Energy storage cabin charging and discharging power distribution method and device

Abstract

The invention provides an energy storage cabin charging and discharging power distribution method and device, and relates to the technical field of energy storage control. The method comprises the steps that in the working process of the energy storage cabin, state parameters of all battery cells in the energy storage cabin are collected every set duration; the set duration dynamically changes along with the overall SOC of the energy storage cabin, and the larger the overall SOC is, the longer the corresponding set duration is; clustering each battery cluster in the energy storage cabin based on the state parameters to obtain a plurality of clusters; and the total power of the energy storage cabin is obtained in real time, the distribution proportion is determined step by step based on the clustering clusters, and the total power is distributed step by step according to the distribution proportion. The method can adapt to the dynamic change of the energy storage cabin, and improves the power balance effect.

Description

Technical Field

[0001] This invention relates to the field of energy storage control technology, and in particular to a method and apparatus for distributing charging and discharging power in an energy storage compartment. Background Technology

[0002] With the large-scale grid connection of renewable energy and the increasing demand for peak shaving and frequency regulation in power systems, battery energy storage modules have been widely used as critical infrastructure. Energy storage modules typically consist of a large number of battery cells connected in series and parallel to form a complex multi-level system (cell-pack-cluster). As the operating time of energy storage modules continues to increase, the dispersion of their system performance continues to grow, and the "weakest link" effect becomes increasingly prominent, severely restricting overall output capacity, cycle life, and operational safety.

[0003] Most related technologies employ fixed grouping or fixed threshold power allocation methods to achieve balanced control of the energy storage compartment. However, these static and fixed power allocation methods cannot adapt to the dynamic changes in battery status during operation, especially under long-term cycling or large ambient temperature fluctuations. This can easily lead to inaccurate power allocation, which in turn exacerbates the imbalance within the battery pack, reduces the system's usable capacity, and even poses safety hazards. Summary of the Invention

[0004] This invention provides a method and apparatus for power distribution during charging and discharging of an energy storage compartment, which solves the problem that existing static and fixed power distribution methods cannot adapt to the dynamic changes of the energy storage compartment, resulting in poor power balancing.

[0005] In a first aspect, embodiments of the present invention provide a method for allocating charging and discharging power in an energy storage compartment, wherein the energy storage compartment comprises multiple battery clusters, each battery cluster comprises multiple battery packs, and each battery pack comprises multiple battery cells. The method includes: During the operation of the energy storage compartment, the state parameters of each battery cell in the compartment are collected at set intervals. The set intervals change dynamically with the overall state of charge (SOC) of the energy storage compartment, and the larger the overall SOC, the longer the set interval. Based on the state parameters, the battery clusters in the energy storage compartment are clustered to obtain multiple clusters; The total power of the energy storage compartment is acquired in real time, and the allocation ratio is determined step by step based on the cluster. The total power is then allocated step by step according to the allocation ratio.

[0006] In one possible implementation, the method for determining the set duration includes: During the operation of the energy storage compartment, the overall State of Charge (SOC) and a preset lookup table are acquired in real time. The lookup table contains multiple adjacent SOC intervals and their corresponding set durations. For each SOC interval, the set duration is: the theoretical charge / discharge time corresponding to that SOC interval. ; An integer greater than or equal to 1; The set duration is dynamically updated based on the SOC range in which the overall SOC is located.

[0007] In one possible implementation, the method further includes: Within the set time period, the operating status of the voltage of each battery cell is detected in real time; If the voltage of any battery cell stops working, the process jumps to the step of collecting the state parameters of each battery cell in the energy storage compartment, and continues to execute subsequent steps to update the cluster.

[0008] In one possible implementation, the method further includes: During the operation of the energy storage compartment, the number of jump update actions that occur within the set time period corresponding to each SOC interval is detected; If the number of jump update actions within the set time period corresponding to any SOC interval exceeds the set threshold, the set time period corresponding to that SOC interval will be adjusted according to the number of jumps.

[0009] In one possible implementation, the clustering of battery clusters in the energy storage compartment based on the state parameters yields multiple clusters, including: Similarity calculations are performed based on the state parameters of each battery cell to obtain the similarity results between the battery cells. Similarity calculations are performed based on the similarity results between individual battery cells to obtain the similarity results between each battery pack. Similarity calculations are performed based on the similarity results between each battery pack to obtain the similarity results between each battery cluster. Based on the similarity results between the battery clusters, the battery clusters are clustered to obtain multiple clusters.

[0010] In one possible implementation, determining the allocation ratio step-by-step based on the clusters includes: Based on the voltage of each battery cell and the charging and discharging parameters of each battery cell in each cluster, the allocation ratio of each cluster is determined. For each cluster, the allocation ratio of each battery cluster is determined based on the voltage of each battery cell in each battery cluster. For each battery cluster, the allocation ratio of each battery pack is determined based on the voltage of each cell in each battery pack within that battery cluster. For each battery pack, the allocation ratio of each battery cell is determined based on the voltage and charging / discharging parameters of each battery cell in the pack.

[0011] In one possible implementation, determining the allocation ratio of each cluster based on the voltage and charge / discharge parameters of each battery cell in each cluster includes: For each cluster, the total number of battery cells in operation within that cluster is determined based on the voltage of each battery cell. For all battery cells in the working state, the average charge and discharge parameters are calculated, and the product of the average charge and discharge parameters and the total number of battery cells in the working state is determined as the amount of charge and discharge to be performed for the cluster. The allocation ratio of each cluster is determined according to the amount of charge and discharge to be performed in each cluster.

