Automatic design method, system and equipment for super-capacitor energy storage system and medium

Abstract

The invention provides an automatic design method, system and device for a super-capacitor energy storage system and a medium, and belongs to the technical field of capacitor energy storage. The method comprises the following steps: carrying out standardized definition, feasibility screening, priority ranking and combination verification on input data designed by the super-capacitor energy storage system, and outputting a cabin and cell combination list; step-by-step topology decomposition is carried out according to the sequence of a system level, a cluster level, a Pack level and an electric core level, and a structured topology scheme document is output; a closed-loop feedback simulation model of an electric domain and a heat domain is established, electric performance, heat safety and service life verification is carried out on the topology schemes, and a qualified topology scheme list is obtained through screening; under the cabin space constraint, cluster block translation and rotation operation is carried out on the screened topology scheme, and an optimized cluster level arrangement scheme is output; performing multi-target quantitative scoring and same-level arbitration on the corresponding topology scheme, and screening to obtain an optimal topology scheme; and generating a flow chart, a layout schematic diagram and a parameter report corresponding to the optimal topology scheme.

Description

Technical Field

[0001] This invention belongs to the field of capacitor energy storage technology, and more specifically relates to an automated design method, system, equipment and medium for a supercapacitor energy storage system. Background Technology

[0002] In the design of supercapacitor energy storage systems, achieving voltage matching, meeting power requirements, and rationally planning the physical layout are key elements to ensure the efficient and stable operation of the system. However, current design technologies have revealed many shortcomings in practical applications that urgently need to be addressed.

[0003] Traditional supercapacitor energy storage system design relies heavily on human experience, with the design process involving numerous trial and error steps. Faced with a wide variety of cell models and various enclosure structures on the market, designers struggle to quickly and accurately achieve efficient compatibility between the two using existing methods, resulting in lengthy design cycles and high costs.

[0004] Existing design tools often focus solely on verifying electrical performance, such as voltage matching. They largely neglect crucial physical factors like cabin size limitations and heat dissipation channel planning. These physical constraints, however, have a significant impact on system performance, stability, and even safety in practical applications.

[0005] When optimizing design schemes, existing technologies typically employ enumeration algorithms to try various possible combinations. However, due to the lack of an effective pruning mechanism, the computational load explodes as the cell type and hull type increase. This not only consumes significant computing resources but also makes it virtually impossible to compare and analyze multiple design schemes in a short period, severely restricting the improvement of design efficiency and the selection of optimal solutions. Summary of the Invention

[0006] To address the above problems, the present invention aims to provide an automated design method, system, equipment, and medium for supercapacitor energy storage systems. Through standardized definition and quantitative screening of input data, four-level topology decomposition, electrothermal closed-loop feedback simulation verification, cluster block translation and rotation optimization under cabin space constraints, and multi-objective normalized scoring and biased arbitration, the invention achieves rapid adaptation of multiple cell types and diverse cabins, coordinated optimization of physical layout and electrical performance, safe and reliable system operation, efficient utilization of computing resources, and flexible adaptation to multiple scenarios.

[0007] To achieve the above objectives, the present invention employs the following technical solution: In a first aspect, embodiments of this application provide an automated design method for a supercapacitor energy storage system, including: The input data for the design of supercapacitor energy storage system is standardized, defined, screened for feasibility, prioritized and verified for combination, and a list of feasible cabin and cell combinations is output. Based on the list of feasible cabin and cell combinations, a hierarchical topology decomposition is performed in the order of system level, cluster level, pack level, and cell level, and a structured topology scheme document is output. A closed-loop feedback simulation model of the electric and thermal domains is established to verify the electrical performance, thermal safety and lifespan of the topology schemes in the topology scheme document, and a list of qualified topology schemes is obtained by screening. Under the constraints of cabin space, perform cluster block translation and rotation operations on the topology schemes in the qualified topology scheme list, and output the optimized cluster-level arrangement scheme. The optimized cluster-level arrangement scheme is subjected to multi-objective quantitative scoring and peer arbitration to select the optimal topology scheme. Generate the full process flowchart, 3D layout diagram and structured parameter report corresponding to the optimal topology scheme.

[0008] In an optional implementation, the standardization, feasibility screening, priority ranking, and combination verification of the input data for the supercapacitor energy storage system design, outputting a list of feasible cabin and cell combinations, includes: Construct a cell library containing parameters for the entire lifecycle of supercapacitor cells, define a cabin library for standard container physical boundaries and constraints, and determine the system requirements that a single design task must meet; these system requirements include the minimum operating voltage of the system. Maximum operating voltage of the system System rated power System continuous discharge time Design cycle life and ambient temperature range; The cell library is traversed, and cell models that do not meet the basic requirements are eliminated through cycle life filtering, voltage matching filtering, and temperature adaptation filtering to determine the cells that pass the initial screening. Among these, the cycle life filtering must meet the following requirements:

[0009] In the formula, κ is the safety factor, which is used to reserve redundancy for the cycle life of the battery cell; When performing voltage matching filtering, the reasonable range for the number of cells connected in series is calculated using the following formula:

[0010]

[0011] In the formula, ceil() is the floor function, and floor() is the floor function. The rated voltage of a single supercapacitor cell. The minimum number of cells connected in series to meet the minimum operating voltage requirement of the system. The maximum number of cells connected in series to meet the system's maximum operating voltage requirement; if > If the cell cannot meet the system voltage requirements through series connection, it should be eliminated. During temperature adaptation filtering, it is verified whether the operating temperature range of the battery cell completely covers the system ambient temperature range; if not, the battery cell is rejected. Based on the cells that pass the initial screening, the specific energy, unit cost, and cycle life of the corresponding cells are obtained from the cell library. How to calculate the overall priority score for each battery cell that passes the initial screening using the following formula:

[0012] In the formula, The overall priority score for the battery cell is determined by Norm(), which is the normalization function, BN is the specific energy, CB is the unit cost, XL is the cycle life, and w1, w2, and w3 are the weighting coefficients for specific energy, 1 / unit cost, and cycle life, respectively. according to Sort the battery cells in descending order to obtain a list of battery cells; Based on the battery cell list, the cabin library is traversed, and the actual usable layout width of the cabin is determined by subtracting the maintenance passage width from the total net width of the cabin; this is then calculated using the formula... Calculate the total energy required by the system; calculate the maximum energy storage of a single cell based on the rated capacity and voltage of the cells, and then... Divide by the maximum energy storage of a single cell to determine the minimum number of cells; determine the effective volume of the cabin based on the space available for arranging the cells inside the cabin; multiply the minimum number of cells by the volume of a single cell to calculate the required volume; Select combinations of cabins and battery cells whose required volume is less than or equal to the effective volume of the cabin and whose total weight of the battery cells is less than or equal to the upper limit of the cabin's load-bearing capacity. For the selected combinations of battery cells and battery packs, their overall score is calculated using the following formula:

[0013] In the formula, PC represents the cabin cost, and RV represents the cabin volume utilization rate; Sort by comprehensive score in descending order and output a list of feasible cabin and battery cell combinations.

[0014] In an optional implementation, based on the list of feasible cabin and cell combinations, a hierarchical topology decomposition is performed in the order of system level, cluster level, pack level, and cell level, outputting a structured topology scheme document, including: Based on the list of feasible cabin and battery cell combinations and the system requirements, a system-level topology decomposition is performed to determine the system-level electrical connection parameters and spatial arrangement parameters under the current combination: The total energy required by the system is obtained. Rated energy of a single cluster Using the formula Calculate the total number of clusters in the system; combine the corresponding effective volume of the cabin with the volume of a single cluster to determine the number of clusters arranged along the x, y, and z axes of the cabin. And must meet ; Based on system-level electrical connection parameters and spatial arrangement parameters, cluster-level topology decomposition is performed to determine the cluster-level electrical connection parameters and spatial arrangement parameters under the current combination: obtaining the cluster rated voltage. With the rated voltage of a single pack Using the formula Calculate the number of PACKs required for a single cluster; combine the internal space dimensions of the cluster with the volume of a single PACK to determine the arrangement of PACKs within the cluster; Based on cluster-level electrical connection parameters and spatial arrangement parameters, a pack-level topology decomposition is performed to determine the pack-level electrical connection parameters and spatial arrangement parameters under the current combination: The pack rated voltage is obtained. With the rated voltage of the battery cell Using the formula Calculate the number of cells connected in series; obtain the rated capacity of the pack. With cell capacity Using the formula Calculate the number of cells connected in parallel; using the formula Calculate the total number of cells in a single PACK; Based on the pack-level electrical connection parameters and spatial arrangement parameters, cell-level topology decomposition is performed: according to the internal space dimensions of the pack and the volume of a single cell, the number of cells arranged in the x, y, and z axes of the pack is determined. , , And satisfy At the same time, a heat dissipation gap is reserved to ensure that the heat generated by the battery cell can be effectively dissipated during operation. The output is a structured topology document containing electrical connection parameters and spatial arrangement parameters at the system, cluster, pack, and cell levels.

