materials. This application can provide a theoretical basis for the design and operation control of high-temperature electrolysis cell stacks. For example, through dynamic parameter optimization, energy conversion efficiency can be improved (such as hydrogen / synthesis gas yield), the life of the cell stack can be extended (reducing the risk of thermal fatigue), and stable operation can be supported under fluctuating renewable energy input scenarios.

[0179] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0180] The electrolytic cell stack analysis device provided in an embodiment of the present application is described below. The electrolytic cell stack analysis device and the above-mentioned electrolytic cell stack analysis method have the same inventive concept, and the implementation solution provided by the device to solve the problem is similar to the implementation solution recorded in the above-mentioned method. Therefore, the specific limitations in one or more electrolytic cell stack analysis device embodiments provided below can refer to the limitations on the electrolytic cell stack analysis method above. The electrolytic cell stack analysis device described below and the electrolytic cell stack analysis method described above can be referenced to each other and will not be repeated here.

[0181] In an exemplary embodiment, Figure 5 This is a schematic diagram of the structure of an electrolytic cell stack analysis device provided in an embodiment of the present application, as shown in FIG. Figure 5As shown, the electrolytic cell stack analysis device 50 includes: an initial state acquisition module 510, an internal state update module 520, an overpotential determination module 530, a multi-scale coupling module 540 and a cell stack analysis module 550, wherein:

[0182] An initial state acquisition module 510 is used to acquire initial internal state data of the battery stack to be analyzed, wherein the initial internal state data of the battery stack includes an initial temperature field, an initial gas concentration field, and an initial electrode damping coefficient;

[0183] Internal state update module 520, configured to input initial internal state data into a pre-built non-isothermal symplectic kinetic model and output the current density, updated temperature field, and updated concentration field of the battery stack. The non-isothermal symplectic kinetic model includes a non-isothermal Hamiltonian for heat exchange, a non-isothermal Hamiltonian for potential energy, and a non-isothermal Hamiltonian for kinetic energy.

[0184] An overpotential determination module 530 is configured to input the updated temperature field and current density into a pre-built dynamic equivalent circuit model and output an overpotential parameter of the battery stack;

[0185] A multi-scale coupling module 540 is used to use the overpotential parameter to perform multi-scale coupling time integration based on the current density to obtain the thermal stress distribution of the battery stack;

[0186] The battery stack analysis module 550 is used to generate analysis results of the battery stack based on the current density, the updated temperature field, the updated concentration field, and the thermal stress distribution.

[0187] In an exemplary embodiment, the apparatus further includes a sample and loss term module, an observation state prediction module, and a thermochemical optimization module.

[0188] The sample and loss term module is used to obtain experimental observation data, initial activation energy data, and initial thermal conductivity data.

[0189] The observation state prediction module is used to input the updated temperature field, updated concentration field, and overpotential parameters into the pre-built observation data mapping model, and output the observation state prediction data.

[0190] The thermochemical optimization module is used to input experimental observation data and observation state prediction data into a preset loss value model and output optimized activation energy and optimized thermal conductivity; the loss value model includes an activation energy term optimized for the initial activation energy data and a thermal conductivity term optimized for the initial thermal conductivity data.

[0191] Among them, the optimized activation energy is used to feed back into the potential energy term, and the optimized thermal conductivity is used to feed back into the multiscale coupling time integral.

[0192] In an exemplary embodiment, the multi-scale coupling module 540 is used to update the current density using the overpotential parameter to obtain an updated current density; based on the updated current density and a preset fast-varying time step, the Joule thermal power parameter of the fast-varying time layer of the battery stack is obtained; according to the Joule thermal power parameter of the fast-varying time layer and the preset slow-varying time step, the global temperature of the slow-varying time layer is obtained; according to the global temperature, the preset reference temperature, and the preset Young's modulus, the thermal stress distribution is obtained.