[0012] In one possible implementation, determining the allocation ratio of each battery cell for each battery pack based on the voltage and charging / discharging parameters of each cell in the battery pack includes: For each battery pack, determine which battery cells in the pack are in operation based on the voltage of each cell. For battery cells in operation, the allocation ratio of each battery cell is determined based on the charging and discharging parameters of each cell.

[0013] Secondly, embodiments of the present invention provide a charging and discharging power distribution device for an energy storage compartment, wherein the energy storage compartment includes multiple battery clusters, each battery cluster includes multiple battery packs, and each battery pack includes multiple battery cells. The device includes: The update module is used for: During the operation of the energy storage compartment, the state parameters of each battery cell in the energy storage compartment are collected at set intervals. The set intervals change dynamically with the overall SOC of the energy storage compartment, and the larger the overall SOC, the longer the corresponding set intervals. Based on the state parameters, the battery clusters in the energy storage compartment are clustered to obtain multiple clusters; The allocation module is used to obtain the total power of the energy storage compartment in real time, determine the allocation ratio step by step based on the cluster, and allocate the total power step by step according to the allocation ratio.

[0014] In one possible implementation, the update module is further configured to: Within the set time period, the operating status of the voltage of each battery cell is detected in real time; If the voltage of any battery cell stops working, the process jumps to the step of collecting the state parameters of each battery cell in the energy storage compartment, and continues to execute subsequent steps to update the cluster.

[0015] Compared to existing technologies, this invention dynamically adjusts the update cycle (i.e., the set duration) of clustering results by controlling the overall SOC of the energy storage module. This allows the update frequency of the clustering results to closely match the state change characteristics of the energy storage module at different stages of actual operation: when the overall SOC is low (where overall SOC changes drastically), the set duration is shortened to improve response speed and control accuracy; when the overall SOC is high (where SOC changes gradually), the set duration is extended to reduce unnecessary computational overhead. This invention can achieve real-time tracking and timely response to fluctuations in the overall state of the energy storage module without increasing the average computational load, thereby significantly enhancing the dynamic adaptability of the power allocation strategy, avoiding power allocation inaccuracies, and improving power balancing effects.

[0016] Furthermore, this embodiment of the invention uses the state parameters of the underlying battery cells to cluster the upper-layer battery clusters. This allows the underlying state parameters to accurately reflect the state differences of the battery clusters, achieving precise clustering. Then, based on the clustering results, the allocation ratio is determined level by level, ensuring a balanced distribution of total power across all levels. This further improves the balancing effect and avoids exacerbating inconsistencies due to uneven distribution. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the energy storage compartment provided in an embodiment of the present invention; Figure 2 This is a flowchart illustrating the implementation of the energy storage compartment charging and discharging power allocation method provided in this embodiment of the invention. Figure 3 This is a schematic diagram of the structure of the energy storage compartment charging and discharging power distribution device provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the control device provided in an embodiment of the present invention. Detailed Implementation

[0018] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0019] In related technologies, fixed grouping or fixed threshold power allocation methods are commonly used. For example, fixed grouping is performed according to parameters such as capacity and internal resistance, or a fixed threshold is set for power adjustment during charging and discharging. These static and fixed power allocation methods cannot adapt to the dynamic changes in battery status during operation. Especially under long-term cycling or large ambient temperature fluctuations, it is easy to cause inaccurate power allocation, which in turn exacerbates the imbalance within the battery pack, reduces the usable capacity of the system, and may even lead to safety hazards.

[0020] To adapt to dynamic changes in battery state and improve power distribution balance, this invention collects battery cell state parameters at set intervals during the operation of the energy storage compartment. Based on these parameters, battery clusters are then clustered to achieve dynamic grouping. The set intervals dynamically change with the overall SOC of the energy storage compartment. When the overall SOC is low (resulting in drastic changes), the set intervals are shortened to improve response speed and control accuracy. When the overall SOC is high (resulting in gradual changes), the set intervals are extended to reduce unnecessary computational overhead. This ensures that the update frequency of the clustering results closely matches the state change characteristics of the energy storage compartment at different stages of actual operation, enabling real-time tracking and timely response to fluctuations in the overall state of the energy storage compartment. This significantly enhances the dynamic adaptability of the power distribution strategy and improves power balancing.

[0021] Figure 1 This is a structural schematic diagram of the energy storage compartment provided in an embodiment of the present invention. Figure 1 As shown, the energy storage compartment contains multiple battery clusters, each battery cluster contains multiple battery packs, and each battery pack contains multiple battery cells. It should be noted that the energy storage compartment may also include a control device (not shown in the figure) for executing the power distribution method provided in this embodiment of the invention to achieve power distribution to each battery cell in the energy storage compartment.

[0022] See Figure 2 The flowchart illustrating the implementation of the energy storage compartment charging and discharging power allocation method provided in this embodiment of the invention is described in detail below: Step 201: During the operation of the energy storage compartment, the status parameters of each battery cell in the energy storage compartment are collected at set intervals.

[0023] Here, the set duration changes dynamically with the overall SOC of the energy storage module, and the larger the overall SOC, the longer the corresponding set duration.

[0024] The operation of the energy storage module includes charging and discharging processes. During this process, the overall State of Charge (SOC) of the module constantly changes. When the overall SOC is low (corresponding to the initial charging and final discharging phases), the SOC changes drastically. In this case, the set duration should be shortened to increase the update frequency of clustering results (i.e., battery grouping), thus responding promptly to fluctuations in the module's state. When the overall SOC is high (corresponding to the final charging and initial discharging phases), the SOC changes more gradually. In this case, the set duration should be extended to reduce unnecessary consumption of computational resources.