[0015] In an optional implementation, the step of establishing a closed-loop feedback simulation model of the electric and thermal domains, verifying the electrical performance, thermal safety, and lifetime of the topology schemes in the topology scheme document, and filtering to obtain a list of qualified topology schemes includes: Obtain the scheme parameters and cell thermal and electrical characteristic parameters from the structured topology scheme document; among them, the electrical characteristic parameters include the cell equivalent internal resistance (ESR). According to the system rated power Calculate the total current of the circuit based on the total system voltage, and combine this with the series-parallel structure of the cells to allocate the operating current of each cell, PACK, and cluster; using the formula... Calculate the power loss of a single battery cell, where... The operating current of a single battery cell; verify whether the output voltage of the system remains at a certain level throughout the entire discharge process. If the range is exceeded, the electrical performance of the topology scheme is marked as substandard. The power loss of each cell obtained from the electric domain simulation As a heat source, it is applied to the corresponding cell location in the thermal simulation model; based on the theories of heat conduction, convection, and radiation, the temperature field distribution of the system during the discharge process is calculated, and the hot spot temperature of the cell is monitored; it is verified whether the hot spot temperature of the cell is less than or equal to 65℃, and if not, the topology is marked as not meeting the thermal safety standards. Based on the temperature data obtained from thermal simulation, the equivalent internal resistance (ESR) and capacity of the battery cell are corrected. Among them, the capacity decay rate must meet the following requirements. In the formula This refers to the capacity decay of the battery cell under the influence of operating temperature. ; This is the initial capacity of the battery cell. The actual capacity of the battery cell after temperature effects; the rate of increase in internal resistance must meet the following requirements. In the formula The increase in internal resistance of the battery cell under the influence of operating temperature , This represents the actual equivalent internal resistance of the battery cell after temperature effects. This represents the initial equivalent internal resistance of the battery cell; Using the corrected equivalent internal resistance (ESR) and capacitance The battery cell is reloaded to the corresponding cell position in the thermal simulation model and an electric domain simulation is performed to verify whether the electrical performance still meets the requirements. If so, the system cycle life is predicted based on the corrected relevant parameters and the preset cell life decay model. The predicted system cycle life is then judged to be greater than or equal to the design cycle life. If it does not meet the requirements, the topology scheme is marked as having a lifespan that does not meet the standards. Topology schemes that meet the standards in terms of electrical performance, thermal safety, and lifespan are retained to form a list of qualified topology schemes.

[0016] In an optional implementation, the step of performing cluster block translation and rotation operations on the topology schemes in the qualified topology scheme list under the constraints of the cabin space, and outputting an optimized cluster-level arrangement scheme, includes: Extract the effective volume boundary of the cabin, the location and width of the maintenance passage, and the location and size of the fixed structure inside the cabin from the cabin library parameters to identify the areas where cluster blocks cannot be arranged. Based on the number of clusters arranged along the x, y, and z axes of the cabin. Generate an initial arrangement scheme for the cluster blocks; within the effective volume of the cabin, translate the cluster blocks along the x, y, and z axes to avoid overlapping the cluster blocks with areas that cannot be arranged and to maximize the use of cabin space; for cluster blocks with regular shapes, rotate them 90° or 180° if space permits to optimize the gap distribution between the cluster blocks and ensure that the heat dissipation channels are uniform and unobstructed. Verify whether the adjusted cluster block arrangement meets all spatial constraints and whether the gaps between cluster blocks meet heat dissipation requirements; if the verification passes, generate and output the optimized cluster-level arrangement scheme.

[0017] In an optional implementation, the topology schemes corresponding to the optimized cluster-level arrangement schemes are subjected to multi-objective quantitative scoring and peer arbitration to select the optimal topology scheme, including: Based on the optimized cluster-level arrangement scheme, its material cost data, volumetric power density data, and cycle life data are determined. Based on the material cost data, volumetric power density data, and cycle life data, normalization is performed using the normalization function Norm() to generate a cost normalization score. Volume normalization score and lifespan normalized score ; The overall score of the topology scheme is calculated using the following formula:

[0018] in, , , These are weight vectors for cost, volume, and lifespan, respectively. right Sort in descending order and filter out The highest-level cluster arrangement is taken as the optimal topology. If it exists For identical and highest-ranking proposals of the same level, the following arbitration rules shall be used for selection: If the optimization of supercapacitor energy storage systems prioritizes cost, the cost difference ratio B1 of comparable solutions is calculated using the following formula: , In the formula, , The total system costs for the two equivalent solutions are respectively. The smaller of the total system cost of the two equivalent solutions; Determine if B1 ≥ 10%. If so, select the scheme with the lower total system cost as the optimal topology. If not, select the scheme with the higher total system cost as the optimal topology. If the optimization preference for supercapacitor energy storage systems is volume-first, the volume difference ratio B2 of the same-level schemes can be calculated using the following formula:

[0019] In the formula, , These represent the actual system volume occupied by the two equivalent schemes. The smaller of the actual occupied volumes of the two equivalent schemes; Determine if B2 ≥ 15%. If so, select the optimal topology scheme from the same level with the smaller actual volume occupied. If not, select the optimal topology scheme from the same level with the larger actual volume occupied. If the optimization preference for supercapacitor energy storage systems is lifespan priority, the lifespan difference ratio B3 of comparable solutions can be calculated using the following formula:

[0020] In the formula, , The predicted system cycle lifetimes for two equivalent schemes are shown below. The smaller of the predicted system cycle lifetimes for the two equivalent schemes; Determine whether B3 ≥ 20%. If so, select the peer scheme with the longer predicted system cycle life as the optimal topology scheme; otherwise, select the peer scheme with the shorter predicted system cycle life as the optimal topology scheme.

[0021] In an optional implementation, generating the full-process flowchart, 3D layout diagram, and structured parameter report corresponding to the optimal topology scheme includes: Based on the selection process of the optimal topology scheme, corresponding input preprocessing flowchart, four-layer topology decomposition flowchart, two-domain coupling verification flowchart, and multi-objective optimization and output flowchart are generated, and key parameters and judgment conditions are marked in each flowchart. A system layout diagram and a cluster / pack layout diagram are generated based on the optimal topology scheme; the system layout diagram shows the arrangement of clusters within the cabin in three dimensions, and indicates the number of clusters in the x, y, and z axes. The cluster / PACK layout diagram shows the arrangement of PACKs within clusters and cells within PACKs, indicating the number of cells arranged in the x, y, and z axes of the PACK. , , and the dimensions of the heat dissipation gap; A structured parameter report is generated based on the optimal topology scheme. The structured parameter report includes: A four-level parameter summary table is used to summarize all key parameters at the system level, cluster level, pack level, and cell level. The verification result table is used to record key data of the dual-domain coupling verification, including electrical performance fluctuation amplitude, hot spot temperature, capacity decay rate, internal resistance rise rate, and predicted lifetime. Decision summary, used to record cost normalization scores Volume normalization score Lifespan normalized score The corresponding weight vector, comprehensive score, and arbitration results at the same level.

