CN-121978548-A - Dynamic SOC calibration method and system based on statistical analysis

CN121978548ACN 121978548 ACN121978548 ACN 121978548ACN-121978548-A

Abstract

The application provides a dynamic SOC calibration method and a system based on statistical analysis, wherein the method comprises the steps of collecting state of charge and voltage curve data of a battery cell under different charge and discharge multiplying powers through an off-line experiment, and determining candidate characteristic peak groups based on the state of charge and the voltage curve data; the method comprises the steps of determining a reference state of charge value according to a candidate characteristic peak group and by utilizing a Monte Carlo sampling law, identifying a battery core in the operation process of an energy storage cabinet system to be calibrated to obtain a characteristic battery core, determining a state of charge value corresponding to a characteristic peak of the characteristic battery core, determining a difference absolute value between the state of charge value corresponding to the characteristic peak of the characteristic battery core and the reference state of charge value, and calibrating the on-screen 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 battery core and the difference absolute value. According to the technical scheme provided by the application, the state of charge value of the battery can be dynamically corrected in the running process of the battery so as to adapt to actual running requirements.

Inventors

XU YONGJIE
HAN CHAO
LIU YONGKUI
QI SHANWEI
CHEN KAIQIANG

Assignees

西安奇点能源股份有限公司
西安弦能科技有限公司

Dates

Publication Date: 20260505
Application Date: 20260407

Claims (10)

1. A method for dynamic SOC calibration based on statistical analysis, the method comprising: Acquiring state of charge and voltage curve data of the battery cells under different charge and discharge multiplying powers through an offline experiment, and determining candidate characteristic peak groups based on the state of charge and voltage curve data; determining a reference state of charge value according to the candidate characteristic peak group and by utilizing a Monte Carlo sampling law; Identifying a battery cell in the operation process of the energy storage cabinet system to be calibrated 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 state of charge value corresponding to the characteristic peak of the characteristic cell and the reference state of charge value, and calibrating the on-screen 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 value.
2. The method of claim 1, wherein the determining a candidate population of characteristic peaks based on the state of charge and voltage curve data comprises: Acquiring second in-situ peaks in charge state and voltage curve data of the battery cells under different charge and discharge multiplying powers, and constructing in-situ characteristic peak groups based on the second in-situ peaks of the battery cells under different charge and discharge multiplying powers; And filtering each data in the in-situ characteristic peak group by adopting an isolated forest algorithm to obtain a candidate characteristic peak group.
3. The method of claim 2, wherein said determining a reference state of charge value from said candidate population of characteristic peaks and using monte carlo sampling law comprises: determining the edge distribution expectation of the candidate characteristic peak group through Monte Carlo sampling law; The edge distribution is expected as a reference state of charge value.
4. The method of claim 3, wherein identifying the cells in the operation of the energy storage cabinet system to be calibrated to obtain the characteristic cells comprises: Monitoring the full charge state and the full discharge state of the energy storage cabinet system to be calibrated, and recording the cell number with the largest voltage in the one-time full charge state of the energy storage cabinet system to be calibrated and the cell number with the smallest voltage in the one-time full discharge state of the energy storage cabinet system to be calibrated; Storing the cell number with the largest voltage in the full charge state and the cell number with the smallest voltage in the full discharge state into a buffer array; And judging whether the cell number with the minimum voltage in the full-discharge state in the buffer array is equal to the cell number with the minimum voltage in the full-discharge state, if so, taking the cell corresponding to the cell number as a characteristic cell, otherwise, taking the cell corresponding to the cell number with the minimum voltage in the full-discharge state as the characteristic cell.
5. The method of claim 4, wherein determining a state of charge value corresponding to a characteristic peak of the characteristic cell comprises: step F1, updating a voltage differential sequence window, a current sequence window and a maximum voltage window of the maintenance characteristic battery cell in real time, wherein: the voltage differential sequence window is used for storing voltage differential data of the characteristic battery cell within a preset duration; the current sequence window is used for storing current data of the characteristic battery cell acquired synchronously with the voltage differential data; The maximum voltage window is used for recording the maximum value in the voltage differential sequence window in real time; When the change amount of the state of charge value of the characteristic battery cell is more than or equal to a preset capacity change amount threshold, the data processing system respectively adds the voltage differential data and the current data of the characteristic battery cell at the current moment into the voltage differential sequence window and the current sequence window, and synchronously updates the tail end value of the maximum voltage window to be the maximum value of the current voltage differential sequence window; Step F2, the data processing system judges whether the operation parameters of the characteristic battery cell meet preset analysis conditions, wherein the preset analysis conditions are that the current SOC of the characteristic battery cell is in a section of 40% < SOC <80%, and the current charge-discharge multiplying power of the characteristic battery cell is in a section of 0.05< charge-discharge multiplying power <0.6, and the charge-discharge multiplying power is the ratio of a current value in the current sequence window to the rated capacity of the characteristic battery cell; step F3, if the data processing system judges that the characteristic battery cell meets the preset analysis condition, carrying out peak value validity verification; Wherein the peak validity verification comprises: when the data processing system detects that the maximum value in the maximum voltage window is equal to the minimum value, the peak value of the voltage change rate corresponding to the current voltage differential sequence window is judged to be the characteristic peak of the characteristic battery cell, and the state of charge value corresponding to the characteristic peak is taken as the state of charge value of the characteristic battery cell.
6. The method of claim 5, wherein calibrating the on-screen 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 value comprises: When the state of charge value corresponding to the characteristic peak of the characteristic battery cell is in a preset confidence interval, determining the sum of the absolute value of the difference value and the on-screen state of charge value, and taking the sum of the absolute value of the difference value and the on-screen state of charge value as the state of charge value after the correction of the energy storage cabinet system; And when the state of charge value corresponding to the characteristic peak of the characteristic battery cell is not in the preset confidence interval, not correcting.
7. A dynamic SOC calibration system based on statistical analysis, the system comprising: The off-line acquisition module is used for acquiring the charge state and voltage curve data of the battery cells under different charge and discharge multiplying powers through an off-line experiment, and determining candidate characteristic peak groups based on the charge state and voltage curve data; The information mining module is used for determining a reference state of charge value according to the candidate characteristic peak group and by utilizing a Monte Carlo sampling law; the determining module is used for identifying the battery cells in the operation process of the energy storage cabinet system to be calibrated to obtain characteristic battery cells, and then determining the state of charge value corresponding to the characteristic peaks of the characteristic battery cells; and the calibration module is used for determining the absolute value of the difference value between the state of charge value corresponding to the characteristic peak of the characteristic battery cell and the reference state of charge value, and calibrating the screen 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 battery cell and the absolute value of the difference value.
8. The system of claim 7, wherein the offline acquisition module is further to: Acquiring second in-situ peaks in charge state and voltage curve data of the battery cells under different charge and discharge multiplying powers, and constructing in-situ characteristic peak groups based on the second in-situ peaks of the battery cells under different charge and discharge multiplying powers; And filtering each data in the in-situ characteristic peak group by adopting an isolated forest algorithm to obtain a candidate characteristic peak group.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method according to any of claims 1-6 when executing the program.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-6.