[0193] In an exemplary embodiment, the overpotential determination module 530 is used to obtain the actual working potential applied externally to the battery stack; determine the real-time thermodynamic potential based on the updated concentration field, the updated temperature field and the reference temperature; the concentrations of each gas corresponding to the updated concentration field are determined based on the current density in the dynamic equivalent circuit model; and use the dynamic equivalent circuit model to iteratively adjust the error between the actual working potential and the real-time thermodynamic potential to obtain the overpotential parameter.

[0194] In an exemplary embodiment, the overpotential determination module 530 is used to determine the thermodynamic equilibrium potential of the battery stack based on the updated concentration field and the updated temperature field; obtain the thermodynamic temperature correction potential based on the updated temperature field and the reference temperature; and obtain the real-time thermodynamic potential based on the thermodynamic equilibrium potential and the thermodynamic temperature correction potential.

[0195] In an exemplary embodiment, the multi-scale coupling module 540 is configured to update the thermodynamic equilibrium potential and the thermodynamic temperature correction potential using the global temperature.

[0196] In an exemplary embodiment, the non-isothermal symplectic kinetic model is constructed by a Hamiltonian, wherein the Hamiltonian is expressed as follows:

[0197]

[0198] in, is the kinetic energy term, is the momentum vector, is the mass matrix; is the potential energy term; is the hot exchange item, is the thermal conductivity coefficient, is the gas temperature, is the initial temperature field.

[0199] In an exemplary embodiment, the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by one or more processors, the one or more processors execute the steps of the electrolytic cell stack analysis method as described in any one of the above embodiments.

[0200] In an exemplary embodiment, the present application also provides a computer device having a computer program stored therein. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of any electrolytic cell stack analysis method in the above-mentioned embodiments.

[0201] In an exemplary embodiment, the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of any electrolytic cell stack analysis method in the above embodiments.

[0202] Schematically, as Figure 6 As shown, Figure 6 This is a schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. The computer device 600 can be provided as a server. Figure 6 Computer device 600 includes a processing component 602, which further includes one or more processors, and memory resources represented by memory 601 for storing instructions executable by processing component 602, such as application programs. The application programs stored in memory 601 may include one or more modules, each corresponding to a set of instructions. Furthermore, processing component 602 is configured to execute the instructions to perform the electrolytic cell stack analysis method according to any of the above-described embodiments.

[0203] The computer device 600 may further include a power supply component 603 configured to perform power management of the computer device 600, a wired or wireless network interface 604 configured to connect the computer device 600 to a network, and an input / output (I / O) interface 605. The computer device 600 may operate based on an operating system stored in the memory 601, such as Windows Server™, Mac OS X™, Unix™, Linux™, Free BSD™, or the like.

[0204] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0205] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0206] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referenced to each other.

[0207] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for analyzing an electrolytic cell stack, characterized in that: The method comprises: Acquiring initial internal state data of the battery stack to be analyzed, wherein the initial internal state data of the battery stack includes an initial temperature field, an initial gas concentration field, and an initial electrode damping coefficient; Inputting the initial internal state data into a pre-built non-isothermal symplectic kinetic model, and outputting the current density, updated temperature field, and updated concentration field of the battery stack, wherein the non-isothermal symplectic kinetic model includes a non-isothermal Hamiltonian for a heat exchange term, a non-isothermal Hamiltonian for a potential energy term, and a non-isothermal Hamiltonian for a kinetic energy term; Inputting the updated temperature field and current density into a pre-built dynamic equivalent circuit model, and outputting overpotential parameters of the battery stack; Using the overpotential parameter, a multi-scale coupled time integration is performed based on the current density to obtain a thermal stress distribution of the battery stack; An analysis result of the battery stack is generated based on the current density, the updated temperature field, the updated concentration field, and the thermal stress distribution.

