Embedded energy storage methods and wall-mounted energy storage systems integrated with building walls

Abstract

This invention relates to the field of specialized building technology, providing an embedded integrated energy storage method and wall-mounted energy storage system integrated with building walls. The method involves acquiring building information and energy storage information; constructing multiphysics coupling equations based on the building and energy storage information; determining the target wall region where energy storage modules can be embedded by iteratively solving the coupling equations; constructing a wall stress distribution cloud map and a nonlinear thermoelectric coupling model based on the target wall region and building information; determining the layout information of the energy storage modules by optimizing the thermoelectric coupling model; and generating an operating strategy for the energy storage modules based on the building information and the layout information of the energy storage modules. Compared with existing technologies, this invention ensures that the embedding location of the energy storage modules simultaneously meets the requirements of wall structural bearing capacity and thermal stability, coordinates electrical performance and thermal management needs to ensure a reasonable layout, and finally dynamically adjusts the energy storage behavior according to the building load characteristics, achieving a safe and stable integration of efficient energy utilization and building integration.

Description

Technical Field

[0001] This invention relates to the field of energy storage technology, and in particular to an embedded fusion energy storage method and a wall-mounted energy storage system that are integrated with building walls. Background Technology

[0002] Solar energy, as one of the most abundant renewable energy sources, is increasingly being used in the building sector. However, the intermittency and volatility of solar energy limit its direct utilization efficiency. Energy storage systems can store excess solar energy and release it when needed, thereby smoothing out energy supply fluctuations and improving energy utilization efficiency.

[0003] The construction industry is one of the major sectors of energy consumption and carbon emissions. Traditional buildings consume large amounts of energy for heating, cooling, and lighting, and their energy utilization efficiency is relatively low. Improving building energy efficiency and reducing energy consumption are important challenges facing the construction industry. However, in practical applications, energy storage devices have a certain weight, and existing technologies cannot ensure that the load-bearing capacity of the walls or installation area meets the requirements. There is a risk that the walls may crack or the equipment may fall due to structural aging or insufficient load-bearing capacity. Therefore, there is a problem of low reliability for embedded integrated energy storage systems that are integrated into building walls. Summary of the Invention

[0004] The purpose of this invention is to overcome the defects of the prior art by providing an embedded fusion energy storage method and wall-mounted energy storage system that is integrated with the building wall, thereby solving the problem of low reliability of embedded fusion energy storage systems integrated with the building wall.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] An embedded energy storage method integrated with building walls, comprising:

[0007] The system acquires building information of the building walls and energy storage information of the energy storage modules, performs mechanical analysis of the building walls, and performs thermal conduction analysis of the energy storage modules embedded in the building walls to determine the target wall areas where energy storage modules can be embedded; it then lays out the energy storage modules in the target wall areas and integrates them with the grid load to determine the operating strategy of the energy storage modules.

[0008] Furthermore, mechanical analysis of the building walls and thermal conduction analysis of embedding energy storage modules within the building walls are performed to determine the target wall areas where energy storage modules can be embedded, including:

[0009] Based on the building information, simulate the wall stress and external volume forces, and construct structural mechanics equations representing the stress state of the building walls;

[0010] Based on the building information and the energy storage information, the heat generation properties of the energy storage module and the heat dissipation properties of the building walls are simulated to construct a heat conduction equation;

[0011] The coupled equations obtained by combining the structural mechanics equations and the heat conduction equations are iteratively solved to determine the target wall region where energy storage modules can be embedded.

[0012] Furthermore, in the process of constructing and coupling the structural mechanics equation and the heat conduction equation, the building wall is discretized into mesh elements through finite element analysis, and each mesh element is assigned material properties according to the wall material parameters in the building information, thereby establishing the structural mechanics equation and the heat conduction equation respectively; the structural mechanics equation simulates the deformation of the wall under load through the principle of elasticity, and the heat conduction equation simulates heat transfer through the principle of energy conservation.

[0013] The structural mechanics equation and the heat conduction equation are coupled through the thermal expansion effect, which includes: the temperature change generated by the heat conduction equation causes the material to expand or contract, thereby generating thermal stress that affects the structural mechanics equation; the structural deformation generated by the structural mechanics equation changes the heat conduction path of the heat conduction equation.

[0014] Furthermore, by applying boundary constraints to the discretized building walls, fixing the bottom nodes of the walls to simulate ground constraints, and loading external forces including wind pressure and self-weight, the structural mechanics equations are constructed. The expression of these structural mechanics equations is as follows:

[0015]

[0016] In the formula, This represents the divergence operator, used to perform derivative operations on the tensor field in spatial coordinates, describing the diffusion effect of stress in space; This indicates the wall stress corresponding to the preset location; F This refers to the external volume force acting on a unit volume at a predetermined location on the wall.

[0017] Furthermore, by setting an ambient temperature convection boundary for the discretized building walls, the heating characteristics of the energy storage module are embedded as an internal heat source into a preset location within the building walls, thereby constructing the heat conduction equation at the preset location. The expression of the heat conduction equation is as follows:

[0018]

[0019] In the formula, These represent the material density, specific heat capacity at constant pressure, and thermal conductivity at the preset location in the building information, respectively. T This represents the temperature field inside the building walls. Denotes the divergence operator,q This indicates the intensity of the internal heat source in the energy storage information, which is characterized by electrochemical heating.

[0020] Furthermore, in each iteration of solving the coupled equation, the thermal analysis results of the discretized building wall are converted into inputs for structural analysis through the heat conduction equation, and the structural analysis results are fed back to adjust the boundary conditions of the thermal analysis.

[0021] Furthermore, the thermal analysis results of the discretized building walls are converted into inputs for structural analysis using the heat conduction equation, including:

[0022] The temperature change is determined based on the heat conduction equation, and the linear displacement change of the building wall due to the temperature change is determined based on the temperature change. The corresponding calculation expression is:

[0023]

[0024] In the formula, It is a linear displacement change. This represents the coefficient of thermal expansion of the building wall material. It represents the change in temperature per unit time. L This indicates the original length of the building wall material.

[0025] Furthermore, the energy storage modules are arranged in the target wall area, including:

[0026] Based on the target wall area and the building information, a wall stress distribution cloud map of the target wall area is constructed;

[0027] Based on the stress distribution cloud map of the wall, a thermoelectric coupling model of the target wall area is constructed, and the layout information of the energy storage module is determined by optimizing the thermoelectric coupling model.

[0028] Furthermore, based on the target wall region and the building information, a wall stress distribution cloud map of the target wall region is constructed, including:

[0029] Based on the regional and architectural information of the target wall area, the target wall area is located at the corresponding position in the three-dimensional wall model;

[0030] The target wall area is divided into grid cells of a preset size, and the stress data of the grid cells is obtained;

[0031] By using a preset display method, the discrete stress data is displayed in the three-dimensional wall model, generating a wall stress distribution cloud map.

[0032] Furthermore, the layout information includes the location layout information of the energy storage module, the piping information for thermal management, and the electrical connection topology.

