Optimization method of heat and cold storage dual-mode energy storage system

Abstract

The application provides an optimization method of a heat storage and cold storage dual-mode energy storage system, and belongs to the technical field of the heat storage and cold storage dual-mode energy storage system.The application covers a multi-element composite phase change material system covering negative 20 DEG C to 80 DEG C, controls the phase change temperature by using a molecular design technology, improves the thermal conductivity to the range of 2.5-4.2 W / (m*K) by using a nano modification technology, realizes material layering gradient configuration by using a minimum spanning tree algorithm, designs a variable-diameter spiral fin and a porous foam metal composite heat transfer strengthening structure, establishes a phase change interface prediction algorithm model to realize interface position monitoring with the accuracy of ±2 mm, constructs a double-layer game optimization model to dynamically adjust heat transfer parameters, realizes heat storage and cold storage dual-mode adaptive switching, and solves the technical problem of unstable heat transfer efficiency of the heat storage and cold storage system in the prior art through the synergistic effect of material performance optimization, structure adaptive design and intelligent control strategy.

Description

Technical Field

[0001] This invention belongs to the technical field of dual-mode energy storage systems for thermal and cold storage, and specifically relates to an optimization method for a dual-mode energy storage system for thermal and cold storage. Background Technology

[0002] Phase change energy storage (PCS) technology, as a highly efficient thermal energy storage method, is widely used in building energy conservation, industrial waste heat recovery, new energy vehicle thermal management, data center cooling, and solar thermal power generation. Traditional technologies mainly use a single PCS material combined with heat transfer elements of fixed geometry for thermal or cold storage, storing and releasing energy through the latent heat of phase change of the material. However, in current PCS applications, due to the low thermal conductivity of PCS materials, the fixed design of traditional heat transfer structures, and the difficulty in precisely controlling the phase change interface position, system operating parameters cannot be adjusted in real time according to changes in operating conditions. This results in large fluctuations in heat transfer efficiency under different ambient temperatures and loads, leading to highly unstable system performance. In other words, existing PCS systems suffer from the technical problem of unstable heat transfer efficiency. Summary of the Invention

[0003] In view of this, the present invention provides an optimization method for a dual-mode energy storage system of thermal and cold storage, which can solve the technical problem of unstable heat transfer efficiency in existing thermal and cold storage systems.

[0004] This invention is implemented as follows: It provides an optimization method for a dual-mode energy storage system (thermal and cold storage). This method achieves stable heat transfer efficiency by constructing a multi-component composite phase change material system, hierarchical gradient configuration optimization, adaptive heat transfer enhancement structural design, establishing a phase change interface prediction algorithm, and implementing a two-layer game-theoretic optimization control. The method includes constructing a multi-component composite phase change material system; controlling the phase change temperature of paraffin-based materials and inorganic salt hydrate materials through molecular design technology to form a gradient phase change material library covering a wide temperature range; adding graphene nanosheets and alumina nanoparticles to the phase change materials using nanotechnology to improve the material's thermal conductivity while maintaining the latent heat of phase change; and establishing a hierarchical gradient configuration optimization model, using the minimum spanning tree algorithm in graph theory to determine the material arrangement order. The paper proposes a method for determining the phase transition temperature of materials. Materials with different phase transition temperatures are arranged in a decreasing order from the outer to the inner layer, using temperature gradient continuity as the edge weight. An adaptive heat transfer enhancement structure is designed, employing a composite structure of variable-diameter spiral fins and porous foam metal, with the fin diameter varying along the axial direction. A phase transition interface prediction algorithm model is established, using data from temperature distribution sensors and phase transition completion sensors to calculate the solid-liquid interface position and movement speed in real time. A two-layer game-theoretic optimization model is established to dynamically adjust heat transfer geometric parameters; the upper model aims to maximize energy storage efficiency, while the lower model aims to minimize energy consumption. A dual-mode switching control strategy for thermal and cold storage is established, activating the cold storage mode when the ambient temperature is higher than the set temperature and the thermal storage mode when it is lower than the set temperature.

[0005] The construction steps of the multi-component composite phase change material system specifically involve a gradient phase change material library containing 15 materials with different phase change temperatures, covering a temperature range from -20℃ to 80℃. Molecular structures with target phase change temperatures are predicted and designed using molecular design techniques, computational chemistry methods, and molecular dynamics simulations, thereby controlling intermolecular forces and crystal structure parameters.

[0006] Specifically, the nano-modification technology involves uniformly dispersing nanoscale high thermal conductivity materials into the phase change material matrix to form a three-dimensional thermally conductive network structure, which significantly improves the thermal conductivity of the composite material and increases the thermal conductivity of the material to the range of thermal conductivity ∈ [2.5, 4.2] W / (m·K), while maintaining the latent heat of phase change at no less than 85% of the initial value.

[0007] Specifically, the step of establishing the hierarchical gradient configuration optimization model is to arrange materials with different phase transition temperatures in an orderly manner according to the temperature gradient, forming a continuous temperature distribution structure from high temperature to low temperature or from low temperature to high temperature, and the phase transition temperature of the outer layer material is set within the range of ambient temperature plus or minus 15°C.

[0008] Specifically, the minimum spanning tree algorithm for determining the material arrangement order uses temperature difference as edge weights and the Kruskal algorithm to find the minimum weight path connecting all material nodes, thereby determining the optimal material arrangement order.

[0009] Specifically, the adaptive heat transfer enhancement structure design steps include a variable diameter spiral fin consisting of a copper fin substrate and a stainless steel support rod, and a porous foam metal made of aluminum alloy with a porosity ∈ [0.85, 0.95). The variable diameter spiral fin refers to a spiral heat transfer enhancement structure in which the fin diameter changes continuously along the axial direction.

[0010] Specifically, the variable diameter helical fin has a diameter variation pattern as follows: ,in denoted as the initial diameter, x as the axial position, and L as the total length. Porous foam metal refers to a metal matrix material with an open-cell structure, characterized by high porosity and large specific surface area, used to enhance heat transfer and promote the flow of phase change materials.

[0011] Specifically, the phase change interface prediction algorithm model establishment step refers to the mathematical model that calculates and predicts the position and movement law of the solid-liquid phase change interface in real time based on the principles of heat transfer and numerical calculation methods, with the prediction accuracy controlled within ±2mm.

[0012] Specifically, the two-layer game optimization model includes an upper-layer energy storage efficiency optimization function and a lower-layer energy consumption minimization function. The energy storage efficiency optimization function is used to maximize the overall energy storage efficiency of the system, and the energy consumption minimization function is used to minimize the total energy consumption of the system. The two objective functions are related through a heat transfer efficiency coupling term.

[0013] Specifically, the energy storage efficiency optimization function takes as input parameters including fin spacing parameters, foam metal porosity parameters, phase change interface migration speed parameters, ambient temperature parameters, and material thermal conductivity parameters, and outputs the optimal fin geometry configuration scheme. The energy consumption minimization function takes as input parameters including auxiliary equipment power parameters, heat transfer enhancement structure operating power parameters, control system power parameters, pump power parameters, and fan power parameters, and outputs the optimal power allocation scheme.

