Thermal storage performance optimization method and device based on solid hydrogen storage

Abstract

The invention provides a thermal storage performance optimization method and device based on solid-state hydrogen storage, and relates to the technical field of solid-state hydrogen storage, and the method comprises the steps: constructing a solid-state hydrogen storage device finite element model containing heat-mass-momentum transmission multi-physics field coupling; analyzing the influence level of the to-be-optimized parameters of the spiral heat exchange tube on the heat storage performance by adopting a Taguchi method; and generating a Pareto frontier solution set through a non-dominant sorting second-generation genetic algorithm, constructing a multi-target evaluation matrix based on weighted grey relational degree analysis, and performing secondary screening by adopting an approximate ideal solution sorting method to determine an optimal parameter combination. According to the method, a multi-physics field coupling simulation model is constructed, a two-stage optimization architecture of a non-dominant sorting second-generation genetic algorithm and an approximate ideal solution sorting method is adopted, and collaborative optimization of spiral pipe parameters and fluid dynamic parameters is achieved. Key factors are pre-screened through a Taguchi method, a three-dimensional evaluation matrix is established in combination with grey correlation analysis, and balanced optimization of reaction scores and outlet temperature is ensured.

Description

Technical Field

[0001] This invention relates to the field of solid-state hydrogen storage technology, and in particular to a method and apparatus for optimizing the thermal storage performance of solid-state hydrogen storage. Background Technology

[0002] Metal hydrides are composed of a metal and hydrogen elements. During hydrogen absorption, the metal and hydrogen react chemically to form metal hydrides, releasing a large amount of heat energy. This reaction is exothermic, allowing the storage of excess heat energy. Furthermore, metal hydrides can store and release significant amounts of heat energy during hydrogen absorption and release, with a heat storage density far exceeding traditional sensible and latent heat storage methods, typically reaching 0.5–3.0 GJ / m³, which is 5–10 times that of sensible or phase-change heat storage. This allows for storing more heat energy in a smaller volume and mass, resulting in higher energy storage efficiency. Metal hydrides are chemically stable and can be stored for extended periods at room temperature without easily deteriorating or failing, thus enabling long-term heat energy storage suitable for applications such as seasonal heat storage. The reaction temperature range of metal hydrides is wide, generally between 200 and 700°C, meeting the heat energy storage and release requirements at different temperature levels. This makes them applicable to various industrial and residential scenarios, such as industrial waste heat recovery, solar thermal utilization, and building heating.

[0003] However, current research on heat exchange devices in solid-state hydrogen storage devices lacks a systematic analysis of the structural parameters of spiral heat exchange tubes and the coupling effects of multiphysics fields, which limits the improvement of heat storage density. Optimizing the performance of solid-state hydrogen storage devices and their thermal storage capabilities requires not only considering heat transfer efficiency but also minimizing flow pressure drop and maximizing the average hydrogen absorption / desorption rate. Summary of the Invention

[0004] The purpose of this invention is to provide a method and apparatus for optimizing the thermal storage performance of solid-state hydrogen storage, in order to solve the problem mentioned in the background art that the current research on heat exchange devices in solid-state hydrogen storage devices lacks a systematic analysis of the structural parameters of spiral heat exchange tubes and the coupling effect of multiple physical fields, which leads to the limitation of improving the thermal storage density.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a solid-state hydrogen storage device, comprising: a thermal reaction vessel; a spiral heat exchange tube disposed inside the thermal reaction vessel; a hydrogen storage alloy uniformly filling the interior of the thermal reaction vessel and covering the outer surface of the spiral heat exchange tube, wherein the hydrogen storage alloy has uniformly distributed pores for hydrogen gas flow, wherein the hydrogen storage alloy participates in hydrogen desorption reaction and releases hydrogen gas when absorbing heat from the spiral heat exchange tube, or participates in hydrogen adsorption reaction and releases heat to the spiral heat exchange tube when absorbing hydrogen gas; and a hydrogen storage tank connected to the thermal reaction vessel, wherein the hydrogen storage tank is used to store hydrogen gas released by the hydrogen desorption reaction or to release hydrogen gas to participate in the hydrogen adsorption reaction.

[0006] On the other hand, the present invention also provides a thermal storage system, including the above-mentioned solid-state hydrogen storage device, and further including: a fluid container for storing or releasing fluid; a solar collector connected to the fluid container and the heat load device for converting received solar radiation into heat energy to heat the fluid and transfer the heat to the heat load device; the solid-state hydrogen storage device is connected to the solar collector, the fluid container and the heat load device respectively; when absorbing the heat transferred by the solar collector, the solid-state hydrogen storage device participates in the hydrogen desorption reaction and stores the released hydrogen gas; when absorbing hydrogen gas, the solid-state hydrogen storage device participates in the hydrogen adsorption reaction and releases heat to heat the fluid transported by the fluid container, thereby transferring the heat to the heat load device.

