Aging estimation method and apparatus for composite electrode battery, device, medium, and program

Abstract

The present application discloses an aging estimation method and apparatus for a composite electrode battery, a device, a medium, and a program. The method comprises: receiving actual running data of a composite electrode battery, wherein the actual running data is configured a function among the battery voltage, battery current, battery temperature, SOC and time; acquiring at least one group of feature data, wherein the feature data is associated with the actual running data, and is configured as a function among the OCV, SOC, temperature, and time; on the basis of pre-set experimental data, determining an aging degree corresponding to each group of feature data, wherein the experimental data corresponds to the battery cell model of the composite electrode battery, and is configured as a function among the temperature, battery aging degree, OCV and SOC; and on the basis of the aging degree corresponding to the at least one group of feature data, determining the aging degree of the composite electrode battery.

Description

Composite electrode battery aging estimation method, device, equipment, medium and program

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to Chinese patent application 202310791402.7, filed in China on June 29, 2023, entitled “Method, device, equipment, medium and program for estimating aging of composite electrode batteries,” the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present application relates to the field of battery state estimation, and in particular to a method, device, equipment, medium and program for estimating aging of a composite electrode battery. Background Art

[0004] Composite electrode batteries refer to batteries that use composite electrode materials. Composite electrode materials are composed of two or more materials, each of which has its own unique properties. Therefore, the advantages of various materials can be fully utilized, making composite electrode batteries have the characteristics of high energy density, high power density, long life, and high safety.

[0005] As an electrochemical system, composite electrode batteries age over time with increasing charge and discharge cycles, leading to a decrease in usable capacity. Improper battery use can lead to serious safety incidents. Therefore, accurately estimating the aging state of composite electrode batteries is of great practical significance. Prior art methods for estimating the aging state of composite electrode batteries use models constructed based on offline battery data to estimate the battery's aging state.

[0006] Summary of the Invention

[0007] The embodiments of the present application provide a composite electrode battery aging estimation method, apparatus, device, medium, and program, which can improve the accuracy of composite electrode battery aging estimation.

[0008] In a first aspect, an embodiment of the present application provides a method for estimating aging of a composite electrode battery, comprising: receiving actual operating data of a composite electrode battery, wherein the actual operating data is configured as a function of battery voltage, battery current, battery temperature, SOC, and time; obtaining at least one set of characteristic data, the characteristic data being associated with the actual operating data and configured as a function of OCV, SOC, temperature, and time; determining a degree of aging corresponding to each set of characteristic data based on pre-set experimental data, wherein the experimental data corresponds to a cell model of the composite electrode battery and is configured as a function of temperature, degree of battery aging, OCV, and SOC; determining the degree of aging of the composite electrode battery based on the degree of aging corresponding to at least one set of characteristic data.

[0009] The above-mentioned composite electrode battery aging estimation method determines the characteristic data based on the actual operating data of the composite electrode battery, which can reflect the operating conditions of the composite electrode battery. The experimental data corresponding to the composite electrode battery includes the aging degree of the composite electrode battery under various operating conditions. Therefore, the composite is determined by combining the actual operating data and the experimental data. Through data analysis, the accurate aging degree of the battery under specific operating conditions can be determined, thereby ensuring the accuracy of the aging estimation of the composite electrode battery.

[0010] As a possible implementation method, obtaining at least one set of characteristic data includes: receiving target data, the target data is associated with actual operating data within a calculation cycle, wherein the calculation cycle is the time for the composite electrode battery to complete at least one usage cycle, and the usage cycle is used to indicate that the cumulative charge capacity or cumulative discharge capacity of the composite electrode battery reaches 100%, and determining at least one set of characteristic data based on the target data.

[0011] Through the above method, the target data is filtered out based on the calculation cycle, so that the feature data determined based on the target data can reflect the battery characteristics within the calculation cycle. Compared with determining the feature data based on all actual operation data, the calculation amount of determining the feature data based on the target data within the calculation cycle is smaller, and feature data can be extracted periodically based on the calculation cycle.

[0012] As a possible implementation, at least one set of characteristic data is determined based on the target data, including: based on the target data, at least one OCV is calculated, and the SOC and temperature corresponding to the calculated OCV, and a set of characteristic data includes a calculated OCV and an SOC and a temperature corresponding to the OCV.

[0013] In the above manner, an OCV, an SOC corresponding to the OCV, and a temperature are used as a set of characteristic data, so that the characteristic data can reflect the SOC-OCV relationship characteristics of the composite electrode battery, and the degree of battery aging can be accurately determined based on the characteristic data.

[0014] As a possible implementation method, for each set of characteristic data, based on pre-set experimental data, the aging degree corresponding to the characteristic data is determined, including: determining the first data in the experimental data, the temperature in the first data is the same as or closest to the temperature in the characteristic data, determining the second data in the first data, the OCV and SOC in the second data are closest to the OCV and SOC in the characteristic data, and taking the aging degree of the second data as the aging degree corresponding to the characteristic data.

[0015] Both experimental data and characteristic data contain multiple parameters. When determining the degree of aging corresponding to the characteristic data based on the experimental data, each parameter has a different impact on the accuracy of determining the degree of aging. Therefore, according to the priority of each parameter, the degree of aging corresponding to the characteristic data is determined based on temperature and OCV-SOC in turn, which can ensure that the degree of aging corresponding to the characteristic data is accurately determined.