[0014] Specifically, the two-layer game optimization model performs the following steps for dynamically adjusting the heat transfer geometric parameters: when the system energy efficiency is below 75%, reduce the operating power of non-critical auxiliary equipment; when the efficiency is between 75% and 90%, maintain the current power allocation; and when the efficiency is above 90%, appropriately improve the overall heat transfer performance.

[0015] Specifically, the steps for establishing the dual-mode switching control strategy for thermal and cold storage are as follows: when the ambient temperature is 5°C higher than the set temperature, the cold storage mode is activated; when the ambient temperature is 5°C lower than the set temperature, the thermal storage mode is activated. During the switching process, the material temperature gradient is maintained continuously, and the mode switching time is controlled within the range of [10, 30] s.

[0016] Before establishing the phase change interface prediction algorithm model, the system also includes the construction of a dual-mode energy storage system for thermal and cold storage. The dual-mode energy storage system for thermal and cold storage includes an outer shell container, a multi-component composite phase change material unit, an adaptive heat transfer enhancement structure, a phase change interface prediction control system, and a mode switching control device.

[0017] Specifically, the dual-mode energy storage system for thermal and cold storage is made of stainless steel, with a multi-component composite phase change material unit inside the outer shell container, an adaptive heat transfer enhancement structure interspersed within the multi-component composite phase change material unit, and a phase change interface prediction and control system including a temperature distribution sensor, a phase change completion sensor, and a data processing unit.

[0018] Specifically, the phase change interface prediction and control system consists of a temperature distribution sensor and a phase change completion sensor connected to a data processing unit via signal lines. The data processing unit is connected to a mode switching control device via control lines. The mode switching control device includes a solenoid valve group, a flow regulator, and a temperature controller.

[0019] The heat transfer efficiency coupling term is specifically a common constraint condition for the two objective functions, expressed as follows: ,in The maximum allowable power of the system is defined as the gradient phase change material library, which is an ordered collection of materials with different phase change temperatures. It is a material database that achieves wide temperature range coverage through material combinations.

[0020] This invention improves the thermal conductivity of materials to the range of 2.5-4.2 W / (m·K) by establishing a multi-component composite phase change material system and nano-modification technology, providing a material basis for stable heat transfer efficiency. The invention employs an adaptive heat transfer enhancement structure design, using a composite configuration of variable-diameter spiral fins and porous foam metal to achieve dynamic optimization of the heat transfer area and intelligent adjustment of flow resistance, effectively solving the problem of poor adaptability of fixed heat transfer structures. The phase change interface prediction algorithm model and two-layer game-theoretic optimization control strategy established in this invention enable precise monitoring of the phase change process and real-time dynamic adjustment of system operating parameters, ensuring stable high heat transfer efficiency under various operating conditions, thus solving the technical problem of unstable heat transfer efficiency in existing thermal and cold storage systems. Attached Figure Description

[0021] Figure 1 This is a flowchart of the method of the present invention.

[0022] Figure 2 This is a temperature gradient distribution diagram for the dual-mode thermal and cold storage in Example 2.

[0023] Figure 3 This is a graph showing the relationship between the phase change interface migration speed and the thermal conductivity of the material in Example 2.

[0024] Figure 4 The graph shows the change in system energy storage efficiency over operating time in Example 2.

[0025] Figure 5 This is a schematic diagram of the dual-mode energy storage system for thermal and cold storage in Example 2. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0027] like Figure 1 The diagram shown is a flowchart of an optimization method for a dual-mode energy storage system (thermal and cold storage) provided by this invention. This method includes the following steps: S01. Construct a multi-component composite phase change material system, and control the phase change temperature of paraffin-based materials and inorganic salt hydrate materials through molecular design technology to form a gradient phase change material library with a temperature range of -20℃ to 80℃. The gradient phase change material library contains 15 materials with different phase change temperatures. S02. Using nanotechnology, graphene nanosheets and alumina nanoparticles are added to the phase change material to improve the thermal conductivity to the range of [2.5, 4.2] W / (m·K), while maintaining the latent heat of phase change at no less than 85% of the initial value; S03. Establish a hierarchical gradient configuration optimization model. Use the minimum spanning tree algorithm in graph theory to determine the material arrangement order. Use the temperature gradient continuity as the edge weight and arrange materials with different phase transition temperatures in a way that the temperature decreases from the outer layer to the inner layer. Set the phase transition temperature of the outer layer material to be within the range of ambient temperature plus or minus 15℃. S04. Design an adaptive heat transfer enhancement structure, which adopts a composite structure of variable diameter spiral fins and porous foam metal. The fin diameter varies along the axial direction. The variable diameter spiral fins include a copper fin substrate and a stainless steel support rod. The porous foam metal is made of aluminum alloy with a porosity of [0.85, 0.95). S05. Establish a phase change interface prediction algorithm model. By monitoring the data obtained by the temperature distribution sensor and the phase change completion sensor, calculate the solid-liquid interface position and movement speed in real time. The prediction accuracy is controlled within ±2mm. S06. Establish a two-layer game optimization model to dynamically adjust the heat transfer geometric parameters. The upper-layer model aims to maximize energy storage efficiency, while the lower-layer model aims to minimize energy consumption. When the system energy efficiency is below 75%, reduce the operating power of non-critical auxiliary equipment; when the efficiency is between 75% and 90%, maintain the current power allocation; when the efficiency is above 90%, appropriately improve the overall heat transfer performance. S07. Establish a dual-mode switching control strategy for thermal and cold storage. When the ambient temperature is more than 5°C higher than the set temperature, the cold storage mode is activated. When the ambient temperature is less than 5°C lower than the set temperature, the thermal storage mode is activated. During the switching process, the material temperature gradient is kept continuous, and the mode switching time is controlled within the range of time ∈ [10, 30]s.

[0028] The dual-mode energy storage system includes a shell container, a multi-component composite phase change material unit, an adaptive heat transfer enhancement structure, a phase change interface prediction and control system, and a mode switching control device. The shell container is made of stainless steel. The multi-component composite phase change material unit is disposed inside the shell container. The adaptive heat transfer enhancement structure is interspersed within the multi-component composite phase change material unit. The phase change interface prediction and control system includes a temperature distribution sensor, a phase change completion sensor, and a data processing unit. The temperature distribution sensor and the phase change completion sensor are connected to the data processing unit via signal lines. The data processing unit is connected to the mode switching control device via control lines. The mode switching control device includes a solenoid valve group, a flow regulator, and a temperature controller.

[0029] The hierarchical gradient configuration optimization model employs the minimum spanning tree algorithm, using temperature difference as edge weights. The Kruskal algorithm is used to find the minimum weight path connecting all material nodes, thus determining the optimal material arrangement order.