[0007] On the other hand, the present invention also provides a method for optimizing thermal storage performance, applied to the aforementioned solid-state hydrogen storage device. The steps include: constructing a finite element model of the solid-state hydrogen storage device that includes multi-physics coupling of heat-mass-momentum transport; using the Taguchi method to analyze the influence level of the parameters to be optimized in the spiral heat exchanger tube on the thermal storage performance; generating a Pareto front solution set through a non-dominant sorting second-generation genetic algorithm; constructing a multi-objective evaluation matrix based on weighted grey relational analysis; and using a sorting method to approximate the ideal solution for secondary screening to determine the optimal parameter combination.

[0008] Optionally, the step of constructing a finite element model of a solid-state hydrogen storage device that includes multi-physics coupling of heat, mass, and momentum transport specifically includes: establishing a finite element model of the solid-state hydrogen storage device; establishing an energy conservation equation that integrates the thermal effects of local thermal capacity storage, gas convection heat transfer, heat conduction, and enthalpy change of chemical reactions; establishing a mass conservation equation that characterizes the mass flow rate of hydrogen adsorption and desorption through changes in the porosity and density of the reaction bed; establishing a momentum conservation equation for the metal hydride reaction bed; and comprehensively quantifying the reaction rate, heat transfer efficiency, and fluid dynamics indicators through the coupling relationship of the various equations in the model.

[0009] Optionally, the steps may also include: constructing a multi-objective optimization function based on reaction rate, heat transfer efficiency, and fluid dynamics quantitative indicators to optimize hydrogen adsorption and desorption rates, thermal conductivity, and flow pressure drop.

[0010] Optionally, the parameters to be optimized specifically include the diameter, outer diameter, number of turns, and flow rate of the heat transfer fluid in the spiral heat exchange tube.

[0011] Optionally, the step of using the Taguchi method to analyze the influence of the parameters to be optimized on the thermal storage performance of the spiral heat exchanger tube specifically includes: defining objective functions for the reaction fraction and the outlet temperature based on the signal-to-noise ratio of the large-scale characteristic; conducting orthogonal experiments to obtain the response of the reaction fraction and the outlet temperature under different parameter combinations; and determining the influence weight of each parameter on the reaction fraction and the outlet temperature based on the signal-to-noise ratio analysis results.

[0012] Optionally, the step of generating the Pareto front solution set using the second-generation genetic algorithm with non-dominant sorting specifically includes: initializing a population containing candidate solutions with helical structure parameters; performing non-dominant sorting on the population and determining the dominance relationship of individuals based on the objective function value; calculating the crowding distance of individuals in each Pareto layer to maintain solution set diversity; prioritizing the selection of individuals with low Pareto rank and large crowding distance through a tournament selection mechanism; generating offspring population using simulated binary crossover and polynomial mutation; merging the parent and offspring populations and selecting a new generation population based on non-dominant sorting and crowding distance; iteratively executing until the maximum number of generations is reached, and outputting the Pareto front solution set.

[0013] Optionally, the step of constructing a multi-objective evaluation matrix based on weighted grey relational analysis specifically includes: standardizing the reaction fraction and heat transfer fluid outlet temperature parameter sequences of each candidate solution in the Pareto front solution set; calculating the weighted grey relational coefficient of each candidate solution according to the parameter weight coefficients predefined by the Taguchi method; constructing an initial multi-objective decision matrix by using the reaction fraction, heat transfer fluid outlet temperature, and weighted grey relational coefficient as three-dimensional evaluation indicators; and performing vector normalization on the decision matrix to eliminate the dimensional differences between different indicators.

[0014] Optionally, the step of using the approximation ideal solution ranking method for secondary screening specifically includes: determining the positive and negative ideal solutions for each evaluation index in the normalized multi-objective evaluation matrix; calculating the Euclidean space distance between each candidate solution and the positive and negative ideal solutions; calculating the comprehensive proximity index based on the closeness of the candidate solution to the positive ideal solution and the distance from the negative ideal solution; sorting the candidates in descending order of the proximity index and selecting the candidate solution with the highest ranking as the optimal parameter combination.