[0025] This invention achieves the goal of dynamically updating clustering results by dynamically adjusting the overall SOC of the energy storage module over a set time. This avoids the computational burden caused by excessively frequent updates and prevents the exacerbation of imbalances between batteries due to delayed updates, thus achieving a balance between precise control and efficient operation.

[0026] In this embodiment of the invention, the state parameters may include, but are not limited to, parameters that affect the performance of the battery cell, such as battery cell voltage, battery cell temperature, battery cell capacity, battery cell SOC, battery cell state of health (SOH), battery cell charge and discharge current, battery cell depth of discharge (DOD), and electrolyte state, so as to facilitate subsequent cluster analysis.

[0027] Step 202: Cluster the battery clusters in the energy storage compartment based on the state parameters to obtain multiple clusters.

[0028] Considering that the aforementioned state parameters such as cell voltage, cell temperature, cell capacity, cell SOC, cell SOH, cell charge / discharge current, cell DOD, and electrolyte state all affect cell performance, and that different state parameters have different degrees of influence on cell performance, this embodiment of the invention can set corresponding weights for each of the above state parameters, and generate multi-dimensional clustering vectors for each cell based on each state parameter and its corresponding weights for cluster analysis.

[0029] Here, the multi-dimensional clustering vector corresponding to each battery cell can be represented as:

[0030] in, This represents the multi-dimensional clustering vector corresponding to the battery cell. Indicates the battery cell voltage. This indicates the weight corresponding to the battery cell voltage. Indicates the battery cell temperature. This indicates the weight corresponding to the battery cell temperature. Indicates the state of charge (SOC) of the battery cell. This indicates the weight corresponding to the state of charge (SOC) of the battery cell. Indicates the SOH of the battery cell. This indicates the weight corresponding to the SOH of the battery cell. Indicates the charging and discharging current of the battery cell. This indicates the weight corresponding to the charging and discharging current of the battery cell. This indicates the battery cell's DOD (Device Occurrence Disposal). This indicates the weight corresponding to the DOD of the battery cell. Indicates the state of the electrolyte. This indicates the weight corresponding to the electrolyte state.

[0031] according to Figure 1 As can be seen, an energy storage compartment is a hierarchical system composed of battery clusters, battery packs, and battery cells. Fundamentally, the performance differences between battery clusters are determined by the performance of the underlying battery cells. This invention can determine multi-dimensional clustering vectors based on the performance parameters of the battery cells, and then calculate the similarity between different levels from bottom to top based on these multi-dimensional clustering vectors, thereby ultimately determining the performance differences between each battery cluster and achieving accurate clustering of battery clusters.

[0032] Clustering is essentially a grouping process. This invention, by setting a time limit, enables dynamic grouping of battery clusters, allowing the grouping results to dynamically adapt to changes in the energy storage compartment's state. Based on this dynamic grouping, this invention performs power allocation, ensuring the power allocation results dynamically adapt to changes in the energy storage compartment's state and improving power allocation balance.

[0033] Step 203: Obtain the total power of the energy storage compartment in real time, and determine the allocation ratio step by step based on the cluster, and allocate the total power step by step according to the allocation ratio.

[0034] Based on accurate clustering, embodiments of the present invention can determine the allocation ratio of each unit (e.g., each cluster, each battery cluster, each battery pack, or each battery cell) in each level from top to bottom, according to the hierarchical order of cluster cluster - battery cluster - battery pack - battery cell, and in combination with the real-time status of each unit in each level, for power allocation.

[0035] It is important to clarify here that the clustering results can be updated periodically according to a set time interval. However, during the entire operation of the energy storage compartment, the control equipment acquires the total power in real time, determines the allocation ratio in real time, and performs hierarchical allocation in real time according to the allocation ratio.

[0036] Compared to existing technologies, this invention dynamically adjusts the update cycle (i.e., the set duration) of clustering results by controlling the overall SOC of the energy storage module. This allows the update frequency of the clustering results to closely match the state changes of the energy storage module at different stages of actual operation: when the overall SOC is low (where overall SOC changes drastically), the set duration is shortened to improve response speed and control accuracy; when the overall SOC is high (where SOC changes gradually), the set duration is extended to reduce unnecessary computational overhead. This invention can achieve real-time tracking and timely response to fluctuations in the overall state of the energy storage module without increasing the average computational load, thereby significantly enhancing the dynamic adaptability of the clustering results, avoiding power allocation inaccuracies, and improving power balancing effects.

[0037] Furthermore, this embodiment of the invention uses the state parameters of the underlying battery cells to cluster the upper-layer battery clusters. This allows the underlying state parameters to accurately reflect the state differences of the battery clusters, achieving precise clustering. Then, based on the clustering results, the allocation ratio is determined level by level, ensuring a balanced distribution of total power across all levels. This further improves the balancing effect and avoids exacerbating inconsistencies due to uneven distribution.

[0038] The specific methods for dynamically updating clustering results are described below.

[0039] During the operation of the energy storage compartment, this embodiment of the invention collects the state parameters of the battery cells at set intervals and performs cluster analysis to obtain the clustering results of the battery clusters.

[0040] In some embodiments, the set duration can be determined as follows: During the operation of the energy storage module, the overall SOC and a preset lookup table are acquired in real time. The lookup table contains multiple adjacent SOC intervals and their corresponding set durations; then, the set durations are dynamically updated based on the SOC interval in which the overall SOC is located.

[0041] In this embodiment of the invention, for each SOC interval, the set duration corresponding to the SOC interval is: the theoretical charge / discharge time corresponding to the SOC interval. ; It is an integer greater than or equal to 1.

