Statistical analysis-based dynamic SOC calibration method and system

Abstract

The invention provides a dynamic SOC calibration method and system based on statistical analysis, and the method comprises the steps: collecting the charge state and voltage curve data of a cell under different charge and discharge rates through an offline experiment, and determining a candidate characteristic peak group based on the charge state and voltage curve data; determining a reference state-of-charge value according to the candidate characteristic peak group by using a Monte Carlo sampling law; the method comprises the following steps: identifying a battery cell in an operation process of a to-be-calibrated energy storage cabinet system to obtain a characteristic battery cell, and then determining a state-of-charge value corresponding to a characteristic peak of the characteristic battery cell; and determining the absolute value of the difference value between the charge state value corresponding to the characteristic peak of the characteristic cell and the reference charge state value, and calibrating the screen display charge state value of the to-be-calibrated energy storage cabinet system based on the charge state value corresponding to the characteristic peak of the characteristic cell and the absolute value of the difference value. According to the technical scheme provided by the invention, the state-of-charge value of the battery can be dynamically corrected in the operation process of the battery so as to adapt to the actual operation requirement.

Description

Technical Field

[0001] This application relates to the field of battery management technology, and in particular to a dynamic SOC calibration method and system based on statistical analysis. Background Technology

[0002] Under the dual-carbon strategy, electrochemical energy storage, as a crucial component of energy storage, effectively reduces the instability factors brought about by new energy sources, while simultaneously enhancing grid dispatch flexibility and alleviating grid load pressure. Furthermore, its rapid adjustment speed, flexible deployment, short construction cycle, and environmental friendliness are also key factors contributing to its continuous increase in installed capacity in recent years. Despite the immense application value of new electrochemical energy storage, its development is still constrained by performance factors. Current mainstream energy storage systems, whether containerized or distributed, essentially employ series / parallel cells to form battery packs, which are then connected to form battery clusters, ultimately constituting a complete energy storage system. In such systems, the cells inevitably experience inconsistent degradation rates during use, causing many cells to fail to fully utilize their capacity, ultimately resulting in a low overall system capacity.

[0003] The key to solving the capacity limitation problem caused by battery inconsistency lies in implementing consistent balancing of the system. In terms of balancing target selection, SOC balancing is often used. The accuracy of the balancing target determines the final balancing effect and the reliability of the balancing command. Considering the bias of SOC results, the industry currently uses two main strategies for SOC calibration: full charge / discharge calibration and OCV (Open Circuit Voltage)-SOC calibration. However, both calibration strategies are static calibrations with relatively stringent triggering conditions, making them difficult to meet practical needs. Therefore, a highly flexible dynamic SOC calibration scheme is urgently needed, capable of dynamically correcting the battery SOC during battery operation to adapt to actual operating requirements. Summary of the Invention

[0004] This application provides a dynamic SOC calibration method and system based on statistical analysis, which at least solves the technical problem that the calibration strategies are all static calibrations and the triggering conditions are too strict to meet actual needs.

[0005] The first aspect of this application proposes a dynamic SOC calibration method based on statistical analysis, the method comprising:

[0006] Offline experiments were conducted to collect the state of charge and voltage curve data of the battery cells at different charge and discharge rates, and candidate characteristic peak groups were determined based on the state of charge and voltage curve data. The reference state of charge value is determined based on the candidate characteristic peak group and using the Monte Carlo sampling law; The characteristic cells are identified during the operation of the energy storage cabinet system to be calibrated, and then the state of charge value corresponding to the characteristic peak of the characteristic cells is determined. The absolute value of the difference between the state of charge value corresponding to the characteristic peak of the characteristic cell and the reference state of charge value is determined, and the display state of charge value of the energy storage cabinet system to be calibrated is calibrated based on the state of charge value corresponding to the characteristic peak of the characteristic cell and the absolute value of the difference.

[0007] Preferably, determining the candidate characteristic peak group based on the state of charge and voltage curve data includes: The second in-situ peak in the state of charge and voltage curve data of the battery cell under different charge and discharge rates is obtained, and then an in-situ characteristic peak group is constructed based on the second in-situ peak of the battery cell under different charge and discharge rates. The isolated forest algorithm is used to filter the data within the in-situ feature peak group to obtain candidate feature peak groups.