[0015] Compared with the prior art, the beneficial effects of the present invention are: This application achieves highly efficient coupling of heat energy, mass transfer, and chemical reaction through a unique design that integrates a spiral heat exchanger structure with a hydrogen storage alloy (metal hydride reaction bed). The turbulence effect generated by the spiral heat exchanger significantly prolongs the residence time of the heat transfer fluid, and combined with the uniformly distributed pore structure inside the hydrogen storage alloy, it greatly improves the thermodynamic efficiency of the hydrogen adsorption / desorption reaction.

[0016] This application integrates a solar collector, a solid-state hydrogen storage reactor, and a fluid storage tank into a closed-loop system, combining the functions of thermal energy storage and hydrogen production. During the hydrogen absorption and heat release phase, the metal hydride provides a stable heat source for the heat-loaded equipment, while during the dehydrogenation and thermal storage phase, it converts excess solar energy into hydrogen for storage. This modular design allows the system to meet both instantaneous high-power heating demands and cross-seasonal energy storage, significantly improving the overall utilization rate of renewable energy.

[0017] This application constructs a multiphysics coupled simulation model based on chemical reaction, gas transport, and heat transfer mechanisms. It employs a two-level optimization architecture combining the non-dominant ranking second-generation genetic algorithm NSGA-II and the TOPSIS approximation ideal solution ranking method to achieve coordinated optimization of the helical tube's geometric and hydrodynamic parameters. Key influencing factors are pre-screened using the Taguchi method, and a three-dimensional evaluation matrix is ​​established using grey relational analysis, enabling the system to automatically adapt to different operating conditions. This effectively reduces the number of trial-and-error experiments while ensuring a balanced optimization of the dual objectives of reaction fraction and outlet temperature. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the thermal reaction device of the present invention.

[0019] Figure 2 This is a schematic diagram of the thermal storage system structure of the present invention.

[0020] Figure 3 This is a schematic diagram of the steps in the thermal storage performance optimization method of the present invention.

[0021] Figure 4 This is a flowchart of the thermal storage performance optimization method of the present invention.

[0022] In the diagram: 1-thermal reaction vessel, 2-hydrogen storage alloy, 3-spiral heat exchange tube, 4-hydrogen storage tank, 10-thermal reaction device, 20-solar collector, 30-fluid container, 40-heat load equipment. Detailed Implementation

[0023] The present invention will now be clearly and completely described in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be used interchangeably where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] Those skilled in the art will understand that, unless explicitly stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in the specification of this application means the presence of features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.

[0026] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0027] It should be understood that the sequence number and size of each step in this embodiment do not imply the order of execution. The execution order of each process is determined by its function and internal logic, and should not constitute any limitation on the implementation process of this application embodiment.

[0028] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0029] Please refer to Figure 1A solid-state hydrogen storage device according to the present invention includes: a thermal reaction vessel; a spiral heat exchange tube disposed inside the thermal reaction vessel; a hydrogen storage alloy uniformly filling the interior of the thermal reaction vessel and covering the outer surface of the spiral heat exchange tube, wherein the hydrogen storage alloy has uniformly distributed pores for hydrogen gas flow, and the hydrogen storage alloy participates in hydrogen desorption reaction and releases hydrogen gas when absorbing heat from the spiral heat exchange tube, or participates in hydrogen adsorption reaction and releases heat to the spiral heat exchange tube when absorbing hydrogen gas; and a hydrogen storage tank connected to the thermal reaction vessel, the hydrogen storage tank being used to store hydrogen gas released by the hydrogen desorption reaction or to release hydrogen gas to participate in the hydrogen adsorption reaction.

[0030] This application achieves highly efficient coupling of heat energy, mass transfer, and chemical reaction through a unique design that integrates a spiral heat exchanger structure with a hydrogen storage alloy (metal hydride reaction bed). The turbulence effect generated by the spiral heat exchanger significantly prolongs the residence time of the heat transfer fluid, and combined with the uniformly distributed pore structure inside the hydrogen storage alloy, it greatly improves the thermodynamic efficiency of the hydrogen adsorption / desorption reaction.

[0031] Please refer to Figure 2 On the other hand, the present invention also provides a thermal storage system, including the above-mentioned solid-state hydrogen storage device, and further including: a fluid container for storing or releasing fluid; a solar collector connected to the fluid container and the heat load device for converting received solar radiation into heat energy to heat the fluid and transfer the heat to the heat load device; the solid-state hydrogen storage device is connected to the solar collector, the fluid container and the heat load device respectively; when absorbing the heat transferred by the solar collector, the solid-state hydrogen storage device participates in the hydrogen desorption reaction and stores the released hydrogen gas; when absorbing hydrogen gas, the solid-state hydrogen storage device participates in the hydrogen adsorption reaction and releases heat to heat the fluid transported by the fluid container, thereby transferring the heat to the heat load device.