[0042] The theoretical charge / discharge times for each SOC range include either the theoretical charging time or the theoretical discharging time. When the energy storage module is in charging mode, the set duration can be determined based on the theoretical charging time. When the energy storage module is in discharging mode, the set duration can be determined based on the theoretical discharging time. Here, the theoretical charging time refers to the theoretical charging time required for the overall SOC of the energy storage module to increase from the lower limit to the upper limit of the SOC range. The theoretical discharging time refers to the theoretical discharging time required for the overall SOC of the energy storage module to decrease from the upper limit to the lower limit of the SOC range.

[0043] For example, taking the charging process as an example, the present invention can set up a lookup table as shown in Table 1 below: Table 1

[0044] For example, when the overall SOC is greater than or equal to 10% and less than 20%, the overall SOC is determined to be in the 10%-20% SOC range. The theoretical charging time T1 corresponding to this SOC range is the theoretical charging time required for the overall SOC of the energy storage compartment to increase from 10% to 20%. Based on this theoretical charging time, the setting duration can be determined to be T1 / 3 in this embodiment of the invention. Other SOC ranges are similar and will not be described in detail here.

[0045] Taking the charging process as an example, the smaller the overall SOC of the energy storage compartment, the greater the rate of SOC change, and the shorter the theoretical charging time required. This embodiment of the invention determines the set duration based on the theoretical charging time, allowing the set duration to be shortened accordingly. This increases the update frequency of dynamic grouping, enabling timely responses to SOC fluctuations in the energy storage compartment and preventing exacerbation of battery imbalance due to clustering lag. Conversely, the larger the overall SOC of the energy storage compartment, the smaller the rate of SOC change, and the longer the theoretical charging time required. This embodiment of the invention extends the set duration accordingly to reduce unnecessary computational resource consumption and avoid increased computational burden due to excessively frequent clustering.

[0046] In this embodiment of the invention, the clustering results are updated at set intervals to dynamically respond to changes in the overall state of the energy storage compartment. Considering that the state of individual battery cells can affect the clustering results, this embodiment of the invention, in addition to the aforementioned periodic updates at set intervals, also introduces the following real-time update mechanism to adapt to changes in the state of individual battery cells.

[0047] In some embodiments, the real-time update mechanism may include: Within a set time period, the working status of the voltage of each battery cell is monitored in real time; if the voltage of any battery cell stops working, the process jumps to the step of collecting the status parameters of each battery cell in the energy storage compartment, and continues to execute subsequent steps to update the cluster.

[0048] Due to the protection mechanism of the battery cells, for each battery cell, if its voltage exceeds the upper voltage limit during the energy storage compartment's power supply process, the battery cell stops working. Conversely, if its voltage falls below the lower voltage limit during the energy storage compartment's discharge process, the battery cell stops working. To avoid the impact of a battery cell that has stopped working, this embodiment of the invention only performs cluster analysis on the state parameters of battery cells in the working state to determine the clustering results of the battery clusters, resulting in multiple clusters.

[0049] If any battery cell stops working, the current clustering result will become invalid. In this embodiment of the invention, during each set time period when the clustering result is not updated, the system monitors in real time whether each battery cell has stopped working. If any battery cell stops working, the system jumps to the step of collecting the state parameters of each battery cell in the energy storage compartment and continues to execute subsequent steps to update the clustering result.

[0050] By introducing the aforementioned real-time update mechanism, this embodiment of the invention can improve the response speed to sudden changes in the state of the corresponding battery cell.

[0051] Based on the above-mentioned real-time update mechanism, this embodiment of the invention also provides the following method for adjusting the set duration, so as to adjust the set duration according to the number of real-time updates.

[0052] In some embodiments, during the operation of the energy storage module, the number of jump update actions within the set duration corresponding to each SOC interval can also be detected; if the number of jump update actions within the set duration corresponding to any SOC interval is greater than the set threshold, the set duration corresponding to that SOC interval is adjusted according to the number of jump update actions.

[0053] Here, the jump update action refers to the action of jumping to execute the step of collecting the state parameters of each battery cell in the energy storage compartment.

[0054] For each set duration, if the number of jump update actions occurs too many times within the set duration (i.e., greater than the set threshold), it indicates that the set duration is too long. In this embodiment of the invention, the set duration can be adjusted according to the number of jump update actions, so that when the energy storage module enters working state again, dynamic clustering can be performed according to the adjusted set duration.

[0055] It should be noted that the set duration in this embodiment of the invention is determined based on the SOC interval. The set duration corresponding to each SOC interval is not the same. When adjusting the set duration, this embodiment of the invention can adjust the set duration of the corresponding SOC interval.

[0056] The adjusted duration can be expressed as: .

[0057] in, This indicates the adjusted set duration. This indicates the theoretical charge / discharge time corresponding to the SOC range. This indicates the number of times a jump update action has occurred.

[0058] Here, the threshold can be set according to the actual situation. For example, the threshold can be set to 3 times.

[0059] To make it easier to understand, the following example illustrates how to adjust the set duration.

[0060] When the overall SOC of the energy storage module is 30%, the module performs clustering every set time interval T3 / 3. However, if the energy storage module experiences four jump update actions within the set time interval T3 / 3 due to battery cell malfunction, it indicates that the set time interval T3 / 3 corresponding to the 30%-40% SOC range is too long. This embodiment of the invention can adjust the set time interval corresponding to the 30%-40% SOC range as follows: This updates the set duration corresponding to the 30%-40% SOC range in the query table, so that when the energy storage module enters the working state again, dynamic clustering can be performed according to the new set duration in the query table.

[0061] To avoid excessive clustering, embodiments of the present invention can set a lower limit for the duration of each SOC interval. If the calculated set duration is less than the lower limit, then the lower limit is set as the adjusted set duration. If based on... If the calculated set duration is greater than or equal to the lower limit of duration, then... The adjusted set duration is then determined. Finally, the final set duration is updated in the lookup table so that when the energy storage module enters its next working state, dynamic clustering can be performed based on the new set duration in the lookup table.