[0033] Furthermore, based on the wall stress distribution cloud map, a thermoelectric coupling model of the target wall region is constructed. The layout information of the energy storage module is determined by optimizing the thermoelectric coupling model, including:

[0034] Based on the stress distribution cloud map of the wall, and combined with the electrochemical heating characteristics of the energy storage module and the thermal conductivity of the wall material, a dynamic interactive thermoelectric coupling analysis model is constructed to simulate the heat diffusion path, temperature distribution uniformity and electrical connection stability under different layout schemes.

[0035] By optimizing the algorithm to adjust the installation location of the energy storage module, the direction of the heat dissipation pipes, and the connection method of the electrical circuits, different layout schemes are generated. In multiple iterations, the impact of each layout scheme on thermal management efficiency, electrical performance, and structural safety is evaluated. Finally, the optimal layout scheme that balances controllable thermal runaway risk, minimum electrical loss, and adaptability to wall stress distribution is selected as the final layout information of the energy storage module.

[0036] Furthermore, the electrochemical heating characteristics of the energy storage module include the variation of internal resistance with temperature and the sensitivity of voltage to temperature.

[0037] Furthermore, in each iteration of different layout schemes, the wall stress distribution cloud map is discretized into a multi-layer thermal resistance network, with each layer corresponding to a different material, and each layer is further divided into grid nodes; based on the thermoelectric coupling analysis model, the heat transfer path from the surface of the energy storage module through the wall to the external environment is calculated through the principle of thermal resistance superposition, and the temperature distribution of each network node is continuously adjusted until the heat inflow and outflow of each layer of thermal resistance network reach equilibrium.

[0038] Furthermore, in each iteration of different layout schemes, the process of evaluating electrical performance includes:

[0039] Based on the current electrical connection topology, the objective function for electrical performance evaluation is determined as follows:

[0040]

[0041] In the formula, The objective function for electrical performance evaluation is... i and M The distribution represents the identifier and total number of electrical nodes; k This represents the preset steepness coefficient, used to control the sensitivity of weight changes; Indicates the node's rated voltage. Indicates electrical node i Voltage fluctuation value, This is the minimum allowable voltage value. This indicates dynamic weights.

[0042] Furthermore, the process of determining the operating strategy of the energy storage module includes:

[0043] The building information and the layout information of the energy storage module are spatiotemporally aligned to generate backup data;

[0044] The priority of energy demand is identified from the backup data, and the candidate charging and discharging periods of the energy storage system are divided based on the priority.

[0045] Based on the building information, the layout information of the energy storage module, and the candidate charging and discharging time periods, an operation strategy for the energy storage module is generated.

[0046] Furthermore, by associating the physical location of the energy storage modules with the functional zoning of buildings, the priority of the energy demand in different areas can be identified.

[0047] Furthermore, the process of generating the operation strategy for the energy storage module specifically includes:

[0048] Based on the building information, the layout information of the energy storage modules, and the candidate charging and discharging time periods, various operation schemes for the energy storage modules are generated.

[0049] Based on real-time building energy consumption forecasts, grid load fluctuation trends, and the remaining capacity and health status of energy storage modules, the energy flow paths of various operating schemes are dynamically simulated. Through iterative optimization algorithms, the optimal operating strategy for energy storage modules is obtained with the goals of minimizing electricity costs, maximizing energy storage utilization, ensuring the reliability of power supply to building loads, and maintaining the safe operating temperature of energy storage modules.

[0050] Furthermore, the method also includes:

[0051] Based on the determined layout of the energy storage modules, the energy storage modules are embedded in the building walls;

[0052] The operation strategy of the energy storage module is converted into a set of control instructions, which includes the charging and discharging sequence of the energy storage module, the power regulation gradient, the start and stop conditions of the thermal management system, and the communication protocol with the building management system and the power grid.

[0053] The control command set is decomposed into executable actions according to the time axis and sent to the energy storage device controller for control.

[0054] The present invention also provides a wall-mounted energy storage system that implements the embedded fusion energy storage method integrated with the building wall as described above, comprising:

[0055] The acquisition module is used to acquire building information of the building walls and energy storage information of the energy storage modules;

[0056] The coupling module is used for mechanical analysis of building walls and thermal conduction analysis of embedding energy storage modules in building walls to determine the target wall area where energy storage modules can be embedded.

[0057] The layout and strategy module is used to lay out the energy storage modules in the target wall area, integrate them with the grid load, and determine the operation strategy of the energy storage modules.

[0058] Furthermore, the energy storage module is a nanocell.

[0059] Compared with the prior art, the present invention has the following advantages:

[0060] (1) Traditional energy storage embedding schemes often adopt an add-on design approach, only considering the availability of building space and ignoring the long-term impact of the weight of the energy storage module and thermal stress on the wall structure, which can easily lead to structural cracking or local instability. At the same time, the thermal conductivity of the wall and the heat dissipation requirements of the energy storage module are not coupled, resulting in low thermal management efficiency or even thermal runaway. This scheme uses multi-physics coupling equations and stress cloud diagram analysis to simultaneously evaluate the mechanical load distribution and heat conduction path after the energy storage module is embedded, ensuring that the embedded area meets the structural bearing capacity threshold and can achieve thermal balance through natural heat dissipation of the wall or auxiliary thermal management system. It avoids structural damage and thermal safety risks from the design source and achieves safe coexistence between the energy storage system and the building body, which is superior to the design mode of post-reinforcement or excessive redundancy in the existing technology.

[0061] (2) Traditional energy storage layout design treats thermal management and electrical connection as independent links, and the routing of thermal pipelines and electrical topology lack coordination, resulting in insufficient local heat dissipation or excessive electrical transmission loss. At the same time, the layout is not adjusted in conjunction with the stress distribution of the wall, which may affect the sealing performance of pipelines or the reliability of the lines due to structural deformation. This solution constructs a nonlinear thermoelectric coupling model based on stress cloud diagrams, uses the stress distribution of the wall as a boundary condition, and dynamically optimizes the pipeline routing, module spacing and electrical connection path to balance heat diffusion efficiency, electrical impedance and structural adaptability. It achieves multi-dimensional performance improvement in thermal, electrical and mechanical aspects. For example, by optimizing the pipeline layout to reduce thermal resistance while avoiding high stress areas, the system life is extended. Compared with the single-objective optimization of existing technologies, the overall efficiency is significantly improved.

[0062] (3) Traditional energy storage operation strategies are mostly based on fixed charging and discharging rules, which cannot respond in real time to changes in building load fluctuations, grid time-of-use pricing, and the health status of energy storage modules, resulting in low energy utilization or increased risk of equipment overload. This solution identifies energy demand priorities and dynamically divides charging and discharging windows by aligning building information and energy storage layout data in time and space; it generates adaptive strategies by combining grid signals and real-time module status, such as prioritizing discharge when building load surges, or adjusting charging power based on wall heat dissipation capacity during periods of low electricity prices. It shifts from passive execution to intelligent decision-making, improving building energy self-sufficiency and operational economy, while avoiding equipment losses caused by rigid strategies, which is superior to the limitations of existing technologies that rely on manual intervention or preset rules.

[0063] In summary, this solution achieves full-chain collaborative optimization through design, layout, and operation, solving the problem of the disconnect between structural safety, system efficiency, and dynamic control in existing technologies, and providing a safer, more reliable, efficient, and intelligent technical path for integrated building energy storage. Attached Figure Description

[0064] Figure 1 The flowchart illustrating an embedded fusion energy storage method integrated with a building wall is shown in one embodiment of this application.

