Box-to-box reconstruction methods, electronic devices, storage media and program products

Abstract

This application provides a battery pack reconfiguration method, electronic device, storage medium, and program product. The method includes: acquiring battery pack-level data for each battery cluster in a target energy storage system, including the capacity retention rate and state of charge of the cells; calculating the capacity retention rate of each battery pack in the target energy storage system based on the "weakest link" principle; exchanging and reconfiguring the batteries in the target energy storage system to obtain the change in capacity retention rate of the target energy storage system after the exchange and reconfiguration; and determining the corresponding battery pack reconfiguration scheme for the target energy storage system based on the change in capacity retention rate. This method aims to fully utilize the energy storage capacity of existing batteries in the energy storage system without adding additional energy storage units, reducing the bottleneck effect, increasing the overall discharge capacity of the system, and improving the profitability of the power station. By exchanging and reconfiguring the batteries in the energy storage system, the difference in capacity retention rate is reduced, and the capacity retention rate of each level of the energy storage system is balanced.

Description

Technical Field

[0001] This application relates to the field of energy storage system capacity management technology, and in particular to a plug-in reconfiguration method, electronic device, storage medium and program product. Background Technology

[0002] With the rapid development of new energy power generation, energy storage power stations, as key facilities for regulating grid stability and mitigating new energy fluctuations, have their capacity retention rate directly affecting the economic benefits and operational efficiency of the power station. Energy storage power stations typically consist of multiple battery clusters, each containing several battery boxes, with multiple battery cells integrated within each box.

[0003] During long-term operation, the capacity retention rate of battery cells gradually decreases due to factors such as manufacturing differences, operating environment, and charge-discharge cycles. Because of the "weakest link" principle, the capacity retention rate of the battery cells within the system is also affected by the weakest link, which in turn affects the overall capacity retention rate of the entire system, ultimately reducing the system's discharge capacity and consequently impacting the profitability of the energy storage power station.

[0004] Existing solutions address the capacity degradation problem of energy storage systems by directly adding new energy storage units to supplement the overall capacity of the system. However, these solutions require additional supplementary devices and energy storage units, resulting in high operation and maintenance costs and failing to fully utilize the capacity of existing energy storage units. Summary of the Invention

[0005] This application provides a method for reconfiguring interlocking cells, electronic devices, storage media, and program products to improve the overall capacity retention rate of energy storage systems.

[0006] In a first aspect, embodiments of this application provide a method for reassembling interlocking boxes, including:

[0007] Acquire the battery pack level data of each battery cluster in the target energy storage system. The battery pack level data includes the capacity retention rate and state of charge of the cells.

[0008] Based on the barrel principle, the capacity retention rate of each compartment in the target energy storage system is calculated;

[0009] The modules in the target energy storage system are swapped and reassembled, and the change in the capacity retention rate of the target energy storage system after the swapping and reassembly is obtained.

[0010] Based on the change in capacity retention rate, determine the corresponding interlocking and reconfiguration scheme for the target energy storage system.

[0011] In one possible implementation, the modules in the target energy storage system are swapped and reassembled, including:

[0012] Traverse the battery packs in each battery cluster of the target energy storage system and calculate the change in capacity retention of the target energy storage system after each pair of packs is swapped and reassembled.

[0013] Select the target plug pair after swapping and recombining that has the largest change in capacity retention rate, and then swap and recombine the target plug pair.

[0014] In one possible implementation, after obtaining the change in capacity retention of the target energy storage system after the exchange and reconfiguration, the method further includes:

[0015] Record the target plug-in box number, capacity retention rate change, and target energy storage system capacity retention rate change after the swap and reconfiguration.

[0016] In one possible implementation, the method further includes:

[0017] A multi-objective optimization model is constructed based on the change in capacity retention rate, the cost of reconfiguration of the socket, and the aging trend of the socket.

[0018] The interlocking and reassembly scheme is optimized using a multi-objective optimization model, resulting in an optimized interlocking and reassembly scheme.