[0032] Specifically, the core components of the thermal storage system in this invention include a solar collector, a solid-state hydrogen storage device, a hydrogen storage tank, and a fluid container.

[0033] Thermal energy storage stage: When there is sunlight, the fluid in the fluid container flows to the solar collector and is heated to a high temperature; the high temperature fluid flows into the solid hydrogen storage device, and the spiral heat exchange tube in the solid hydrogen storage device transfers heat to the hydrogen storage alloy (i.e., metal hydride), causing the metal hydride bed to begin desorbing hydrogen; the hydrogen is stored in the hydrogen cylinder due to the pressure difference generated by the heating of the metal hydride bed.

[0034] Heat release stage: Hydrogen flows from the hydrogen cylinder into the solid hydrogen storage device, where it is adsorbed by the metal hydride, a process that releases heat; a low-temperature hot fluid flows through the spiral heat exchange tube, carrying away the heat from the metal hydride bed; continuous removal of heat from the metal hydride reactor ensures that the adsorption process is uninterrupted and also raises the temperature of the hot fluid.

[0035] This invention employs solid-state hydrogen storage technology, which boasts a significantly higher heat storage density than traditional sensible and latent heat storage methods. It can store more thermal energy within a smaller volume and mass, resulting in higher energy storage efficiency. Furthermore, the chemical properties of metal hydrides are relatively stable, allowing for long-term storage at room temperature without significant deterioration or failure. Therefore, it enables long-term thermal energy storage and is suitable for extended energy reserves.

[0036] This application integrates a solar collector, a solid-state hydrogen storage reactor, and a fluid storage tank into a closed-loop system, combining the functions of thermal energy storage and hydrogen production. During the hydrogen absorption and heat release phase, the metal hydride provides a stable heat source for the heat-loaded equipment, while during the dehydrogenation and thermal storage phase, it converts excess solar energy into hydrogen for storage. This modular design allows the system to meet both instantaneous high-power heating demands and cross-seasonal energy storage, significantly improving the overall utilization rate of renewable energy.

[0037] Please refer to Figure 3 and Figure 4 On the other hand, the present invention also provides a method for optimizing thermal storage performance, applied to the aforementioned solid-state hydrogen storage device. The steps include: constructing a finite element model of the solid-state hydrogen storage device that includes multi-physics field coupling of heat-mass-momentum transport; using the Taguchi method to analyze the influence level of the parameters to be optimized in the spiral heat exchanger tube on the thermal storage performance; generating a Pareto front solution set through a non-dominant sorting second-generation genetic algorithm, constructing a multi-objective evaluation matrix based on weighted grey relational analysis, and performing secondary screening using an approximation ideal solution sorting method to determine the optimal parameter combination.

[0038] This application constructs a multiphysics coupled simulation model based on chemical reaction, gas transport, and heat transfer mechanisms. It employs a two-level optimization architecture combining a non-dominant ranking second-generation genetic algorithm and an approximation-ideal-solution ranking method to achieve coordinated optimization of the helical tube's geometric and hydrodynamic parameters. Key influencing factors are pre-screened using the Taguchi method, and a three-dimensional evaluation matrix is ​​established using grey relational analysis, enabling the system to automatically adapt to different operating conditions. This effectively reduces the number of trial-and-error experiments while ensuring a balanced optimization of the dual objectives of reaction fraction and outlet temperature.

[0039] In some embodiments, the step of constructing a finite element model of a solid-state hydrogen storage device that includes multi-physics coupling of heat-mass-momentum transport specifically includes: establishing a finite element model of the solid-state hydrogen storage device; establishing an energy conservation equation that integrates the thermal effects of local thermal capacity storage, gas convection heat transfer, heat conduction, and enthalpy change of chemical reaction; establishing a mass conservation equation that characterizes the mass flow rate of hydrogen adsorption and desorption through changes in the porosity and density of the reaction bed; establishing a momentum conservation equation for the metal hydride reaction bed; and comprehensively quantifying the reaction rate, heat transfer efficiency, and fluid dynamics indicators through the coupling relationship of the various equations in the model.