[0065] Figure 2 The flowchart illustrating the determination of a target wall region is shown in one embodiment of this application.

[0066] Figure 3 The illustration shows a schematic diagram of an embedded fusion energy storage system integrated with a building wall in one embodiment of this application.

[0067] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown. Detailed Implementation

[0068] 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. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0069] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0070] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0071] Example 1

[0072] This embodiment provides an embedded integrated energy storage method that is integrated with building walls, including:

[0073] Obtain building information of the building walls and energy storage information of the energy storage modules;

[0074] Mechanical analysis of building walls and thermal conduction analysis of embedding energy storage modules in building walls are conducted to determine the target wall areas where energy storage modules can be embedded.

[0075] The energy storage modules are laid out in the target wall area and integrated with the grid load to determine the operation strategy of the energy storage modules.

[0076] Preferably, the energy storage modules are arranged in the target wall area, including:

[0077] Based on the target wall area and building information, construct a cloud map of wall stress distribution in the target wall area;

[0078] Based on the stress distribution cloud map of the wall, a thermoelectric coupling model of the target wall area is constructed, and the layout information of the energy storage module is determined by optimizing the thermoelectric coupling model.

[0079] Figure 1 A flowchart illustrating an embedded fusion energy storage method integrated with a building wall according to an embodiment of this application is shown. (Refer to...) Figure 1 As shown, the embedded integrated energy storage method that integrates with the building wall includes at least steps S110 to S150, which are described in detail below:

[0080] S110 acquires building information and energy storage information; among which, building information includes building structure information, wall material parameters, and grid load information.

[0081] In this embodiment, raw data is obtained from different sources through a preset interface protocol. For building information, the building information model file or building structural design drawing is read, and the geometric analysis module is called to extract the geometric dimensions, spatial coordinates, and structural hierarchy of the walls. Wall material parameters are retrieved from the material property database, and corresponding physical properties such as thermal conductivity and elastic modulus are matched according to the building zoning. Grid load information is connected to the energy management system through a real-time data interface to collect historical load curves or real-time power flow data. Energy storage information, including parameters such as module size, capacity range, charge / discharge efficiency, and thermodynamic characteristics, is read from energy storage device configuration files or manufacturer databases.

[0082] Specifically, the process involves reading the building structure design drawings and extracting the geometric features of the walls, such as thickness, shape, and connection relationships, using geometric analysis algorithms to repair geometric defects in the model, such as gaps and overlapping surfaces. Subsequently, time-series data corresponding to the regional power grid load curve and the user-defined energy storage capacity requirements are stored as simulation parameters. Simultaneously, the physical properties of the wall materials, such as thermal conductivity and elastic strength, are retrieved from the material database. After standardization, this data is mapped to the corresponding areas of the wall geometric model, forming a computable digital model.

[0083] Optionally, all data acquisition operations verify the interface response status through an exception handling mechanism to ensure data transmission integrity and compliance.

[0084] Optionally, after acquiring the raw data, data cleaning is initiated to standardize the multi-source heterogeneous data and generate standard data. For example, the geometric coordinates in the building structural design drawings are converted into a unified spatial reference system, the units of material parameters are normalized to the International System of Units (SI), and missing timestamp values ​​in the load data are filled in. Subsequently, data associations are established based on building spatial identifiers (such as wall numbers), binding structural information and material properties with corresponding wall areas to form data objects with spatial context. Energy storage module data is mapped according to equipment models and building adaptation areas, providing structured input for subsequent analysis.

[0085] The cleaned data is encapsulated into a preset data structure, generating building information and energy storage information, which are then stored in shared memory or a temporary storage area. After the building information and energy storage information are linked by identifiers, a comprehensive dataset is formed, triggering the execution of subsequent coupled analysis processes. At this stage, the data version and acquisition timestamp are recorded to ensure the traceability of the analysis process. Simultaneously, the data monitoring module is activated to detect in real time whether key parameters exceed preset thresholds. If an anomaly is detected, an alarm is triggered and the process is interrupted to prevent erroneous data from being passed to the next stage.

[0086] The above process collects raw data from multiple sources through a pre-defined interface protocol, ensuring lossless integration of building design and energy storage equipment data in different formats and eliminating information silos. Standardized processing unifies data dimensions and units, resolving heterogeneous data compatibility issues and improving the accuracy of subsequent analysis. Encapsulation transforms data into structured information modules, facilitating rapid access and transfer, establishing a reliable data foundation for multiphysics coupling analysis, and avoiding computational errors caused by data corruption. This constructs a data foundation encompassing building physical properties and energy storage equipment characteristics, providing reliable input for multiphysics coupling calculations.

[0087] S120 constructs a multiphysics coupling equation based on building information and energy storage information, and determines the target wall area where energy storage modules can be embedded by iteratively solving the coupling equation.

[0088] In one embodiment of this application, based on the acquired building structural features, material properties, and energy storage module characteristics, a dynamic interaction model of two core physical fields—structural mechanics and heat conduction—is constructed in memory. By simulating the stress distribution changes and temperature field diffusion effects of the wall under different embedding scenarios, the influence of heat generation from the energy storage module on wall material deformation, and the coupling relationship between structural deformation and the heat conduction path, are tracked. Then, by iteratively calculating the assumed embedding location and capacity parameters of the energy storage module, the impact of each adjustment on the overall bearing capacity and thermal stability of the wall is dynamically evaluated. Based on preset safety thresholds, feasible areas that meet energy storage requirements without causing structural strength exceeding limits or localized heat accumulation are selected, ultimately determining the target wall region that conforms to the multi-physics field collaborative constraints.

[0089] like Figure 2 As shown, in one embodiment of this application, a multiphysics coupling equation is constructed based on building information and energy storage information. The target wall region where an energy storage module can be embedded is determined by iteratively solving the coupling equation, including:

[0090] S210, based on building information simulation of wall stress and external volume forces, constructs structural mechanics equations representing the stress state of building walls;

[0091] S220, based on building information and energy storage information, simulates the heat generation properties of energy storage modules and the heat dissipation properties of building walls to construct heat conduction equations;

[0092] S230, the coupled equations obtained by combining the structural mechanics equations and the heat conduction equations are solved iteratively to determine the target wall area where the energy storage module can be embedded.

[0093] The wall continuum is discretized into mesh elements, such as tetrahedrons or hexahedrons, based on finite element analysis. Material properties are assigned to each mesh element, and a coupling relationship is established between two physical fields: structural mechanics and heat conduction. The deformation of the wall under load is simulated using the principles of elasticity, while heat transfer is simulated using the principle of energy conservation. These two fields are coupled through the thermal expansion effect; temperature changes cause material expansion or contraction, resulting in thermal stress. Simultaneously, structural deformation may alter the heat conduction path, such as cracks affecting thermal conductivity. Then, an iterative algorithm dynamically coordinates the interaction of these two physical fields to determine the target wall region where energy storage modules can be embedded.