[0016] As a possible implementation method, there are multiple first data, and determining the second data in the first data includes: calculating the first difference between the OCV in each first data and the OCV in the characteristic data, and the second difference between the SOC in each first data and the SOC in the characteristic data; for each first data, normalizing and summing the first difference and the second difference corresponding to the first data to obtain multiple first data differences corresponding to the multiple first data, and taking the first data corresponding to the minimum first data difference as the second data.

[0017] When determining the second data, the first difference and the second difference corresponding to each first data are calculated respectively. The first difference can reflect the difference between the OCV in the first data and the OCV in the characteristic data, and the second difference can reflect the difference between the SOC in the first data and the SOC in the characteristic data. The first difference and the second difference are normalized and summed, and the obtained first data difference can reflect the difference between the SOC-OCV relationship characteristics in the first data and the SOC-OCV relationship characteristics in the characteristic data. Then, based on the first data difference, the second data whose SOC-OCV relationship characteristics are closest to the characteristic data can be determined from multiple first data.

[0018] As a possible implementation method, there are multiple groups of characteristic data, and the aging degree of the composite electrode battery is determined based on the aging degree corresponding to at least one group of characteristic data, including: determining target characteristic data from the multiple groups of characteristic data, the SOC of the target characteristic data is the same as or closest to a preset SOC value, calculating the average of the aging degrees corresponding to the multiple groups of target characteristic data, and determining the average as the aging degree of the composite electrode battery.

[0019] For a composite electrode battery, only one aging estimate—that is, a single aging degree—needs to be derived within a calculation cycle. However, due to objective errors, the aging degrees corresponding to multiple sets of feature data may not be identical. Therefore, multiple SOC values ​​are pre-set and used as calculation nodes to filter target feature data from the multiple sets of feature data. The accurate aging degree of the composite electrode battery is then determined based on the aging degree corresponding to the target feature data.

[0020] In a second aspect, an embodiment of the present application provides a composite electrode battery aging estimation device, comprising: an actual data receiving module for receiving actual operating data of the composite electrode battery, wherein the actual operating data is configured as a function of battery voltage, battery current, battery temperature, SOC and time; a storage module for storing the actual operating data; a characteristic data acquisition module for acquiring at least one set of characteristic data, the characteristic data being associated with the actual operating data and configured as a function of OCV, SOC, temperature and time; an aging degree experimental value determination module for determining the aging degree corresponding to each set of characteristic data based on pre-set experimental data, wherein the experimental data corresponds to the cell model of the composite electrode battery and is configured as a function of temperature, battery aging degree, OCV and SOC; an aging degree determination module for determining the aging degree of the composite electrode battery based on the aging degree corresponding to at least one set of characteristic data.

[0021] As a possible implementation, the characteristic data acquisition module includes: a target data determination submodule for receiving target data, where the target data is associated with actual operating data within a calculation cycle, wherein the calculation cycle is the time for the composite electrode battery to complete at least one usage cycle, and the usage cycle is used to indicate that the cumulative charge capacity or cumulative discharge capacity of the composite electrode battery reaches 100%; a characteristic data determination submodule for determining at least one set of characteristic data based on the target data.

[0022] As a possible implementation method, the characteristic data determination submodule is specifically used to: calculate at least one OCV, and the SOC and temperature corresponding to the calculated OCV based on the target data, wherein a set of characteristic data includes a calculated OCV and an SOC and a temperature corresponding to the OCV.

[0023] As a possible implementation method, the aging degree experimental value determination module includes: a first data determination submodule, used to determine the first data in the experimental data, the temperature in the first data is the same as or closest to the temperature in the characteristic data; a second data determination submodule, used to determine the second data in the first data, the OCV and SOC in the second data are closest to the OCV and SOC in the characteristic data; and an experimental value determination submodule, used to use the aging degree of the second data as the aging degree corresponding to the characteristic data.

[0024] As a possible implementation method, there are multiple first data, and the second data determination submodule includes: a first calculation submodule, used to calculate the first difference between the OCV in each first data and the OCV in the characteristic data; a second calculation submodule, used to calculate the second difference between the SOC in each first data and the SOC in the characteristic data; a normalization submodule, used to normalize and sum the first difference and the second difference corresponding to the first data for each first data, to obtain the first data difference corresponding to the multiple first data; a screening submodule, used to use the first data corresponding to the minimum first data difference as the second data.

[0025] As a possible implementation method, the characteristic data is multiple groups, and the aging degree determination module includes: a target characteristic determination submodule, which is used to determine the target characteristic data among the multiple groups of characteristic data, and the SOC of the target characteristic data is the same as or closest to the preset SOC value; a mean calculation submodule, which is used to calculate the mean of the aging degrees corresponding to the multiple groups of target characteristic data; and an aging degree determination submodule, which is used to determine the mean as the aging degree of the composite electrode battery.

[0026] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor and a memory storing computer program instructions;

[0027] When the processor executes the computer program instructions, the composite electrode battery aging estimation method of the first aspect is implemented.

[0028] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, the composite electrode battery aging estimation method according to the first aspect is implemented.

[0029] In a fifth aspect, an embodiment of the present application provides a computer program product. When instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the composite electrode battery aging estimation method of the first aspect.