[0040] Specifically, the solid-state hydrogen storage device model in this invention is constructed using the finite element method and numerically simulated using COMSOL multiphysics simulation software. The mass conservation relationship of the solid-state hydrogen storage reaction bed can be expressed as follows: (1) In the formula: This indicates the density of the solid hydrogen storage reaction bed. The porosity of the solid hydrogen storage reaction bed is represented by t, which represents time. This indicates the mass flow rate of adsorbed or desorbed hydrogen.

[0041] Energy conservation in solid-state hydrogen storage reaction beds: (2) In the formula: This indicates the effective heat capacity of the solid hydrogen storage reaction bed. The value represents the temperature of the solid hydrogen storage reaction bed, and t represents time. Indicates gas density, This represents the specific heat capacity of a gas at constant pressure. Indicates gas flow rate, Indicates the effective thermal conductivity. Indicates the enthalpy change of a chemical reaction. This indicates the molar mass of hydrogen gas.

[0042] The law of conservation of hydrogen mass is expressed as: (3) In the formula: This indicates the porosity of the solid hydrogen storage reaction bed. Let t represent the gas density and t represent time. This indicates the mass flow rate of adsorbed or desorbed hydrogen. Indicates gas density, This indicates the gas flow rate.

[0043] The flow rate of hydrogen gas was calculated using Darcy's law: (4) In the formula: Indicates gas flow rate, Indicates penetration rate. Indicates gas viscosity. This represents the pressure gradient.

[0044] Chemical reaction kinetics is expressed as: (5) In the formula: This represents the adsorption rate constant. Indicates activation energy. Represents the universal gas constant. Indicates gas pressure. Indicates balanced pressure. This indicates the density of the solid hydrogen storage reaction bed under saturated conditions. This indicates the density of the solid hydrogen storage reaction bed.

[0045] This application establishes a comprehensive equation that includes heat capacity effect, convective heat transfer, and chemical reaction kinetics. The model accurately quantifies the coupling mechanism of energy-mass-momentum in the reaction bed, providing a reliable data foundation for subsequent optimization.

[0046] In some embodiments, the steps further include: constructing a multi-objective optimization function based on reaction rate, heat transfer efficiency, and fluid dynamics quantification indicators to optimize hydrogen adsorption and desorption rates, thermal conductivity, and flow pressure drop.

[0047] Specifically, an optimization function based on core indicators such as reaction rate and heat transfer efficiency enables simultaneous optimization of hydrogen adsorption / desorption kinetics, thermal conductivity, and fluid resistance. This multi-dimensional evaluation system ensures that parameter adjustments do not sacrifice the overall system balance by unilaterally pursuing a single performance indicator.

[0048] In some embodiments, the parameters to be optimized specifically include the diameter, outer diameter, number of turns, and flow rate of the heat transfer fluid in the spiral heat exchange tube.

[0049] Specifically, this application synergistically optimizes geometric parameters such as the diameter and outer diameter of the helical tube with the flow velocity, improving heat transfer performance from both structural design and operational control perspectives. These parameters directly affect turbulence intensity and fluid residence time, thereby increasing heat transfer efficiency.

[0050] In some embodiments, the step of using the Taguchi method to analyze the influence of the parameters to be optimized on the thermal storage performance of the spiral heat exchanger specifically includes: defining objective functions for the reaction fraction and the outlet temperature based on the signal-to-noise ratio of the large-scale characteristic; conducting orthogonal experiments to obtain the response of the reaction fraction and the outlet temperature under different parameter combinations; and determining the influence weight of each parameter on the reaction fraction and the outlet temperature based on the signal-to-noise ratio analysis results.

[0051] Specifically, this invention takes values ​​for the helical tube diameter, helical tube outer diameter, number of helical tube turns, and flow velocity of the heat transfer fluid inside the helical tube within a conventional range, and introduces these values ​​as input parameters into a multiphysics coupling model. Based on the high signal-to-noise ratio characteristic, the reaction fraction and the outlet temperature of the heat transfer fluid are defined as objective functions respectively: (6) (7) In the formula: Indicates the reaction fraction. This indicates the outlet temperature of the high-temperature fluid.

[0052] The effects of different parameter combinations on system performance are simulated to obtain the reaction fraction and outlet temperature response under each parameter combination. Finally, the Taguchi method is used to systematically evaluate the influence of each parameter on performance indicators, thereby identifying the design factors that have the greatest impact on thermal response and heat transfer efficiency.

[0053] By employing orthogonal experimental design, the complex multi-parameter optimization problem is transformed into a systematic signal-to-noise ratio analysis, quickly identifying the control variables most sensitive to reaction fraction and outlet temperature. This method significantly reduces the number of experiments and defines an effective parameter search space for subsequent genetic algorithm optimization.