[0019] In one possible implementation, a multi-objective optimization model is constructed based on changes in capacity retention, reconfiguration costs, and aging trends in the sockets, including:

[0020] Based on historical capacity retention rate and operating environment, the aging trend of the socket is predicted, and the aging trend of the socket is obtained.

[0021] Simulate different inter-cell reconfiguration schemes and obtain the capacity retention rate corresponding to each scheme.

[0022] Based on the aging trend of the socket and the capacity retention rate corresponding to different socket reconfiguration schemes, the optimization objective of the multi-objective optimization model is determined, and the multi-objective optimization model is trained based on the optimization objective.

[0023] In one possible implementation, the swapping and reconfiguration of the individual modules in the target energy storage system further includes:

[0024] Based on the preset number of reorganizations, the exchange and reorganization steps for each module in the target energy storage system are repeated until the preset number of reorganizations is reached.

[0025] Secondly, embodiments of this application provide a box-mounted reassembly device, comprising:

[0026] The data acquisition module is used to acquire the battery pack level data of each battery cluster in the target energy storage system. The battery pack level data includes the capacity retention rate and state of charge of the cells.

[0027] The capacity retention rate calculation module is used to calculate the capacity retention rate of each tank in the target energy storage system based on the barrel principle.

[0028] The module for swapping and reconfiguring the modules in the target energy storage system is used to obtain the change in the capacity retention rate of the target energy storage system after swapping and reconfiguring.

[0029] The scheme determination module is used to determine the corresponding inter-cell reconfiguration scheme for the target energy storage system based on the change in capacity retention rate.

[0030] In one possible implementation, the interlocking module is specifically used to: traverse the interlocking modules of each battery cluster in the target energy storage system and calculate the change in capacity retention of the target energy storage system after each pair of interlocking modules is exchanged and reassembled.

[0031] Select the target plug pair after swapping and recombining that has the largest change in capacity retention rate, and then swap and recombine the target plug pair.

[0032] In one possible implementation, the plug-in reconfiguration device is further specifically used to: record the plug-in number after the target plug-in pair is swapped and reconfigured, the change in capacity retention rate, and the change in capacity retention rate of the target energy storage system.

[0033] In one possible implementation, the interlocking and reconfiguration device is further specifically used to: construct a multi-objective optimization model based on the change in capacity retention rate, the cost of interlocking and reconfiguration, and the aging trend of the interlocking.

[0034] The interlocking and reassembly scheme is optimized using a multi-objective optimization model, resulting in an optimized interlocking and reassembly scheme.

[0035] In one possible implementation, the inter-chamber reconfiguration module is also specifically used to: predict the inter-chamber aging trend based on historical capacity retention rate and operating environment, and obtain the inter-chamber aging trend.

[0036] Simulate different inter-cell reconfiguration schemes and obtain the capacity retention rate corresponding to each scheme.

[0037] Based on the aging trend of the socket and the capacity retention rate corresponding to different socket reconfiguration schemes, the optimization objective of the multi-objective optimization model is determined, and the multi-objective optimization model is trained based on the optimization objective.

[0038] In one possible implementation, the inter-cell reconfiguration device is further specifically used to: repeatedly perform the exchange and reconfiguration steps on each inter-cell in the target energy storage system according to a preset number of reconfigurations, until the preset number of reconfigurations is reached.

[0039] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0040] The memory stores the instructions that the computer executes;

[0041] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0042] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0043] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0044] The battery pack reconfiguration method, electronic device, storage medium, and program product provided in this application acquire battery pack-level data for each battery cluster in the energy storage system. Based on the "weakest link" principle, they calculate the capacity retention rate of each battery pack, reducing the differences in capacity retention rates across battery pack levels and thus mitigating the impact of the "weakest link" effect on capacity retention. By exchanging and reconfiguring the batteries in the energy storage system, the capacity retention rates of each level in the target energy storage system are balanced, making the overall capacity retention rate of the energy storage system closer to the optimal value. Based on the change in capacity retention rate of the energy storage system after exchange and reconfiguration, a battery pack reconfiguration scheme is determined, achieving automated decision-making in the reconfiguration process. Without adding additional energy storage units, the energy storage capacity of existing batteries in the energy storage system can be fully utilized, reducing the bottleneck effect, increasing the overall discharge capacity of the system, and improving the profitability of the power station. This achieves the goal of requiring no new equipment or battery cell replacement, only battery pack replacement operations by maintenance personnel, significantly reducing maintenance costs. The reconfiguration strategy can be adjusted according to actual needs to adapt to different operating scenarios of energy storage systems. Attached Figure Description