[0008] Furthermore, determining the reference state of charge value based on the candidate characteristic peak group and using the Monte Carlo sampling law includes: The expected edge distribution of the candidate feature peak group is determined by the Monte Carlo sampling theorem; The expected edge distribution is used as the reference state of charge value.

[0009] Furthermore, the process of identifying characteristic cells during the operation of the energy storage cabinet system to be calibrated includes: The full charge state and full discharge state of the energy storage cabinet system to be calibrated are monitored, and the cell number with the largest voltage when the energy storage cabinet system to be calibrated completes a full charge state and the cell number with the smallest voltage when the energy storage cabinet system to be calibrated completes a full discharge state are recorded. Store the cell number with the highest voltage under full charge and the cell number with the lowest voltage under full discharge in a buffer array; Determine whether the cell number with the lowest voltage in the fully discharged state in the buffer array is equal to the cell number with the lowest voltage in the fully discharged state. If so, the cell corresponding to the cell number is taken as the feature cell; otherwise, the cell corresponding to the cell number with the lowest voltage in the fully discharged state is taken as the feature cell.

[0010] Furthermore, determining the state of charge value corresponding to the characteristic peak of the characteristic cell includes: Step F1: Update and maintain the voltage differential sequence window, current sequence window, and maximum voltage window of the characteristic cell in real time, where: The voltage differential sequence window is used to store the voltage differential data of the feature cell within a preset time period; The current sequence window is used to store the current data of the characteristic battery cell collected synchronously with the voltage differential data; The maximum voltage window is used to record the maximum value in the voltage differential sequence window in real time; The update trigger conditions for the voltage differential sequence window, the current sequence window, and the maximum voltage window are as follows: when the change amount of the state of charge value of the characteristic battery cell is greater than or equal to a preset capacity change threshold, the data processing system adds the current voltage differential data and current data of the characteristic battery cell to the voltage differential sequence window and the current sequence window respectively, and synchronously updates the end value of the maximum voltage window to the maximum value of the current voltage differential sequence window; Step F2: The data processing system determines whether the operating parameters of the characteristic battery cell meet the preset analysis conditions. The preset analysis conditions are: the current SOC of the characteristic battery cell is in the range of 40% < SOC < 80%, and the current charge-discharge rate of the characteristic battery cell is in the range of 0.05 < charge-discharge rate < 0.6, where the charge-discharge rate is the ratio of the current value in the current sequence window to the rated capacity of the characteristic battery cell; Step F3: If the data processing system determines that the characteristic battery cell meets the preset analysis conditions, peak validity verification is performed; Among them, the peak validity verification includes: When the data processing system detects that the maximum value in the maximum voltage window is equal to the minimum value, it is determined that the peak of the voltage change rate corresponding to the current voltage differential sequence window is the characteristic peak of the characteristic battery cell, and the state of charge value corresponding to the characteristic peak is used as the state of charge value of the characteristic battery cell.

[0011] Further, the calibration of the displayed state of charge value of the to-be-calibrated energy storage cabinet system based on the state of charge value corresponding to the characteristic peak of the characteristic battery cell and the absolute value of the difference includes: When the state of charge value corresponding to the characteristic peak of the characteristic battery cell is within a preset confidence interval, the sum of the absolute value of the difference and the displayed state of charge value is determined, and the sum of the absolute value of the difference and the displayed state of charge value is used as the corrected state of charge value of the energy storage cabinet system; When the state of charge value corresponding to the characteristic peak of the characteristic battery cell is not within the preset confidence interval, no correction is performed.

[0012] An embodiment of the second aspect of the present application proposes a dynamic SOC calibration system based on statistical analysis, including: An off-line acquisition module, configured to acquire the state of charge and voltage curve data of the battery cell at different charge-discharge rates through off-line experiments, and determine a candidate characteristic peak group based on the state of charge and voltage curve data; The information mining module is used to determine the reference state of charge value based on the candidate feature peak group and using the Monte Carlo sampling law; The determination module is used to identify characteristic cells during the operation of the energy storage cabinet system to be calibrated, and then determine the state of charge value corresponding to the characteristic peak of the characteristic cell. The calibration module is used to determine the absolute value of the difference between the state of charge value corresponding to the characteristic peak of the characteristic cell and the reference state of charge value, and to calibrate the display state of charge value of the energy storage cabinet system to be calibrated based on the state of charge value corresponding to the characteristic peak of the characteristic cell and the absolute value of the difference.