[0030] The composite electrode battery aging estimation method, apparatus, device, medium, and program of the embodiments of the present application receive actual operating data of the composite electrode battery, wherein the actual operating data is configured as a function of battery voltage, battery current, battery temperature, state of charge (SOC), and time, obtain at least one set of characteristic data, the characteristic data being associated with the actual operating data and configured as a function of OCV, SOC, temperature, and time, and determine the degree of aging corresponding to each set of characteristic data based on pre-set experimental data, wherein the experimental data corresponds to the cell model of the composite electrode battery and is configured as a function of temperature, degree of battery aging, OCV, and SOC. The degree of aging of the composite electrode battery is determined based on the degree of aging corresponding to the at least one set of characteristic data. According to this embodiment, the accurate degree of battery aging is determined based on the experimental data through data analysis, thereby improving the accuracy of composite electrode battery aging estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the embodiments of the present application, a brief introduction to the drawings required for use in the embodiments of the present application is given below.

[0032] FIG1 is a schematic flow chart of a composite electrode battery aging estimation method provided by one embodiment of the present application;

[0033] FIG2 is a schematic structural diagram of a composite electrode battery aging estimation device provided by another embodiment of the present application;

[0034] FIG3 is a schematic structural diagram of an electronic device provided in yet another embodiment of the present application. DETAILED DESCRIPTION

[0035] The features and exemplary embodiments of various aspects of the present application will be described in detail below. To make the objectives, technical solutions, and advantages of the present application more clearly understood, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are intended only to explain the present application and not to limit the present application.

[0036] It should be noted that, in this document, relational terms such as first and second, etc. are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations.

[0037] In order to facilitate understanding of the embodiments of the present application, the terms involved in the embodiments of the present application are first explained.

[0038] Composite electrode battery, the battery usually includes a cell and an electrode, wherein the electrode includes a positive electrode and a negative electrode, and the composite electrode battery refers to a battery in which a composite electrode material is used for the positive electrode and / or the negative electrode. For example, a battery in which the positive electrode includes two or more positive electrode active materials, and / or the negative electrode includes two or more negative electrode active materials is a composite electrode battery. The positive electrode active materials include but are not limited to lithium cobalt oxide (LCO), lithium manganese oxide (LMO), lithium iron phosphate (LFP), ternary materials (lithium nickel cobalt manganese oxide: NCM) and lithium nickel cobalt aluminum oxide (NCA)), etc., and the negative electrode active materials include but are not limited to graphite, silicon, hard carbon, soft carbon, etc. The cell of the composite electrode battery can be a sodium ion cell, a lithium ion cell, or other types of secondary cells.

[0039] SOC, the full name of which is State of Charge, is the ratio of the remaining capacity of a battery after it has been used for a period of time or has been left unused for a long time to its capacity in a fully charged state.

[0040] OCV, the full name of which is open circuit voltage, refers to the potential difference between the positive and negative electrodes of the battery when the battery is in a non-working state, that is, when no current flows through the circuit.

[0041] BMS, the full name of Battery Management System, is a battery management system that can monitor the status of the battery and manage the charging and discharging of the battery.

[0042] A usage cycle refers to the time when the battery's cumulative charge or discharge capacity reaches 100%. For example, a battery's discharge from 100% to 0% is one usage cycle, also known as a discharge cycle, and a battery's charge from 0% to 100% is another usage cycle, also known as a charge cycle.

[0043] In order to improve the problem of inaccurate battery aging estimation in related technologies, the embodiments of the present application provide a novel composite electrode battery aging estimation method, device, equipment, medium and program for estimating the aging of a composite electrode battery.

[0044] The following describes the aging estimation method for a composite electrode battery provided in an embodiment of the present application.

[0045] Figure 1 shows a schematic flow chart of a method for estimating aging of a composite electrode battery according to an embodiment of the present application. As shown in Figure 1 , the method may include the following steps S110-S140.

[0046] S110. Receive actual operating data of the composite electrode battery.

[0047] The actual operating data is configured as a function of battery voltage, battery current, battery temperature, SOC and time.

[0048] In this embodiment, the actual operating data may be expressed as a functional relationship among the battery voltage, battery current, battery temperature, SOC, and time in the form of a table, a function, or a graph.

[0049] In practical applications, the corresponding voltage, current, temperature, SOC, time and other data of the composite electrode battery can be collected through relevant collection devices during its use, and actual operation data can be generated based on the collected data. The actual operation data is transmitted to the execution device of the composite electrode battery aging estimation method through data transmission, so that the execution device can receive the actual operation data of the composite electrode battery.

[0050] As an example, during the use of a composite electrode battery, the voltage, current, temperature, SOC, and time of the composite electrode battery can be collected in real time by a BMS. A functional relationship between the voltage, current, temperature, SOC, and time can be established to obtain actual operating data. The actual operating data can then be uploaded to an execution device, which then receives the actual operating data of the composite electrode battery. The time can be the data collection time recorded by the clock module in the BMS or the upload time of the actual operating data by a tracker in the BMS.

[0051] The execution device may be a device with big data processing capabilities. Using an execution device with big data processing capabilities to estimate battery aging can fully consider the battery aging situation and help to accurately grasp the degree of battery aging.

[0052] As an example, a cloud server can be used as an execution device. The cloud server has good scalability, supports users to flexibly adjust the server's memory, system disk, and bandwidth online at any time, has the ability to coordinate and calculate massive amounts of data, and can monitor the aging degree of composite electrode batteries on a large scale in real time, all at a low cost. In other embodiments of the present application, a BMS can also be used as an execution device.

[0053] S120 . Obtain at least one set of characteristic data, where the characteristic data is associated with actual operation data and is configured as a function of OCV, SOC, temperature, and time.