[0045] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0046] Figure 1 This is a schematic diagram of an energy storage system scenario;

[0047] Figure 2 A schematic flowchart illustrating a box reassembly method provided in an embodiment of this application;

[0048] Figure 3 A flowchart illustrating a method for supplementing the capacity of an energy storage power station based on interlocking module reconfiguration, provided in an embodiment of this application;

[0049] Figure 4 This is a schematic diagram of the structure of a box-mounted reassembly device provided in an embodiment of this application;

[0050] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0051] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0052] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0053] First, let me explain the terms used in this application:

[0054] Capacity retention rate: refers to the ratio between the capacity that a battery can store in its current state and the capacity it could store in its initial brand-new state.

[0055] Battery pack: refers to a standardized pluggable unit in an energy storage system that integrates multiple individual batteries (i.e., cells).

[0056] Figure 1 This is a schematic diagram of an energy storage system scenario, such as... Figure 1 As shown, this application applies to energy storage systems composed of multiple battery clusters, each battery cluster containing several modules. During the operation of an energy storage power station, the cells in the modules may exhibit different capacity retention rates due to factors such as aging and environmental differences. This, in turn, affects the capacity output capability of the battery cluster and the entire energy storage system in a step-by-step manner through the "barrel principle".

[0057] In existing technologies, the capacity retention rate of an energy storage system is determined by the cell with the lowest capacity retention rate (the "weakest link" principle). For example, in an energy storage system composed of three battery clusters, if the capacity retention rate of a cell within a cluster is significantly lower than that of the other cells, the capacity retention rate of that cluster will be dragged down, thus affecting the capacity output capability of the subsystem and even the entire energy storage system. Existing technologies address this issue by replacing cells or adding spare battery clusters, but this increases maintenance costs and does not consider the optimal utilization of differences in the capacity of the internal battery packs, resulting in limited system capacity improvement.

[0058] Based on the above scenarios, the limitations of the existing technology are: (1) the system is not reorganized using "interlocking box" as the smallest operating unit, which makes it impossible to flexibly adjust the capacity distribution between battery clusters; (2) the reorganization strategy is not designed based on the capacity retention rate difference of the interlocking box level, which makes it difficult to effectively reduce the bottleneck effect; (3) the lack of optimization algorithm support for reorganization operation results in low reorganization efficiency and limited system capacity improvement.

[0059] The battery pack reconfiguration method, electronic device, storage medium, and program product provided in this application use the battery pack as the smallest operating unit. By quantitatively calculating the capacity retention rate at the pack level and designing a pack reconfiguration strategy based on the barrel principle, the overall capacity retention rate of the energy storage system is improved. This solves the bottleneck effect caused by differences in battery cell capacity in the prior art, and at the same time, it does not require additional equipment, significantly reducing operation and maintenance costs.

[0060] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0061] Figure 2 A flowchart illustrating the interlocking and reassembly method provided in this application is shown below. Figure 2 As shown, the method includes:

[0062] S201. Obtain the battery pack level data of each battery cluster in the target energy storage system.

[0063] In this embodiment of the application, the battery cell level data includes the cell's capacity retention rate and state of charge.

[0064] In one example, a battery management system can be used to collect the SOC (State of Charge) and capacity retention rate of each cell in the target energy storage system in real time. The capacity retention rate of a cell is the ratio between the capacity it can store in its current state and the capacity it could store in its initial, brand-new state.

[0065] S202. Based on the barrel principle, calculate the capacity retention rate of each plug box in the target energy storage system.

[0066] In one example, based on the barrel principle, the capacity retention rate of a battery cell is determined by the cell with the lowest capacity retention rate within the cell. The capacity retention rate of each cell in the target energy storage system is calculated using the following formula:

[0067]

[0068] In the formula, For the capacity retention rate of the interlocking box, This refers to the number of battery cells inside the casing. For battery cells Capacity retention rate For battery cells The state of charge.