[0013] Preferably, the offline acquisition module is further used for: The second in-situ peak in the state of charge and voltage curve data of the battery cell under different charge and discharge rates is obtained, and then an in-situ characteristic peak group is constructed based on the second in-situ peak of the battery cell under different charge and discharge rates. The isolated forest algorithm is used to filter the data within the in-situ feature peak group to obtain candidate feature peak groups. A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the method described in the first aspect embodiment.

[0014] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method described in the first aspect.

[0015] The technical solutions provided by the embodiments of this application bring at least the following beneficial effects: This application proposes a dynamic SOC calibration method and system based on statistical analysis. The method includes: collecting state-of-charge (SOC) and voltage curve data of battery cells at different charge / discharge rates through offline experiments, and determining candidate characteristic peak groups based on the SOC and voltage curve data; determining a reference SOC value based on the candidate characteristic peak groups and using the Monte Carlo sampling theorem; identifying characteristic cells during the operation of the energy storage cabinet system to be calibrated, and then determining the SOC value corresponding to the characteristic peak of the characteristic cell; determining the absolute value of the difference between the SOC value corresponding to the characteristic peak of the characteristic cell and the reference SOC value; and calibrating the displayed SOC value of the energy storage cabinet system to be calibrated based on the SOC value corresponding to the characteristic peak of the characteristic cell and the absolute value of the difference. The technical solution proposed in this application can dynamically correct the battery SOC value during battery operation to adapt to actual operating requirements.

[0016] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0017] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 A flowchart illustrating a dynamic SOC calibration method based on statistical analysis according to an embodiment of this application; Figure 2 This is a structural diagram of a dynamic SOC calibration system based on statistical analysis provided according to an embodiment of this application. Detailed Implementation

[0018] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0019] This application proposes a dynamic SOC calibration method and system based on statistical analysis. The method includes: collecting state-of-charge (SOC) and voltage curve data of battery cells at different charge / discharge rates through offline experiments, and determining candidate characteristic peak groups based on the SOC and voltage curve data; determining a reference SOC value based on the candidate characteristic peak groups and using the Monte Carlo sampling theorem; identifying characteristic cells during the operation of the energy storage cabinet system to be calibrated, and then determining the SOC value corresponding to the characteristic peak of the characteristic cell; determining the absolute value of the difference between the SOC value corresponding to the characteristic peak of the characteristic cell and the reference SOC value; and calibrating the displayed SOC value of the energy storage cabinet system to be calibrated based on the SOC value corresponding to the characteristic peak of the characteristic cell and the absolute value of the difference. The technical solution proposed in this application can dynamically correct the battery SOC value during battery operation to adapt to actual operating requirements.

[0020] The following description, with reference to the accompanying drawings, illustrates a dynamic SOC calibration method and system based on statistical analysis, according to embodiments of this application.

[0021] Example 1 Figure 1 This is a flowchart illustrating a dynamic SOC calibration method based on statistical analysis according to an embodiment of this application, as shown below. Figure 1 As shown, the method includes: Step 1: Collect the state of charge and voltage curve data of the battery cell at different charge and discharge rates through offline experiments, and determine the candidate characteristic peak group based on the state of charge and voltage curve data; In this embodiment of the disclosure, determining the candidate characteristic peak group based on the state of charge and voltage curve data includes: The second in-situ peak in the state of charge and voltage curve data of the battery cell under different charge and discharge rates is obtained, and then an in-situ characteristic peak group is constructed based on the second in-situ peak of the battery cell under different charge and discharge rates. The isolated forest algorithm is used to filter the data within the in-situ feature peak group to obtain candidate feature peak groups.

