Battery health state estimation method, device, equipment, medium and program product

Abstract

The invention relates to a battery health state estimation method, device and equipment, a medium and a program product. The method comprises the following steps: acquiring first electrochemical impedance spectroscopy (EIS) data of a battery under a plurality of health state classifications at a specific frequency point, determining a health state estimation strategy according to the first EIS data of the battery at the specific frequency point, and estimating the health state of a to-be-detected battery according to second EIS data of the to-be-detected battery at the specific frequency point and the health state estimation strategy. The to-be-tested battery does not need to be fully charged and discharged, so that the time required for estimating the health state of the battery is shortened, and the cost required for estimating the health state of the battery is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of battery state of health estimation, and particularly to a method, device, equipment, medium and program product for estimating the state of health of a battery. Background Art

[0002] With the wide application of batteries in devices such as new energy vehicles and ships, their use safety has attracted increasing attention. The state of health (SOH) is an indication of the battery's health and life status. Therefore, by estimating the SOH of the battery, different treatments can be carried out on the battery according to the estimated SOH. For example, for some batteries with particularly severe SOH decay, continued cascade utilization may lead to serious safety accidents, while for some batteries with less severe SOH decay, they can continue to be used for other purposes.

[0003] Current state of health estimation methods require full charge and discharge of each battery, resulting in a long time consumption problem. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide a method, device, equipment, medium and program product for estimating the state of health of a battery that can reduce the estimation time of the state of health of the battery.

[0005] In a first aspect, the present application provides a method for estimating the state of health of a battery. The method includes:

[0006] Obtain first electrochemical impedance spectroscopy (EIS) data of batteries under multiple state of health classifications at a specific frequency point;

[0007] Determine a state of health estimation strategy based on the first EIS data of the battery at the specific frequency point;

[0008] Estimate the state of health of the battery to be measured based on the second EIS data of the battery to be measured at the specific frequency point and the state of health estimation strategy.

[0009] In the above method for estimating the state of health of a battery, the state of health of the battery to be measured can be directly estimated based on the second EIS data of the battery to be measured at the specific frequency point and the state of health estimation strategy. Therefore, when estimating the state of health of the battery to be measured, it is not necessary to perform full charge and discharge on the battery to be measured, reducing the time required for estimating the state of health of the battery and the cost required for estimating the state of health of the battery.

[0010] In one of the embodiments, the number of batteries and specific frequency points is multiple. Determining a state of health estimation strategy based on the first EIS data of the battery at the specific frequency point includes:

[0011] For each health state classification, based on the first EIS data of each battery at each specific frequency point under the health state classification, determine the third EIS data corresponding to each specific frequency point under the health state classification;

[0012] Based on the third EIS data corresponding to each specific frequency point under each health state classification, determine the health state estimation strategy.

[0013] In this embodiment, by determining the health state estimation strategy, the health state of the battery under test can be estimated based on the health state estimation strategy, reducing the time and cost required for health state estimation.

[0014] In one of the embodiments, the method further includes:

[0015] Obtain the EIS data of the battery sample in the preset frequency band;

[0016] Determine the specific frequency points according to the EIS data in the preset frequency band.

[0017] In this embodiment, by determining the specific frequency points according to the EIS data in the preset frequency band, a foundation is laid for obtaining the first EIS data of each battery at each specific frequency point under the health state classification, without scanning each frequency point in the preset frequency band of each battery under the health state classification, thus saving the time required to obtain the first EIS data, and therefore further improving the health state estimation efficiency.

[0018] In one of the embodiments, determining the specific frequency points according to the EIS data in the preset frequency band includes:

[0019] Obtain the first Nyquist plot corresponding to the EIS data in the preset frequency band;

[0020] Determine the specific frequency points based on the first Nyquist plot.

[0021] In this embodiment, determining the specific frequency points based on the first Nyquist plot can more intuitively determine the specific frequency points, improving the determination efficiency of the specific frequency points. And since only one scan of the battery sample in the preset frequency band is required to obtain the EIS data of the battery sample in the preset frequency band and then determine the specific frequency points, and subsequently only the first EIS data of the batteries under multiple health state classifications at the specific frequency points needs to be obtained, without obtaining the first EIS data of the batteries under multiple health state classifications at each frequency point in the preset frequency band, therefore, the time for obtaining the first EIS data is saved, and further the efficiency of determining the health state estimation strategy based on the first EIS data and the estimation efficiency of the health state of the battery under test are improved.