[0022] Secondly, embodiments of this application also provide an automated design system for a supercapacitor energy storage system, comprising: The input preprocessing module is used to standardize, define, screen, prioritize, and verify the input data for the design of supercapacitor energy storage systems, and output a list of feasible combinations of cabin and cells. The four-level topology decomposition module is used to perform topology decomposition in the order of system level, cluster level, pack level and cell level based on the feasible cabin and cell combination list, and output a structured topology scheme document. The dual-domain coupling verification module is used to establish a closed-loop feedback simulation model of the electric and thermal domains, and to verify the electrical performance, thermal safety and lifespan of the topology schemes in the topology scheme document, and to obtain a list of qualified topology schemes. The cluster-level reconfigurable arrangement module is used to perform cluster block translation and rotation operations on the topology schemes in the qualified topology scheme list under the constraints of cabin space, and output the optimized cluster-level arrangement scheme. The multi-objective rapid optimization module is used to perform multi-objective quantitative scoring and peer arbitration on the topology scheme corresponding to the optimized cluster-level arrangement scheme, and to select the optimal topology scheme. The visualization output module is used to generate a full-process flowchart, a 3D layout diagram, and a structured parameter report corresponding to the optimal topology scheme.

[0023] Thirdly, embodiments of this application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the automated design method for a supercapacitor energy storage system as described in any of the above.

[0024] Fourthly, embodiments of this application also provide a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the automated design method for a supercapacitor energy storage system as described in any of the above claims.

[0025] As can be seen from the above technical solutions, the present invention has the following advantages: The automated design method for supercapacitor energy storage systems provided in this application standardizes and defines input data, combines quantitative screening of cycle life, voltage matching, and temperature adaptability with verification of cabin and cell space and load-bearing capacity, and incorporates a comprehensive cell priority scoring and cabin and cell combination scoring mechanism. This replaces the traditional manual trial-and-error mode, enabling rapid and accurate adaptation to multiple cell models and diverse cabins, significantly shortening the design cycle. A four-level topology decomposition (system-level, cluster-level, pack-level, and cell-level) clarifies the correlation between electrical and spatial parameters at each level, and optimizes the translation and rotation of clusters under cabin space constraints, simultaneously ensuring… The rationality of electrical performance and physical layout effectively solves the problem of traditional designs neglecting physical constraints such as cabin size and heat dissipation channels. Relying on the electrical and thermal closed-loop feedback simulation model, it realizes triple verification of electrical performance, thermal safety and lifespan. At the same time, through normalized quantitative scoring of cost, volume and lifespan and biased peer arbitration, it efficiently selects the optimal solution, avoiding the drawback of the explosive amount of computation of enumeration algorithm. It not only reduces the consumption of computing resources, but also completes the comparison of multiple solutions in a short time. Ultimately, it ensures that the designed supercapacitor energy storage system has high efficiency, stability, safety and economy, and meets the design requirements of different scenarios.

[0026] This application, through standardized definition of input data, combined with quantitative screening of cycle life, voltage matching, and temperature adaptability, as well as space and load-bearing verification of the battery compartment and cells, along with a comprehensive priority scoring mechanism for cells and a scoring mechanism for the combination of battery compartment and cells, replaces the traditional manual trial-and-error mode. It can quickly and accurately adapt to multiple models of cells and various battery compartments, significantly shorten the design cycle, and avoid the inefficiency caused by human experience bias.

[0027] This application utilizes a four-level topology decomposition at the system, cluster, pack, and cell levels to clarify the logical relationship between electrical and spatial parameters at each level. Simultaneously, by combining the translation and rotation optimization of cluster blocks under cabin space constraints, it can ensure that electrical performance meets the standards while also taking into account physical requirements such as cabin size limitations and heat dissipation channel planning. This achieves coordinated optimization of physical layout and electrical performance, solving the shortcomings of traditional designs that neglect physical constraints.

[0028] This application establishes an electrical and thermal closed-loop feedback simulation model to perform triple verification of the topology scheme in terms of electrical performance, thermal safety, and lifespan. Thermal safety is ensured by controlling the hot spot temperature to not exceed 65℃, and lifespan is guaranteed by ensuring the cycle life is not lower than the design value. Simultaneously, by recalculating the electrical performance through temperature-corrected cell parameters, problems such as thermal runaway, capacity decay, and increased internal resistance are effectively avoided, significantly improving the stability and reliability of the system operation.

[0029] This application addresses the drawback of the explosive computational cost of traditional enumeration algorithms by using normalized quantitative scoring of cost, size, and lifetime, combined with a biased arbitration rule for similar solutions, to quickly select the optimal solution. It eliminates the need for indiscriminate iterative calculations on all solutions, reducing wasted computational resources and enabling rapid comparison of multiple solutions, thus improving design optimization efficiency.

[0030] The design process of this application comprehensively considers multiple dimensions such as cost economy, volume space utilization, and lifespan durability. Through dynamic weight allocation and comprehensive scoring mechanism, the priority of objectives can be adjusted according to the optimization tendencies of different scenarios, and finally outputs a solution that takes into account efficiency, economy and security, and flexibly adapts to the needs of diverse application scenarios. Attached Figure Description

[0031] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 A flowchart illustrating the automated design method for the supercapacitor energy storage system provided in this application.

[0033] Figure 2 This is a flowchart illustrating the input preprocessing method provided in this application.

[0034] Figure 3 This is a flowchart illustrating the four-layer topology decomposition method provided in this application.

[0035] Figure 4 A schematic diagram of the structure of the automated design system for the supercapacitor energy storage system provided in this application.

[0036] Figure 5 A schematic diagram of the structure of the electronic device provided in this application. Detailed Implementation

[0037] The various embodiments of this disclosure will be described more fully in the detailed steps of the automated design method for supercapacitor energy storage systems described below. This disclosure may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of this disclosure to the specific embodiments disclosed herein, but rather this disclosure should be understood to cover all adjustments, equivalents, and / or alternatives falling within the spirit and scope of the various embodiments of this disclosure.

[0038] In the following, the terms “comprising” or “may include”, which may be used in various embodiments of this disclosure, indicate the presence of the disclosed functions, operations, or elements, and do not limit the addition of one or more functions, operations, or elements. Furthermore, as used in various embodiments of this disclosure, the terms “comprising,” “having,” and their cognates are intended only to indicate a particular feature, number, step, operation, element, component, or combination of the foregoing, and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing, or the possibility of adding one or more combinations of the foregoing.

[0039] 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. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] Please see Figure 1 The diagram shows a flowchart of an automated design method for a supercapacitor energy storage system in a specific embodiment. The method includes: S1: Standardize the input data for the design of supercapacitor energy storage systems, perform feasibility screening, priority ranking, and combination verification, and output a list of feasible cabin and cell combinations.

[0041] In a specific implementation, firstly, a cell library containing the full lifecycle parameters of supercapacitor cells and a cabin library defining the physical boundaries and constraints of standard containers are constructed, clarifying system requirements such as minimum / maximum operating voltage and rated power. Next, the cell library is traversed, and cells are initially screened based on cycle life, voltage matching, and temperature adaptability. The initially screened cells are then ranked and scored according to their specific energy, unit cost, and cycle life. Then, based on the cell list, the cabin library is traversed to determine parameters such as the actual usable layout width of the cabin and the total energy required by the system. Cabin and cell combinations that meet the cabin conditions in terms of volume and weight are selected, and their comprehensive scores are calculated. A list of feasible cabin and cell combinations is then output in descending order.

[0042] S2: Based on the list of feasible cabin and cell combinations, perform a step-by-step topology decomposition in the order of system level, cluster level, pack level, and cell level, and output a structured topology scheme document.

[0043] In a specific implementation, based on a feasible list of cabin and cell combinations and system requirements, a system-level topology decomposition is first performed. The total number of clusters is calculated using the total required energy of the system and the rated energy of a single cluster. The number of clusters arranged along each axis of the cabin is then determined by considering the cabin and cluster volumes. Next, cluster-level topology decomposition is performed. The number of packs required per cluster is calculated based on the rated voltage of the cluster and a single pack. The arrangement of packs within the cluster is determined by considering the cluster and pack volumes. Then, pack-level topology decomposition is performed. The number of cells connected in series and parallel, and the total number of cells per pack, are calculated based on the rated voltage and capacity of the packs and cells. Finally, cell-level topology decomposition is performed. The number of cells arranged along each axis of the pack is determined based on the pack and cell volumes, with heat dissipation gaps reserved. The final output is a structured topology scheme document containing electrical connection and spatial arrangement parameters for each level.