[0022] It should be noted that, through extensive offline experiments, SOC and voltage curve data for different cells at different rate limits were collected. By selecting a preset capacity interval and performing differential calculations on the voltage, the state of charge and voltage curves were obtained. , The specific capacity interval is not subject to any particular requirement in actual implementation and can be flexibly selected according to the actual application scenario. diff For difference operations, This represents the voltage sequence obtained from the data acquisition. For the collection sequence.

[0023] According to the above The curve can locate two characteristic peaks, referred to as the first and second in-situ peaks. In the volumetric experimental data, the inventors observed that the first in-situ peak was "eliminated" at a larger magnification, therefore the second in-situ peak was used as the reference in-situ peak.

[0024] The collection of second in-situ peaks from a large amount of test data is called the in-situ characteristic peak group: Specifically, This represents a voltage differential sequence; due to equally spaced capacity sampling, the peak value can be approximated using only the voltage derivative. Represents the corresponding voltage sequence.

[0025] Considering that the characteristic peak set is closely related to the experimental conditions, the isolated forest method was first used to determine the optimal peak set. and The data is filtered using features to determine which data needs to be retained, and unreasonable data is filtered according to the following abnormal scores to obtain candidate groups.

[0026] Specifically, after obtaining an array consisting of a large number of offline feature peaks, an isolation forest (composed of multiple isolation trees) is constructed from the array. The isolation trees continuously split the samples by randomly selecting split points until the number of samples equals 1.

[0027] After constructing the isolation forest, calculate the expected depth of each sample in the array within the isolation forest. E ( h ( x )).

[0028] Assuming we construct 5 isolated trees, and the depths of the first feature peak in the five trees are 3, 2, 3, 5, and 7 respectively, then the average depth is (3+2+3+5+7) / 5=4. The calculation formula is as follows:

[0029] In the formula, T is the number of isolated trees generated, and h t (x) specifies the depth of the characteristic peak sample in each tree.

[0030] Next, the standardized term c(n) is calculated. , where n is the number of nodes used to construct a single isolated tree. is Euler's constant, approximately equal to 0.5772156649.

[0031] By substituting the calculated E(h(x)) and c(n) into the anomaly score calculation formula Anomaly scores for each characteristic peak can be obtained. It's important to note that the above formula is a slightly modified version; its core essence is to measure the relative magnitude of outliers and standardized terms. Outliers tend to be isolated earlier, resulting in a relatively smaller average depth and thus a lower overall score. Conversely, normal values ​​tend to have a more concentrated score distribution.

[0032] Step 2: Determine the reference state of charge value based on the candidate characteristic peak group and using the Monte Carlo sampling law; In this embodiment of the disclosure, step 2 specifically includes: The expected edge distribution of the candidate feature peak group is determined by the Monte Carlo sampling theorem; The expected edge distribution is used as the reference state of charge value.

[0033] It should be noted that Monte Carlo sampling (MCMC) approximation is used to replace integral calculations to obtain the benchmark. ; Specifically, using formulas Determine the expected marginal distribution of candidate feature peak groups , for The corresponding edge distribution, For the corresponding probability density function, The baseline value is obtained by approximating the expected value by sampling the sample mean.

[0034] Step 3: Identify the characteristic cells in the operation of the energy storage cabinet system to be calibrated, and then determine the state of charge value corresponding to the characteristic peak of the characteristic cell; In this embodiment of the disclosure, the process of identifying characteristic cells during the operation of the energy storage cabinet system to be calibrated includes: The full charge state and full discharge state of the energy storage cabinet system to be calibrated are monitored, and the cell number with the largest voltage when the energy storage cabinet system to be calibrated completes a full charge state and the cell number with the smallest voltage when the energy storage cabinet system to be calibrated completes a full discharge state are recorded. Store the cell number with the highest voltage under full charge and the cell number with the lowest voltage under full discharge in a buffer array; Determine whether the cell number with the lowest voltage in the fully discharged state in the buffer array is equal to the cell number with the lowest voltage in the fully discharged state. If so, the cell corresponding to the cell number is taken as the feature cell; otherwise, the cell corresponding to the cell number with the lowest voltage in the fully discharged state is taken as the feature cell.