2. The method according to claim 1, characterized in that The method further comprises: Obtain experimental observation data, initial activation energy data, and initial thermal conductivity data; Inputting the updated temperature field, the updated concentration field, and the overpotential parameter into a pre-built observation data mapping model, and outputting observation state prediction data; Inputting the experimental observation data and the observation state prediction data into a preset loss value model, and outputting optimized activation energy and optimized thermal conductivity; wherein the loss value model includes an activation energy term optimized for the initial activation energy data and a thermal conductivity term optimized for the initial thermal conductivity data; The optimized activation energy is used to feed back to the potential energy term, and the optimized thermal conductivity is used to feed back to the multi-scale coupling time integral.

3. The method according to claim 1, characterized in that The method of utilizing the overpotential parameter and performing multi-scale coupled time integration based on the current density to obtain the thermal stress distribution of the battery stack includes: Using the overpotential parameter, updating the current density to obtain an updated current density; Obtaining a Joule thermal power parameter of a fast-changing time layer of the battery stack based on the updated current density and a preset fast-changing time step; Obtaining the global temperature of the slow-varying time layer according to the Joule heat power parameter of the fast-varying time layer and a preset slow-varying time step; The thermal stress distribution is obtained according to the global temperature, a preset reference temperature, and a preset Young's modulus.

4. The method according to claim 3, characterized in that Inputting the updated temperature field and current density into a pre-built dynamic equivalent circuit model and outputting the overpotential parameters of the battery stack includes: obtaining an actual operating potential externally applied to the battery stack; Determining a real-time thermodynamic potential based on the updated concentration field, the updated temperature field, and the reference temperature; wherein the concentrations of the gases corresponding to the updated concentration field are determined based on the current density in a dynamic equivalent circuit model; The error between the actual working potential and the real-time thermodynamic potential is iteratively adjusted using a dynamic equivalent circuit model to obtain the overpotential parameter.

5. The method according to claim 4, characterized in that The determining of the real-time thermodynamic potential according to the updated concentration field, the updated temperature field and the reference temperature comprises: determining a thermodynamic equilibrium potential of the battery stack based on the updated concentration field and the updated temperature field; Obtaining a thermodynamic temperature correction potential based on the updated temperature field and the reference temperature; A real-time thermodynamic potential is obtained according to the thermodynamic equilibrium potential and the thermodynamic temperature correction potential.

6. The method according to claim 5, characterized in that After obtaining the global temperature of the slow-varying time layer according to the Joule heat power parameter of the fast-varying time layer and the preset slow-varying time step, the method further includes: The global temperature is used to update the thermodynamic equilibrium potential and the thermodynamic temperature correction potential.

7. The method according to claim 1, characterized in that The non-isothermal symplectic kinetic model is constructed by Hamiltonian, wherein the Hamiltonian is expressed as follows: in, is the kinetic energy term, is the momentum vector, is the mass matrix; is the potential energy term; is the hot exchange item, is the thermal conductivity coefficient, is the gas temperature, is the initial temperature field.

8. An electrolytic cell stack analysis device, characterized in that: The device comprises: An initial state acquisition module, configured to acquire initial internal state data of the battery stack to be analyzed, wherein the initial internal state data of the battery stack includes an initial temperature field, an initial gas concentration field, and an initial electrode damping coefficient; an internal state update module, configured to input the initial internal state data into a pre-built non-isothermal symplectic kinetic model and output the current density, updated temperature field, and updated concentration field of the battery stack, wherein the non-isothermal symplectic kinetic model includes a non-isothermal Hamiltonian for a heat exchange term, a non-isothermal Hamiltonian for a potential energy term, and a non-isothermal Hamiltonian for a kinetic energy term; an overpotential determination module, configured to input the updated temperature field and current density into a pre-built dynamic equivalent circuit model and output an overpotential parameter of the battery stack; a multiscale coupling module, configured to utilize the overpotential parameter and perform multiscale coupling time integration based on the current density to obtain a thermal stress distribution of the battery stack; A battery stack analysis module is used to generate an analysis result of the battery stack based on the current density, the updated temperature field, the updated concentration field, and the thermal stress distribution.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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