[0044] S3: Establish a closed-loop feedback simulation model for the electric and thermal domains, verify the electrical performance, thermal safety, and lifespan of the topology schemes in the topology scheme document, and obtain a list of qualified topology schemes.

[0045] In specific implementations, the topology scheme and cell thermal and electrical characteristic parameters are obtained from the topology scheme document. Based on the system's rated power and total voltage, the loop current is calculated and allocated to each level. The power loss of a single cell is calculated using formulas, and the output voltage during discharge is verified to be within a specified range to assess electrical performance. The cell power loss is used as a heat source in a thermal simulation model to calculate the temperature field distribution, and the cell hotspot temperature is monitored to assess thermal safety. Based on the thermal simulation temperature data, the cell's equivalent internal resistance and capacity are corrected. The corrected parameters are then reloaded for electrical domain simulation to verify electrical performance. If the requirements are met, the system cycle life is predicted using a life decay model to determine if the design cycle life is met. Schemes that meet the standards in terms of electrical performance, thermal safety, and lifespan are retained to form a list of qualified topology schemes.

[0046] S4: Under the constraints of cabin space, perform cluster block translation and rotation operations on the topology schemes in the qualified topology scheme list, and output the optimized cluster-level arrangement scheme.

[0047] In a specific implementation, information on the effective volume boundary, maintenance channels, and fixed structures of the cabin is extracted from the cabin library to identify areas where clusters cannot be arranged. An initial arrangement scheme is generated based on the number of clusters arranged along each axis of the cabin. Within the effective volume of the cabin, clusters are translated along each axis to avoid overlapping with areas that cannot be arranged and to maximize space utilization. For regular-shaped clusters, 90° or 180° rotation is performed when space permits to optimize the gap distribution and ensure unobstructed heat dissipation channels. The adjusted arrangement is verified to ensure that it meets the space constraints and heat dissipation requirements. If it passes, the optimized cluster-level arrangement scheme is output.

[0048] S5: Perform multi-objective quantitative scoring and peer arbitration on the topology scheme corresponding to the optimized cluster-level arrangement scheme to select the optimal topology scheme.

[0049] In a specific implementation, material cost, volumetric power density, and cycle life data are obtained based on the optimized cluster-level arrangement scheme and normalized to obtain normalized scores for cost, volume, and lifespan. A specific formula is used to calculate the comprehensive score of the topology schemes, and the highest-scoring schemes are selected in descending order. If there are schemes with the same highest score, the percentage difference in cost, volume, or lifespan among schemes of the same level is calculated based on the optimization preference for cost, volume, or lifespan in the supercapacitor energy storage system, and the optimal topology scheme is selected based on a set threshold.

[0050] S6: Generate the full process flowchart, 3D layout diagram and structured parameter report corresponding to the optimal topology scheme.

[0051] In a specific implementation, a flowchart is generated based on the optimal topology scheme selection process, including input preprocessing, four-layer topology decomposition, dual-domain coupling verification, multi-objective optimization, and output, with key parameters and decision conditions annotated. Simultaneously, a system layout diagram is generated showing the three-dimensional arrangement of clusters within the cabin and the number of clusters in each axial direction. A cluster / PACK layout diagram shows the arrangement of PACKs within clusters, the arrangement of cells within PACKs, and the dimensions of heat dissipation gaps. Structured parameter reports are also generated, including a four-level parameter summary table summarizing key parameters at each level, a verification result table recording key data from the dual-domain coupling verification, and a decision summary recording scores, weight vectors, and arbitration results.

[0052] In this embodiment, by standardizing, filtering, sorting, and verifying the input data, feasible combinations of cabin and battery cells can be quickly determined, improving the accuracy and efficiency of the initial design phase. Next, a hierarchical topology decomposition is performed in the order of system, cluster, pack, and battery cell, outputting a structured topology solution document to provide clear architectural guidance for system construction. Multi-faceted verification is conducted using closed-loop feedback simulation models in the electrical and thermal domains to ensure that the topology solution meets standards in terms of electrical performance, thermal safety, and lifespan, improving system stability and reliability. Under cabin space constraints, cluster block translation and rotation operations are performed on qualified solutions to optimize cluster-level arrangement and improve space utilization and heat dissipation. The optimal topology solution is selected through multi-objective quantitative scoring and peer arbitration, comprehensively considering factors such as cost, volume, and lifespan to meet different design priorities. Finally, a full-process flowchart, a 3D layout diagram, and structured parameter reports are generated, providing comprehensive and intuitive information for system implementation, maintenance, and optimization. Overall, this method automates and scientizes the entire process from design to verification, optimization, and output, effectively improving the design quality and efficiency of supercapacitor energy storage systems, reducing costs, and enhancing the overall performance of the system.

[0053] In one embodiment of the present invention, based on step S1, the following will provide a possible embodiment and describe its specific implementation in a non-limiting manner.

[0054] refer to Figure 2 As shown, this embodiment discloses an input preprocessing method, which specifically includes the following steps: S101: Build the database and define system requirements.

[0055] First, a cell library is constructed, recording detailed parameters of the supercapacitor cells throughout their entire lifecycle, such as rated voltage, specific energy, unit cost, and cycle life. Next, a container library is established, defining the physical boundaries and constraints of standard containers, including the total net width of the container, internal space volume, maximum load-bearing capacity, and maintenance access width. Simultaneously, the system requirements that a single design task must meet are clearly defined, including the minimum operating voltage. Maximum operating voltage of the system System rated power System continuous discharge time Design cycle life and ambient temperature range.

[0056] S102: Initial screening of battery cells.

[0057] First, iterate through the cell database and check whether the cycle life of each cell model meets the requirements. κ is the safety factor, used to reserve redundancy for the cycle life of the battery cell. Battery cell models that do not meet this condition are eliminated.

[0058] For each cell, the formula is used. and Calculate the reasonable range for the number of battery cells connected in series. The minimum number of cells connected in series to meet the minimum operating voltage requirement of the system. The maximum number of battery cells connected in series to meet the system's maximum operating voltage requirement. If > This indicates that the battery cell cannot meet the system voltage requirements through series connection, and therefore it is rejected.

[0059] Verify whether the operating temperature range of the battery cell fully covers the system ambient temperature range. If not, reject the battery cell.

[0060] S103: Comprehensive priority scoring and ranking of battery cells.

[0061] For cells that pass the initial screening, their specific energy (BN), unit cost (CB), and cycle life (XL) are obtained from the cell library.

[0062] Formula used: Calculate the overall priority score for each cell, where Norm() is the normalization function, and w1, w2, and w3 are the weighting coefficients for specific energy, 1 / unit cost, and cycle life, respectively. These weighting coefficients are pre-set according to actual design requirements and priorities.

[0063] Finally, press Sort the battery cells in descending order to obtain a list of battery cells.

[0064] S104: Screening and scoring of the combination of cabin and battery cells.

[0065] First, based on the cell list, the cell library is traversed. The actual usable layout width of the cell is determined by subtracting the maintenance passage width from the total net width of the cell. This is done using a formula... Calculate the total energy requirement of the system. Based on the rated capacity and voltage of the battery cells, calculate the maximum energy storage capacity of a single cell, and then... Divide by the maximum energy storage of a single cell to determine the minimum number of cells. Determine the effective volume of the cabin based on the available space inside the cabin for arranging the cells. Multiply the minimum number of cells by the volume of a single cell to calculate the required volume.

[0066] Then, combinations of cabins and battery cells with a required volume less than or equal to the effective volume of the cabin and a total battery cell weight less than or equal to the upper limit of the cabin's load-bearing capacity are selected.

[0067] At this point, for the selected combinations, the formula is used... Calculate its overall score, where PC is the cabin cost and RV is the cabin volume utilization rate.

[0068] Finally, sort the results in descending order of overall scores and output a list of feasible cabin and battery cell combinations.