[0054] The inventors of this application discovered that the SOC-OCV relationship characteristic of composite electrode batteries changes with the number of battery cycles, and that the number of cycles is linearly correlated with the degree of aging. Based on this, this embodiment associates the SOC-OCV relationship characteristic of composite electrode batteries with the aging estimation of composite electrode batteries. Furthermore, the SOC-OCV relationship characteristic is also affected by temperature. Therefore, to ensure the accuracy of battery aging estimation results, the characteristic data is configured as a function of OCV, SOC, temperature, and time. The battery aging degree is determined based on the characteristic data.

[0055] In this embodiment, the characteristic data is calculated based on the actual operation data in S110.

[0056] The characteristic data can be expressed in the form of tables, functions or images to represent the functional relationship between OCV, SOC, temperature and time.

[0057] As an example, the characteristic data may be expressed as a functional relationship between OCV, SOC, temperature, and time using a table as shown in Table 1 below:

[0058] Table 1:

[0059] The product types and cell models in Table 1 can be directly determined based on the product parameters of the composite electrode battery.

[0060] In Table 1, except for the first row, which is also the title row, each row corresponds to a set of characteristic data. The temperature, OCV, and SOC in the same set of characteristic data have the same corresponding time.

[0061] S130. Based on pre-set experimental data, determine the aging degree corresponding to each set of characteristic data.

[0062] The experimental data corresponds to the cell model of the composite electrode battery in S110 (hereinafter referred to as the "current composite electrode battery" for ease of description) and is configured as a function of temperature, battery aging, OCV and SOC.

[0063] Before performing aging estimation on the current composite electrode battery, the execution device pre-stores the experimental data corresponding to the cell model of the current composite electrode battery. In this way, the execution device can directly obtain the corresponding experimental data when performing aging estimation on the current composite electrode battery.

[0064] In this embodiment, for the current composite electrode battery, its corresponding experimental data can be obtained by performing a standard cycle life test on a sample of cells with the same cell model as the composite electrode battery. Specifically, utilizing the characteristic that the SOC and OCV characteristics of the composite electrode battery change with the cycle data, a standard cycle life test is carried out in the laboratory on the sample of cells of this cell model to obtain the aging parameters of the cell over its entire life cycle. The aging parameters include the battery SOC and OCV data of the cell at different temperatures and different cycle numbers. The number of cycles under standard laboratory conditions is linearly correlated with the degree of battery aging. From this, a mapping relationship between the number of cycles and the degree of aging can be obtained. Based on this mapping relationship, the degree of aging corresponding to each number of cycles can be determined. Based on this, a functional relationship between temperature, degree of aging, OCV and SOC can be further established to obtain the experimental data corresponding to the cell model, that is, the experimental data corresponding to the current composite electrode battery. The experimental data is uploaded to the execution device, which receives and stores the experimental data.

[0065] In this embodiment, the experimental data may be expressed in the form of a table, a functional expression, or a graph to represent the functional relationship among the temperature, battery aging, OCV, and SOC.

[0066] As an example, the experimental data can be expressed as a table as shown in Table 2 below to represent the functional relationship between temperature, battery aging, OCV, and SOC:

[0067] Table 2:

[0068] In Table 2, except for the first row, which is also the title row, each row corresponds to a set of aging parameters obtained based on experiments. For each row, the corresponding aging degree can be determined when the battery cell model, temperature, OCV and SOC are known.

[0069] It can be seen from Table 1 and Table 2 that both the experimental data and the characteristic data include three parameter items: temperature, OCV and SOC. When the battery cell model is consistent, the aging degree corresponding to the characteristic data can be determined from the experimental data based on the characteristic data.

[0070] S140. Determine the aging degree of the composite electrode battery based on the aging degree corresponding to at least one set of characteristic data.

[0071] The composite electrode battery aging estimation method in this embodiment receives actual operating data of a composite electrode battery, where the actual operating data is configured as a function of battery voltage, battery current, battery temperature, state of charge (SOC), and time. It then obtains at least one set of characteristic data associated with the actual operating data and configured as a function of OCV, SOC, temperature, and time. The aging degree corresponding to each set of characteristic data is determined based on pre-set experimental data, where the experimental data corresponds to the cell model of the composite electrode battery and is configured as a function of temperature, battery aging degree, OCV, and SOC. The aging degree of the composite electrode battery is determined based on the aging degree corresponding to the at least one set of characteristic data. According to this embodiment, the accurate aging degree of the battery is determined based on the experimental data through data analysis, ensuring the accuracy of aging estimation for the composite electrode battery.

[0072] In addition, based on the fact that the SOC-OCV relationship characteristics of the composite electrode battery change with the number of battery cycles, that is, the degree of aging, this embodiment estimates the degree of aging based on characteristic data including OCV and SOC, further ensuring the accuracy of the estimation result.

[0073] In some embodiments, the implementation of obtaining at least one set of feature data may include:

[0074] receiving target data, where the target data is associated with actual operating data within a calculation cycle;

[0075] Wherein, the calculation period is the time for the composite electrode battery to complete at least one use cycle, and the use cycle is used to indicate that the cumulative charge capacity or cumulative discharge capacity of the composite electrode battery reaches 100%;

[0076] Based on the target data, at least one set of feature data is determined.

[0077] Understandably, battery aging cycles are long, and aging changes are less noticeable in short periods of time. Therefore, calculation cycles are divided. When estimating aging for the current composite electrode battery, the execution device extracts data from a specific time period from the operating data of the current composite electrode battery based on the calculation cycle as target data for centralized calculation to ensure the accuracy of the final battery aging estimation results.