[0094] In one embodiment of this application, boundary constraints are applied according to actual working conditions. In the structural analysis, the bottom nodes of the wall are fixed to simulate ground constraints, and external forces such as wind pressure and self-weight are applied to construct the structural mechanics equations representing the stress state of the building wall as follows:

[0095]

[0096] in, This represents the divergence operator, used to perform derivative operations on the tensor field in spatial coordinates, describing the diffusion effect of stress in space; The wall stress at the preset location is calculated from the strain tensor using the constitutive relation of the material based on Hooke's law. F This represents the external volume force acting on a unit volume of the wall at a predetermined location. In this embodiment, the structural mechanics equations are used to describe the static equilibrium of the wall under external loads, ensuring that the structure does not fail.

[0097] In one embodiment of this application, during thermal analysis, an ambient temperature convection boundary is set, and the heating characteristics of the energy storage module are used as an internal heat source and loaded into the embedded region. The heat conduction equation corresponding to the preset location is constructed as follows:

[0098]

[0099] in, These represent the material density, specific heat capacity at constant pressure, and thermal conductivity at the preset location in the building information, respectively. T This represents the temperature field inside the wall over time. t and spatial changes; Denotes the divergence operator, q This indicates the intensity of the internal heat source in the energy storage information, which is characterized by electrochemical heating.

[0100] The above process simulates the heat transfer process inside the wall by constructing a heat conduction equation, and evaluates the temperature rise and heat distribution during the operation of the energy storage module; at the same time, the structural mechanics equation is coupled with the heat conduction equation to obtain a coupled equation, which is used to jointly evaluate the thermal stress caused by temperature changes.

[0101] Subsequently, structural and thermal analyses are performed alternately using a coupled solver. In each iteration, the thermal analysis results (temperature distribution) are converted into inputs for the structural analysis through the heat conduction equations, while the structural analysis results, such as displacement or deformation, are fed back to adjust the boundary conditions of the thermal analysis.

[0102] Specifically, during the iteration process, the temperature change is determined based on the heat conduction equation, and the linear displacement change of the building wall due to the temperature change is determined based on the temperature change. for:

[0103]

[0104] in, The coefficient of thermal expansion of the building wall material is obtained from a material database and characterizes the relative change rate of the material length under a unit temperature change. It represents the temperature change per unit time, and is the temperature field difference calculated by the heat conduction equation, reflecting the local temperature rise or fall of the energy storage module during operation. L This indicates the original length of the building wall material.

[0105] Based on the above process, the linear displacement change of the building wall due to temperature changes is determined. The wall position is updated based on this data, and the corresponding stress and thermal conductivity at that position are recalculated. The solver continuously monitors key indicators, such as temperature difference. When the deviation between the results of two consecutive iterations is less than a preset threshold, convergence is determined and the calculation is terminated. Target wall areas that simultaneously meet the conditions of stress below allowable values ​​and temperature rise within a safe range are marked, and the location information of target wall areas where energy storage modules can be embedded is determined.

[0106] The above process, by simulating the stress distribution of the wall under its own weight, external loads, and additional loads from the energy storage module, ensures that the embedded area will not suffer structural damage due to mechanical overload. Simultaneously, it captures the diffusion pattern of heat generated during energy storage module operation within the wall material, preventing material performance degradation or thermal runaway risks caused by localized overheating. By dynamically balancing mechanical stability and thermal safety, wall areas that simultaneously meet structural strength and thermal management requirements are selected, avoiding the limitations of single-situation analysis and improving the feasibility of the embedded scheme.

[0107] S130, based on the target wall area and building information, constructs a cloud map of the wall stress distribution in the target wall area.

[0108] In this embodiment, the target wall area where the energy storage module can be embedded is precisely located in the three-dimensional structural model of the building. Combining the material properties and structural connection relationships in the building information, the area is automatically divided into a fine grid. Then, taking into account the wall's own weight, external loads, and local load changes brought about by the embedding of the energy storage module, the stress state of each grid point is calculated through structural mechanics simulation. These discrete stress data are mapped onto the three-dimensional model according to their spatial location. Finally, color gradient technology is used to transform different stress levels into intuitive visual patterns, with high stress areas appearing dark and low stress areas appearing light, thereby generating a wall stress distribution cloud map that can clearly identify stress concentration and dispersion areas, realizing dynamic modeling of building energy storage.

[0109] In one embodiment of this application, a wall stress distribution cloud map of the target wall area is constructed based on the target wall area and building information, including:

[0110] Based on the regional and architectural information of the target wall area, the target wall area is located at the corresponding position in the 3D wall model;

[0111] The target wall area is divided into grid cells of a preset size, and the stress data of the grid cells is obtained;

[0112] By using a preset display method, discrete stress data is displayed in a three-dimensional wall model, generating a wall stress distribution cloud map.

[0113] In this embodiment, the regional and architectural information of the target wall area is first read. The regional information may include the wall range into which the energy storage module can be embedded, and the architectural information may include the wall geometry, material type, and boundary constraints. A geometric matching algorithm is used to accurately locate the target wall area to its corresponding position in the wall model, ensuring that subsequent analysis is performed only on the relevant area, thus improving computational efficiency.

[0114] The target wall area is divided into fine mesh cells, similar to cutting the wall into countless tiny building blocks. Each mesh cell is assigned material properties and location information, where material properties can be the elastic strength of concrete or steel reinforcement. The fineness of the mesh directly affects the accuracy of the results; the denser the mesh, the more accurately local stress changes are captured, but the computational load also increases accordingly. The mesh density and computational efficiency can be automatically balanced according to the complexity of the wall.

[0115] The stress data for each grid cell is acquired. For example, based on load conditions in the building information, such as gravity and wind pressure, the mechanical force borne by each grid cell is determined. The thermal stress analysis results obtained in the previous steps are superimposed on the mechanical force to simulate the effect of temperature changes on the stress in the target wall area. Hooke's law is used to convert the external force into stress data within each grid cell. After the stress data of all cells are determined, a discrete data field covering the entire target wall area is formed.

[0116] Next, the discrete stress data is converted into a continuous visual representation. Specifically, colors are assigned to different stress ranges, such as red for high stress and blue for low stress, and interpolation algorithms are used to ensure smooth color transitions between grids. Color boundary points are automatically adjusted to ensure that high-stress areas are highlighted, which may include parts approaching the material's load-bearing limit; areas of stress concentration or abrupt data changes are marked to indicate potential structural risks.

[0117] Optionally, the generated stress distribution cloud map can be logically validated to confirm that the stress distribution at the wall edges and constraint locations conforms to mechanical laws, automatically marking areas exceeding the material's allowable stress and generating early warning alerts. Cloud map displays from different angles can also be provided to assist engineers in comprehensively assessing structural safety.

[0118] Optionally, area analysis can be used to identify hazardous areas in the wall where stress concentration or temperature exceeds limits, and these areas can be marked as no-go zones where energy storage modules cannot be embedded. Simultaneously, by combining energy storage capacity requirements with wall safety thresholds, such as maximum allowable temperature rise and stress limits, the power density and total capacity of the energy storage modules can be calculated in reverse to ensure that their operation does not exceed the physical limits of the wall. For example, if the temperature rise in a certain area exceeds 5°C, the charging and discharging power or capacity of the energy storage modules in that area needs to be reduced.