[0069] In one embodiment of the present invention, based on step S2, the following will provide a possible embodiment and describe its specific implementation in a non-limiting manner.

[0070] refer to Figure 3 As shown, this embodiment discloses a four-layer topology decomposition method, which specifically includes the following steps: S201: System-level topology decomposition.

[0071] Based on the list of feasible cabin and battery cell combinations and system requirements, the total energy required for the system is obtained. Rated energy of a single cluster .

[0072] Using formula Calculate the total number of clusters in the system.

[0073] Based on the effective volume of the corresponding cabin and the volume of a single cluster, determine the number of clusters to be arranged along the x, y, and z axes of the cabin. It is necessary to ensure This allows us to determine the system-level electrical connection parameters and spatial layout parameters.

[0074] S202: Cluster-level topological decomposition.

[0075] The cluster rated voltage is obtained based on the system-level electrical connection parameters and spatial layout parameters. With the rated voltage of a single pack .

[0076] Using formula Calculate the number of PACKs required for a single cluster.

[0077] By combining the internal space dimensions of the cluster with the volume of a single PACK, the arrangement of the PACKs within the cluster is determined, and the cluster-level electrical connection parameters and spatial arrangement parameters are obtained.

[0078] S203: Pack-level topology decomposition.

[0079] The PACK rated voltage is obtained based on cluster-level electrical connection parameters and spatial arrangement parameters. With the rated voltage of the battery cell .

[0080] Using formula Calculate the number of cells connected in series; obtain the rated capacity of the pack. With cell capacity Using the formula Calculate the number of cells connected in parallel.

[0081] Through formula Calculate the total number of cells in a single PACK and determine the PACK-level electrical connection parameters and spatial arrangement parameters.

[0082] S204: Cell-level topology decomposition.

[0083] Based on the pack-level electrical connection parameters and spatial arrangement parameters, and according to the internal space dimensions of the pack and the volume of a single cell, determine the number of cells to be arranged in the x, y, and z axes of the pack. , , , must meet .

[0084] When arranging the battery cells, leave a heat dissipation gap to ensure that the heat generated by the battery cells during operation can be effectively dissipated.

[0085] S205: Generate topology scheme document.

[0086] The output includes a structured topology document containing electrical connection parameters and spatial arrangement parameters at the system, cluster, pack, and cell levels. This document records the specific parameters and arrangement methods of each level in detail, providing clear guidance for subsequent design.

[0087] In one embodiment of the present invention, based on step S3, the following will provide a possible embodiment and describe its specific implementation in a non-limiting manner.

[0088] S301: Get parameters.

[0089] Extract scheme parameters from the structured topology scheme document, and obtain the cell thermal characteristic parameters and electrical characteristic parameters, including the cell equivalent internal resistance (ESR).

[0090] S302: Electrical performance verification.

[0091] According to the system rated power The total current of the circuit is calculated in conjunction with the total voltage of the system, and the operating current of each cell, PACK, and cluster is accurately allocated based on the series and parallel structure of the cells.

[0092] Through formula Calculate the power loss of a single battery cell, where This represents the operating current of a single battery cell.

[0093] Whether the output voltage of the verification system remains at the specified level throughout the entire discharge process. If the value exceeds the specified range, the electrical performance of the topology scheme is marked as substandard.

[0094] S303: Thermal safety check.

[0095] The power loss of each cell obtained from the electric domain simulation As a heat source, it is applied to the corresponding cell location in the thermal simulation model.

[0096] Based on the theories of heat conduction, convection, and radiation, the temperature field distribution of the system during the discharge process is calculated in detail, and the hot spot temperature of the battery cell is monitored in real time.

[0097] Verify that the hot spot temperature of the battery cell is less than or equal to 65℃. If this condition is not met, mark the topology scheme as having substandard thermal safety.

[0098] S304: Parameter Correction and Lifetime Verification.

[0099] Based on the temperature data obtained from thermal simulation, the equivalent internal resistance (ESR) and capacity of the battery cell are compared. Corrections are required. Specifically, the capacity decay rate must meet the following requirements. In the formula This refers to the capacity decay of the battery cell under the influence of operating temperature. , This is the initial capacity of the battery cell. The actual capacity of the battery cell after temperature effects; the rate of increase in internal resistance must meet the following requirements. In the formula This represents the increase in the internal resistance of the battery cell under the influence of operating temperature. , This represents the actual equivalent internal resistance of the battery cell after temperature effects. This represents the initial equivalent internal resistance of the battery cell.

[0100] Using the corrected equivalent internal resistance (ESR) and capacitance The battery cell is reloaded to the corresponding cell location in the thermal simulation model, and the electrical domain simulation is performed again to verify whether the electrical performance still meets the requirements.

[0101] If so, based on the corrected relevant parameters and combined with the preset cell life decay model, the system cycle life is predicted.

[0102] Determine whether the predicted system cycle life meets the requirement of being greater than or equal to the design cycle life. If it does not meet the requirement, mark the topology scheme as having a lifespan that does not meet the standard.

[0103] S305: Select qualified solutions.

[0104] Topology schemes that meet the standards in terms of electrical performance, thermal safety, and lifespan are retained to form a list of qualified topology schemes, providing a basis for subsequent optimization.

[0105] In one embodiment of the present invention, based on step S4, the following will provide a possible embodiment and describe its specific implementation in a non-limiting manner.

[0106] S401: Specify the constraints.

[0107] Extract the effective volume boundary of the cabin, the location and width of the maintenance passage, and the location and dimensions of the fixed structure inside the cabin from the cabin library parameters to clarify the areas where cluster blocks cannot be arranged.

[0108] S402: Generate the initial layout scheme.

[0109] Based on the number of clusters arranged along the x, y, and z axes of the cabin. Generate the initial arrangement scheme of cluster blocks.

[0110] S403: Translation and rotation operations.

[0111] Within the effective volume of the cabin, the cluster blocks are translated along the x, y, and z axes to ensure that the cluster blocks do not overlap with areas that cannot be arranged, while maximizing the use of cabin space.

[0112] For regularly shaped clusters, rotate them 90° or 180° if space permits to optimize the gap distribution between clusters and ensure uniform and unobstructed heat dissipation channels.

[0113] S404: Verification and Output.

[0114] Verify whether the adjusted cluster arrangement meets all spatial constraints and whether the gaps between clusters meet heat dissipation requirements.

[0115] If the verification passes, an optimized cluster-level arrangement scheme is generated and output, providing a better basic scheme for subsequent multi-objective quantitative scoring.

[0116] In one embodiment of the present invention, based on step S5, a possible embodiment will be given below, and its specific implementation will be described in a non-limiting manner.

[0117] S501: Data acquisition and normalization processing.

[0118] Based on the optimized cluster-level arrangement scheme, its material cost data, volumetric power density data, and cycle life data were determined.

[0119] Material cost data, volumetric power density data, and cycle life data were normalized using the normalization function Norm() to generate a cost normalization score. Volume normalization score and lifespan normalized score .

[0120] S502: Calculation of comprehensive score.

[0121] Using formula The overall score of the topology scheme is calculated, where , , These are weight vectors for cost, volume, and lifespan, which are pre-set according to the design goals of the supercapacitor energy storage system.

[0122] S503: Preliminary screening.

[0123] right Sort in descending order and filter out The highest cluster-level arrangement scheme is used as the initial optimal topology scheme.

[0124] If there are identical highest-scoring schemes under S504, arbitration at the same level shall be conducted.

[0125] Cost Priority: If the optimization of supercapacitor energy storage systems prioritizes cost, then the formula... Calculate the percentage of cost difference among similar options, where , The total system costs for the two equivalent solutions are respectively. Let B1 be the smaller of the total system costs of the two equivalent solutions. Determine if B1 ≥ 10%. If yes, select the equivalent solution with the lower total system cost as the optimal topology; otherwise, select the equivalent solution with the higher total system cost as the optimal topology.

[0126] Volume Priority: If the optimization preference is volume priority, then by formula Calculate the proportion of volume difference among schemes of the same level, where , These represent the actual system volume occupied by the two equivalent schemes. Let B2 be the smaller of the actual occupied volumes of the two equivalent schemes. Determine if B2 ≥ 15%. If yes, the equivalent scheme with the smaller actual occupied volume is selected as the optimal topology scheme; otherwise, the equivalent scheme with the larger actual occupied volume is selected as the optimal topology scheme.