[0054] In some embodiments, the step of generating the Pareto front solution set using a second-generation genetic algorithm with non-dominant sorting specifically includes: initializing a population containing candidate solutions with helical structure parameters; performing non-dominant sorting on the population and determining the dominance relationship of individuals based on the objective function value; calculating the crowding distance of individuals in each Pareto layer to maintain solution set diversity; prioritizing individuals with low Pareto rank and large crowding distance through a tournament selection mechanism; generating offspring populations using simulated binary crossover and polynomial mutation; merging parent and offspring populations and selecting a new generation population based on non-dominant sorting and crowding distance; iteratively executing until the maximum number of generations is reached, and outputting the Pareto front solution set.

[0055] Specifically, this study employs the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to perform multi-objective optimization of the solenoid structure parameters, including the solenoid diameter, outer diameter, number of turns, and the flow velocity of the heat transfer fluid inside the solenoid. The core idea of ​​the NSGA-II algorithm is to select candidate solutions based on Pareto optimal sorting, maintain the diversity of the solution set using crowding distance, and introduce an elite retention strategy to improve the algorithm's convergence stability. Its main steps are as follows: Initialize the population: First, generate a population of size 1. initial population Each individual Represent a candidate solution and compute its multi-objective value. .

[0056] Non-dominated sorting: Performing non-dominated sorting on a population. A population is considered non-dominated if the following conditions are met. Dominate .

[0057] (8) In the formula: and For each individual in the population (candidate solution). To optimize the total number of objectives, For individuals In the The value of the objective function For individuals In the The values ​​on the objective function.

[0058] Crowding distance calculation: In each Pareto level, individuals Crowding is defined as: (9) In the formula: and yes Individuals in the goal The adjacent solutions on the solution reflect the sparsity of the solution. and For the goal The population extreme values ​​are used to calculate the normalized crowding degree.

[0059] Tournament Selection: A binary tournament selection mechanism is adopted, prioritizing individuals with low Pareto ratings and large crowding distances.

[0060] Crossover and Mutation: Using Simulated Binary Crossover and polynomial variation Generate offspring: (10) In the formula: and For each individual in the population (candidate solution). These are intermediate offspring individuals generated through crossover operations. It is a polynomial mutation operator. This refers to the mutated offspring individuals that are ultimately generated.

[0061] Merging and Selecting New Populations: Merging Parent and Offspring Generations ,in, This is the temporary population formed after merging, with a size of 2N. The parent population of generation t contains N individuals. Let N be the offspring population generated through crossover mutation in generation t, containing N individuals, and let N be the offspring population generated through crossover mutation in generation t. Perform non-dominated sorting, and select N individuals based on Pareto rank and crowding distance to form the next generation. .

[0062] Stopping condition: Repeat the non-dominated sorting-merging and new population selection steps until the maximum number of iterations is reached. .

[0063] This application combines non-dominated sorting with a crowding distance mechanism to ensure a uniform distribution of the Pareto solution set in the target space. By simulating the crossover and mutation operations in biological evolution, the algorithm can both escape local optima and preserve the inheritance of desirable features, ultimately obtaining a diverse set of optimized solutions.

[0064] In some embodiments, the step of constructing a multi-objective evaluation matrix based on weighted grey relational analysis specifically includes: standardizing the reaction fraction and heat transfer fluid outlet temperature parameter sequences of each candidate solution in the Pareto front solution set; calculating the weighted grey relational coefficient of each candidate solution according to the parameter weight coefficients predefined by the Taguchi method; constructing an initial multi-objective decision matrix by using the reaction fraction, heat transfer fluid outlet temperature, and weighted grey relational coefficient as three-dimensional evaluation indicators; and performing vector normalization on the decision matrix to eliminate the dimensional differences between different indicators.

[0065] Specifically, this application combines standardized performance indicators with predefined weights, eliminating the barriers to comparison of parameters with different dimensions. The establishment of a three-dimensional evaluation matrix makes heterogeneous indicators such as reaction efficiency and heat output comparable, providing a quantitative basis for TOPSIS decision-making.

[0066] In some embodiments, the step of secondary screening using the approximation ideal solution ranking method specifically includes: determining the positive and negative ideal solutions for each evaluation index in the normalized multi-objective evaluation matrix; calculating the Euclidean space distance between each candidate solution and the positive and negative ideal solutions; calculating the comprehensive proximity index based on the closeness of the candidate solution to the positive ideal solution and the distance from the negative ideal solution; sorting the candidate solutions in descending order of the proximity index and selecting the candidate solution with the highest ranking as the optimal parameter combination.