Description

Dynamic SOC calibration method and system based on statistical analysis Technical Field The application relates to the technical field of battery management, in particular to a dynamic SOC calibration method and system based on statistical analysis. Background Under the background of a double-carbon strategy, electrochemical energy storage is taken as one of important components in the field of energy storage, so that unstable factors caused by novel energy can be effectively reduced, the scheduling flexibility of a power grid is effectively improved, and the load pressure of the power grid is reduced. In addition, the speed of adjustment is fast, the arrangement is flexible, the construction period is short, environmental friendliness also becomes one of the important factors that the installation amount is continuously increased in recent years. Although the novel electrochemical energy storage has great application value, the development of the novel electrochemical energy storage is still limited by performance factors. The main current energy storage system is of a container type or a distributed type, and the essence of the main current energy storage system is that serial/parallel cells are adopted to form a battery pack, then the battery packs are connected to form a battery cluster, and finally the whole energy storage system is formed. In such a system, the situation that the attenuation rates of the battery cells are inconsistent in the use process can be avoided, so that most battery cells cannot fully exert capacity, and finally, the phenomenon that the overall capacity of the system is low is shown. The key to solving the limited capacity exertion problem caused by the battery inconsistency is to implement consistency balancing on the system. In the aspect of equalization target selection, SOC equalization is often used. The accuracy of the equalization target at this time determines the final effect of equalization and the reliability of the equalization command. Considering the bias of SOC results, there are currently two strategies in the industry, mainly full charge discharge calibration and OCV (open circuit voltage ) -SOC calibration for calibration of SOC. However, both calibration strategies belong to static calibration, and the trigger conditions are severe, so that the actual requirements are difficult to meet. Therefore, there is a need for a dynamic SOC calibration scheme with high flexibility, which can dynamically correct the battery SOC during the battery operation process, so as to adapt to the actual operation requirement. Disclosure of Invention The application provides a dynamic SOC calibration method and a system based on statistical analysis, which at least solve the technical problems that the calibration strategies belong to static calibration, the triggering conditions are severe, and the actual requirements are difficult to meet. An embodiment of a first aspect of the present application provides a dynamic SOC calibration method based on statistical analysis, the method including: Acquiring state of charge and voltage curve data of the battery cells under different charge and discharge multiplying powers through an offline experiment, and determining candidate characteristic peak groups based on the state of charge and voltage curve data; determining a reference state of charge value according to the candidate characteristic peak group and by utilizing a Monte Carlo sampling law; Identifying a battery cell in the operation process of the energy storage cabinet system to be calibrated 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 state of charge value corresponding to the characteristic peak of the characteristic cell and the reference state of charge value, and calibrating the on-screen 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 value. Preferably, the determining the candidate characteristic peak group based on the state of charge and voltage curve data includes: Acquiring second in-situ peaks in charge state and voltage curve data of the battery cells under different charge and discharge multiplying powers, and constructing in-situ characteristic peak groups based on the second in-situ peaks of the battery cells under different charge and discharge multiplying powers; And filtering each data in the in-situ characteristic peak group by adopting an isolated forest algorithm to obtain a candidate characteristic peak group. Further, the determining the reference state of charge value according to the candidate characteristic peak group and by using the monte carlo sampling law includes: determining the edge distribu