[0035] It should be noted that the characteristic cell is the cell that is fully charged and then discharged first, and it determines the overall performance of the system. Performance.

[0036] Record the cell serial number corresponding to the maximum voltage at each full charge cutoff point. The minimum voltage corresponding to the cell serial number at the time of full discharge cutoff is recorded. In the ideal state of passive equilibrium execution, there exists In reality, in most cases... Therefore, when such a cell does not exist, the cell corresponding to the minimum voltage at the fully discharged cutoff moment can be selected as the characteristic cell and used as the input of the next dynamic differential module.

[0037] In this embodiment of the disclosure, determining the state of charge value corresponding to the characteristic peak of the characteristic cell includes: Step F1: Update and maintain the voltage differential sequence window, current sequence window, and maximum voltage window of the characteristic cell in real time, where: The voltage differential sequence window is used to store the voltage differential data of the feature cell within a preset time period; The current sequence window is used to store the current data of the characteristic cell acquired simultaneously with the voltage differential data; The maximum voltage window is used to record the maximum value within the voltage differential sequence window in real time; The update trigger conditions for the voltage differential sequence window, the current sequence window, and the maximum voltage window are as follows: when the change amount of the state of charge value of the characteristic battery cell is greater than or equal to a preset capacity change threshold, the data processing system adds the voltage differential data and current data of the characteristic battery cell at the current moment to the voltage differential sequence window and the current sequence window respectively, and synchronously updates the end value of the maximum voltage window to the maximum value of the current voltage differential sequence window; Step F2: The data processing system determines whether the operating parameters of the characteristic battery cell meet the preset analysis conditions. The preset analysis conditions are that the current SOC of the characteristic battery cell is in the range of 40% < SOC < 80%, and the current charge-discharge rate of the characteristic battery cell is in the range of 0.05 < charge-discharge rate < 0.6, where the charge-discharge rate is the ratio of the current value in the current sequence window to the rated capacity of the characteristic battery cell; Step F3: If the data processing system determines that the characteristic battery cell meets the preset analysis conditions, peak validity verification is performed; Among them, the peak validity verification includes: When the data processing system detects that the maximum value in the maximum voltage window is equal to the minimum value, it determines that the peak of the voltage change rate corresponding to the current voltage differential sequence window is the characteristic peak of the characteristic battery cell, and uses the state of charge value corresponding to the characteristic peak as the state of charge value of the characteristic battery cell.

[0038] It should be noted that to obtain the state of charge value corresponding to the characteristic peak of the characteristic battery cell, that is, to locate the SOC position corresponding to the characteristic peak. By separately maintaining the voltage differential sequence and the current sequence window, and at the same time maintaining the maximum voltage window, every time the SOC change amount reaches a predetermined value (that is, each time the control capacity change amount reaches the set capacity change amount), the voltage differential window, the current differential window, and the end value of the maximum voltage window are updated. The maximum voltage window is the maximum value of the current voltage differential window. When the SOC interval and the倍率 information meet the preset conditions (the preset conditions can be optionally set as 40% < SOC < 80% and 0.05 < 倍率 < 0.6), peak validity calculation is performed. When the maximum value and the minimum value in the maximum voltage window are equal, the peak is valid, and the current SOC is the SOC value corresponding to the characteristic peak.

[0039] Step 4: Determine the absolute value of the difference between the state of charge value corresponding to the characteristic peak of the characteristic battery cell and the reference state of charge value, and calibrate the displayed state of charge value of the to-be-calibrated energy storage cabinet system based on the state of charge value corresponding to the characteristic peak of the characteristic battery cell and the absolute value of the difference;

[0040] In the embodiments of the present disclosure, the calibration of the displayed state of charge value of the to-be-calibrated energy storage cabinet system based on the state of charge value corresponding to the characteristic peak of the characteristic battery cell and the absolute value of the difference includes: When the state of charge value corresponding to the characteristic peak of the characteristic cell is within a preset confidence interval, the sum of the absolute value of the difference and the displayed state of charge value is determined, and the sum of the absolute value of the difference and the displayed state of charge value is used as the corrected state of charge value of the energy storage cabinet system. No correction is performed when the state of charge value corresponding to the characteristic peak of the characteristic cell is not within the preset confidence interval.