[0062] Here, the lower limit of duration can be determined according to the actual situation. For example, the lower limit of duration can be 3 minutes.

[0063] Based on the above-mentioned dynamic update setting time, the next step is to elaborate on the clustering process of battery clusters and the method for determining the allocation ratio.

[0064] In some embodiments, similarity calculations can be performed layer by layer from bottom to top based on the state parameters of the battery cells to obtain similarity results between battery clusters, and the battery clusters can be clustered based on the similarity results to obtain multiple clusters.

[0065] Specifically, similarity calculations can be performed based on the state parameters of each battery cell to obtain the similarity results between each battery cell; similarity calculations can be performed based on the similarity results between each battery cell to obtain the similarity results between each battery pack; similarity calculations can be performed based on the similarity results between each battery pack to obtain the similarity results between each battery cluster; and each battery cluster can be clustered based on the similarity results between each battery cluster to obtain multiple clusters.

[0066] In this embodiment of the invention, a multi-dimensional clustering vector corresponding to each battery cell can first be constructed based on the state parameters of each battery cell. Then, for each battery pack, the cosine similarity between each battery cell in the battery pack is calculated to obtain the similarity result.

[0067] The similarity results between different battery cells can be expressed as: .

[0068] in, Indicates battery cell and battery cells Similarity results between them Indicates battery cell The corresponding multi-dimensional clustering vectors, Indicates battery cell The corresponding multi-dimensional clustering vector.

[0069] Here, similarity calculations are only performed on battery cells that are in operation. For battery cells that are not in operation, the similarity results can be set to zero.

[0070] For each battery pack, embodiments of the present invention can combine the similarity results between the battery cells in the battery pack to form a similarity matrix. This similarity matrix is ​​then used as the battery pack clustering vector for that battery pack.

[0071] For each battery cluster, based on the clustering vectors of each battery pack within that cluster, the similarity results between the battery packs in that cluster are calculated. The calculation process for the similarity results between battery packs is the same as the calculation process for the similarity results between battery cells, and will not be repeated here.

[0072] For each battery cluster, embodiments of the present invention can combine the similarity results between the battery packs in the battery cluster to form a similarity matrix, and use the similarity matrix as the battery cluster clustering vector of the battery cluster.

[0073] In this embodiment of the invention, the similarity results between each battery cluster are calculated based on the clustering vector of each battery cluster. The calculation process for the similarity results between each battery cluster is the same as the calculation process for the similarity results between each battery cell, and will not be repeated here.

[0074] This invention embodiment can perform clustering based on the clustering vectors of each battery cluster using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Specifically, based on the clustering vector B of each battery cluster, the distance matrix (1-B) corresponding to each battery cluster is calculated, and each distance matrix is ​​input into the DBSCAN algorithm to obtain the clustering results output by the algorithm.

[0075] For example, before running the DBSCAN algorithm, a k-distance graph can be pre-plotted (e.g., k=5); try parameter combinations such as neighborhood radius Eps=0.5 and minimum number of points MinPts=5, and fine-tune the parameters based on the clustering results to find the optimal clustering parameters.

[0076] Based on the determination of multiple clusters, the embodiments of the present invention determine the allocation ratio of each cluster, each battery cluster, each battery pack, and each battery cell from top to bottom.

[0077] In some embodiments, the method for determining the allocation ratio step by step includes: First, based on the voltage of each battery cell and the charging / discharging parameters of each battery cell in each cluster, the allocation ratio of each cluster is determined. Next, for each cluster, based on the voltage of each battery cell in each battery cell within that cluster, the allocation ratio of each battery cell is determined. Subsequently, for each battery cell, based on the voltage of each battery cell in each battery pack within that cluster, the allocation ratio of each battery pack is determined. Finally, for each battery pack, based on the voltage of each battery cell and the charging / discharging parameters of each battery cell, the allocation ratio of each battery cell is determined.

[0078] This invention performs power allocation from top to bottom, first determining the allocation ratio for each cluster: In some embodiments, for each cluster, the total number of battery cells in the cluster that are in operation is determined based on the voltage of each battery cell; for all battery cells in operation, the average charge and discharge parameters are calculated, and the product of the average charge and discharge parameters and the total number of battery cells in operation is determined as the amount to be charged and discharged for that cluster; finally, the allocation ratio of each cluster is determined according to the amount to be charged and discharged for each cluster.

[0079] It is important to clarify here that average charge / discharge parameters include average charging parameters (e.g., average DOD, calculated from the cell DOD) or average discharging parameters (e.g., average SOC, calculated from the cell SOC). The amount to be charged / discharged includes either the amount to be charged or the amount to be discharged. When the energy storage compartment is in charging operation, this embodiment of the invention determines the amount to be charged for each cluster by statistically analyzing the average charging parameters, thereby determining the allocation ratio. When the energy storage compartment is in discharging operation, this embodiment of the invention determines the amount to be discharged for each cluster by statistically analyzing the average discharging parameters, thereby determining the allocation ratio.

[0080] Given the large number of battery cells, for each cluster, any one of them can be selected. x A battery cell in working condition, and according to xThe charge / discharge parameters (cell SOC or cell DOD) of each battery cell are used to calculate the average charge / discharge parameters (average SOC or average DOD). The product of this average charge / discharge parameter and the total number of battery cells in operation is the amount of charge / discharge to be performed for that cluster.