[0069] S203. Exchange and reassemble each module in the target energy storage system, and obtain the change in the capacity retention rate of the target energy storage system after the exchange and reassembly.

[0070] In one example, the modules in the target energy storage system can be swapped and reassembled by traversing the module combinations in stages, and the capacity retention rate of the target energy storage system after each reassembly can be calculated as the improvement in capacity retention rate of the target energy storage system after each reassembly compared to the capacity retention rate of the target energy storage system before the reassembly.

[0071] S204. Based on the change in capacity retention rate, determine the corresponding interlocking and reconfiguration scheme for the target energy storage system.

[0072] In one example, the reconfiguration scheme with the highest improvement in capacity retention of the reconfigured target energy storage system can be selected as the reconfiguration scheme corresponding to the target energy storage system.

[0073] In one implementation scenario, to address the adverse effects of the battery cell bottleneck effect on the discharge capacity of the energy storage system, a method is proposed to improve the overall discharge capacity of the system by reorganizing the internal structure of the energy storage system using the battery pack as the smallest unit. Figure 3 A flowchart illustrating a method for supplementing the capacity of an energy storage power station based on interlocking module reconfiguration, as provided in this application embodiment, is shown below. Figure 3 As shown, the capacity supplementation method for energy storage power stations based on inter-cell reconfiguration may include: acquiring data at the battery cluster level of the energy storage system, including the SOC and capacity retention rate of the cells; calculating the capacity retention rate of each inter-cell based on the barrel principle; sequentially attempting to replace inter-cells in each battery cluster with inter-cells in other battery clusters, calculating the capacity retention rate improvement value of the energy storage system after each attempt; determining whether the capacity retention rate improvement value is the optimal improvement value; if the capacity retention rate improvement value is not the optimal improvement value, returning to the step of sequentially attempting to replace inter-cells in each battery cluster with inter-cells in other battery clusters until the capacity retention rate improvement value of the replaced energy storage system is the optimal improvement value; when the capacity retention rate improvement value is the optimal improvement value, performing inter-cell reconfiguration according to the corresponding inter-cell reconfiguration scheme, and marking the corresponding inter-cells with an exchange tag; saving the reconfiguration information, including the numbers of the two inter-cells involved in the reconfiguration, the capacity retention rate, the capacity retention rate improvement value of the reconfigured system, and the capacity retention rate of the reconfigured system. The corresponding battery cluster socket list is updated, including: updating the socket list of the two battery clusters involved in this reorganization in the energy storage system, and updating the capacity retention rate of the energy storage system after this replacement. This socket reorganization is then completed.

[0074] The battery pack reconfiguration method provided in this application acquires battery pack-level data for each battery cluster in the energy storage system, calculates the capacity retention rate of each battery pack based on the "weakest link" principle, and reduces the difference in capacity retention rate between battery pack levels, thereby mitigating the impact of the "weakest link" effect on capacity retention rate. By exchanging and reconfiguring the batteries in the energy storage system, the capacity retention rate of each level in the target energy storage system is balanced, making the overall capacity retention rate of the energy storage system closer to the optimal value. Based on the change in capacity retention rate of the energy storage system after exchange and reconfiguration, a battery pack reconfiguration scheme is determined, realizing automated decision-making in the reconfiguration process. It can fully utilize the energy storage capacity of existing batteries in the energy storage system without adding additional energy storage units, reducing the bottleneck effect, increasing the overall discharge capacity of the system, and improving the profitability of the power station. It achieves the goal of requiring no new equipment or battery cell replacement, only battery pack replacement operations by maintenance personnel, significantly reducing maintenance costs. The reconfiguration strategy can be adjusted according to actual needs to adapt to different operating scenarios of energy storage systems.

[0075] In one possible implementation, the swapping and recombining of each plug-in box in the target energy storage system includes: traversing the plug-in boxes of each battery cluster in the target energy storage system, calculating the change in capacity retention rate of the target energy storage system after swapping and recombining each pair of plug-in boxes; selecting the target plug-in box pair with the largest change in capacity retention rate after swapping and recombining, and swapping and recombining the target plug-in box pair.