[0041] It should be noted that the current characteristic peak corresponds to... Find the difference to get Deviation amount: , For the calculation Deviation can be determined by the difference. offset degree and on the screen display Perform the correction operation. and The current system The time corresponding to the occurrence of the characteristic peak and the in-situ characteristic peaks obtained from offline data analysis .

[0042] The deviation will be used as a correction factor for the current screen display. Perform corrective procedures. , For the revised version , Display the current moment on the screen. ,every time The correction operation runs within the interval between the current feature peak and the next identified feature peak.

[0043] In summary, the dynamic SOC calibration method based on statistical analysis proposed in this embodiment, according to the principles of big data mining, uncovers the underlying patterns in the data and calibrates based on the inherent characteristics of the data. This improves model interpretability. Furthermore, it eliminates the need for stringent triggering conditions, allowing for dynamic adjustments. Calibration greatly improves Calibration flexibility.

[0044] Example 2 Figure 2 This is a structural diagram of a dynamic SOC calibration system based on statistical analysis according to an embodiment of this application, as shown below. Figure 2 As shown, the system includes: The offline acquisition module 100 is used to acquire the state of charge and voltage curve data of the battery cell under different charge and discharge rates through offline experiments, and to determine candidate characteristic peak groups based on the state of charge and voltage curve data. Information mining module 200 is used to determine the reference state of charge value based on the candidate feature peak group and using the Monte Carlo sampling law; The determination module 300 is used to identify the characteristic cells of the energy storage cabinet system under test during operation, and then determine the state of charge value corresponding to the characteristic peak of the characteristic cell. The calibration module 400 is used to determine the absolute value of the difference between the state of charge value corresponding to the characteristic peak of the characteristic cell and the reference state of charge value, and to calibrate the display state of charge value of the energy storage cabinet system to be calibrated based on the state of charge value corresponding to the characteristic peak of the characteristic cell and the absolute value of the difference.

[0045] In this embodiment of the disclosure, the offline acquisition module 100 is further configured to: The second in-situ peak in the state of charge and voltage curve data of the battery cell under different charge and discharge rates is obtained, and then an in-situ characteristic peak group is constructed based on the second in-situ peak of the battery cell under different charge and discharge rates. The isolated forest algorithm is used to filter the data within the in-situ feature peak group to obtain candidate feature peak groups. The step of determining the reference state of charge value based on the candidate characteristic peak group and using the Monte Carlo sampling law includes: The expected edge distribution of the candidate feature peak group is determined by the Monte Carlo sampling theorem; The expected edge distribution is used as the reference state of charge value.

[0046] In this embodiment of the disclosure, the determining module 300 is further configured to: The full charge state and full discharge state of the energy storage cabinet system to be calibrated are monitored, and the cell number with the largest voltage when the energy storage cabinet system to be calibrated completes a full charge state and the cell number with the smallest voltage when the energy storage cabinet system to be calibrated completes a full discharge state are recorded. Store the cell number with the highest voltage under full charge and the cell number with the lowest voltage under full discharge in a buffer array; Determine whether the cell number with the lowest voltage in the fully discharged state in the buffer array is equal to the cell number with the lowest voltage in the fully discharged state. If so, the cell corresponding to the cell number is taken as the feature cell; otherwise, the cell corresponding to the cell number with the lowest voltage in the fully discharged state is taken as the feature cell.