[0030] The two-layer game optimization model includes an upper-layer energy storage efficiency optimization function and a lower-layer energy consumption minimization function. The energy storage efficiency optimization function is used to maximize the overall energy storage efficiency of the system. Its inputs include fin spacing parameters, foam metal porosity parameters, phase change interface migration speed parameters, ambient temperature parameters, and material thermal conductivity parameters. The output is the optimal fin geometry configuration scheme. The energy consumption minimization function is used to minimize the total energy consumption of the system operation. Its inputs include auxiliary equipment power parameters, heat transfer enhancement structure operating power parameters, control system power parameters, pump power parameters, and fan power parameters. The output is the optimal power allocation scheme. The two objective functions are correlated through a heat transfer efficiency coupling term.

[0031] Molecular design technology refers to the use of computational chemistry methods and molecular dynamics simulations to predict and design molecular structures with target phase transition temperatures, thereby controlling intermolecular forces and crystal structure parameters.

[0032] Among them, nano-modification technology refers to uniformly dispersing nanoscale high thermal conductivity materials into a phase change material matrix to form a three-dimensional thermally conductive network structure, which significantly improves the thermal conductivity of composite materials.

[0033] Among them, the layered gradient configuration refers to arranging materials with different phase transition temperatures in an orderly manner according to the temperature gradient to form a continuous temperature distribution structure from high temperature to low temperature or from low temperature to high temperature.

[0034] Among them, variable diameter spiral fins refer to spiral heat transfer enhancement structures in which the fin diameter changes continuously along the axial direction, and the diameter change law is as follows: ,in Let x be the initial diameter, x be the axial position, and L be the total length.

[0035] Among them, porous foam metal refers to a metal matrix material with an open-cell structure, which has high porosity and large specific surface area, and is used to enhance heat transfer and promote the flow of phase change materials.

[0036] Among them, the phase change interface prediction algorithm refers to a mathematical model that calculates and predicts the position and movement law of the solid-liquid phase change interface in real time based on the principles of heat transfer and numerical calculation methods.

[0037] Among them, the gradient phase change material library refers to an ordered collection of materials with different phase change temperatures, which achieves a wide temperature range coverage through material combinations.

[0038] Thermal conductivity refers to the amount of heat transferred through a unit area per unit time under a unit temperature gradient, and is a physical parameter that characterizes the thermal conductivity of a material.

[0039] Among them, the fin spacing parameter refers to the distance between adjacent fins, which is a geometric parameter that affects the heat transfer area and fluid flow resistance.

[0040] Among them, the porosity parameter of foam metal refers to the ratio of pore volume to total volume in porous foam metal, which is a structural parameter that affects heat transfer performance and flow characteristics.

[0041] Among them, the phase change interface migration speed parameter refers to the instantaneous speed at which the solid-liquid phase change interface moves along the spatial direction, and is a dynamic parameter that reflects the speed of the phase change process.

[0042] Among them, ambient temperature parameter refers to the temperature conditions of the environment in which the energy storage system is located, and is an external parameter that affects the heat transfer boundary conditions of the system.

[0043] Among them, the thermal conductivity parameter of a material refers to the value of the heat conduction capacity of a phase change material, which is a physical property parameter that determines the heat transfer efficiency inside the material.

[0044] Among them, the power parameters of auxiliary equipment refer to the electrical power consumed by the operation of equipment other than the main heat transfer components.

[0045] Among them, the operating power parameter of the heat transfer enhancement structure refers to the electrical power required for the operation of heat transfer enhancement devices such as variable diameter spiral fins and porous foam metal.

[0046] Among them, the power parameters of the control system refer to the electrical power consumed by the phase change interface predictive control system and the mode switching control device during operation.

[0047] Among them, pump power parameters refer to the electrical power consumed by the pump equipment used to drive the circulation of heat transfer fluid.

[0048] Among them, the fan power parameter refers to the electrical power consumed by the fan equipment used for forced convection heat transfer.

[0049] Wherein, the energy storage efficiency optimization function is: Where s is the fin spacing parameter, p is the porosity parameter of the foam metal, and v is the phase change interface migration velocity parameter. Here, is the ambient temperature parameter, and k is the thermal conductivity parameter of the material.

[0050] Wherein, the energy consumption minimization function is ,in For auxiliary equipment power parameters, For the operating power parameters of the heat transfer enhancement structure, To control the system power parameters, For pump power parameters, These are the power parameters of the wind turbine.

[0051] The heat transfer efficiency coupling term is a common constraint on the two objective functions, expressed as follows: ,in This represents the system's maximum permissible power.

[0052] The specific implementation methods of the above steps are described in detail below.

[0053] The specific implementation of step S01 involves constructing a multi-component composite phase change material system using molecular design techniques. The purpose of this step is to obtain a gradient phase change material library covering a wide temperature range. First, density functional theory in computational chemistry is used to calculate the relationship between the molecular chain length and phase change temperature of paraffin-based materials. The phase change temperature is controlled by adjusting the number of carbon atoms in the alkane molecule; as the number of carbon atoms increases from 12 to 30, the phase change temperature increases from -20℃ to 80℃. For inorganic hydrate materials, molecular dynamics simulation is used to predict the influence of different hydration numbers on the phase change temperature, and the phase change temperature range is adjusted by controlling the number of water of crystallization molecules. When establishing the material database, the range from -20℃ to 80℃ is divided into 15 temperature nodes according to a 6.7℃ temperature interval, with each node corresponding to a phase change material. A high-throughput material screening algorithm is used to select material combinations from candidate molecular structures with a latent heat of phase change of not less than 150 kJ / kg and a thermal stability of not less than 100 cycles. Precise control of the phase transition temperature is achieved by adjusting the strength of intermolecular van der Waals forces and hydrogen bonding forces, with the control accuracy within ±2℃.

[0054] The specific implementation of step S02 involves adding thermally conductive enhancing particles to the phase change material using nanotechnology. This step significantly improves the thermal conductivity of the composite material. First, graphene nanosheets are dispersed in an organic solvent using ultrasonic dispersion technology to form a stable suspension. The dispersion time is controlled at 45 minutes, and the ultrasonic power is set to 800 watts. Surface functionalization technology is used to introduce carboxyl and hydroxyl functional groups onto the surface of the graphene nanosheets, improving their compatibility with the phase change material matrix. Alumina nanoparticles are prepared by ball milling, with an average particle size controlled at 50 nanometers and a specific surface area of ​​not less than 80 square meters per gram. Graphene nanosheets and alumina nanoparticles are mixed at a mass ratio of 2:1 and then composited with the phase change material matrix via melt blending. The mixing temperature is controlled at 10°C above the melting point of the phase change material, and the stirring speed is set to 200 rpm. The addition amount is controlled within the range of 3% to 8% of the phase change material matrix mass, ensuring that the thermal conductivity is increased to within the range of 2.5 to 4.2 W / m Kelvin. The latent heat retention rate of the composite material was determined by thermogravimetric analysis and differential scanning calorimetry, and it was required to be no less than 85% of the initial value.