[0119] The above process transforms complex mechanical analysis into intuitive visual output. Discretization transforms the continuous wall into computable micro-units; data mapping converts abstract numerical values ​​into color signals; and interpolation algorithms map the location information of the target wall area onto a color field, generating a visualized image. This allows for rapid identification of risk areas using the human eye's sensitivity to color. The process from mesh generation to color rendering interpolates and maps the solution for the target wall area's location information into a visualized cloud map, ensuring rapid result generation and reducing human intervention. Through this process, the internal mechanical state of the wall is transformed into a stress map that engineers can directly interpret, assisting decision-makers in avoiding high-risk embedding locations, providing a visual basis for the safe embedding of energy storage modules, and improving the efficiency of structural safety assessment.

[0120] S140. Based on the wall stress distribution cloud map, construct a thermoelectric coupling model of the target wall area, and determine the layout information of the energy storage module by optimizing the thermoelectric coupling model. The layout information includes the location layout information of the energy storage module, the pipeline information of thermal management, and the electrical connection topology.

[0121] In this embodiment, the wall stress distribution cloud map is analyzed, and a dynamic interactive thermoelectric coupling analysis model is constructed by combining the electrochemical heating characteristics of the energy storage module with the thermal conductivity of the wall material. This model simulates the heat diffusion path, temperature distribution uniformity, and electrical connection stability under different layout schemes. An intelligent optimization algorithm is used to adjust the installation position of the energy storage module, the routing of the heat dissipation pipes, and the electrical wiring connection method. Through multiple iterations, the impact of each layout on thermal management efficiency, electrical performance, and structural safety is evaluated. Finally, the optimal layout scheme that balances controllable thermal runaway risk, minimum electrical loss, and adaptability to the wall stress distribution is selected, determining the spatial coordinates of the energy storage module, the cooling pipe layout, and the circuit topology.

[0122] In one embodiment of this application, a thermoelectric coupling model of the target wall region is constructed based on the wall stress distribution cloud map. The layout information of the energy storage module is determined by optimizing the thermoelectric coupling model, including:

[0123] Based on the electrochemical characteristics of the energy storage module and the building information of the wall in the stress distribution cloud map of the wall, an objective function is constructed through a dynamic weight allocation mechanism;

[0124] The electrical connection topology of the energy storage module is determined by detecting the electrical state of the objective function at its minimum value.

[0125] In this embodiment, after generating the wall stress distribution cloud map, the electrochemical characteristics of the energy storage module in the wall stress distribution cloud map are integrated with the building information of the wall. The electrochemical characteristics include the variation of internal resistance with temperature and the voltage sensitivity to temperature, while the building information includes the three-dimensional wall model of the building and the thermal resistance parameters of the wall decorative layer. Through parameter mapping, the thermal generation characteristics of the battery are associated with the three-dimensional wall model, and the thermal insulation or thermal conductivity properties of the wall decorative layer are superimposed on the three-dimensional wall model to form a complete thermoelectric coupling analysis model.

[0126] Specifically, when constructing a nonlinear thermoelectric coupling analysis model, the self-heating behavior of the battery at different temperatures is simulated. Dynamic response rules for battery internal resistance and voltage drift are established through experimental calibration or data fitting. Specifically, when the surface temperature changes, pre-stored characteristic curves are consulted to adjust the heat generation intensity. For example, an increase in temperature triggers an increase in internal resistance, leading to an increase in Joule heating; simultaneously, based on the direction of the electrochemical reaction during charging or discharging, the additional heat caused by entropy change is dynamically calculated. This process is achieved through iterative feedback: temperature changes drive the update of the heat generation rate, and changes in the heat generation rate in turn affect the temperature field, forming a closed-loop coupling and generating a nonlinear thermoelectric coupling model.

[0127] In the location layout of energy storage modules, the stress distribution cloud map of the wall is discretized into a multi-layer thermal resistance network, with each layer corresponding to different materials, such as structural and decorative layers. Each layer is further divided into grid nodes. Based on a thermoelectric coupling model, the heat transfer path from the surface of the energy storage module through the wall to the external environment is calculated using the principle of thermal resistance superposition. A steady-state thermal analysis algorithm is employed. Assuming thermal equilibrium is reached, the temperature values ​​of each node are iteratively solved. Heat in high-temperature areas will diffuse along low-thermal-resistance paths, and the thermal resistance characteristics of the decorative layer directly affect the heat transfer efficiency into the room. During this process, the node temperature distribution is continuously adjusted until the heat inflow and outflow of the entire network reach equilibrium, generating the location layout information of the energy storage modules.

[0128] In the pipeline layout of the energy storage module, an optimization engine is activated based on the temperature field distribution to adjust the pipeline routing for thermal management. Priority is given to covering areas at risk of exceeding temperature limits. Path search techniques, such as ant colony optimization or gradient-driven optimization, are used to find the shortest and most efficient cooling loops. During the optimization process, the heat dissipation effect under different pipeline layouts is simulated to evaluate their improvement on the temperature field, and the physical feasibility of pipeline installation is considered, such as avoiding steel reinforcement skeleton nodes. Ultimately, pipeline information that balances heat dissipation efficiency and construction convenience is generated.

[0129] When constructing the electrical network topology, each electrical node is considered a graph node, and wires are considered edges. A dynamic weight allocation mechanism assigns higher priority to key nodes corresponding to voltage-sensitive device access points. A weighted graph model of the electrical connection topology is constructed based on the electrical nodes, with voltage stability of the electrical nodes as the optimization objective. An objective function is built based on a thermo-electric coupling model. Y for:

[0130]

[0131] in, i and M The distribution represents the identifier and total number of electrical nodes; k This represents the preset steepness coefficient, used to control the sensitivity of weight changes; This indicates the rated voltage of the node, such as the standard voltage of a building's power distribution system; Indicates electrical node i The voltage fluctuation value, that is, the deviation between the actual voltage and the rated voltage; This represents dynamic weighting, applied when the node voltage approaches the minimum allowable value. At that time, the weight increases sharply to prioritize ensuring the voltage stability of that node.

[0132] In this embodiment, the electrical connection topology of the energy storage module is determined by identifying the electrical state corresponding to the minimum value of the objective function. Specifically, during the calculation process, when the node voltage approaches the safe lower limit, a dynamic weight is automatically increased to prioritize ensuring the voltage stability of that node. In this state, graph partitioning and redundant path search techniques are used to minimize the amount of conductor while satisfying voltage fluctuation constraints. For example, backup circuits are added or conductor cross-sectional areas are increased for high-weight nodes to ensure power supply reliability during grid load fluctuations.

[0133] Finally, the optimization results are converted into executable energy storage module layout information, which can include: the location layout information of the energy storage modules, the piping information for thermal management, and the electrical connection topology. Specifically, the energy storage module layout determines the module spacing and orientation based on temperature uniformity and structural load-bearing capacity to avoid heat accumulation or localized overload; the piping information for thermal management includes a three-dimensional path of pipe diameter, flow velocity, and direction, supporting construction layout; the electrical connection topology includes wiring diagrams with redundant configurations, marking key node protection measures to ensure seamless integration with the building's power distribution system.