[0067] Specifically, in each iteration, the algorithm generates different parameter combinations and inputs them into the constructed multiphysics model to obtain the corresponding reaction fraction and heat transfer fluid outlet temperature. The grey relational coefficient of each parameter combination is calculated to assess its impact on system performance. Based on this, according to the influence levels of each parameter previously determined by the Taguchi method, different parameters are assigned corresponding weights, and the weighted grey relational degree of all Pareto fronts of the NSGA-II algorithm is calculated accordingly, as shown in the following formula: (11) (12) (13) (14) (15) In the formula: These are the weighting coefficients. For the weighted grey relational coefficient, To represent the original sequence values, To represent the standardized sequence values, As a reference sequence, It is a deviation sequence. The resolution coefficient, is the grey relational coefficient.

[0068] Based on population response score Heat transfer fluid outlet temperature and the aforementioned weighted grey relational coefficient Construct a multi-objective matrix The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used to select the parameter combination with the best overall performance. TOPSIS is a multi-index decision-making method that ranks solutions by calculating the distances between each solution and the positive and negative ideal solutions. The ideal solution should have the smallest distance to the positive ideal solution and the largest distance to the negative ideal solution. The specific steps of TOPSIS are as follows: Constructing the decision matrix: Given... Decision matrix, where As a candidate solution, These are the evaluation indicators.

[0069] Normalized decision matrix: Normalize each indicator to eliminate the influence of dimensions.

[0070] (16) In the formula: This represents the original value of the i-th sample in the original decision matrix on the j-th evaluation index. This is the normalized value, and m is the total number of samples.

[0071] Constructing a weighted normalization matrix to determine the positive and negative ideal solutions: (17) In the formula: For the j-th index, the positive ideal solution is... It is the negative ideal solution for the j-th index. The weighted index value. Represents a weighted matrix. Let be the weight of the j-th indicator. The positive ideal solution contains the optimal value of each indicator, and the negative ideal solution contains the worst value of each indicator.

[0072] Calculate the distance to the ideal solution: Euclidean distance is used to calculate the distance between the positive and negative ideal solutions. and The specific formula is as follows: (18) In the formula, and The distance between the positive and negative ideal solutions. For the j-th index, It is the negative ideal solution for the j-th index. This is the weighted index value.

[0073] Calculate relative proximity And sort and select: (19) In the formula: For relative closeness, and The distance is the distance between the positive and negative ideal solutions.

[0074] Ultimately, by selecting the parameter combination with the highest approximation, the dual objectives of reaction efficiency and heat output are synergistically optimized.

[0075] This application transforms the multidimensional optimization problem into a proximity ranking problem by calculating the Euclidean distance between candidate solutions and the ideal solution. This spatial distance-based decision-making method intuitively reflects the overall merits of each solution, ensuring that the final selected parameter combination achieves the optimal balance across various performance indicators.

[0076] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0077] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, database, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0078] The above are merely embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention's specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A solid-state hydrogen storage device, characterized in that, include: Thermal reaction vessel; Spiral heat exchange tubes are disposed inside the thermal reaction vessel; A hydrogen storage alloy is uniformly filled inside the thermal reaction vessel and covers the outer surface of the spiral heat exchange tube. The hydrogen storage alloy has uniformly distributed pores for hydrogen gas to flow through. When the hydrogen storage alloy absorbs heat from the spiral heat exchange tube, it participates in the hydrogen desorption reaction and releases hydrogen gas, or when it absorbs hydrogen gas, it participates in the hydrogen adsorption reaction and releases heat to the spiral heat exchange tube. A hydrogen storage tank is connected to the thermal reaction vessel. The hydrogen storage tank is used to store hydrogen gas released during the hydrogen desorption reaction or to control the release of hydrogen gas to participate in the hydrogen adsorption reaction.

2. A thermal storage system, comprising the solid-state hydrogen storage device of claim 1, characterized in that, Also includes: Fluid containers are used to store or release fluids; A solar collector, connected to the fluid container and the heat load device, is used to convert received solar radiation into heat energy to heat the fluid and transfer the heat to the heat load device. The solid hydrogen storage device is connected to the solar collector, the fluid container, and the heat load device. When the solid hydrogen storage device absorbs the heat transferred by the solar collector, it participates in the hydrogen desorption reaction and stores the released hydrogen. When the solid hydrogen storage device absorbs hydrogen, it participates in the hydrogen adsorption reaction and releases heat to heat the fluid transported by the fluid container, thereby transferring the heat to the heat load device.