[0055] The specific implementation of step S03 involves establishing a hierarchical gradient configuration optimization model to determine the material arrangement order. The purpose of this step is to achieve a continuous distribution of the temperature gradient. The minimum spanning tree algorithm from graph theory is used to optimize the arrangement of 15 materials with different phase transition temperatures. Each material is considered a node in the graph, and the temperature difference between adjacent materials is used as the edge weight to construct a complete graph. Kruskal's algorithm is used to find the minimum weight path connecting all material nodes. This algorithm first sorts all edges by weight from smallest to largest, and then sequentially selects the edge with the smallest weight that does not form a cycle to add to the spanning tree. The optimal arrangement order obtained by this algorithm ensures that the sum of temperature differences between adjacent materials is minimized, achieving a smooth transition of the temperature gradient. The phase transition temperature of the outer layer material is dynamically adjusted according to the ambient temperature. When the ambient temperature is 25℃, the phase transition temperature of the outer layer material is set within the range of 10℃ to 40℃. The inner layer materials are arranged in descending order of temperature, and the temperature gradient slope is controlled within the range of 2℃ to 5℃ per centimeter. A topological sorting algorithm is used to verify the uniqueness of the material arrangement order, ensuring that there are no loop structures.

[0056] The specific implementation of step S04 involves designing an adaptive heat transfer enhancement structure to improve heat transfer efficiency. This step aims to enhance the heat exchange capacity between the phase change material and the heat transfer fluid. The diameter variation of the variable-diameter spiral fins is designed based on the heat transfer boundary layer theory. The initial diameter is set at 20 mm, increasing by 2 mm for every 10 cm along the axial length, with a maximum diameter not exceeding 50 mm. The fin helix angle is controlled within the range of 30 to 45 degrees, and the pitch is set to 1.5 times the fin diameter. The fin substrate is made of copper with a thermal conductivity of 385 W / m Kelvin, and the surface roughness is controlled within 1.6 micrometers. The stainless steel support rod has a diameter of 5 mm and is made of 316L stainless steel to ensure corrosion resistance. The porous foam metal is prepared from aluminum alloy, with the porosity controlled within the range of 85% to 95% using a foaming agent, and the pore size distribution controlled within the range of 0.5 mm to 2 mm. The foam metal is manufactured using powder metallurgy, with the sintering temperature controlled at 580℃ and the holding time set at 4 hours. The variable diameter spiral fins are assembled with porous foam metal using a mechanical interlocking method to ensure that the contact thermal resistance does not exceed 0.01 square kelvin per watt.

[0057] The specific implementation of step S05 involves establishing a phase change interface prediction algorithm model to achieve real-time tracking of the interface position. The purpose of this step is to accurately grasp the dynamic characteristics of the phase change process. The temperature distribution sensor uses a thermocouple array arrangement with a sensor spacing of 5 mm, a measurement accuracy of no less than ±0.1℃, and a response time of no more than 0.5 seconds. The phase change completion sensor operates based on the capacitance measurement principle, using the change in dielectric constant during the solid-liquid phase change to determine the degree of phase change completion, with a measurement accuracy controlled within ±2%. The data processing unit uses a Kalman filter algorithm to reduce noise in the sensor signals, with filter parameters adaptively adjusted according to the signal spectrum characteristics. A numerical calculation model based on the finite difference method is established to solve the heat transfer differential equation, with a mesh density of 10 nodes per millimeter and a time step controlled within 0.01 seconds. The Newton-Raphson iterative method is used to solve the nonlinear equations to determine the phase change interface position, with an iterative convergence accuracy set to 1 x 10^-6. The interface movement trend is predicted using a linear extrapolation method, with a prediction time window set to the next 10 seconds and a prediction accuracy controlled within ±2 mm. The algorithm is set to run 10 times per second to ensure real-time performance.

[0058] The specific implementation of step S06 involves establishing a two-layer game optimization model for dynamic parameter adjustment. This step aims to achieve coordinated optimization of energy storage efficiency and energy consumption. The upper-layer energy storage efficiency optimization function uses fin spacing, foam metal porosity, phase change interface migration speed, ambient temperature, and material thermal conductivity as input variables, constructing the objective function through a weighted summation method. The lower-layer energy consumption minimization function uses power parameters of various devices as input variables, employing a nonlinear programming method to solve for the optimal power allocation scheme. The two-layer optimization model establishes correlation constraints through heat transfer efficiency coupling terms and uses Nash equilibrium theory to find the optimal game solution. When the system efficiency is below 75%, an energy-saving mode is activated, using a genetic algorithm to search for the optimal parameter combination. The population size is set to 50 individuals, the number of generations is set to 100, and the mutation probability is controlled at 1%. When the efficiency is between 75% and 90%, a maintenance strategy is adopted, with parameter adjustment controlled within ±5%. When the efficiency exceeds 90%, a performance enhancement mode is activated, using a particle swarm optimization algorithm to find the global optimal solution. The number of particles is set to 30, and the maximum number of iterations is set to 200. The optimization algorithm is set to run once per minute to ensure timely parameter adjustments.

[0059] The specific implementation of step S07 involves establishing a dual-mode switching control strategy for thermal and cold storage to achieve intelligent switching of operating modes. The purpose of this step is to automatically select the optimal operating mode based on environmental conditions. The temperature comparison module continuously monitors the deviation between the ambient temperature and the set temperature, with a sampling frequency set to once per second and a measurement accuracy of no less than ±0.5℃. When the ambient temperature exceeds the set temperature by more than 5℃ for more than 300 seconds, a cold storage mode start signal is triggered. At this time, the solenoid valve group switches to the refrigeration circuit, and the flow regulator sets the flow rate to 80% of the rated flow rate. When the ambient temperature is lower than the set temperature by more than 5℃ for more than 300 seconds, a thermal storage mode start signal is triggered. The solenoid valve group switches to the heating circuit, and the flow regulator increases the flow rate to 100% of the rated flow rate. A gradual adjustment strategy is adopted during mode switching to avoid thermal shock caused by sudden changes in system parameters. The switching time is controlled within the range of 10 to 30 seconds. The temperature controller uses a proportional-integral-derivative (PID) control algorithm to maintain stable output temperature, with a proportional coefficient set to 0.8, an integral time constant set to 120 seconds, and a derivative time constant set to 30 seconds. During the switching process, a predictive control algorithm is used to adjust the material temperature distribution in advance to ensure the continuity of the temperature gradient. The prediction time window is set to 60 seconds. A database of mode switching history records is established, and a machine learning algorithm is used to optimize the switching judgment threshold. The number of training samples is no less than 1000 sets, and the model update cycle is set to once a month.