[0127] Lifetime Priority: If the optimization bias is lifetime priority, then by formula... Calculate the percentage of lifetime difference among similar schemes, where... , The predicted system cycle lifetimes for two equivalent schemes are shown below. Let B3 be the smaller of the predicted system cycle lifetimes of the two equivalent schemes. Determine if B3 ≥ 20%. If yes, the equivalent scheme with the longer predicted system cycle lifetime is selected as the optimal topology; otherwise, the equivalent scheme with the shorter predicted system cycle lifetime is selected as the optimal topology.

[0128] In one embodiment of the present invention, based on step S6, a possible embodiment will be given below, and its specific implementation will be described in a non-limiting manner.

[0129] First, based on the selection process of the optimal topology scheme, corresponding input preprocessing flowchart, four-layer topology decomposition flowchart, two-domain coupling verification flowchart, and multi-objective optimization and output flowchart are generated. Key parameters and judgment conditions are marked in each flowchart to clearly show the logic and key decision points of the entire design process.

[0130] Then, a system layout diagram and a cluster / pack layout diagram are generated based on the optimal topology scheme; the system layout diagram shows the arrangement of clusters in the cabin in three dimensions, and marks the number of clusters in the x, y, and z axes. The cluster / PACK layout diagram shows the arrangement of PACKs within clusters and cells within PACKs, indicating the number of cells arranged in the x, y, and z axes of the PACK. , , The dimensions of the heat dissipation gaps visually present the internal structural layout of the system.

[0131] Simultaneously, a structured parameter report is generated based on the optimal topology scheme. This structured parameter report includes: The four-level parameter summary table is used to summarize all key parameters at the system level, cluster level, pack level, and cell level, including electrical connection parameters (such as voltage, current, capacity, etc.) and spatial arrangement parameters (such as the quantity and size of each level in different directions, etc.), which facilitates overall viewing and comparison of parameters at each level.

[0132] The verification result table records key data for dual-domain coupling verification, including electrical performance fluctuation amplitude, hot spot temperature, capacity decay rate, internal resistance rise rate, and predicted lifetime; this data can be used to evaluate the performance and reliability of the design scheme.

[0133] Decision summary, used to record cost normalization scores Volume normalization score Lifespan normalized score The decision summary includes the corresponding weight vector, comprehensive score, and arbitration results at the same level. It summarizes the key decision-making criteria and final results during the design process.

[0134] like Figure 4 As shown, the following are embodiments of the automated design system for supercapacitor energy storage systems provided in this disclosure. This system and the automated design method for supercapacitor energy storage systems in the above embodiments belong to the same inventive concept. For details not described in detail in the embodiments of the automated design system for supercapacitor energy storage systems, please refer to the embodiments of the automated design method for supercapacitor energy storage systems described above.

[0135] An automated design system for a supercapacitor energy storage system includes: The input preprocessing module is used to standardize, define, screen, prioritize, and verify the input data for the design of supercapacitor energy storage systems, and output a list of feasible combinations of cabin and cells. The four-level topology decomposition module is used to perform topology decomposition in the order of system level, cluster level, pack level and cell level based on the feasible cabin and cell combination list, and output a structured topology scheme document. The dual-domain coupling verification module is used to establish a closed-loop feedback simulation model of the electric and thermal domains, and to verify the electrical performance, thermal safety and lifespan of the topology schemes in the topology scheme document, and to obtain a list of qualified topology schemes. The cluster-level reconfigurable arrangement module is used to perform cluster block translation and rotation operations on the topology schemes in the qualified topology scheme list under the constraints of cabin space, and output the optimized cluster-level arrangement scheme. The multi-objective rapid optimization module is used to perform multi-objective quantitative scoring and peer arbitration on the topology scheme corresponding to the optimized cluster-level arrangement scheme, and to select the optimal topology scheme. The visualization output module is used to generate a full-process flowchart, a 3D layout diagram, and a structured parameter report corresponding to the optimal topology scheme.

[0136] The automated design system for supercapacitor energy storage systems provided in this embodiment, through standardized definition of input data and quantitative screening of cycle life, voltage matching, and temperature adaptability, combined with space and load-bearing verification of the cabin and cells and a comprehensive scoring mechanism, can quickly and accurately adapt to multiple cell models and diverse cabins, significantly shortening the design cycle. It relies on a four-level topology decomposition at the system, cluster, pack, and cell levels to clarify the correlation of parameters at each level, and combines the optimization of cluster translation and rotation under cabin space constraints to achieve synergistic unity of physical layout and electrical performance. It uses an electro-thermal closed-loop feedback simulation model to conduct triple verification of electrical performance, thermal safety, and lifespan, effectively ensuring the stability and reliability of system operation. Through normalized quantitative scoring of cost, volume, and lifespan, and biased arbitration at the same level, it avoids the problem of computational explosion in enumeration algorithms, reduces computational resource consumption, and efficiently completes multi-scheme comparisons. Simultaneously, it can adjust the priority of targets according to different optimization tendencies, ultimately achieving a comprehensive improvement in the efficiency, safety, economy, and scenario adaptability of the supercapacitor energy storage system.

[0137] Figure 5 A schematic diagram of the hardware structure of an electronic device for implementing various embodiments of the present invention.

[0138] The automated design method for supercapacitor energy storage systems provided in this application can be applied to electronic devices. Those skilled in the art will understand that the electronic device structures involved in the embodiments of this invention do not constitute a limitation on the electronic device. An electronic device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. In the embodiments of this invention, electronic devices include, but are not limited to, laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of this application described and / or claimed herein.

[0139] Electronic devices may include processors, external memory interfaces, internal memory, universal serial bus (USB) interfaces, charging management modules, power management modules, batteries, wireless communication modules, audio modules, speakers, microphones, sensor modules, buttons, cameras, displays, and SIM card interfaces, etc.

[0140] A processor may include one or more processing units, such as: a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU). Different processing units may be independent devices or integrated into one or more processors.

[0141] The processor can serve as the nerve center and command center of an electronic device. The controller can generate operation control signals based on the instruction opcode and timing signals to control the fetching and execution of instructions.

[0142] The processor may also include memory for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. This memory can store instructions or data that the processor has just used or that are used repeatedly. If the processor needs to use the instruction or data again, it can retrieve it directly from this memory. This avoids repeated accesses, reduces processor latency, and thus improves system efficiency.

[0143] An external storage interface (ESI) can be used to connect external memory cards, such as microSD cards, to expand the storage capacity of electronic devices. The external memory card communicates with the processor through the ESI to perform data storage functions, such as saving music and video files on the external memory card.

[0144] Internal memory can be used to store computer executable program code, which includes instructions. The processor executes various functional applications and data processing of electronic devices by running the instructions stored in internal memory. Internal memory can include a program storage area and a data storage area. Internal memory can include high-speed random access memory, and can also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc.

[0145] Wireless communication functionality in electronic devices can be achieved through antennas, wireless communication modules, modem processors, and baseband processors.

[0146] Wireless communication modules can provide solutions for wireless communication applications in electronic devices, including wireless local area networks (WLANs) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), and infrared (IR) technologies.

[0147] Electronic devices can implement audio functions through audio modules, speakers, receivers, microphones, headphone jacks, and application processors.

[0148] Electronic devices can achieve shooting functions through ISPs, cameras, video codecs, GPUs, displays, and application processors.

[0149] Electronic devices can achieve display functions through GPUs, displays, and application processors.

[0150] A GPU is a microprocessor for image processing, connected to the display screen and application processor. GPUs are used to perform mathematical and geometric calculations for graphics rendering. A processor may include one or more GPUs, which execute program instructions to generate or modify display information.

[0151] A display screen is used to display images, videos, etc. A display screen includes a display panel.