[0134] Through the above simulation process, combined with the real-time variation characteristics of battery internal resistance and voltage drift, the impact of temperature fluctuations on the electrical performance of energy storage modules is simulated, and the system behavior under thermoelectric interaction is predicted. This achieves a dynamic balance between battery heat generation and dissipation, avoiding thermal runaway; redundancy design improves power supply stability under grid fluctuations; and efficient utilization of internal resources eliminates physical conflicts between subsystems. By iteratively approximating the global optimal solution, replacing traditional manual trial-and-error design, the integration efficiency and reliability of the energy storage system are significantly improved.

[0135] S150 generates an operation strategy for the energy storage module based on building information and the layout information of the energy storage module.

[0136] In this embodiment, building information and energy storage module layout information are deeply integrated to analyze the urgency and sustainability of energy demand in different regions. Then, combining the time-of-use characteristics of the power grid and the fluctuation patterns of building load, the charging and discharging candidate windows of the energy storage modules are dynamically matched. During simulation operation, thermal diffusion constraints, electrical transmission efficiency, and equipment health status are comprehensively evaluated. Finally, a control instruction set that balances economy, reliability, and safety is generated to achieve intelligent coordination between the energy storage system and building energy consumption.

[0137] In one embodiment of this application, an operation strategy for the energy storage module is generated based on building information and layout information of the energy storage module, including:

[0138] The building information and the layout information of the energy storage modules are spatiotemporally aligned to generate backup data;

[0139] Identify the priority of energy demand from backup data, and divide the candidate charging and discharging periods of the energy storage system based on the priority;

[0140] Based on building information, energy storage module layout information, and candidate charging and discharging periods, an operation strategy for the energy storage module is generated.

[0141] In one embodiment of this application, the grid load curve, building energy consumption pattern, and energy storage module layout information (such as installation location, thermal management pipeline distribution, and electrical connection relationships) in the building information are first spatiotemporally aligned. By associating the physical location of the energy storage module with the building's functional zoning, the energy demand priorities of different areas are identified, such as prioritizing power supply to high-energy-consuming areas or emergency backup needs for critical loads. Simultaneously, combined with the grid's time-of-use pricing signal, the candidate charging and discharging periods for the energy storage system are initially defined.

[0142] Subsequently, based on real-time building energy consumption forecasts, grid load fluctuation trends, and the remaining capacity and health status of energy storage modules, such as the impact of temperature on charging and discharging efficiency, the energy flow paths of various operating schemes are dynamically simulated. For example, energy storage modules are prioritized for charging during periods of low electricity prices, while energy is released during peak building energy consumption periods. However, the discharge power needs to be adjusted according to thermal management constraints in the module layout to avoid the risk of thermal runaway.

[0143] Based on the deduction results of the strategy model, multiple objectives are weighed through iterative optimization algorithms: minimizing electricity costs, maximizing energy storage utilization, ensuring the reliability of power supply to building loads, and maintaining the safe operating temperature of energy storage modules. During this process, the optimization results are verified in real time to ensure they meet the physical constraints of the energy storage module layout, such as whether the electrical connection topology supports the target discharge power and whether the heat dissipation pipe layout can effectively absorb the heat generated during charging and discharging.

[0144] The optimized operating strategy is translated into a specific set of control instructions, including the charging and discharging sequence of the energy storage module, power regulation gradient, start-up and shutdown conditions of the thermal management system, and communication protocols with the building management system and the power grid. These instructions are broken down into executable actions along a timeline and sent to the energy storage device controller via a standardized interface. Simultaneously, a monitoring module is activated to track the strategy's execution effect in real time.

[0145] Optionally, during the operation of the monitoring module, in conjunction with IoT technology, real-time data is acquired based on the IoT chips in the preset sensor nodes, and a wireless local area network is built between the sensor nodes via WIFI to form a microelectromechanical system composed of various sensors, thereby tracking the effect of policy execution in real time.

[0146] Optionally, if the actual operating data deviates from the predicted value, a strategy re-optimization process will be triggered to form a closed-loop control.

[0147] The above process synchronizes building load demand with energy storage layout information in time and space, accurately identifying energy usage priorities in different areas and ensuring stable power supply to critical loads. By combining grid time-of-use pricing with building energy consumption patterns, a low-cost charging and discharging plan is developed, while power is dynamically adjusted based on module thermal status to avoid overheating and triggering protection mechanisms. Control commands linked to the building management system are generated, supporting the engineering design of energy-efficient buildings, enabling intelligent interaction between the energy storage system, the grid, and loads, improving building energy self-sufficiency and reducing operating costs.

[0148] This application's technical solution involves acquiring building and energy storage information; constructing multi-physics coupling equations based on these information; determining the target wall region where energy storage modules can be embedded by iteratively solving these equations; constructing a wall stress distribution cloud map of the target wall region based on the target wall region and building information; constructing a thermoelectric coupling model of the target wall region based on the wall stress distribution cloud map; determining the layout information of the energy storage modules by optimizing the thermoelectric coupling model; and generating an operation strategy for the energy storage modules based on the building information and the layout information of the energy storage modules. By coupling building and energy storage information, a multi-physics coupling analysis system is constructed, ensuring that the embedding location of the energy storage modules simultaneously meets the requirements of wall structure bearing capacity and thermal stability, avoiding the risk of mechanical damage or thermal runaway; visually presenting structural weak points through the stress distribution cloud map to guide the optimization of the embedding area; further coordinating electrical performance and thermal management requirements through the thermoelectric coupling model to ensure a reasonable layout; and finally, dynamically adjusting the energy storage behavior according to the building load characteristics to achieve a safe and stable integration of energy efficiency and building-integrated new energy building engineering.

[0149] Example 2

[0150] This embodiment describes a wall-mounted energy storage system that can be used to execute the embedded fusion energy storage method integrated with building walls described in the above embodiments of this application. It is understood that the wall-mounted energy storage system can be a computer program (including program code) running on a computer device; for example, the wall-mounted energy storage system can load industrial application software or industrial control software. The wall-mounted energy storage system can be used to execute the corresponding steps in the methods provided in the embodiments of this application. For details not disclosed in the embodiments of the wall-mounted energy storage system of this application, please refer to the above embodiments of the embedded fusion energy storage method integrated with building walls described in this application.

[0151] Figure 3 A block diagram of a wall-mounted energy storage system according to an embodiment of this application is shown.

[0152] Reference Figure 3 As shown, a wall-mounted energy storage system according to an embodiment of this application includes:

[0153] The acquisition module 310 is used to acquire building information and energy storage information; wherein, the building information includes building structure information, wall material parameters and power grid load information;

[0154] The coupling module 320 is used to construct the coupling equation of multiphysics based on building information and energy storage information, and to determine the target wall area into which the energy storage module can be embedded by iteratively solving the coupling equation.

[0155] Distribution module 330 is used to construct a wall stress distribution cloud map of the target wall area based on the target wall area and building information;

[0156] The layout module 340 is used to construct a thermoelectric coupling model of the target wall area based on the wall stress distribution cloud map, and to determine the layout information of the energy storage module by optimizing the thermoelectric coupling model.

[0157] Strategy module 350 is used to generate operating strategies for energy storage modules based on building information and layout information of energy storage modules.

[0158] The preferred building wall is a structure, which refers to a fixed facility that does not have human habitation function and is mainly built for specific engineering or production purposes, such as the outer wall of a community, flower bed, and outer columns.