[0060] It should be noted that the key technical ideas of this invention are mainly reflected in the following aspects. First, the molecular design technology for multi-component composite phase change material systems. This technology achieves precise control of the phase change temperature through computational chemistry methods and molecular dynamics simulations. Compared with traditional trial-and-error material development, this method can predict material properties before synthesis, significantly shortening the material development cycle and reducing R&D costs. Simultaneously, by constructing a gradient material library of 15 different phase change temperatures, it achieves full coverage of a wide temperature range from -20℃ to 80℃, solving the technical problem of limited temperature adaptability of single phase change materials. Second, the hierarchical gradient configuration optimization based on the minimum spanning tree algorithm. This technology transforms the material arrangement problem into a minimum spanning tree problem in graph theory, determining the optimal material arrangement order through mathematical optimization methods. Compared with traditional empirical arrangement methods, this technology can ensure the continuity and smoothness of the temperature gradient, avoid local thermal stress concentration problems, and improve the thermodynamic performance and operational stability of the system. The third key technology is the dynamic parameter adjustment mechanism of the two-layer game optimization model. This technology treats energy storage efficiency optimization and energy consumption minimization as two game subjects, and seeks the optimal solution through Nash equilibrium theory. Compared with traditional single-objective optimization methods, this technology can effectively control system energy consumption while improving energy storage efficiency, achieving multi-objective coordinated optimization and improving the overall economic performance of the system. The synergistic effect of these key technological ideas forms a complete optimization solution for dual-mode energy storage systems of thermal and cold storage. Through performance improvements at the material level, configuration optimization at the structural level, and intelligent adjustment at the control level, significant improvements have been achieved compared with traditional energy storage systems in terms of temperature adaptability, energy storage efficiency, system stability, and operational economy, providing important technical support for the industrial application of energy storage technology.

[0061] It should be noted that the thermal conductivity of traditional phase change materials, such as organic phase change materials like paraffin and fatty acids, is generally in the range of 0.2-0.5 W / (m·K), while that of inorganic salt hydrate phase change materials is only 0.5-1.0 W / (m·K), far below the requirements for heat transfer applications, severely restricting the heat transfer efficiency and charge / discharge rate of energy storage systems. This invention utilizes precisely designed nano-modification technology, selecting graphene nanosheets with ultra-high thermal conductivity as the thermally conductive reinforcing phase. Its two-dimensional sheet structure is used to construct continuous thermally conductive channels within the phase change material matrix. Simultaneously, alumina nanoparticles are added to form a three-dimensional thermally conductive network. By controlling the dispersion uniformity of the nanoparticles and the interfacial bonding strength, while maintaining the latent heat of phase change at no less than 85% of the initial value, the thermal conductivity of the composite phase change material is increased to the range of 2.5-4.2 W / (m·K), a 5-20 times improvement compared to traditional materials, thus solving the technical problem of severely insufficient thermal conductivity in phase change materials. Furthermore, most existing thermal and cold storage systems employ fixed heat transfer structures and constant operating parameters. When ambient temperature changes, heat load fluctuates, or the system ages, they cannot automatically adjust to maintain optimal performance, leading to a significant drop in efficiency or even performance degradation when deviating from design conditions. This invention establishes a multi-layered operating condition adaptation mechanism. It monitors the system's operating status in real time using temperature distribution sensors and phase change completion sensors, accurately calculates the phase change process using a phase change interface prediction algorithm, and achieves coordinated optimization of maximizing energy storage efficiency and minimizing energy consumption using a two-layer game optimization model. When the system efficiency deviates from the optimal state, it automatically adjusts key parameters such as fin spacing, foam metal porosity, and heat transfer fluid flow rate. Simultaneously, it intelligently switches between thermal and cold storage modes based on ambient temperature changes, with the switching time controlled within 10-30 seconds, ensuring the system always operates in a highly efficient and stable state. This effectively solves the technical problem of poor operating condition adaptability in energy storage systems.

[0062] Specifically, the principle of this invention is as follows: The technical solution of this invention can solve the problem of unstable heat transfer efficiency in thermal and cold storage systems by establishing a stable heat transfer system integrating material performance optimization, structural adaptive adjustment, and intelligent control. First, a gradient material library of 15 different phase change temperatures is constructed using molecular design technology. Combined with nanotechnology modification, graphene nanosheets and alumina nanoparticles are added to the phase change materials to form a highly efficient three-dimensional thermal conductivity network, increasing the thermal conductivity from 0.2-0.5 W / (m·K) of traditional materials to 2.5-4.2 W / (m·K), providing high-performance material assurance for stable heat transfer. Second, a hierarchical gradient configuration model is established using the minimum spanning tree algorithm, arranging materials with different phase change temperatures according to the continuity of the temperature gradient. This avoids fluctuations in heat transfer resistance caused by sudden temperature changes, ensuring the smooth progress of the heat transfer process. Furthermore, the designed composite heat transfer enhancement structure of variable-diameter spiral fins and porous foam metal, through the continuous axial variation of the fin diameter and the high porosity configuration of the foam metal (85%-95%), achieves adaptive adjustment of the heat transfer area as the heat flux density changes, effectively balancing the relationship between heat transfer efficiency and flow resistance. Finally, the phase change interface prediction algorithm accurately calculates the solid-liquid interface position and movement speed by real-time monitoring of temperature distribution and phase change completion, with prediction accuracy controlled within ±2mm, providing accurate state information for system control. The two-layer game optimization model dynamically adjusts the heat transfer geometry parameters and power distribution based on the prediction results. When the efficiency is below 75%, the power of non-critical equipment is reduced; between 75% and 90%, the current state is maintained; and above 90%, the overall performance is improved, achieving active and stable control of heat transfer efficiency.

[0063] The following provides a specific embodiment 1 of the present invention, and the specific implementation of each step in this embodiment 1 is described in detail below.

[0064] The specific implementation of step S01 involves constructing a multi-component composite phase change material system using molecular design techniques. The relationship between the phase change temperature and molecular chain length of paraffin-based materials is expressed as follows: In the formula, The phase transition temperature of paraffin-based materials is expressed in °C. The number of carbon atoms, dimensionless; The linear coefficient is 2.85℃. This is the exponential coefficient, with a value of 0.75, and is dimensionless. The constant term has a value of -25.6℃. The relationship between the phase transition temperature and hydration number of inorganic salt hydrate materials is expressed as: In the formula, The phase transition temperature of inorganic salt hydrates is expressed in °C. The number of water molecules of crystallization is dimensionless. The logarithmic coefficient is 15.2℃. The coefficients are power function coefficients, with a value of -8.9℃; This is a constant term with a value of 42.7℃. The material selection function is expressed as: In the formula, The overall score for the materials is dimensionless. The latent heat of phase change is expressed in kJ / kg. The reference latent heat value is 150 kJ / kg; The target phase transition temperature is expressed in °C. This represents the actual phase transition temperature, expressed in °C. Temperature range: 100℃; The thermal stability cycle number is dimensionless. For reference, the number of cycles is 100, dimensionless; These are weighting coefficients, with values ​​of 0.4, 0.35, and 0.25, all of which are dimensionless.

[0065] The specific implementation of step S02 is the same as described above, and will not be repeated in detail here.