[0047] In this embodiment of the disclosure, the determining module 300 is further configured to: Step R1: Update and maintain the voltage differential sequence window, current sequence window, and maximum voltage window of the characteristic cell in real time, where: The voltage differential sequence window is used to store the voltage differential data of the feature cell within a preset time period; The current sequence window is used to store the current data of the feature cell collected synchronously with the voltage differential data; The maximum voltage window is used to record the maximum value in the voltage differential sequence window in real time; The update trigger conditions for the voltage differential sequence window, the current sequence window, and the maximum voltage window are as follows: when the change amount of the state of charge value of the feature cell is greater than or equal to the preset capacity change threshold, the data processing system adds the current voltage differential data and current data of the feature cell to the voltage differential sequence window and the current sequence window respectively, and synchronously updates the end value of the maximum voltage window to the maximum value of the current voltage differential sequence window; Step R2: The data processing system determines whether the operating parameters of the feature cell meet the preset analysis conditions. The preset analysis conditions are that the current SOC of the feature cell is in the interval of 40% < SOC < 80%, and the current charge-discharge rate of the feature cell is in the interval of 0.05 < charge-discharge rate < 0.6, where the charge-discharge rate is the ratio of the current value in the current sequence window to the rated capacity of the feature cell; Step R3: If the data processing system determines that the feature cell meets the preset analysis conditions, peak validity verification is performed; Among them, the peak validity verification includes: When the data processing system detects that the maximum value in the maximum voltage window is equal to the minimum value, it determines that the peak of the voltage change rate corresponding to the current voltage differential sequence window is the characteristic peak of the feature cell, and uses the state of charge value corresponding to the characteristic peak as the state of charge value of the feature cell.

[0048] In the embodiment of the present disclosure, the calibration module 400 is further configured to: When the state of charge value corresponding to the characteristic peak of the feature cell is within the preset confidence interval, determine the sum of the absolute value of the difference and the screen-displayed state of charge value, and use the sum of the absolute value of the difference and the screen-displayed state of charge value as the corrected state of charge value of the energy storage cabinet system; When the state of charge value corresponding to the characteristic peak of the feature cell is not within the preset confidence interval, no correction is performed.

[0049] In summary, a dynamic SOC calibration system based on statistical analysis proposed in this embodiment can dynamically correct the state of charge value of the battery during the operation of the battery to meet the actual operation requirements.

[0050] Embodiment III To implement the above embodiments, this disclosure also proposes an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the method described in Embodiment 1.

[0051] Example 4 To implement the above embodiments, this disclosure also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in Embodiment 1.

[0052] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0053] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0054] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A dynamic SOC calibration method based on statistical analysis, characterized in that, The method includes: Offline experiments were conducted to collect the state of charge and voltage curve data of the battery cells at different charge and discharge rates, and candidate characteristic peak groups were determined based on the state of charge and voltage curve data. The reference state of charge value is determined based on the candidate characteristic peak group and using the Monte Carlo sampling law; The characteristic cells are identified during the operation of the energy storage cabinet system to be calibrated, and then the state of charge value corresponding to the characteristic peak of the characteristic cells is determined. The absolute value of the difference between the state of charge value corresponding to the characteristic peak of the characteristic cell and the reference state of charge value is determined, and the display state of charge value of the energy storage cabinet system to be calibrated is calibrated based on the state of charge value corresponding to the characteristic peak of the characteristic cell and the absolute value of the difference.

2. The method as described in claim 1, characterized in that, The process of determining candidate characteristic peak groups based on the state of charge and voltage curve data includes: The second in-situ peak in the state of charge and voltage curve data of the battery cell under different charge and discharge rates is obtained, and then an in-situ characteristic peak group is constructed based on the second in-situ peak of the battery cell under different charge and discharge rates. The isolated forest algorithm is used to filter the data within the in-situ feature peak group to obtain candidate feature peak groups.

3. The method as described in claim 2, characterized in that, The step of determining the reference state of charge value based on the candidate characteristic peak group and using the Monte Carlo sampling law includes: The expected edge distribution of the candidate feature peak group is determined by the Monte Carlo sampling theorem; The expected edge distribution is used as the reference state of charge value.

4. The method as described in claim 3, characterized in that, The process of identifying characteristic cells during the operation of the energy storage cabinet system to be calibrated includes: The full charge state and full discharge state of the energy storage cabinet system to be calibrated are monitored, and the cell number with the largest voltage when the energy storage cabinet system to be calibrated completes a full charge state and the cell number with the smallest voltage when the energy storage cabinet system to be calibrated completes a full discharge state are recorded. Store the cell number with the highest voltage under full charge and the cell number with the lowest voltage under full discharge in a buffer array; Determine whether the cell number with the lowest voltage in the fully discharged state in the buffer array is equal to the cell number with the lowest voltage in the fully discharged state. If so, the cell corresponding to the cell number is taken as the feature cell; otherwise, the cell corresponding to the cell number with the lowest voltage in the fully discharged state is taken as the feature cell.