[0078] Because different battery products have different total battery capacities, operating environments, and conditions, the calculation cycles for different composite electrode batteries may differ. Therefore, the execution device determines the calculation cycle corresponding to the current composite electrode battery before extracting the target data.

[0079] In this embodiment, the calculation cycle corresponding to the current composite electrode battery may be pre-stored in the execution device, or may be input into the execution device by the user when performing aging estimation on the current composite electrode battery.

[0080] For the current composite electrode battery, its corresponding calculation cycle can be set according to actual needs. In order to ensure that the target data can reflect the aging changes of the battery, it is necessary to ensure that the battery can complete at least one use cycle within one calculation cycle, that is, the calculation cycle corresponding to the current composite electrode battery is the time it takes for the current composite electrode battery to complete at least one use cycle. For example, taking the current composite electrode battery as a composite electrode battery product with a total capacity of 20AH as an example, the current composite electrode battery can use the cumulative charging capacity or cumulative discharge capacity ≥ 24Ah as a calculation cycle, and 24Ah is the minimum value of the calculation cycle of the product. The calculation cycle can also be extended in the future according to the actual use of the product.

[0081] In this embodiment, the calculation cycle refers to the time interval corresponding to two adjacent aging estimations, which is a duration. The execution device can periodically perform aging estimation on the current composite electrode battery based on the calculation cycle.

[0082] When extracting target data from the actual operating data of the current composite electrode battery based on a calculation cycle, the time when aging estimation was last performed on the composite electrode battery can be determined as the first time. If aging estimation has not been performed on the composite electrode battery before, the minimum time value in the actual operating data of the composite electrode battery can be used as the first time. The first time is added to the calculation cycle to obtain a second time. The first time and the second time are combined to form a target time interval. The target data is extracted from the actual operation of the composite electrode battery based on the target time interval, where the time corresponding to the target data falls within the target time interval. For example, if the first time is 14:00 on January 15, 2022, and the second time is 14:00 on January 22, 2022, the actual operating data uploaded between 14:00 on January 15, 2022 and 14:00 on January 22, 2022, is used as the target data.

[0083] Through the above method, the target data is filtered out based on the calculation cycle, so that the feature data determined based on the target data can reflect the battery characteristics within the calculation cycle. Compared with determining the feature data based on all actual operation data, the calculation amount of determining the feature data based on the target data within the calculation cycle is smaller, and feature data can be extracted periodically based on the calculation cycle.

[0084] After the target data is extracted, at least one set of feature data may be determined based on the target data.

[0085] In some embodiments, in order to improve the quality of the target data, before extracting the target data from the actual operation data, the actual operation data can be preprocessed, and then the target data can be extracted from the preprocessed actual operation data. This can improve the data quality of the target data and further improve the accuracy of the aging estimation results.

[0086] Preprocessing the actual operation data may include: constructing time series data corresponding to the actual operation data, and performing missing value processing and outlier processing on the time series data.

[0087] Constructing the time series data corresponding to the actual operation data includes: constructing the time series data based on the time in the actual operation data and sorting the data in chronological order.

[0088] Missing value processing for time series data can include: finding missing values ​​in the time series, and processing missing values ​​through interpolation or deletion.

[0089] Outlier processing for time series data may include: detecting outliers based on an outlier detection method, deleting outliers, and other processing. The outlier detection method may include a statistical method and a machine learning method.

[0090] In some embodiments, determining at least one set of feature data based on the target data may include:

[0091] Based on the target data, at least one OCV, and an SOC and a temperature corresponding to the calculated OCV are calculated;

[0092] A set of characteristic data includes a calculated OCV, an SOC corresponding to the OCV, and a temperature.

[0093] In this embodiment, the actual operating data includes the battery voltage instead of directly including the OCV, so the OCV needs to be calculated based on the target data.

[0094] As an example, based on the previous explanation of terminology, OCV stands for open-circuit voltage, which refers to the potential difference between the positive and negative electrodes of a battery when the battery is not operating, meaning when no current is flowing through the circuit. Based on this, OCV can be calculated by counting the static data in the target data. This static data refers to data collected after the battery has been in a state of rest (i.e., no longer charging or discharging) for a preset period of time. This preset period can be set based on actual conditions, for example, two hours. Specifically, the voltage in the static data can be determined as the OCV.

[0095] As another example, OCV may be extracted from the target data based on a parameter identification model set in the execution device, where the parameter identification model is a model that can identify OCV.

[0096] Because OCV is actually a battery voltage determined based on target data, and the target data is a function of battery voltage, battery current, battery temperature, SOC and time, the corresponding SOC and temperature can be extracted from the target data based on OCV.

[0097] After obtaining the OCV, the SOC corresponding to the OCV, and the temperature, at least one group of characteristic data is obtained by dividing the data according to a rule that a group of characteristic data includes an OCV, a temperature corresponding to the OCV, and an SOC.

[0098] In this way, an OCV, an SOC corresponding to the OCV, and a temperature are used as a set of characteristic data, so that the characteristic data can reflect the SOC-OCV relationship characteristics of the composite electrode battery, and the battery aging degree can be accurately determined based on the characteristic data.

[0099] In some embodiments, after obtaining at least one set of characteristic data, determining the aging degree corresponding to each set of characteristic data based on pre-set experimental data may include:

[0100] Determining first data in the experimental data, where a temperature in the first data is the same as or closest to a temperature in the characteristic data;

[0101] Determining second data in the first data, wherein OCV and SOC in the second data are closest to OCV and SOC in the characteristic data;

[0102] The aging degree of the second data is used as the aging degree corresponding to the characteristic data.