[0152] The aforementioned electronic equipment realizes the automated design method of the supercapacitor energy storage system of this application through standardized definition and quantitative screening of input data, four-level topology decomposition, electro-thermal closed-loop feedback simulation verification, cluster block translation and rotation optimization under cabin space constraints, and multi-objective normalized scoring and bias arbitration. It achieves the beneficial effects of rapid adaptation of multiple types of cells and diverse cabins in the supercapacitor energy storage system, coordinated optimization of physical layout and electrical performance, safe and reliable system operation, efficient utilization of computing resources, and flexible adaptation to multiple scenarios.

[0153] The storage medium provided in this application stores a program product capable of realizing an automated design method for a supercapacitor energy storage system.

[0154] The automated design methods for supercapacitor energy storage systems include: The input data for the design of supercapacitor energy storage system is standardized, defined, screened for feasibility, prioritized and verified for combination, and a list of feasible cabin and cell combinations is output. Based on the list of feasible cabin and cell combinations, a hierarchical topology decomposition is performed in the order of system level, cluster level, pack level, and cell level, and a structured topology scheme document is output. A closed-loop feedback simulation model of the electric and thermal domains is established to verify the electrical performance, thermal safety and lifespan of the topology schemes in the topology scheme document, and a list of qualified topology schemes is obtained by screening. Under the constraints of cabin space, perform cluster block translation and rotation operations on the topology schemes in the qualified topology scheme list, and output the optimized cluster-level arrangement scheme. The optimized cluster-level arrangement scheme is subjected to multi-objective quantitative scoring and peer arbitration to select the optimal topology scheme. Generate the full process flowchart, 3D layout diagram and structured parameter report corresponding to the optimal topology scheme.

[0155] In some possible implementations, the automated design method for supercapacitor energy storage systems disclosed herein can be implemented as a program product comprising program code that, when run on a terminal device, causes the terminal device to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure.

[0156] The storage medium disclosed herein may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may 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 (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0157] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An automated design method for a supercapacitor energy storage system, characterized in that, include: The input data for the design of supercapacitor energy storage system is standardized, defined, screened for feasibility, prioritized and verified for combination, and a list of feasible cabin and cell combinations is output. Based on the list of feasible cabin and cell combinations, a hierarchical topology decomposition is performed in the order of system level, cluster level, pack level, and cell level, and a structured topology scheme document is output. A closed-loop feedback simulation model of the electric and thermal domains is established to verify the electrical performance, thermal safety and lifespan of the topology schemes in the topology scheme document, and a list of qualified topology schemes is obtained by screening. Under the constraints of cabin space, perform cluster block translation and rotation operations on the topology schemes in the qualified topology scheme list, and output the optimized cluster-level arrangement scheme. The optimized cluster-level arrangement scheme is subjected to multi-objective quantitative scoring and peer arbitration to select the optimal topology scheme. Generate the full process flowchart, 3D layout diagram and structured parameter report corresponding to the optimal topology scheme.

2. The automated design method for a supercapacitor energy storage system according to claim 1, characterized in that, The input data for the design of the supercapacitor energy storage system is standardized, defined, screened for feasibility, prioritized, and validated for combination. The resulting list of feasible cabin and cell combinations is output, including: Construct a cell library containing parameters for the entire lifecycle of supercapacitor cells, define a cabin library for standard container physical boundaries and constraints, and determine the system requirements that a single design task must meet; these system requirements include the minimum operating voltage of the system. Maximum operating voltage of the system System rated power System continuous discharge time Design cycle life and ambient temperature range; The cell library is traversed, and cell models that do not meet the basic requirements are eliminated through cycle life filtering, voltage matching filtering, and temperature adaptation filtering to determine the cells that pass the initial screening. Among these, the cycle life filtering must meet the following requirements: In the formula, κ is the safety factor, which is used to reserve redundancy for the cycle life of the battery cell; When performing voltage matching filtering, the reasonable range for the number of cells connected in series is calculated using the following formula: In the formula, ceil() is the floor function, and floor() is the floor function. The rated voltage of a single supercapacitor cell. The minimum number of cells connected in series to meet the minimum operating voltage requirement of the system. The maximum number of cells connected in series to meet the system's maximum operating voltage requirement; if > If the cell cannot meet the system voltage requirements through series connection, it should be eliminated. During temperature adaptation filtering, it is verified whether the operating temperature range of the battery cell completely covers the system ambient temperature range; if not, the battery cell is rejected. Based on the cells that pass the initial screening, the specific energy, unit cost, and cycle life of the corresponding cells are obtained from the cell library. How to calculate the overall priority score for each battery cell that passes the initial screening using the following formula: In the formula, The overall priority score for the battery cell is determined by Norm(), which is the normalization function, BN is the specific energy, CB is the unit cost, XL is the cycle life, and w1, w2, and w3 are the weighting coefficients for specific energy, 1 / unit cost, and cycle life, respectively. according to Sort the battery cells in descending order to obtain a list of battery cells; Based on the battery cell list, the cabin library is traversed, and the actual usable layout width of the cabin is determined by subtracting the maintenance passage width from the total net width of the cabin; this is then calculated using the formula... Calculate the total energy required by the system; calculate the maximum energy storage of a single cell based on the rated capacity and voltage of the cells, and then... Divide by the maximum energy storage of a single cell to determine the minimum number of cells; determine the effective volume of the cabin based on the space available for arranging the cells inside the cabin; multiply the minimum number of cells by the volume of a single cell to calculate the required volume; Select combinations of cabins and battery cells whose required volume is less than or equal to the effective volume of the cabin and whose total weight of the battery cells is less than or equal to the upper limit of the cabin's load-bearing capacity. For the selected combinations of battery cells and battery packs, their overall score is calculated using the following formula: In the formula, PC represents the cabin cost, and RV represents the cabin volume utilization rate; Sort by comprehensive score in descending order and output a list of feasible cabin and battery cell combinations.

3. The automated design method for a supercapacitor energy storage system according to claim 2, characterized in that, Based on the list of feasible cabin and cell combinations, a hierarchical topology decomposition is performed in the order of system level, cluster level, pack level, and cell level, outputting a structured topology scheme document, including: Based on the list of feasible cabin and battery cell combinations and the system requirements, a system-level topology decomposition is performed to determine the system-level electrical connection parameters and spatial arrangement parameters under the current combination: The total energy required by the system is obtained. Rated energy of a single cluster Using the formula Calculate the total number of clusters in the system; combine the corresponding effective volume of the cabin with the volume of a single cluster to determine the number of clusters arranged along the x, y, and z axes of the cabin. And must meet ; Based on system-level electrical connection parameters and spatial arrangement parameters, cluster-level topology decomposition is performed to determine the cluster-level electrical connection parameters and spatial arrangement parameters under the current combination: obtaining the cluster rated voltage. With the rated voltage of a single pack Using the formula Calculate the number of PACKs required for a single cluster; combine the internal space dimensions of the cluster with the volume of a single PACK to determine the arrangement of PACKs within the cluster; Based on cluster-level electrical connection parameters and spatial arrangement parameters, a pack-level topology decomposition is performed to determine the pack-level electrical connection parameters and spatial arrangement parameters under the current combination: The pack rated voltage is obtained. With the rated voltage of the battery cell Using the formula Calculate the number of cells connected in series; obtain the rated capacity of the pack. With cell capacity Using the formula Calculate the number of cells connected in parallel; using the formula Calculate the total number of cells in a single PACK; Based on the pack-level electrical connection parameters and spatial arrangement parameters, cell-level topology decomposition is performed: according to the internal space dimensions of the pack and the volume of a single cell, the number of cells arranged in the x, y, and z axes of the pack is determined. , , And satisfy At the same time, a heat dissipation gap is reserved to ensure that the heat generated by the battery cell can be effectively dissipated during operation. The output is a structured topology document containing electrical connection parameters and spatial arrangement parameters at the system, cluster, pack, and cell levels.