[0066] The specific implementation of step S03 is to establish a hierarchical gradient configuration optimization model. The material node adjacency matrix is ​​expressed as: In the formula, For nodes and nodes The weights between them, in °C; and Materials and materials The phase transition temperatures are all in °C. The edge weight sorting vector for Kruskal's algorithm is: ,in Indicates the first Edge connection node and The weight is ,satisfy In the formula, For the first Edge; Node numbering, dimensionless; The edge weight is expressed in °C. Let be the total number of edges, dimensionless. The total weight of the minimum spanning tree is: In the formula, The total weight of the minimum spanning tree is expressed in °C. For the selected number The weight of each edge, in °C; The total number of material nodes is 15, dimensionless. The temperature gradient function is expressed as: In the formula, This represents the radial temperature gradient, in °C / m. These are radial coordinates, in meters (m). Temperature of the outer layer material, in °C; Temperature of the inner layer material, in °C; This represents the total radial distance, in meters (m).

[0067] The specific implementation of step S04 is to design an adaptive heat transfer enhancement structure. The diameter variation law of the variable diameter helical fins is as follows: In the formula, Axial position The fin diameter at that location is in mm. The initial diameter is 20mm; This refers to the axial position, in mm. This represents the total length of the fins, in mm. The formula for calculating the fin surface area is: In the formula, This represents the total surface area of ​​the fins, in units of... ; The pitch is in mm. The specific surface area of ​​the porous foam metal is: In the formula, Specific surface area of ​​foamed metal, in units of ; The baseline specific surface area is 800. ; Porosity is a dimensionless quantity. The average aperture is expressed in mm. The reference aperture is 1mm.

[0068] The specific implementation of step S05 involves establishing a phase change interface prediction algorithm model. The heat transfer differential equation is: In the formula, Density, unit: ; Specific heat capacity, expressed in J / (kg·K); Temperature, in °C; Time, in seconds; is the thermal conductivity, with units of W / (m·K); Internal heat source, unit is The phase transition interface position function is: In the formula, for The interface position at any given time, in mm; This is the initial interface position, in mm; The interface movement speed is expressed in mm / s. Let be the integration variable, representing time in seconds. The Kalman filter state equation is: The observation equation is: In the formula, It is a state vector; This is the state transition matrix; To control the input matrix; For control input; This is process noise; These are the observed values; The observation matrix; To observe noise, the Newton-Raphson iteration formula is: In the formula, For the first The result of the next iteration; For the first The value of the next iteration; The objective function value; The derivative of the objective function.

[0069] The specific implementation of step S06 involves establishing a two-layer game optimization model. The energy storage efficiency optimization function is: In the formula, Energy storage efficiency is dimensionless; This refers to the fin spacing parameter, in mm. is the porosity parameter of the foamed metal, which is dimensionless; This is the phase change interface migration speed parameter, in mm / s; This refers to the ambient temperature parameter, in °C. This is the thermal conductivity parameter of the material, with units of W / (m·K); The weighting coefficients are 0.25, 0.18, 0.15, 0.22, and 0.20, all dimensionless. The energy minimization function is: In the formula, Total power consumption, in watts (W). Power parameters for auxiliary equipment, in watts (W). These are the operating power parameters for the heat transfer enhancement structure, in W. These are the power parameters for the control system, in watts (W). These are pump power parameters, in watts (W). These are fan power parameters, in watts (W). The heat transfer efficiency coupling term is: In the formula, For heat transfer efficiency, dimensionless; Let be the maximum allowable power of the system, taken as 5000W. The Nash equilibrium condition is expressed as: and In the formula, It is a Lagrange multiplier, dimensionless.

[0070] The specific implementation of step S07 is to establish a dual-mode switching control strategy for thermal and cold storage. The mode determination function is: In the formula, The mode identifier is 1 for cold storage mode, -1 for heat storage mode, and 0 for maintaining the current mode. All are dimensionless. The current ambient temperature is expressed in °C. The temperature is set in °C. The duration is in seconds. The proportional-integral-derivative (PID) control algorithm is as follows: In the formula, To control the output, the unit is ℃; This is the error signal, in °C. Let be the proportional, integral, and differential coefficients, with values ​​of 0.8, 0.0083 / s, and 0.033s, respectively, and dimensions of dimensionless, dimensionless, and 0.033 / s, respectively. The flow regulation equation is: In the formula, Actual flow rate, unit: ; Rated flow rate, unit: .

[0071] It should be explained that the phase transition temperature relationship of paraffin-based materials is based on molecular thermodynamics theory, using a nonlinear function of the number of carbon atoms. This formula, which expresses the phase transition temperature variation, considers the influence of molecular chain length on intermolecular van der Waals forces. Compared to traditional linear relationships, it can more accurately predict the actual phase transition temperature, improving prediction accuracy by more than 15%. The formula for the phase transition temperature of inorganic salt hydrates combines the thermodynamics of hydration reactions and crystal field theory, through the application of several terms... Sum of power function terms The coordination interactions of water molecules and changes in lattice energy are described separately, enabling accurate modeling of the phase transition behavior of complex hydrate systems. The material selection function employs a multi-objective weighted evaluation method, quantifying latent heat of phase transition, temperature matching degree, and thermal stability in a unified manner, avoiding the limitations of traditional single-index evaluation and improving the scientific rigor and accuracy of material selection. The minimum spanning tree algorithm determines the material arrangement order through graph theory optimization. This algorithm guarantees the global optimality of the temperature gradient and, compared to heuristic arrangement methods, reduces thermal stress concentration caused by temperature discontinuities, improving the thermodynamic stability of the system. The formula for the diameter variation of the variable-diameter spiral fins is designed based on boundary layer theory, utilizing an axial linear growth law. It adapts to changes in fluid boundary layer thickness, effectively improving the heat transfer coefficient, with a heat transfer effect that is about 25% better than that of constant diameter fins.

[0072] ; The heat transfer differential equation is based on Fourier's law of thermal conductivity and the principle of energy conservation. The left side represents the material's heat storage term. The right side contains thermal conductivity terms. and internal heat source items This equation accurately describes the heat transfer mechanism during the phase transition process, providing a theoretical basis for interface prediction.

[0073] ; The phase transition interface position integral equation achieves time tracking of the interface position through velocity integration. This equation establishes a mathematical relationship between the interface position and the moving velocity, enabling an accurate description of the dynamic characteristics of the phase transition process. The Kalman filter algorithm uses a state-space model to optimally estimate the sensor signal. This algorithm effectively suppresses measurement noise and system disturbances, improves the prediction accuracy of the phase transition interface position, and controls the prediction error within ±2mm.

[0074] ; The energy storage efficiency optimization function combines multiple influencing factors through nonlinear weighted combination, where the exponential term... , , , sum of logarithmic terms It reflects the nonlinear characteristics of the impact of different parameters on efficiency. This function can accurately describe complex coupling relationships and guide the optimization of system parameters.

[0075] ; The energy minimization function uses different powers. , , , Reflecting the differences in power characteristics among various devices, this formula takes into account the nonlinear characteristics of the device efficiency curves, and can predict total energy consumption more accurately than the linear superposition method. The heat transfer efficiency coupling term establishes a correlation constraint between energy storage efficiency and energy consumption, realizing multi-objective coordinated optimization and avoiding other performance degradation problems that may be caused by single-objective optimization.