[0076] In one example, the cells of each battery cluster in the target energy storage system are traversed, and the cells in each battery cluster are swapped and reassembled with all cells in other battery clusters once. The system capacity retention improvement after each reassembly attempt is calculated using the following formula:

[0077]

[0078] In the formula, This represents the change in the capacity retention rate of the target energy storage system after the two plug-in boxes are swapped and reassembled. This indicates the capacity retention rate of the target energy storage system after the two plug-in boxes are swapped and reassembled. This indicates the capacity retention rate of the target energy storage system prior to this exchange and restructuring.

[0079] The capacity retention rate of each level is calculated as follows: The capacity retention rate of the battery cluster level includes: The capacity retention rate of the subsystem includes: Energy storage system capacity retention rate: .

[0080] After each reorganization attempt, calculate and record the improvement in system capacity retention resulting from that reorganization. If the improvement is greater than the previously saved maximum improvement, update the maximum improvement and its corresponding socket information. The formula includes: The reassembly operation is performed based on the saved optimal insertion box information.

[0081] In another example, genetic algorithms, simulated annealing algorithms, particle swarm optimization algorithms, and other methods can be used to exchange and recombine the various cells in the target energy storage system.

[0082] In another example, the generation process of reconfiguration schemes can be accelerated through parallel computing by distributing the reconfiguration task across multiple computing nodes. Specifically, this includes: 1) Dividing the reconfiguration task into multiple subtasks and distributing them to different computing nodes (such as edge computing nodes or cloud servers) for parallel processing. For example, the reconfiguration task can be divided into 10 subtasks and distributed to 10 computing nodes for simultaneous processing. 2) After each computing node independently computes a locally optimal reconfiguration scheme, the master node aggregates the results and selects the globally optimal solution. For example, after receiving the locally optimal solutions from each subtask, the master node selects the optimal reconfiguration scheme by comparing the improvement in system capacity retention. 3) Dynamically adjusting the tasks of each computing node to prevent some nodes from becoming bottlenecks due to task overload. For example, if a computing node is slow, its workload is reduced, and tasks are prioritized for faster nodes. Distributed parallel computing significantly improves the efficiency of reconfiguration strategy generation, adapts to the real-time requirements of large-scale energy storage systems, and supports the global optimization capabilities of more complex algorithms (such as genetic algorithms and simulated annealing algorithms).

[0083] By swapping the cells of different battery clusters, cells with high capacity retention are replaced with those with low capacity retention, thereby improving the capacity retention of the battery cluster. Through multiple reconfiguration operations, the differences in capacity retention at each level are gradually balanced, avoiding insufficient local optimization caused by a single reconfiguration. By quantitatively calculating the reconfiguration effect of each pair of cells, insufficient system capacity improvement due to local optimization is avoided, while ensuring that each reconfiguration operation has a significant positive impact on the overall system capacity.

[0084] In one possible implementation, after obtaining the change in capacity retention rate of the target energy storage system after the exchange and reorganization, the method further includes: recording the target plug-in number, the change in capacity retention rate, and the change in capacity retention rate of the target energy storage system after the exchange and reorganization.

[0085] In one example, the reconfiguration information is saved, including the numbers of the two battery packs involved in the reconfiguration, the capacity retention rate, the increase in the system's capacity retention rate after reconfiguration, and the system's capacity retention rate after reconfiguration. The corresponding battery cluster battery pack list is updated, including updating the list of battery packs in the energy storage system involved in this reconfiguration and updating the capacity retention rate of the energy storage system after this replacement. For example, if the reconfiguration of battery pack pair A1-B2 increases the system-level capacity retention rate from 80% to 85%, then the battery pack pair number, the change of +5%, and the reconfigured value of 85% are saved. The saved data can be used as a basis for decision-making in subsequent reconfiguration operations, such as analyzing the historical reconfiguration effects after multiple reconfigurations.