4. The automated design method for a supercapacitor energy storage system according to claim 3, characterized in that, The established closed-loop feedback simulation model of the electric and thermal domains is used to verify the electrical performance, thermal safety, and lifespan of the topology schemes in the topology scheme document, and a list of qualified topology schemes is obtained, including: Obtain the scheme parameters and cell thermal and electrical characteristic parameters from the structured topology scheme document; among them, the electrical characteristic parameters include the cell equivalent internal resistance (ESR). According to the system rated power Calculate the total current of the circuit based on the total system voltage, and combine this with the series-parallel structure of the cells to allocate the operating current of each cell, PACK, and cluster; using the formula... Calculate the power loss of a single battery cell, where... The operating current of a single battery cell; verify whether the output voltage of the system remains at a certain level throughout the entire discharge process. If the range is exceeded, the electrical performance of the topology scheme is marked as substandard. The power loss of each cell obtained from the electric domain simulation As a heat source, it is applied to the corresponding cell location in the thermal simulation model; based on the theories of heat conduction, convection, and radiation, the temperature field distribution of the system during the discharge process is calculated, and the hot spot temperature of the cell is monitored; it is verified whether the hot spot temperature of the cell is less than or equal to 65℃, and if not, the topology is marked as not meeting the thermal safety standards. Based on the temperature data obtained from thermal simulation, the equivalent internal resistance (ESR) and capacity of the battery cell are corrected. Among them, the capacity decay rate must meet the following requirements. In the formula This refers to the capacity decay of the battery cell under the influence of operating temperature. ; This is the initial capacity of the battery cell. The actual capacity of the battery cell after temperature effects; the rate of increase in internal resistance must meet the following requirements. In the formula The increase in internal resistance of the battery cell under the influence of operating temperature , This represents the actual equivalent internal resistance of the battery cell after temperature effects. This represents the initial equivalent internal resistance of the battery cell; Using the corrected equivalent internal resistance (ESR) and capacitance The battery cell is reloaded to the corresponding cell position in the thermal simulation model and an electric domain simulation is performed to verify whether the electrical performance still meets the requirements. If so, the system cycle life is predicted based on the corrected relevant parameters and the preset cell life decay model. The predicted system cycle life is then judged to be greater than or equal to the design cycle life. If it does not meet the requirements, the topology scheme is marked as having a lifespan that does not meet the standards. Topology schemes that meet the standards in terms of electrical performance, thermal safety, and lifespan are retained to form a list of qualified topology schemes.

5. The automated design method for a supercapacitor energy storage system according to claim 4, characterized in that, Under the constraints of the cabin space, the process involves performing cluster block translation and rotation operations on the topology schemes in the list of qualified topology schemes to output an optimized cluster-level arrangement scheme, including: Extract the effective volume boundary of the cabin, the location and width of the maintenance passage, and the location and size of the fixed structure inside the cabin from the cabin library parameters to identify the areas where cluster blocks cannot be arranged. Based on the number of clusters arranged along the x, y, and z axes of the cabin. Generate an initial arrangement scheme for the cluster blocks; within the effective volume of the cabin, translate the cluster blocks along the x, y, and z axes to avoid overlapping the cluster blocks with areas that cannot be arranged and to maximize the use of cabin space; for cluster blocks with regular shapes, rotate them 90° or 180° if space permits to optimize the gap distribution between the cluster blocks and ensure that the heat dissipation channels are uniform and unobstructed. Verify whether the adjusted cluster block arrangement meets all spatial constraints and whether the gaps between cluster blocks meet heat dissipation requirements; if the verification passes, generate and output the optimized cluster-level arrangement scheme.

6. The automated design method for a supercapacitor energy storage system according to claim 5, characterized in that, The optimized cluster-level arrangement scheme is subjected to multi-objective quantitative scoring and peer arbitration to select the optimal topology scheme, including: Based on the optimized cluster-level arrangement scheme, its material cost data, volumetric power density data, and cycle life data are determined. Based on the material cost data, volumetric power density data, and cycle life data, normalization is performed using the normalization function Norm() to generate a cost normalization score. Volume normalization score and lifespan normalized score ; The overall score of the topology scheme is calculated using the following formula: in, , , These are weight vectors for cost, volume, and lifespan, respectively. right Sort in descending order and filter out The highest-level cluster arrangement is taken as the optimal topology. If it exists For identical and highest-ranking proposals of the same level, the following arbitration rules shall be used for selection: If the optimization of supercapacitor energy storage systems prioritizes cost, the cost difference ratio B1 of comparable solutions is calculated using the following formula: ， In the formula, , The total system costs for the two equivalent solutions are respectively. The smaller of the total system cost of the two equivalent solutions; Determine if B1 ≥ 10%. If so, select the scheme with the lower total system cost as the optimal topology. If not, select the scheme with the higher total system cost as the optimal topology. If the optimization preference for supercapacitor energy storage systems is volume-first, the volume difference ratio B2 of the same-level schemes can be calculated using the following formula: In the formula, , These represent the actual system volume occupied by the two equivalent schemes. The smaller of the actual occupied volumes of the two equivalent schemes; Determine if B2 ≥ 15%. If so, select the optimal topology scheme from the same level with the smaller actual volume occupied. If not, select the optimal topology scheme from the same level with the larger actual volume occupied. If the optimization preference for supercapacitor energy storage systems is lifespan priority, the lifespan difference ratio B3 of comparable solutions can be calculated using the following formula: In the formula, , The predicted system cycle lifetimes for two equivalent schemes are shown below. The smaller of the predicted system cycle lifetimes for the two equivalent schemes; Determine whether B3 ≥ 20%. If so, select the peer scheme with the longer predicted system cycle life as the optimal topology scheme; otherwise, select the peer scheme with the shorter predicted system cycle life as the optimal topology scheme.

7. The automated design method for a supercapacitor energy storage system according to claim 6, characterized in that, The generation of the full-process flowchart, 3D layout diagram, and structured parameter report corresponding to the optimal topology scheme includes: Based on the selection process of the optimal topology scheme, corresponding input preprocessing flowchart, four-layer topology decomposition flowchart, two-domain coupling verification flowchart, and multi-objective optimization and output flowchart are generated, and key parameters and judgment conditions are marked in each flowchart. A system layout diagram and a cluster / pack layout diagram are generated based on the optimal topology scheme; the system layout diagram shows the arrangement of clusters within the cabin in three dimensions, and indicates the number of clusters in the x, y, and z axes. The cluster / PACK layout diagram shows the arrangement of PACKs within clusters and cells within PACKs, indicating the number of cells arranged in the x, y, and z axes of the PACK. , , and the dimensions of the heat dissipation gap; A structured parameter report is generated based on the optimal topology scheme. The structured parameter report includes: A four-level parameter summary table is used to summarize all key parameters at the system level, cluster level, pack level, and cell level. The verification result table is used to record key data of the dual-domain coupling verification, including electrical performance fluctuation amplitude, hot spot temperature, capacity decay rate, internal resistance rise rate, and predicted lifetime. Decision summary, used to record cost normalization scores Volume normalization score Lifespan normalized score The corresponding weight vector, comprehensive score, and arbitration results at the same level.

8. An automated design system for a supercapacitor energy storage system, characterized in that, The system employs the automated design method for supercapacitor energy storage systems as described in any one of claims 1 to 7; The system includes: The input preprocessing module is used to standardize, define, screen, prioritize, and verify the input data for the design of supercapacitor energy storage systems, and output a list of feasible combinations of cabin and cells. The four-level topology decomposition module is used to perform topology decomposition in the order of system level, cluster level, pack level and cell level based on the feasible cabin and cell combination list, and output a structured topology scheme document. The dual-domain coupling verification module is used to establish a closed-loop feedback simulation model of the electric and thermal domains, and to verify the electrical performance, thermal safety and lifespan of the topology schemes in the topology scheme document, and to obtain a list of qualified topology schemes. The cluster-level reconfigurable arrangement module is used to perform cluster block translation and rotation operations on the topology schemes in the qualified topology scheme list under the constraints of cabin space, and output the optimized cluster-level arrangement scheme. The multi-objective rapid optimization module is used to perform multi-objective quantitative scoring and peer arbitration on the topology scheme corresponding to the optimized cluster-level arrangement scheme, and to select the optimal topology scheme. The visualization output module is used to generate a full-process flowchart, a 3D layout diagram, and a structured parameter report corresponding to the optimal topology scheme.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the automated design method for a supercapacitor energy storage system as described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the automated design method for the supercapacitor energy storage system as described in any one of claims 1 to 7.

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