[0159] The energy storage module is preferably a nanocell, which is more stable and reliable when placed in the building wall.

[0160] In this application, based on the aforementioned scheme, obtaining building information and energy storage information includes: obtaining raw data from different sources through a preset interface protocol; wherein the raw data includes building information model files or building structural design drawings; standardizing the raw data to generate standard data; and encapsulating the standard data based on a preset data structure to generate building information and energy storage information.

[0161] In this application, based on the aforementioned scheme, a multiphysics coupling equation is constructed based on building information and energy storage information. The target wall region where an energy storage module can be embedded is determined by iteratively solving the coupling equation. This includes: simulating wall stress and external volume forces based on building information to construct a structural mechanics equation representing the stress state of the building wall; simulating the heat generation properties of the energy storage module and the heat dissipation properties of the building wall based on building information and energy storage information to construct a heat conduction equation; and iteratively solving the coupling equation obtained by combining the structural mechanics equation and the heat conduction equation to determine the target wall region where an energy storage module can be embedded.

[0162] In this application, based on the aforementioned scheme and based on the target wall area and building information, a wall stress distribution cloud map of the target wall area is constructed, including: locating the target wall area to the corresponding position in the three-dimensional wall model based on the area information and building information of the target wall area; dividing the target wall area into grid cells of a preset size and obtaining the stress data of the grid cells; and displaying the discrete stress data in the three-dimensional wall model through a preset display method to generate a wall stress distribution cloud map.

[0163] In this application, based on the aforementioned scheme, the layout information includes the location layout information of the energy storage module, the piping information for thermal management, and the electrical connection topology.

[0164] In this application, based on the aforementioned scheme, a thermoelectric coupling model of the target wall region is constructed according to the wall stress distribution cloud map. The layout information of the energy storage module is determined by optimizing the thermoelectric coupling model, including: constructing an objective function through a dynamic weight allocation mechanism based on the electrochemical characteristics of the energy storage module and the building information of the wall in the wall stress distribution cloud map; and determining the electrical connection topology of the energy storage module by detecting the electrical state of the objective function at its minimum value.

[0165] In this application, based on the aforementioned scheme and the layout information of the building information and the energy storage module, an operation strategy for the energy storage module is generated, including: spatiotemporally aligning the building information and the layout information of the energy storage module to generate backup data; identifying the priority of energy demand from the backup data and dividing the charging and discharging candidate periods of the energy storage system based on the priority; and generating an operation strategy for the energy storage module based on the building information, the layout information of the energy storage module, and the charging and discharging candidate periods.

[0166] This application's technical solution involves acquiring building and energy storage information; constructing multiphysics coupling equations based on these information; determining the target wall region where energy storage modules can be embedded by iteratively solving these equations; constructing a wall stress distribution cloud map of the target wall region based on the target wall region and building information; constructing a thermoelectric coupling model of the target wall region based on the wall stress distribution cloud map; determining the layout information of the energy storage modules by optimizing the thermoelectric coupling model; and generating an operation strategy for the energy storage modules based on the building information and the layout information of the energy storage modules. By coupling building and energy storage information, a multiphysics coupling analysis system is constructed, ensuring that the embedding location of the energy storage modules simultaneously meets the requirements of wall structure bearing capacity and thermal stability, avoiding the risk of mechanical damage or thermal runaway; visually presenting structural weak points through the stress distribution cloud map to guide the optimization of the embedding area; further coordinating electrical performance and thermal management requirements through the thermoelectric coupling model to ensure a reasonable layout; and finally, dynamically adjusting the energy storage behavior according to the building load characteristics to achieve a safe and stable integration of energy efficiency and building integration.

[0167] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown.

[0168] It should be noted that the computer system of the electronic device in this embodiment is only an example and should not impose any limitations on the function and scope of use of the embodiments of this application.

[0169] In this embodiment, the computer system includes a central processing unit 401, which can perform various appropriate actions and processes based on a program stored in the read-only memory 402 or a program loaded from the storage section 408 into the random access memory 403, such as executing the embedded fusion energy storage method integrated with the building wall in the above embodiment. The random access memory 403 also stores various programs and data required for system operation, thereby realizing big data storage and big data management. The central processing unit 401, the read-only memory 402, and the random access memory 403 are interconnected via a bus 404. An input / output interface 405 is also connected to the bus 404.

[0170] The following components are connected to the input / output interface 405: an input section 406 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the input / output interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 410 as needed so that computer programs read from it can be installed into the storage section 408 as needed.

[0171] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by central processing unit 401, it performs various functions defined in the system of this application.

[0172] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. The transmitted data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0173] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0174] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0175] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various alternative implementations described above.

[0176] In another aspect, this application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to implement the embedded fusion energy storage method integrated with building walls as described in the above embodiments.

[0177] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0178] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the methods according to the embodiments of this application.

[0179] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0180] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. An embedded integrated energy storage method that integrates with building walls, characterized in that, include: The system acquires building information of the building walls and energy storage information of the energy storage modules, performs mechanical analysis of the building walls, and performs thermal conduction analysis of embedding energy storage modules in the building walls to determine the target wall areas where energy storage modules can be embedded; it then lays out the energy storage modules in the target wall areas and integrates them with the grid load to determine the operation strategy of the energy storage modules. The mechanical analysis of the building wall includes: discretizing the building wall into mesh elements through finite element analysis, assigning material properties to each mesh element according to the wall material parameters in the building information; applying boundary constraints to the discretized building wall, fixing the bottom nodes of the wall to simulate ground constraints, and loading wall stress and external volume forces including wind pressure and self-weight, thereby constructing structural mechanical equations.

2. The embedded fusion energy storage method integrated with building walls according to claim 1, characterized in that, Thermal conduction analysis of energy storage modules embedded in building walls is used to determine target wall areas where energy storage modules can be embedded, including: Based on the building information and the energy storage information, the heat generation properties of the energy storage module and the heat dissipation properties of the building walls are simulated to construct a heat conduction equation; The coupled equations obtained by combining the structural mechanics equations and the heat conduction equations are iteratively solved to determine the target wall region where energy storage modules can be embedded.

3. The embedded fusion energy storage method integrated with building walls according to claim 2, characterized in that, The structural mechanics equations simulate the deformation of the wall under load using the principle of elasticity, and the heat conduction equations simulate heat transfer using the principle of energy conservation. The structural mechanics equation and the heat conduction equation are coupled through the thermal expansion effect, which includes: the temperature change generated by the heat conduction equation causes the material to expand or contract, thereby generating thermal stress that affects the structural mechanics equation; the structural deformation generated by the structural mechanics equation changes the heat conduction path of the heat conduction equation.

4. The embedded fusion energy storage method integrated with building walls according to claim 3, characterized in that, The expression for the structural mechanics equation is: In the formula, This represents the divergence operator, used to perform derivative operations on the tensor field in spatial coordinates, describing the diffusion effect of stress in space; This indicates the wall stress corresponding to the preset location; F This refers to the external volume force acting on a unit volume at a predetermined location on the wall.