[0076] ; The proportional-integral-derivative control algorithm uses the proportional term Provides rapid response and points system Eliminating steady-state error and differential terms Improved dynamic characteristics enable precise temperature control, achieving more than three times the control accuracy compared to traditional on / off control methods, and significantly improving system stability.

[0077] To better understand and implement this invention, the following is a specific application scenario of this invention, Example 2: The technical team needs to design a dual-mode energy storage system that can provide cooling for a data center in the summer and heating support in the winter, while achieving efficient energy storage and release.

[0078] The technical team first constructed a multi-component composite phase change material system. Employing molecular design techniques, they selected paraffin-based materials such as n-octadecane, n-eicosane, and n-docosahexadecane as the primary phase change materials, controlling the phase change temperature by adjusting the carbon chain length. Simultaneously, they selected inorganic salt hydrate materials such as sodium sulfate decahydrate, calcium chloride hexahydrate, and disodium hydrogen phosphate dodecahydrate as auxiliary phase change materials. Through molecular dynamics simulations and experimental verification, they ultimately established a gradient phase change material library containing 15 materials with different phase change temperatures, ranging from -18℃ to 78℃, with temperature intervals of approximately 6.4℃.

[0079] To improve the thermal conductivity of the material, the research team employed nanotechnology modification. They added 3% graphene nanosheets and 2% alumina nanoparticles by mass to the phase change material. The graphene nanosheets had dimensions of 500 nm × 500 nm × 2 nm, and the alumina nanoparticles had an average diameter of 50 nm. Through ultrasonic dispersion and mechanical stirring, the nanomaterials formed a uniform three-dimensional thermally conductive network within the phase change material matrix. The modified composite phase change material achieved a thermal conductivity of 3.2 W / (m·K), a 15-fold increase compared to the original paraffin material's 0.2 W / (m·K), while maintaining 88% of its initial latent heat of phase change.

[0080] When establishing the hierarchical gradient configuration optimization model, the technical team used 15 phase change materials as nodes in the graph, with the temperature difference between materials as the edge weights. The Kruskal algorithm was used to construct a minimum spanning tree to determine the optimal arrangement order of the materials. Based on the actual needs of the data center, the outer layer material was selected as hexadecane composite material with a phase change temperature of 20℃, while the inner layer gradually transitioned to calcium chloride hydrate composite material with a phase change temperature of -10℃, forming a continuous temperature gradient distribution. The entire energy storage unit adopts a concentric cylindrical structure with an outer diameter of 1.2 m and an inner diameter of 0.3 m, divided into 7 layers, each approximately 0.13 m thick.

[0081] The design of the adaptive heat transfer enhancement structure is one of the key technologies of the system. The variable diameter spiral fins are made of copper, with an initial diameter of... = 25 mm, total length L = 1000 mm, pitch 80 mm. The fin diameter varies continuously along the axial direction, reaching a maximum diameter of 27.5 mm. The fin thickness is 2 mm, and the surface is polished to reduce flow resistance. The porous foam metal is made of 6061 aluminum alloy, prepared using a foaming process, with a porosity of 0.90, an average pore size of 2.5 mm, and a specific surface area of ​​1200. The foamed metal and fins are arranged alternately to form a composite heat transfer enhancement structure.

[0082] The phase transition interface prediction algorithm model is established based on real-time monitoring data. The system is equipped with 36 temperature distribution sensors, employing PT1000 platinum resistance temperature sensors with an accuracy of ±0.1℃ and a response time of less than 2 seconds. The phase transition completion sensor uses a capacitive measurement principle, determining the phase transition process by detecting changes in the dielectric constant, achieving a measurement accuracy of ±1%. The data processing unit uses an ARM Cortex-A72 quad-core processor with a main frequency of 1.8 GHz and is equipped with 4 GB of DDR4 memory. The prediction algorithm is based on the finite difference method and the theory of solving moving boundary problems, calculating the solid-liquid interface position in real time, with prediction accuracy controlled within ±1.5 mm.

[0083] The implementation of the two-layer game optimization model involves a complex multi-objective optimization problem. In the upper-layer energy storage efficiency optimization function, the fin spacing parameter s is set to 15 mm, the foam metal porosity parameter p is 0.90, the phase change interface migration velocity parameter v is 0.8 mm / min, and the ambient temperature parameter... At 25℃, the material's thermal conductivity parameter k is 3.2 W / (m·K). The optimal fin geometry configuration is obtained through genetic algorithm optimization. In the lower-level energy consumption minimization function, the auxiliary equipment power parameters... The operating power parameters of the heat transfer enhanced structure are 2.5 kW. The power parameters of the control system are 1.8 kW. The pump power parameters are 0.6 kW. The power parameters of the wind turbine are 3.2 kW. It is 1.5 kW.

[0084] The dual-mode switching control strategy for thermal and cold storage relies on precise temperature control and a rapid response mechanism. When the ambient temperature exceeds 30°C, the system automatically activates the cold storage mode, the refrigerant circulation loop begins operation, and the low-temperature phase change material begins to solidify and release cooling energy. When the ambient temperature falls below 20°C, the system switches to thermal storage mode, the heat pump system starts, and the high-temperature phase change material begins to melt and absorb heat. During mode switching, the solenoid valve group has a response time of 0.5 s, the flow regulator has an adjustment time of 3 s, and the entire switching process is completed within 18 s. The temperature controller employs a PID control algorithm with a proportional gain Kp = 1.2, an integral time Ti = 8 s, and a derivative time Td = 2 s, ensuring temperature control accuracy within ±1°C.

[0085] like Figure 2As shown, the system exhibits a clear temperature gradient under different operating modes. In cold storage mode, the temperature of the outer material remains around 18℃, while the temperature of the inner material drops to -8℃, forming a continuous temperature gradient. In heat storage mode, the temperature distribution is reversed, with the inner temperature rising to 45℃ and the outer temperature remaining at 22℃. Figure 3 As shown, the phase change interface migration rate is positively correlated with the material's thermal conductivity; the higher the thermal conductivity, the faster the phase change process and the greater the interface migration rate. Figure 4 As shown, the relationship between system energy storage efficiency and operating time indicates that the energy storage efficiency can still be maintained above 85% after 72 hours of continuous operation. The structure of the entire system is as follows. Figure 5 As shown.

[0086] During actual operation, the technical team collected a large amount of operational data, as shown in Table 1: Table 1 System performance parameters under different operating modes

[0087] To verify the system's performance under extreme conditions, the technical team conducted 30 consecutive days of performance testing, as shown in Table 2: Table 2 System stability test results under extreme conditions

[0088] After six months of operation, this dual-mode thermal and cooling energy storage system has performed exceptionally well. During the high temperatures of summer, the system provides stable cooling capacity for the data center, effectively reducing the energy consumption of the air conditioning system. During the low temperatures of winter, the system releases stored heat energy to provide heating support for the data center. The system's adaptive control capability ensures stable operation under various conditions, and the high-precision prediction capability of the phase change interface prediction algorithm allows the system to adjust operating parameters in advance, avoiding energy loss.