5. The method as described in claim 4, characterized in that, Determining the state of charge value corresponding to the characteristic peak of the characteristic cell includes: Step F1: Update and maintain the voltage differential sequence window, current sequence window, and maximum voltage window of the characteristic cell in real time, where: The voltage differential sequence window is used to store the voltage differential data of the feature cell within a preset time period; The current sequence window is used to store the current data of the characteristic cell acquired simultaneously with the voltage differential data; The maximum voltage window is used to record the maximum value within the voltage differential sequence window in real time; The update trigger conditions for the voltage differential sequence window, current sequence window, and maximum voltage window are as follows: When the change amount of the state of charge value of the characteristic battery cell is greater than or equal to a preset capacity change threshold, the data processing system adds the voltage differential data and current data of the characteristic battery cell at the current moment to the voltage differential sequence window and current sequence window respectively, and synchronously updates the end value of the maximum voltage window to the maximum value of the current voltage differential sequence window; Step F2: The data processing system determines whether the operating parameters of the characteristic battery cell meet the preset analysis conditions. The preset analysis conditions are: the current SOC of the characteristic battery cell is in the range of 40% < SOC < 80%, and the current charge-discharge rate of the characteristic battery cell is in the range of 0.05 < charge-discharge rate < 0.6, where the charge-discharge rate is the ratio of the current value in the current sequence window to the rated capacity of the characteristic battery cell; Step F3: If the data processing system determines that the characteristic battery cell meets the preset analysis conditions, then perform peak validity verification; Among them, the peak validity verification includes: When the data processing system detects that the maximum value in the maximum voltage window is equal to the minimum value, it determines that the peak value of the voltage change rate corresponding to the current voltage differential sequence window is the characteristic peak of the characteristic battery cell, and uses the state of charge value corresponding to the characteristic peak as the state of charge value of the characteristic battery cell.

6. The method as described in claim 5, characterized in that, The calibration of the displayed state of charge value of the to-be-calibrated energy storage cabinet system based on the state of charge value corresponding to the characteristic peak of the characteristic battery cell and the absolute value of the difference includes: When the state of charge value corresponding to the characteristic peak of the characteristic battery cell is within the preset confidence interval, determine the sum of the absolute value of the difference and the displayed state of charge value, and use the sum of the absolute value of the difference and the displayed state of charge value as the corrected state of charge value of the energy storage cabinet system; When the state of charge value corresponding to the characteristic peak of the characteristic battery cell is not within the preset confidence interval, no correction is performed.

7. A dynamic SOC calibration system based on statistical analysis, characterized in that, The system includes: An offline acquisition module, which is used to acquire the state of charge and voltage curve data of the battery cell at different charge-discharge rates through offline experiments, and determine a candidate characteristic peak group based on the state of charge and voltage curve data; An information mining module, which is used to determine the reference state of charge value according to the candidate characteristic peak group and using the Monte Carlo sampling law; A determination module, which is used to identify the battery cell during the operation of the to-be-calibrated energy storage cabinet system to obtain the characteristic battery cell, and then determine the state of charge value corresponding to the characteristic peak of the characteristic battery cell; A calibration module, which is used to determine the absolute value of the difference between the state of charge value corresponding to the characteristic peak of the characteristic battery cell and the reference state of charge value, and calibrate the displayed state of charge value of the to-be-calibrated energy storage cabinet system based on the state of charge value corresponding to the characteristic peak of the characteristic battery cell and the absolute value of the difference.

8. The system as described in claim 7, characterized in that, The offline acquisition module is further used for: Obtain the second in-situ peak in the state of charge and voltage curve data of the battery cell at different charge-discharge rates, and then construct an in-situ characteristic peak group based on the second in-situ peaks of the battery cell at different charge-discharge rates; The isolated forest algorithm is used to filter the data within the in-situ feature peak group to obtain candidate feature peak groups.

9. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method as described in any one of claims 1-6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-6.

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