[0081] For each cluster, if the total number of battery cells in operation within that cluster is less than or equal to 50, then it is determined that... x The value of is the total number of battery cells in working condition; If the total number of battery cells in operation within the cluster is greater than 50 and less than or equal to 200, then according to calculate x The value of .

[0082] in, This represents the standard normal distribution quantile corresponding to the confidence level (e.g., 1.96 for a 95% confidence level). This represents the overall standard deviation of the charge and discharge parameters. This indicates the allowable error (e.g., 1.5%). This indicates the total number of battery cells in operation.

[0083] Considering the relatively small total number of battery cells in operation, this embodiment of the invention utilizes... The calculated quantity X Perform finite population correction to obtain the final result. x Values.

[0084] If the total number of battery cells in operation within the cluster is greater than 200, then directly based on... calculate x The value of does not need to be corrected.

[0085] Based on the determined charge / discharge quantities of each cluster, the sum of the charge / discharge quantities of all clusters is calculated. For each cluster, the ratio of the charge / discharge quantity of that cluster to the above sum is the allocation ratio for that cluster. In this embodiment of the invention, the total power is allocated to each cluster according to the allocation ratio.

[0086] Taking three clusters as an example, if the charge / discharge quantities of each cluster are respectively Bdl 1. Bdl 2. Bdl 3. Then the allocation power of each cluster can be determined as follows: P bt1 , P bt2 , P bt3 :

[0087] In the formula, This indicates the total power of the energy storage compartment. , and These represent the allocation ratio of each cluster.

[0088] After determining the allocation ratio and power of each cluster, the next step is to determine the allocation ratio and power of each battery cluster: For each cluster, embodiments of the present invention can determine the total number of battery cells in operation within each battery cluster based on the voltage of each battery cell in that cluster. Embodiments of the present invention also determine the allocation ratio of each battery cluster within that cluster based on the total number of battery cells in operation within each battery cluster.

[0089] Specifically, for each cluster, the total number of battery cells in operation within that cluster can be determined (referred to as the total cluster size for ease of distinction). For each battery cluster within that cluster, the total number of battery cells in operation within that cluster can be determined (referred to as the total battery cluster size for ease of distinction). For each battery cluster within that cluster, the ratio of the total battery cluster size to the total cluster size is determined as the allocation ratio for that battery cluster.

[0090] Taking the three clusters mentioned above as an example, if the first cluster contains battery cluster B1 and battery cluster B2, the total number of battery cells in operation in B1 and B2 are respectively... Bcn 1 and Bcn 2. Therefore, the power distribution of battery clusters B1 and B2 can be determined as follows: P b1 , P b2 :

[0091] In the formula, and These represent the allocation ratios of battery clusters B1 and B2, respectively.

[0092] After determining the allocation ratio and power of each battery cluster, the next step is to determine the allocation ratio and power of each battery pack: Similar to the process of determining the allocation ratio and power of each battery cluster described above, for each battery cluster, this embodiment of the invention can determine the total number of battery cells in the working state in each battery pack based on the voltage of each battery cell in each battery pack within that battery cluster. This embodiment of the invention determines the allocation ratio of each battery pack in the battery cluster based on the total number of battery cells in the working state in each battery pack.

[0093] Taking battery cluster B1 as an example again, battery cluster B1 contains 16 battery packs. The total number of battery cells in operation in each battery pack is as follows: Pcn 1, Pcn 2, ..., Pcn 16. Then the th battery in this battery cluster... The power distribution of each battery pack is as follows:

[0094] in, Indicates the first Power distribution of each battery pack Indicates the first The total number of battery cells in a battery pack that are in operation. Indicates the first The allocation ratio of each battery pack.

[0095] Finally, based on the determination of the allocation ratio and power of each battery pack, the final allocation ratio and power of each battery cell are determined: In some embodiments, for each battery pack, the battery cells in the battery pack that are in operation are determined according to the voltage of each battery cell; for the battery cells in operation, the allocation ratio of each battery cell is determined according to the charging and discharging parameters of each battery cell.

[0096] Based on the above, when the energy storage compartment is in charging mode, if the battery cell voltage is greater than the upper voltage limit, the battery cell is determined to stop working. When the energy storage compartment is in discharging mode, if the battery cell voltage is less than the lower voltage limit, the battery cell is determined to stop working. This embodiment of the invention can determine which battery cells are in operation based on their voltage.

[0097] Here, charging and discharging parameters include charging parameters (battery DOD) or discharging parameters (battery SOC). When the energy storage compartment is in charging mode, the allocation ratio of each battery cell can be determined based on the charging parameters. When the energy storage compartment is in discharging mode, the allocation ratio of each battery cell can be determined based on the discharging parameters.

[0098] Specifically, when the energy storage compartment is in the charging working state, for each battery pack, the total charging parameters of each battery cell in the working state in the battery pack can be calculated. For each battery cell in the battery pack, the ratio of the charging parameters of the battery cell to the total charging parameters is calculated. This ratio is the allocation ratio of the battery cell.

[0099] Taking the first battery pack in battery cluster B1 as an example, this battery pack contains 16 battery cells. When the energy storage compartment is in charging operation, the first... The power distribution of each battery cell can be expressed as:

[0100] in, Indicates the first Power distribution per battery cell This indicates the power allocation of the first battery pack in battery cluster B1. Indicates the first Charging parameters of each battery cell Indicates the first The allocation ratio of each battery cell.

[0101] This invention, by performing similarity calculations and aggregation from the battery cell level upwards (battery cell → battery pack → battery cluster), fully preserves the differential distribution information of the underlying battery cells. This allows the final clustering results of the battery clusters to accurately reflect their internal consistency, health gradient, and potential shortcomings, thus improving the clustering accuracy. Compared to methods that directly cluster battery clusters based on battery cluster parameters, this approach ensures the authenticity and precision of battery cluster state assessment from the data source, overcoming the drawback of relying on macroscopic average parameters (i.e., battery cluster parameters) to mask internal differences.