[0103] Each parameter in the characteristic data has a different impact on the accuracy of determining the degree of aging corresponding to the characteristic data. Therefore, the degree of aging corresponding to the characteristic data can be determined based on temperature and OCV-SOC in turn according to the priority of each parameter, which can ensure that the degree of aging corresponding to the characteristic data is accurately determined.

[0104] Specifically, you can first search for data with the same temperature as the temperature in the characteristic data from the experimental data, and use the found data as the first data. If no data with the same temperature is found, then search for data with the closest temperature to the temperature in the characteristic data, and use the found data with the closest temperature as the first data.

[0105] After obtaining the first data, search the first data for data whose SOC-OCV data is identical to the SOC-OCV data in the characteristic data, and use the found data as the second data. Similarly, if no data whose SOC-OCV data is identical is found, search the first data for data whose SOC-OCV data is closest to the SOC-OCV data in the characteristic data, and determine the found data as the second data.

[0106] In this way, the second data obtained is the data closest to the uniform characteristic data of temperature and SOC-OCV data. Therefore, the aging degree in the second data is the aging degree closest to the corresponding characteristic data, so the aging degree in the second data can be determined as the aging degree corresponding to the characteristic data.

[0107] In the case where there are multiple groups of characteristic data, the above process can usually be repeated to obtain the aging degree corresponding to each group of characteristic data.

[0108] Through the above method, the accuracy of the aging degree corresponding to the characteristic data can be improved.

[0109] It is understood that the experimental data includes aging parameters of composite electrode batteries under various operating conditions. Therefore, for the same temperature, there may be multiple sets of different SOC-OCV data. In other words, for each characteristic data, multiple first data are usually determined based on the experimental data. Based on this, when determining the second data within the first data, the following may be included:

[0110] Calculating a first difference between the OCV in each first data and the OCV in the characteristic data, and a second difference between the SOC in each first data and the SOC in the characteristic data;

[0111] For each first data, normalize and sum the first difference and the second difference corresponding to the first data to obtain first data difference values ​​corresponding to multiple first data;

[0112] The first data corresponding to the minimum first data difference is used as the second data.

[0113] In practical applications, for each first data, the corresponding first data difference can be calculated based on the following formula:

[0114] In the formula, δ represents the first data difference corresponding to the first data, SOC2 represents the SOC in the characteristic data, SOC1 represents the SOC in the first data, OCV2 represents the OCV in the characteristic data, OCV1 represents the OCV in the first data, V max Indicates the current full charge voltage of the composite electrode battery, V min Indicates the current discharge cut-off voltage of the composite electrode battery.

[0115] The smaller the δ value corresponding to the first data is, the closer it is to the characteristic data. Based on this, the data with the smallest δ value is selected from the plurality of first data and determined as the second characteristic data.

[0116] Based on the above method, when determining the second data, the first difference and the second difference corresponding to each first data are calculated respectively. The first difference can reflect the difference between the OCV in the first data and the OCV in the characteristic data, and the second difference can reflect the difference between the SOC in the first data and the SOC in the characteristic data. The first difference and the second difference are normalized and summed, and the obtained first data difference can reflect the difference between the SOC-OCV relationship characteristics in the first data and the SOC-OCV relationship characteristics in the characteristic data. Then, based on the first data difference, the second data whose SOC-OCV relationship characteristics are closest to the SOC-OCV relationship characteristics of the characteristic data can be found from multiple first data.

[0117] In some embodiments, if a set of characteristic data is obtained in S120, then when determining the aging degree of the composite electrode battery in S140 based on the aging degree corresponding to at least one set of characteristic data, the aging degree corresponding to the set of characteristic data can be directly determined as the aging degree of the composite electrode battery.

[0118] In some other embodiments of the present application, an aging estimation result determined based on multiple sets of feature data is more accurate than a single set of feature data. Multiple sets of feature data may be obtained in S120. Based on this, determining the aging degree of the composite electrode battery based on the aging degrees corresponding to the multiple sets of feature data in S140 may include:

[0119] Determining target characteristic data from the plurality of sets of characteristic data, wherein the SOC of the target characteristic data is the same as or closest to a preset SOC value;

[0120] Calculate the mean of the aging degree corresponding to multiple groups of target feature data;

[0121] The mean value is determined as the aging degree of the composite electrode battery.

[0122] For a composite electrode battery, only one aging estimation result needs to be obtained within one calculation cycle. However, due to objective errors, the aging degrees corresponding to multiple sets of feature data may not be the same. The SOC-OCV relationship characteristics of the composite electrode battery system change with the number of cycles, that is, the characteristics of the aging degree, and the aging degree is different under different SOCs. In addition, depending on the different usage of the composite electrode battery and the different OCV calculation methods, the sparsity of the SOC distribution in the multiple sets of feature data screened out may be different, and there may be a situation where the data in a certain SOC interval is too dense. Therefore, multiple SOC values ​​are set in advance, and the preset SOC values ​​are used as calculation nodes to screen out target feature data from multiple sets of feature data, and then the aging degree of the composite electrode battery is determined based on the aging degree corresponding to the target feature data. Among them, the preset SOC can be set according to actual needs.

[0123] As an example, using 20%, 40%, 60%, and 80% SOC values ​​as preset SOCs, or calculation nodes, the system selects, from multiple sets of characteristic data, first characteristic data with an SOC value in the range of 0% to 20% and closest to 20%, second characteristic data with an SOC value in the range of 20% to 40% and closest to 40%, third characteristic data with an SOC value in the range of 40% to 60% and closest to 60%, and fourth characteristic data with an SOC value in the range of 60% to 80% and closest to 80%. The system then calculates the average of the aging degrees corresponding to the first, second, third, and fourth characteristic data, and uses this average to determine the aging degree of the composite electrode battery within the calculation period.