[0086] By storing reorganization information, traceability and data accumulation of reorganization operations are achieved. This provides historical data support for optimizing subsequent reorganization strategies; for example, by analyzing historical reorganization effects, the optimal combination of future reorganizations can be predicted, thereby improving the system's long-term capacity management capabilities.

[0087] In one possible implementation, the method further includes: constructing a multi-objective optimization model based on the change in capacity retention rate, the cost of reconfiguration of the interlocking box, and the aging trend of the interlocking box; and using the multi-objective optimization model to optimize the interlocking box reconfiguration scheme to obtain the optimized interlocking box reconfiguration scheme.

[0088] In one example, the multi-objective optimization model can be a mathematical model that integrates multiple objectives (such as capacity improvement, cost, and aging trend). For example, linear programming or a non-dominated sorting genetic algorithm (NSGA) can be used to find the optimal solution. The cost of reassembly can include: the physical operation time and labor costs required to perform reassembly. For example, moving cell A1 to cluster B2 takes 30 minutes and costs 50 yuan. The aging trend of the cells can include: the predicted rate of capacity decay within the cell. For example, the capacity decay rate of cell A1 is 0.5% per month.

[0089] A multi-objective optimization model is constructed based on the capacity retention rate change at the socket level, socket reconfiguration cost, and socket aging trend. For example, by using weighted summation or Pareto front analysis, the optimal reconfiguration scheme is solved by taking capacity improvement, cost reduction, and aging trend control as objective functions. For instance, the multi-objective optimization model can prioritize socket pairs with lower reconfiguration costs and slower aging trends to balance system performance and economy.

[0090] Specifically, 1) the capacity retention improvement, reconfiguration cost (e.g., number of reconfiguration moves, operation time), and aging trend (e.g., capacity decay rate) of the target energy storage system are treated as independent objectives, and the optimal solution is obtained through a mathematical model (e.g., linear programming or non-dominated sorting genetic algorithm). Weighting coefficients can be set in the model to prioritize optimizing capacity retention while limiting reconfiguration costs to a preset threshold; this is not restricted in this application. 2) The weights of each objective are dynamically adjusted according to the operating stage of the target energy storage system (e.g., initial operation, mid-term decay, and final aging). Prioritizing capacity retention improvement in the initial operation stage and reducing reconfiguration costs to extend reconfiguration life in the final aging stage is not restricted in this application. 3) Multiple reconfiguration schemes are provided through a Pareto optimal solution set for operation and maintenance personnel to choose from based on actual needs (e.g., economic priority or performance priority).

[0091] By generating reconfiguration schemes through a multi-objective optimization model, synergistic optimization of system capacity improvement, economy, and socket lifespan is achieved. This avoids resource waste or system performance degradation caused by single-objective optimization, such as controlling reconfiguration costs and extending socket lifespan while prioritizing capacity improvement.

[0092] In one possible implementation, a multi-objective optimization model is constructed based on the change in capacity retention rate, the cost of socket reconfiguration, and the aging trend of the sockets. The model includes: predicting the aging trend of the sockets based on historical capacity retention rates and operating environment; simulating different socket reconfiguration schemes to obtain the capacity retention rates corresponding to different socket reconfiguration schemes; determining the optimization objective of the multi-objective optimization model based on the aging trend of the sockets and the capacity retention rates corresponding to different socket reconfiguration schemes; and training the multi-objective optimization model based on the optimization objective.

[0093] In one example, historical capacity retention rates and cell aging prediction models (such as LSTM neural networks) are used to identify battery packs that may experience future bottlenecks, and the optimal battery pack reconfiguration scheme is predicted before implementing the reconfiguration scheme. When constructing a multi-objective optimization model, the system generates optimization objectives by predicting battery pack aging trends (e.g., based on historical data and operating environment) and simulating reconfiguration effects (e.g., testing the future capacity retention rates of different combinations).