[0102] Based on the above-mentioned precise clustering, the embodiments of the present invention decompose and execute the allocation ratio step by step from top to bottom according to the physical power path of "energy storage compartment → cluster → battery cluster → battery pack → battery cell", so that the total power of the energy storage compartment can be accurately and errorlessly adapted to each specific battery unit (i.e. each cluster, each battery cluster, each battery pack, and each battery cell) along a clear control link, thereby improving the balance of power distribution.

[0103] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0104] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0105] Figure 3 A schematic diagram of the energy storage compartment charging and discharging power distribution device provided in an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below: like Figure 3 As shown, the energy storage compartment charging and discharging power distribution device 3 includes: an update module 31 and a distribution module 32.

[0106] Update module 31, used for: During the operation of the energy storage compartment, the state parameters of each battery cell in the compartment are collected at set intervals. The set intervals change dynamically with the overall SOC of the energy storage compartment, and the larger the overall SOC, the longer the corresponding set intervals. Based on the state parameters, the battery clusters in the energy storage compartment are clustered to obtain multiple clusters; The allocation module 32 is used to obtain the total power of the energy storage compartment in real time, and determine the allocation ratio step by step based on the cluster, and allocate the total power step by step according to the allocation ratio.

[0107] In one possible implementation, the method for determining the duration includes: During the operation of the energy storage compartment, the overall State of Charge (SOC) and a preset lookup table are acquired in real time. The lookup table contains multiple adjacent SOC intervals and their corresponding set durations. For each SOC interval, the set duration is: the theoretical charge / discharge time for that SOC interval. ; An integer greater than or equal to 1; The set duration is dynamically updated based on the overall SOC range in which it is located.

[0108] In one possible implementation, update module 31 is also used for: Within a set time period, the operating status of the voltage of each battery cell is monitored in real time; If the voltage of any battery cell stops working, the process will jump to the step of collecting the state parameters of each battery cell in the energy storage compartment, and continue to execute subsequent steps to update the cluster.

[0109] In one possible implementation, update module 31 is also used for: During the operation of the energy storage compartment, the number of jump update actions that occur within the set time period corresponding to each SOC interval is detected; If the number of jump update actions within the set time period corresponding to any SOC interval exceeds the set threshold, the set time period corresponding to that SOC interval will be adjusted according to the number of actions.

[0110] In one possible implementation, update module 31 is specifically used for: Similarity calculations are performed based on the state parameters of each battery cell to obtain the similarity results between the battery cells. Similarity calculations are performed based on the similarity results between individual battery cells to obtain the similarity results between each battery pack. Similarity calculations are performed based on the similarity results between each battery pack to obtain the similarity results between each battery cluster. Based on the similarity results between the battery clusters, the battery clusters are clustered to obtain multiple clusters.

[0111] In one possible implementation, allocation module 32 is specifically used for: Based on the voltage of each battery cell and the charging and discharging parameters of each battery cell in each cluster, the allocation ratio of each cluster is determined. For each cluster, the allocation ratio of each battery cluster is determined based on the voltage of each battery cell in each battery cluster. For each battery cluster, the allocation ratio of each battery pack is determined based on the voltage of each cell in each battery pack within that battery cluster. For each battery pack, the allocation ratio of each battery cell is determined based on the voltage and charging / discharging parameters of each battery cell in the pack.

[0112] In one possible implementation, allocation module 32 is specifically used for: For each cluster, the total number of battery cells in operation within that cluster is determined based on the voltage of each battery cell. For all battery cells in operation, the average charge and discharge parameters are calculated, and the product of the average charge and discharge parameters and the total number of battery cells in operation is determined as the amount of charge and discharge to be performed for that cluster. The allocation ratio of each cluster is determined according to the amount of charge and discharge to be performed in each cluster.

[0113] In one possible implementation, allocation module 32 is specifically used for: For each battery pack, determine which battery cells in the pack are in operation based on the voltage of each cell. For battery cells in operation, the allocation ratio of each battery cell is determined based on the charging and discharging parameters of each cell.

[0114] This device embodiment can be used to implement the above method embodiment, and its technical principle and implementation effect are the same as those of the above method embodiment, so they will not be repeated here.

[0115] Figure 4 This is a schematic diagram of the control device provided in an embodiment of the present invention. Figure 4 As shown, the control device 4 in this embodiment includes a processor 40 and a memory 41. The memory 41 stores a computer program 42. When the processor 40 executes the computer program 42, it implements the steps in the various method embodiments described above. Alternatively, when the processor 40 executes the computer program 42, it implements the functions of each module / unit in the various device embodiments described above.

[0116] For example, computer program 42 may be divided into one or more modules / units, which are stored in memory 41 and executed by processor 40 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 42 in control device 4.

[0117] The control device 4 may include, but is not limited to, a processor 40 and a memory 41. Those skilled in the art will understand that... Figure 4 This is merely an example of control device 4 and does not constitute a limitation on control device 4. It may include more or fewer components than shown, or combine certain components, or different components. For example, control device 4 may also include input / output devices, network access devices, buses, etc.

[0118] The processor 40 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0119] The memory 41 can be an internal storage unit of the control device 4, such as a hard disk or RAM of the control device 4. The memory 41 can also be an external storage device of the control device 4, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the control device 4. Furthermore, the memory 41 can include both internal and external storage units of the control device 4. The memory 41 is used to store the computer program 42 and other programs and data required by the control device 4. The memory 41 can also be used to temporarily store data that has been output or will be output.