[0124] By the above method, the accurate aging degree of the composite electrode battery can be obtained.

[0125] Furthermore, after obtaining the aging degree of the composite electrode battery within the calculation period, the result can be saved to the execution device for subsequent viewing or calling.

[0126] Based on the composite electrode battery aging estimation method provided in the above embodiment, the present application also provides a specific implementation of a composite electrode battery aging estimation device. Please refer to the following embodiment.

[0127] 2 , a composite electrode battery aging estimation device 200 provided in an embodiment of the present application includes the following modules: an actual data receiving module 201 , a characteristic data acquiring module 202 , an aging degree experimental value determining module 203 , and an aging degree determining module 204 .

[0128] Actual data receiving module 201, for receiving actual operation data of the composite electrode battery;

[0129] The actual operating data is configured as a function of battery voltage, battery current, battery temperature, SOC and time;

[0130] A storage module, configured to store the actual operation data;

[0131] a characteristic data acquisition module 202 for acquiring at least one set of characteristic data, the characteristic data being associated with actual operating data and configured as a function of OCV, SOC, temperature, and time;

[0132] An aging degree experimental value determination module 203 is used to determine the aging degree corresponding to each set of characteristic data based on pre-set experimental data;

[0133] The experimental data corresponds to the cell model of the composite electrode battery and is configured as a function of temperature, battery aging, OCV and SOC;

[0134] The aging degree determination module 204 is configured to determine the aging degree of the composite electrode battery based on the aging degree corresponding to at least one set of characteristic data.

[0135] The composite electrode battery aging estimation device provided in this embodiment receives actual operating data of the composite electrode battery, where the actual operating data is configured as a function of battery voltage, battery current, battery temperature, state of charge (SOC), and time. It then obtains at least one set of characteristic data, which is associated with the actual operating data and configured as a function of OCV, SOC, temperature, and time. The device then determines the degree of aging corresponding to each set of characteristic data based on pre-set experimental data. The experimental data corresponds to the cell model of the composite electrode battery and is configured as a function of temperature, battery aging, OCV, and SOC. The device determines the degree of aging of the composite electrode battery based on the aging degree corresponding to the at least one set of characteristic data. According to this embodiment, the accurate degree of battery aging is determined based on the experimental data through data analysis, thereby improving the accuracy of composite electrode battery aging estimation.

[0136] In some embodiments, the feature data acquisition module 202 may include:

[0137] a target data determination submodule for receiving target data associated with actual operating data within a calculation period. The calculation period is the time it takes for the composite electrode battery to complete at least one usage cycle, where the usage cycle indicates when the cumulative charge capacity or cumulative discharge capacity of the composite electrode battery reaches 100%;

[0138] The feature data determination submodule is used to determine at least one set of feature data based on the target data.

[0139] In some embodiments, the characteristic data determination submodule is specifically configured to calculate, based on the target data, at least one OCV, and an SOC and a temperature corresponding to the calculated OCV. A set of characteristic data includes a calculated OCV, an SOC and a temperature corresponding to the OCV.

[0140] In some embodiments, the aging degree experimental value determination module 203 may include: a first data determination submodule, used to determine the first data in the experimental data, the temperature in the first data is the same as or closest to the temperature in the characteristic data; a second data determination submodule, used to determine the second data in the first data, the OCV and SOC in the second data are closest to the OCV and SOC in the characteristic data; an experimental value determination submodule, used to use the aging degree of the second data as the aging degree corresponding to the characteristic data.

[0141] In some embodiments, the first data is multiple, and the second data determination submodule may include:

[0142] A first calculation submodule, configured to calculate a first difference between the OCV in each first data and the OCV in the feature data;

[0143] A second calculation submodule, configured to calculate a second difference between the SOC in each first data and the SOC in the characteristic data;

[0144] a normalization submodule, configured to normalize and sum the first difference and the second difference corresponding to each first data, to obtain a plurality of first data difference values ​​corresponding to the plurality of first data;

[0145] The screening submodule is configured to use the first data corresponding to the minimum first data difference as the second data.

[0146] In some embodiments, the characteristic data is multiple groups, and the aging degree determination module 204 may include:

[0147] A target feature determination submodule is used to determine target feature data from the plurality of sets of feature data, wherein the SOC of the target feature data is the same as or closest to a preset SOC value;

[0148] The mean calculation submodule is used to calculate the mean of the aging degree corresponding to multiple groups of target feature data;

[0149] The aging degree determination submodule is used to determine the mean value as the aging degree of the composite electrode battery.

[0150] The composite electrode battery aging estimation device provided in the embodiments of the present application can implement each process implemented in any of the above-mentioned composite electrode battery aging estimation method embodiments, and to avoid repetition, they are not described here.

[0151] FIG3 shows a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application.

[0152] The electronic device 300 may include a processor 301 and a memory 302 storing computer program instructions.

[0153] Specifically, the processor 301 may include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0154] The memory 302 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 302 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 302 may include removable or non-removable (or fixed) media. Where appropriate, the memory 302 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 302 is a non-volatile solid-state memory.

[0155] The memory 302 may include read-only memory (ROM), random access memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical / tangible memory storage devices. Therefore, generally, the memory 302 includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it may perform the operations described in any of the composite electrode battery aging estimation methods described in the above embodiments.