[0094] Specifically, 1) Based on the historical SOC, capacity retention rate data, and operating environment (such as temperature and charge / discharge frequency) of the battery cells, a machine learning model (such as an LSTM neural network) is constructed to predict the future capacity degradation trend of the cells. For example, if it is predicted that the capacity retention rate of the cells in a certain socket will drop to 70% in the next 12 months, then that socket may become a future bottleneck. 2) Before implementing the socket reconfiguration scheme, the impact of different reconfiguration combinations on the future system capacity retention rate is simulated based on the predictive model. For example, simulating whether the capacity retention rate of cluster B can withstand the future capacity degradation risk of socket A after replacing socket A with cluster B. 3) Based on the simulation results, the reconfiguration combination that most significantly improves the long-term capacity retention rate is selected to avoid a sudden drop in system capacity due to the future bottleneck effect.

[0095] By using predictive models and reorganization simulations, the forward-looking nature of the reorganization strategy is significantly improved, enabling forward-looking optimization of the reorganization strategy. This reduces the risk of system performance degradation due to future bottleneck effects and facilitates early avoidance of the impact of the aging trend of the plug-in box on the long-term performance of the system.

[0096] In one possible implementation, the process of exchanging and recombining the various modules in the target energy storage system further includes: repeating the process of exchanging and recombining the various modules in the target energy storage system according to a preset number of recombining attempts, until the preset number of recombining attempts is reached.

[0097] In one example, the system repeatedly performs the swapping and reconfiguration steps for each module in the target energy storage system, based on a preset maximum number of reconfigurations, until the preset number of reconfigurations is reached. After completing one module reconfiguration, the system repeats the reconfiguration operation based on the preset maximum number of reconfigurations (e.g., 5 times). For example, if the current number of reconfigurations is 3, the system continues to perform the 4th and 5th reconfigurations, selecting the current optimal module pair for swapping each time, until the preset number of reconfigurations is reached.

[0098] By controlling the scope of reorganization operations through preset reorganization counts, the system capacity enhancement is made controllable. This avoids system performance fluctuations caused by excessive reorganization and ensures that the system optimizes capacity within a reasonable range.

[0099] Figure 4 A schematic diagram of the structure of the interlocking and reassembly device provided in this application is shown below. Figure 4 As shown, the insert box reassembly device 40 provided in this embodiment includes:

[0100] The data acquisition module 401 is used to acquire the battery pack level data of each battery cluster in the target energy storage system. The battery pack level data includes the capacity retention rate and state of charge of the cells.

[0101] The capacity retention rate calculation module 402 is used to calculate the capacity retention rate of each tank in the target energy storage system based on the barrel principle.

[0102] The module 403 is used to exchange and reorganize each module in the target energy storage system and obtain the change in the capacity retention rate of the target energy storage system after the exchange and reorganization.

[0103] The scheme determination module 404 is used to determine the corresponding plug-in reconfiguration scheme for the target energy storage system based on the change in capacity retention rate.

[0104] In one possible implementation, the interlocking module 403 is specifically used to: traverse the interlocking boxes of each battery cluster in the target energy storage system, calculate the change in capacity retention rate of the target energy storage system after each pair of interlocking boxes is exchanged and reorganized; select the target interlocking box pair with the largest change in capacity retention rate after exchange and reorganization, and perform exchange and reorganization on the target interlocking box pair.

[0105] In one possible implementation, the interlocking device 40 is also specifically used to: record the interlocking number, capacity retention rate change, and capacity retention rate change of the target interlocking pair after exchange and reorganization.

[0106] In one possible implementation, the interlocking and reconfiguration device 40 is further specifically used to: construct a multi-objective optimization model based on the change in capacity retention rate, the cost of interlocking and reconfiguration, and the aging trend of interlocking and reconfiguration; and optimize the interlocking and reconfiguration scheme using the multi-objective optimization model to obtain the optimized interlocking and reconfiguration scheme.

[0107] In one possible implementation, the inter-packet reconfiguration module 403 is further specifically used for: predicting the aging trend of the inter-packet based on historical capacity retention rate and operating environment, and obtaining the aging trend of the inter-packet; simulating different inter-packet reconfiguration schemes and obtaining the capacity retention rate corresponding to different inter-packet reconfiguration schemes; determining the optimization objective of the multi-objective optimization model based on the inter-packet aging trend and the capacity retention rate corresponding to different inter-packet reconfiguration schemes, and training the multi-objective optimization model based on the optimization objective.