[0120] For the sake of simplicity and clarity, only the above-described functional modules / units are used as examples. In practical applications, the functions described above can be assigned to different functional modules / units as needed. These modules / units can be implemented in hardware, software, or a combination of both.

[0121] This invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the methods described in the above-described method embodiments.

[0122] This invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the methods described in the above-described method embodiments.

[0123] Computer programs include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. Computer-readable media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0124] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0125] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for distributing charging and discharging power in an energy storage compartment, characterized in that, The energy storage compartment contains multiple battery clusters, each battery cluster contains multiple battery packs, and each battery pack contains multiple battery cells. The method includes: During the operation of the energy storage compartment, the state parameters of each battery cell in the energy storage compartment are collected at set intervals. The set intervals change dynamically with the overall SOC of the energy storage compartment, and the larger the overall SOC, the longer the corresponding set intervals. Based on the state parameters, the battery clusters in the energy storage compartment are clustered to obtain multiple clusters; The total power of the energy storage compartment is acquired in real time, and the allocation ratio is determined step by step based on the cluster. The total power is then allocated step by step according to the allocation ratio.

2. The energy storage chamber charging and discharging power distribution method according to claim 1, characterized in that, The method for determining the set duration includes: During the operation of the energy storage compartment, the overall State of Charge (SOC) and a preset lookup table are acquired in real time. The lookup table contains multiple adjacent SOC intervals and their corresponding set durations. For each SOC interval, the set duration is: the theoretical charge / discharge time corresponding to that SOC interval. ; An integer greater than or equal to 1; The set duration is dynamically updated based on the SOC range in which the overall SOC is located.

3. The energy storage chamber charging and discharging power distribution method according to claim 2, characterized in that, The method further includes: Within the set time period, the operating status of the voltage of each battery cell is detected in real time; If the voltage of any battery cell stops working, the process jumps to the step of collecting the state parameters of each battery cell in the energy storage compartment, and continues to execute subsequent steps to update the cluster.

4. The energy storage chamber charging and discharging power distribution method according to claim 3, characterized in that, The method further includes: During the operation of the energy storage compartment, the number of jump update actions that occur within the set time period corresponding to each SOC interval is detected; If the number of jump update actions within the set time period corresponding to any SOC interval exceeds the set threshold, the set time period corresponding to that SOC interval will be adjusted according to the number of jumps.

5. The energy storage chamber charging and discharging power distribution method according to any one of claims 1-4, characterized in that, The energy storage compartment is clustered based on the state parameters to obtain multiple clusters, including: Similarity calculations are performed based on the state parameters of each battery cell to obtain the similarity results between the battery cells. Similarity calculations are performed based on the similarity results between individual battery cells to obtain the similarity results between each battery pack. Similarity calculations are performed based on the similarity results between each battery pack to obtain the similarity results between each battery cluster. Based on the similarity results between the battery clusters, the battery clusters are clustered to obtain multiple clusters.

6. The energy storage chamber charging and discharging power distribution method according to any one of claims 1-4, characterized in that, The step-by-step determination of allocation ratios based on the clusters includes: Based on the voltage of each battery cell and the charging and discharging parameters of each battery cell in each cluster, the allocation ratio of each cluster is determined. For each cluster, the allocation ratio of each battery cluster is determined based on the voltage of each battery cell in each battery cluster. For each battery cluster, the allocation ratio of each battery pack is determined based on the voltage of each cell in each battery pack within that battery cluster. For each battery pack, the allocation ratio of each battery cell is determined based on the voltage and charging / discharging parameters of each battery cell in the pack.

7. The energy storage chamber charging and discharging power distribution method according to claim 6, characterized in that, The determination of the allocation ratio for each cluster based on the voltage and charge / discharge parameters of each battery cell in each cluster includes: For each cluster, the total number of battery cells in operation within that cluster is determined based on the voltage of each battery cell. For all battery cells in the working state, the average charge and discharge parameters are calculated, and the product of the average charge and discharge parameters and the total number of battery cells in the working state is determined as the amount of charge and discharge to be performed for the cluster. The allocation ratio of each cluster is determined according to the amount of charge and discharge to be performed in each cluster.

8. The energy storage chamber charging and discharging power distribution method according to claim 6, characterized in that, For each battery pack, based on the voltage and charging / discharging parameters of each battery cell, the allocation ratio of each battery cell is determined, including: For each battery pack, determine which battery cells in the pack are in operation based on the voltage of each cell. For battery cells in operation, the allocation ratio of each battery cell is determined based on the charging and discharging parameters of each cell.

9. A power distribution device for charging and discharging an energy storage compartment, characterized in that, The energy storage compartment contains multiple battery clusters, each battery cluster contains multiple battery packs, and each battery pack contains multiple battery cells. The device includes: The update module is used for: During the operation of the energy storage compartment, the state parameters of each battery cell in the energy storage compartment are collected at set intervals. The set intervals change dynamically with the overall SOC of the energy storage compartment, and the larger the overall SOC, the longer the corresponding set intervals. Based on the state parameters, the battery clusters in the energy storage compartment are clustered to obtain multiple clusters; The allocation module is used to obtain the total power of the energy storage compartment in real time, determine the allocation ratio step by step based on the cluster, and allocate the total power step by step according to the allocation ratio.

10. The energy storage compartment charging and discharging power distribution device according to claim 9, characterized in that, The update module is also used for: Within the set time period, the operating status of the voltage of each battery cell is detected in real time; If the voltage of any battery cell stops working, the process jumps to the step of collecting the state parameters of each battery cell in the energy storage compartment, and continues to execute subsequent steps to update the cluster.

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