[0156] The processor 301 reads and executes computer program instructions stored in the memory 302 to implement any one of the composite electrode battery aging estimation methods in the above embodiments.

[0157] In one example, the electronic device 300 may further include a communication interface 303 and a bus 310. As shown in FIG3, the processor 301, the memory 302, and the communication interface 303 are connected via the bus 310 and communicate with each other.

[0158] The communication interface 303 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0159] Bus 310 includes hardware, software or both, and the components of online data flow metering equipment are coupled to each other. For example, but not limitation, bus can include accelerated graphics port (AGP) or other graphics bus, enhanced industry standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industry standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 310 can include one or more buses. Although the present application embodiment describes and shows specific bus, the application considers any suitable bus or interconnection.

[0160] In addition, in conjunction with the composite electrode battery aging estimation method described in the above embodiments, embodiments of the present application may provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when executed by a processor, the computer program instructions implement any of the composite electrode battery aging estimation methods described in the above embodiments.

[0161] It should be understood that the present application is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and illustrated as examples. However, the method process of the present application is not limited to the specific steps described and illustrated. Those skilled in the art can make various changes, modifications, and additions, or change the order of the steps after understanding the spirit of the present application.

[0162] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium that can store or transmit information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memories, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0163] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps. In other words, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0164] Aspects of the present disclosure have been described above with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor or a field programmable logic circuit. It is also understood that each box in the block diagram and / or flowchart and the combination of the boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0165] The above description is only a specific embodiment of the present application. Those skilled in the art will clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the scope of protection of the present application.

Claims

1. A composite electrode battery aging estimation method, wherein: include: receiving actual operating data of the composite electrode battery; Wherein, the actual operation data is configured as a function of battery voltage, battery current, battery temperature, SOC and time; Acquire at least one set of characteristic data, wherein the characteristic data is associated with the actual operation data and is configured as a function of OCV, SOC, temperature, and time; Based on the preset experimental data, determining the aging degree corresponding to each group of the characteristic data; Wherein, the experimental data corresponds to the cell model of the composite electrode battery and is configured as a function of temperature, battery aging, OCV and SOC; The aging degree of the composite electrode battery is determined based on the aging degree corresponding to at least one set of the characteristic data.

2. The method according to claim 1, wherein: The obtaining of at least one set of characteristic data comprises: receiving target data, the target data being associated with actual operating data within a calculation cycle; Wherein, the calculation period is the time for the composite electrode battery to complete at least one use cycle, and the use cycle is used to indicate that the cumulative charge capacity or cumulative discharge capacity of the composite electrode battery reaches 100%; Based on the target data, at least one set of the feature data is determined.

3. The method according to claim 2, wherein: The determining, based on the target data, the at least one set of feature data comprises: Based on the target data, at least one OCV, and an SOC and a temperature corresponding to the calculated OCV are calculated; A set of the characteristic data includes the calculated OCV and an SOC and a temperature corresponding to the OCV.

4. The method according to any one of claims 1 to 3, wherein: For each set of the characteristic data, based on the preset experimental data, determining the aging degree corresponding to the characteristic data includes: Determine first data in the experimental data, wherein a temperature in the first data is the same as or closest to a temperature in the characteristic data; Determine second data in the first data, wherein OCV and SOC in the second data are closest to OCV and SOC in the characteristic data; The aging degree of the second data is used as the aging degree corresponding to the characteristic data.

5. The method according to claim 4, wherein: The first data is a plurality of data, and determining the second data in the first data includes: Calculating a first difference between the OCV in each of the first data and the OCV in the characteristic data, and a second difference between the SOC in each of the first data and the SOC in the characteristic data; For each of the first data, normalize and sum the first difference value and the second difference value corresponding to the first data to obtain a plurality of first data difference values ​​corresponding to the plurality of the first data; The first data corresponding to the minimum first data difference is used as the second data.

6. The method according to claim 5, wherein: The characteristic data is a plurality of groups, and determining the aging degree of the composite electrode battery based on the aging degree corresponding to at least one group of the characteristic data includes: Determining target characteristic data among the plurality of sets of characteristic data, wherein the SOC of the target characteristic data is the same as or closest to a preset SOC value; Calculating the mean of the aging degrees corresponding to the multiple groups of target characteristic data; The average value is determined as the aging degree of the composite electrode battery.

7. A composite electrode battery aging estimation device, wherein: include: An actual data receiving module, used for receiving actual operation data of the composite electrode battery; Wherein, the actual operation data is configured as a function of battery voltage, battery current, battery temperature, SOC and time; A storage module, used for storing the actual operation data; a characteristic data acquisition module, for acquiring at least one set of characteristic data, wherein the characteristic data is associated with the actual operation data and is configured as a function of OCV, SOC, temperature and time; An aging degree experimental value determination module, used to determine the aging degree corresponding to each set of characteristic data based on preset experimental data; Wherein, the experimental data corresponds to the cell model of the composite electrode battery and is configured as a function of temperature, battery aging, OCV and SOC; The aging degree determination module is used to determine the aging degree of the composite electrode battery based on the aging degree corresponding to at least one set of the characteristic data.

8. An electronic device, wherein: The device comprises: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the composite electrode battery aging estimation method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, wherein: The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the composite electrode battery aging estimation method according to any one of claims 1 to 6 is implemented.

10. A computer program product, wherein: When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the composite electrode battery aging estimation method according to any one of claims 1 to 6.

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