5. The embedded fusion energy storage method integrated with building walls according to claim 3, characterized in that, By setting an ambient temperature convection boundary for the discretized building wall, the heating characteristics of the energy storage module are embedded as an internal heat source into a predetermined location within the building wall, thereby constructing the heat conduction equation at the predetermined location. The expression of the heat conduction equation is as follows: In the formula, These represent the material density, specific heat capacity at constant pressure, and thermal conductivity at the preset location in the building information, respectively. T This represents the temperature field inside the building walls. Denotes the divergence operator, q This indicates the intensity of the internal heat source in the energy storage information, which is characterized by electrochemical heating.

6. The embedded fusion energy storage method integrated with building walls according to claim 3, characterized in that, In each iteration of solving the coupled equation, the thermal analysis results of the discretized building wall are converted into inputs for structural analysis through the heat conduction equation, and the structural analysis results are fed back to adjust the boundary conditions of the thermal analysis.

7. The embedded fusion energy storage method integrated with building walls according to claim 6, characterized in that, The thermal analysis results of the discretized building walls are converted into inputs for structural analysis using heat conduction equations, including: The temperature change is determined based on the heat conduction equation, and the linear displacement change of the building wall due to the temperature change is determined based on the temperature change. The corresponding calculation expression is: In the formula, It is a linear displacement change. This represents the coefficient of thermal expansion of the building wall material. It represents the change in temperature per unit time. L This indicates the original length of the building wall material.

8. The embedded fusion energy storage method integrated with building walls according to claim 1, characterized in that, The energy storage modules are arranged in the target wall area, including: Based on the target wall area and the building information, a wall stress distribution cloud map of the target wall area is constructed; Based on the stress distribution cloud map of the wall, a thermoelectric coupling model of the target wall area is constructed, and the layout information of the energy storage module is determined by optimizing the thermoelectric coupling model.

9. The embedded fusion energy storage method integrated with building walls according to claim 8, characterized in that, Based on the target wall region and the building information, a wall stress distribution cloud map of the target wall region is constructed, including: Based on the regional and architectural information of the target wall area, the target wall area is located at the corresponding position in the three-dimensional wall model; The target wall area is divided into grid cells of a preset size, and the stress data of the grid cells is obtained; By using a preset display method, the discrete stress data is displayed in the three-dimensional wall model, generating a wall stress distribution cloud map.

10. The embedded fusion energy storage method integrated with building walls according to claim 8, characterized in that, The layout information includes the location layout information of the energy storage modules, the piping information for thermal management, and the electrical connection topology.

11. The embedded fusion energy storage method integrated with building walls according to claim 10, characterized in that, Based on the wall stress distribution cloud map, a thermoelectric coupling model of the target wall region is constructed. The layout information of the energy storage module is determined by optimizing the thermoelectric coupling model, including: Based on the stress distribution cloud map of the wall, and combined with the electrochemical heating characteristics of the energy storage module and the thermal conductivity of the wall material, a dynamic interactive thermoelectric coupling analysis model is constructed to simulate the heat diffusion path, temperature distribution uniformity and electrical connection stability under different layout schemes. By optimizing the algorithm to adjust the installation location of the energy storage module, the direction of the heat dissipation pipes, and the connection method of the electrical circuits, different layout schemes are generated. In multiple iterations, the impact of each layout scheme on thermal management efficiency, electrical performance, and structural safety is evaluated. Finally, the optimal layout scheme that balances controllable thermal runaway risk, minimum electrical loss, and adaptability to wall stress distribution is selected as the final layout information of the energy storage module.

12. The embedded fusion energy storage method integrated with building walls according to claim 11, characterized in that, The electrochemical heating characteristics of the energy storage module include the variation of internal resistance with temperature and the sensitivity of voltage to temperature.

13. The embedded fusion energy storage method integrated with building walls according to claim 11, characterized in that, In each iteration of different layout schemes, the stress distribution cloud map of the wall is discretized into a multi-layer thermal resistance network, with each layer corresponding to a different material, and each layer is further divided into grid nodes. Based on the thermoelectric coupling analysis model, the heat transfer path from the surface of the energy storage module through the wall to the external environment is calculated through the principle of thermal resistance superposition. The temperature distribution of each network node is continuously adjusted until the heat inflow and outflow of each thermal resistance network layer reach equilibrium.

14. The embedded fusion energy storage method integrated with building walls according to claim 11, characterized in that, The process of evaluating electrical performance during each iteration of different layout schemes includes: Based on the current electrical connection topology, the objective function for electrical performance evaluation is determined as follows: In the formula, The objective function for electrical performance evaluation is... i and M The distribution represents the identifier and total number of electrical nodes; k This represents the preset steepness coefficient, used to control the sensitivity of weight changes; Indicates the node's rated voltage. Indicates electrical node i Voltage fluctuation value, This is the minimum allowable voltage value. This indicates dynamic weights.

15. The embedded fusion energy storage method integrated with building walls according to claim 11, characterized in that, The process of determining the operating strategy of the energy storage module includes: The building information and the layout information of the energy storage module are spatiotemporally aligned to generate backup data; The priority of energy demand is identified from the backup data, and the candidate charging and discharging periods of the energy storage system are divided based on the priority. Based on the building information, the layout information of the energy storage module, and the candidate charging and discharging time periods, an operation strategy for the energy storage module is generated.

16. The embedded fusion energy storage method integrated with building walls according to claim 15, characterized in that, By associating the physical location of energy storage modules with building functional zoning, the priority of energy demand in different areas can be identified.

17. The embedded fusion energy storage method integrated with building walls according to claim 15, characterized in that, The process of generating the operation strategy for the energy storage module specifically includes: Based on the building information, the layout information of the energy storage modules, and the candidate charging and discharging time periods, various operation schemes for the energy storage modules are generated. Based on real-time building energy consumption forecasts, grid load fluctuation trends, and the remaining capacity and health status of energy storage modules, the energy flow paths of various operating schemes are dynamically simulated. Through iterative optimization algorithms, the optimal operating strategy for energy storage modules is obtained with the goals of minimizing electricity costs, maximizing energy storage utilization, ensuring the reliability of power supply to building loads, and maintaining the safe operating temperature of energy storage modules.

18. The embedded fusion energy storage method integrated with building walls according to claim 1, characterized in that, The method further includes: Based on the determined layout of the energy storage modules, the energy storage modules are embedded in the building walls; The operation strategy of the energy storage module is converted into a set of control instructions, which includes the charging and discharging sequence of the energy storage module, the power regulation gradient, the start and stop conditions of the thermal management system, and the communication protocol with the building management system and the power grid. The control command set is decomposed into executable actions according to the time axis and sent to the energy storage device controller for control.

19. A wall-mounted energy storage system that implements the embedded fusion energy storage method integrated with building walls as described in any one of claims 1-18, characterized in that, include: The acquisition module is used to acquire building information of the building walls and energy storage information of the energy storage modules; The coupling module is used for mechanical analysis of building walls and thermal conduction analysis of embedding energy storage modules in building walls to determine the target wall area where energy storage modules can be embedded. The layout and strategy module is used to lay out the energy storage modules in the target wall area, integrate them with the grid load, and determine the operation strategy of the energy storage modules.

20. The wall-mounted energy storage system according to claim 19, characterized in that, The building walls are structures, and the energy storage module is a nanocell.

Images