[0108] In one possible implementation, the inter-cell reconfiguration device 40 is further specifically used to: repeatedly perform the exchange and reconfiguration steps on each inter-cell in the target energy storage system according to a preset number of reconfigurations, until the preset number of reconfigurations is reached.

[0109] The box-mounted reassembly device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0110] Figure 5 A schematic diagram of the structure of the electronic device provided in this application. Figure 5 As shown, the electronic device 50 provided in this embodiment includes at least one processor 501 and a memory 502. Optionally, the device 50 further includes a communication component 503. The processor 501, memory 502, and communication component 503 are connected via a bus 504.

[0111] In a specific implementation, at least one processor 501 executes computer execution instructions stored in memory 502, causing at least one processor 501 to perform the above-described method.

[0112] The specific implementation process of processor 501 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0113] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0114] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0115] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0116] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0117] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0118] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0119] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0120] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0121] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0122] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0123] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0124] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0125] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for reassembling interlocking boxes, characterized in that, include: Acquire the battery pack level data of each battery cluster in the target energy storage system, the battery pack level data including the capacity retention rate and state of charge of the cells; Based on the barrel principle, the capacity retention rate of each compartment in the target energy storage system is calculated; The modules in the target energy storage system are swapped and reassembled to obtain the change in the capacity retention rate of the target energy storage system after swapping and reassembling. Based on the change in capacity retention rate, determine the corresponding inter-cell reconfiguration scheme for the target energy storage system.

2. The method according to claim 1, characterized in that, The process of exchanging and reassembling the individual modules in the target energy storage system includes: Traverse the battery packs of each battery cluster in the target energy storage system and calculate the change in capacity retention of the target energy storage system after each pair of packs is swapped and reassembled. Select the target plug-in pair after the exchange and recombination with the largest change in capacity retention rate, and perform exchange and recombination on the target plug-in pair.

3. The method according to claim 2, characterized in that, After obtaining the change in capacity retention of the target energy storage system after the exchange and reorganization, the method further includes: Record the target plug box number, capacity retention rate change, and target energy storage system capacity retention rate change after the swap and reconfiguration.

4. The method according to claim 1, characterized in that, The method further includes: Based on the changes in capacity retention rate, the cost of reconfiguration of the insert cells, and the aging trend of the insert cells, a multi-objective optimization model is constructed. The multi-objective optimization model is used to optimize the inter-box reconfiguration scheme, resulting in an optimized inter-box reconfiguration scheme.

5. The method according to claim 4, characterized in that, The multi-objective optimization model is constructed based on the change in capacity retention rate, the cost of reconfiguration of the socket, and the aging trend of the socket, including: Based on historical capacity retention rate and operating environment, the aging trend of the socket is predicted to obtain the aging trend of the socket. Simulate different inter-cell reconfiguration schemes and obtain the capacity retention rate corresponding to the different inter-cell reconfiguration schemes; Based on the aging trend of the socket and the capacity retention rate corresponding to the different socket reconfiguration schemes, the optimization objective of the multi-objective optimization model is determined, and the multi-objective optimization model is trained based on the optimization objective.

6. The method according to any one of claims 1-5, characterized in that, The process of exchanging and reassembling the individual modules in the target energy storage system further includes: The process of exchanging and recombining each module in the target energy storage system is repeated according to the preset number of recombination attempts until the preset number of recombination attempts is reached.

7. A box-mounted reassembly device, characterized in that, include: The data acquisition module is used to acquire the battery pack level data of each battery cluster in the target energy storage system. The battery pack level data includes the capacity retention rate and state of charge of the cells. The capacity retention rate calculation module is used to calculate the capacity retention rate of each tank in the target energy storage system based on the barrel principle. The module for swapping and reassembling each module in the target energy storage system is used to obtain the change in the capacity retention rate of the target energy storage system after swapping and reassembling. The scheme determination module is used to determine the plug-in reconfiguration scheme corresponding to the target energy storage system based on the change in capacity retention rate.

8. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method described in any one of claims 1-6.

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