3. A method for optimizing thermal storage performance, applied to the solid-state hydrogen storage device of claim 1 or the thermal storage system of claim 2, characterized in that the steps include... include: A finite element model of a solid-state hydrogen storage device incorporating multi-physics coupling of heat, mass, and momentum transport was constructed. The Taguchi method was used to analyze the impact of the parameters to be optimized in spiral heat exchangers on thermal storage performance. The Pareto front solution set is generated by a second-generation genetic algorithm with non-dominance sorting. A multi-objective evaluation matrix is ​​constructed based on weighted grey relational analysis. Then, a secondary screening is performed using the approximation of ideal solution sorting method to determine the optimal parameter combination.

4. The method for optimizing thermal storage performance according to claim 3, characterized in that, The steps for constructing a finite element model of a solid-state hydrogen storage device that includes multiphysics coupling of heat, mass, and momentum transport specifically include: Establish a finite element model of a solid-state hydrogen storage device; Establish an energy conservation equation that integrates the thermal effects of local heat capacity storage, gas convection heat transfer, heat conduction, and enthalpy change of chemical reactions; A mass conservation equation was established to characterize the mass flow rates of hydrogen adsorption and desorption by changes in the porosity and density of the reaction bed. Establish the momentum conservation equation for the metal hydride reaction bed; The reaction rate, heat transfer efficiency, and fluid dynamics indicators are comprehensively quantified by the coupling relationship of various equations in the model.

5. The method for optimizing thermal storage performance according to claim 4, characterized in that, The steps also include: constructing a multi-objective optimization function based on reaction rate, heat transfer efficiency, and fluid dynamics quantitative indicators to optimize hydrogen adsorption and desorption rates, thermal conductivity, and flow pressure drop.

6. The method for optimizing thermal storage performance according to claim 3, characterized in that, The parameters to be optimized specifically include the diameter, outer diameter, number of turns, and flow rate of the heat transfer fluid in the spiral heat exchanger tube.

7. The method for optimizing thermal storage performance according to claim 3, characterized in that, The steps for analyzing the impact of the parameters to be optimized in the spiral heat exchanger tube on the thermal storage performance using the Taguchi method specifically include: Based on the signal-to-noise ratio of the large-scale characteristic, objective functions for reaction fraction and outlet temperature are defined respectively; Orthogonal experiments were conducted to obtain the reaction fraction and outlet temperature response under different parameter combinations; The influence weights of each parameter on the reaction fraction and outlet temperature are determined based on the signal-to-noise ratio analysis results.

8. The method for optimizing thermal storage performance according to claim 3, characterized in that, The steps for generating the Pareto front solution set using the second-generation genetic algorithm with non-dominance sorting specifically include: Initialize a population containing candidate solutions for the helical tube structure parameters; Perform non-dominated ranking of the population and determine the dominance relationship of individuals based on the objective function value; Calculate the crowding distance of individuals in each Pareto layer to maintain solution set diversity; The tournament selection mechanism prioritizes individuals with low Pareto levels and large crowding distances. The offspring population is generated using simulated binary crossover and polynomial mutation. The parent and offspring populations were merged, and the next generation of populations was selected based on non-dominated ranking and crowding distance. Iterate until the maximum algebra is reached, and output the Pareto front solution set.

9. The method for optimizing thermal storage performance according to claim 8, characterized in that, The steps for constructing a multi-objective evaluation matrix based on weighted grey relational analysis specifically include: The reaction fractions and heat transfer fluid outlet temperature parameter sequences of each candidate solution in the Pareto front solution set are standardized. Calculate the weighted grey relational coefficients for each candidate solution based on the predefined parameter weighting coefficients of the Taguchi method. The reaction fraction, heat transfer fluid outlet temperature, and weighted grey relational coefficient are used as three-dimensional evaluation indicators to construct an initial multi-objective decision matrix. The decision matrix is ​​vector normalized to eliminate the dimensional differences between different indicators.

10. The method for optimizing thermal storage performance according to claim 9, characterized in that, The steps for secondary screening using the approximation of ideal solution sorting method specifically include: In the normalized multi-objective evaluation matrix, the positive ideal solution and negative ideal solution of each evaluation index are determined respectively; Calculate the Euclidean distance between each candidate solution and the positive and negative ideal solutions; The comprehensive similarity index is calculated based on the degree of closeness between the candidate solution and the positive ideal solution and the degree of distance between the candidate solution and the negative ideal solution. Sort the solutions in descending order of their proximity index and select the candidate solution with the highest ranking as the optimal parameter combination.

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