[0089] This invention represents a significant technological advancement over traditional energy storage methods. Traditional energy storage systems typically employ a single energy storage mode, either thermal or cold storage, lacking the flexibility to switch between modes as needed. This invention, through the construction of a multi-component composite phase change material system, achieves wide-range energy storage coverage, enabling a single system to simultaneously perform both thermal and cold storage functions. Traditional phase change materials inherently suffer from low thermal conductivity, resulting in slow energy charging and discharging processes. This invention significantly improves the thermal conductivity of the materials through nanotechnology, accelerating the phase change process. Traditional energy storage systems lack optimized material arrangement. This invention employs graph theory algorithms to optimize material arrangement, ensuring the continuity of the temperature gradient and maximizing heat transfer efficiency. Traditional systems lack intelligent prediction and adaptive control capabilities. This invention establishes a phase change interface prediction algorithm and a two-layer game optimization model, enabling intelligent system operation. Operating parameters are dynamically adjusted based on real-time conditions, maximizing energy storage efficiency and minimizing energy consumption. Mode switching in traditional energy storage systems is typically time-consuming and prone to energy loss. This invention's rapid switching control strategy keeps switching time within 30 seconds and avoids energy waste by maintaining temperature gradient continuity.

[0090] It should be noted that the variables involved in this invention are explained in detail in Tables 3 and 4.

[0091] Table 3. Variable Explanation Table (Part 1)

[0092] Table 4. Variable Explanation Table (Part Two)

[0093] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. An optimization method for a dual-mode energy storage system of thermal and cold storage, characterized in that, Stable heat transfer efficiency is achieved by constructing a multi-component composite phase change material system, optimizing hierarchical gradient configuration, designing an adaptive heat transfer enhancement structure, establishing a phase change interface prediction algorithm, and implementing bi-layer game-theoretic optimization control. This includes constructing a multi-component composite phase change material system, controlling the phase change temperature of paraffin-based materials and inorganic salt hydrate materials through molecular design technology to form a gradient phase change material library covering a temperature range, and using nanotechnology to add graphene nanosheets and alumina nanoparticles to the phase change materials to improve the thermal conductivity of the materials while maintaining the latent heat of phase change. A hierarchical gradient configuration optimization model is established, using the minimum spanning tree algorithm in graph theory to determine the material arrangement order. Temperature gradient continuity is used as the edge weight, arranging materials with different phase transition temperatures in a decreasing order from the outer layer to the inner layer. An adaptive heat transfer enhancement structure is designed, employing a composite structure of variable-diameter spiral fins and porous foam metal, with the fin diameter varying along the axial direction. A phase transition interface prediction algorithm model is established, using data from temperature distribution sensors and phase transition completion sensors to calculate the solid-liquid interface position and movement speed in real time. A two-layer game theory optimization model is established for dynamic adjustment of heat transfer geometric parameters; the upper model aims to maximize energy storage efficiency, while the lower model aims to minimize energy consumption. A dual-mode switching control strategy for thermal and cold storage is established, activating the cold storage mode when the ambient temperature is higher than the set temperature and the thermal storage mode when it is lower than the set temperature.

2. The optimization method for the dual-mode energy storage system of thermal and cold storage according to claim 1, characterized in that, The steps for constructing the multi-component composite phase change material system are as follows: a gradient phase change material library contains 15 materials with different phase change temperatures, covering a temperature range of -20℃ to 80℃. Molecular design techniques are used, along with computational chemistry methods and molecular dynamics simulations, to predict and design molecular structures with target phase change temperatures, thereby controlling intermolecular forces and crystal structure parameters.

3. The optimization method for the dual-mode energy storage system of thermal and cold storage according to claim 2, characterized in that, The specific steps of the nano-modification technology are to uniformly disperse nanoscale high thermal conductivity materials into the phase change material matrix to form a three-dimensional thermally conductive network structure, which significantly improves the thermal conductivity of the composite material and increases the thermal conductivity of the material to the range of thermal conductivity ∈ [2.5, 4.2] W / (m·K), while maintaining the latent heat of phase change at no less than 85% of the initial value.

4. The optimization method for the dual-mode energy storage system of thermal and cold storage according to claim 3, characterized in that, The steps for establishing the hierarchical gradient configuration optimization model are as follows: hierarchical gradient configuration refers to arranging materials with different phase transition temperatures in an orderly manner according to the temperature gradient to form a continuous temperature distribution structure from high temperature to low temperature or from low temperature to high temperature. The phase transition temperature of the outer layer material is set within the range of ambient temperature plus or minus 15°C.

5. The optimization method for the dual-mode energy storage system of thermal and cold storage according to claim 4, characterized in that, The minimum spanning tree algorithm determines the material arrangement order by using temperature difference as edge weights and finding the minimum weight path connecting all material nodes using the Kruskal algorithm to determine the optimal material arrangement order.

6. The optimization method for the dual-mode energy storage system of thermal and cold storage according to claim 5, characterized in that, The steps of the adaptive heat transfer enhancement structure design are as follows: the variable diameter spiral fins include a copper fin substrate and a stainless steel support rod, and the porous foam metal is made of aluminum alloy with a porosity of [0.85, 0.95). The variable diameter spiral fins refer to a spiral heat transfer enhancement structure in which the fin diameter changes continuously along the axial direction.

7. The optimization method for the dual-mode energy storage system of thermal and cold storage according to claim 6, characterized in that, The variable-diameter spiral fins specifically have a diameter variation pattern as follows: ,in denoted as the initial diameter, x as the axial position, and L as the total length. Porous foam metal refers to a metal matrix material with an open-cell structure, characterized by high porosity and large specific surface area, used to enhance heat transfer and promote the flow of phase change materials.

8. The optimization method for the dual-mode energy storage system of thermal and cold storage according to claim 7, characterized in that, The steps for establishing the phase change interface prediction algorithm model are as follows: The phase change interface prediction algorithm refers to a mathematical model that calculates and predicts the position and movement law of the solid-liquid phase change interface in real time based on the principles of heat transfer and numerical calculation methods, with the prediction accuracy controlled within ±2mm.

9. The optimization method for the dual-mode energy storage system of thermal and cold storage according to claim 8, characterized in that, The two-layer game optimization model specifically includes an upper-layer energy storage efficiency optimization function and a lower-layer energy consumption minimization function. The energy storage efficiency optimization function is used to maximize the overall energy storage efficiency of the system, and the energy consumption minimization function is used to minimize the total energy consumption of the system. The two objective functions are related through a heat transfer efficiency coupling term.

10. The optimization method for the dual-mode energy storage system of thermal and cold storage according to claim 9, characterized in that, The energy storage efficiency optimization function takes as input parameters including fin spacing parameters, foam metal porosity parameters, phase change interface migration speed parameters, ambient temperature parameters, and material thermal conductivity parameters, and outputs the optimal fin geometry configuration scheme. The energy consumption minimization function takes as input parameters including auxiliary equipment power parameters, heat transfer enhancement structure operating power parameters, control system power parameters, pump power parameters, and fan power parameters, and outputs the optimal power allocation scheme.

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