[0022] In one of the embodiments, determining the specific frequency points based on the first Nyquist plot includes:

[0023] Determine the first frequency interval corresponding to the semicircle region of the first Nyquist plot and the second frequency interval corresponding to the linear region; the preset frequency band includes the first frequency interval and the second frequency interval;

[0024] Determine a first frequency point from the first frequency interval and a second frequency point from the second frequency interval; the specific frequency points include the first frequency point and the second frequency point.

[0025] In this embodiment, by determining a first frequency point from the first frequency interval and a second frequency point from the second frequency interval, the specific frequency points are determined, thus laying a foundation for obtaining the first EIS data of each battery in each specific frequency point under the health state classification. There is no need to scan each frequency point of each battery in the preset frequency band under the health state classification, thus saving the time required to obtain the first EIS data, and therefore the health state estimation efficiency can be further improved.

[0026] In one of the embodiments, determining a first frequency point from the first frequency interval and a second frequency point from the second frequency interval includes:

[0027] Taking the frequency point corresponding to the vertex of the semicircle region in the first frequency interval as the first frequency point, and taking the center frequency point of the second frequency interval as the second frequency point.

[0028] In one of the embodiments, the health state classification includes a health state interval; according to the first EIS data of each battery in each specific frequency point under the health state classification, determining the third EIS data corresponding to each specific frequency point under the health state classification includes:

[0029] According to the real part data of the first EIS data of the first frequency point under the health state interval, determining the first parameter corresponding to the first frequency point under the health state interval, and according to the imaginary part data of the first EIS data of the second frequency point under the health state interval, determining the second parameter corresponding to the second frequency point under the health state interval; the first parameter includes the first average value or the first median, and the second parameter includes the second average value or the second parameter;

[0030] Taking the first parameter as the third EIS data corresponding to the first frequency point under the health state interval, and taking the second parameter as the third EIS data corresponding to the second frequency point under the health state interval.

[0031] In this embodiment, by determining the average value or the median to determine the third EIS data corresponding to the first frequency point and the second frequency point under the health state interval, the accuracy of the determined third EIS data is improved, and further the accuracy of the health state estimation strategy determined according to the third EIS data corresponding to each specific frequency point under each health state classification is improved.

[0032] In one embodiment, determining a health state estimation strategy according to the third EIS data corresponding to each specific frequency point under each health state classification includes:

[0033] For each health state interval, determining a first weighted value corresponding to the health state interval according to the real part data and the first weight of the third EIS data corresponding to the first frequency point under the health state interval, and the imaginary part data and the second weight of the third EIS data corresponding to the second frequency point; the first weight is less than the second weight;

[0034] Determining a health state estimation strategy according to the first weighted value corresponding to each health state interval.

[0035] In this embodiment, by determining a health state estimation strategy according to the first weighted value corresponding to each health state interval, the accuracy of the determined health state estimation strategy is improved, so that a more accurate health state of the battery under test can be obtained based on a more accurate health state estimation strategy.

[0036] In one embodiment, determining a health state estimation strategy according to the first weighted value corresponding to each health state interval includes:

[0037] For each health state interval, taking the first weighted value corresponding to the health state interval as the health state reference value of the health state interval;

[0038] Taking the health state reference values of each health state interval as the health state estimation strategy.

[0039] In this embodiment, by taking the first weighted value corresponding to the health state interval as the health state reference value of the health state interval and taking the health state reference values of each health state interval as the health state estimation strategy, the accuracy of the determined health state estimation strategy is improved, so that a more accurate health state of the battery under test can be obtained based on a more accurate health state estimation strategy.

[0040] In one embodiment, estimating the health state of the battery under test according to the second EIS data of the battery under test at a specific frequency point and the health state estimation strategy includes:

[0041] Determining a second weighted value according to the real part second EIS data of the battery under test at the first frequency point, the first weight, the imaginary part second EIS data of the battery under test at the second frequency point, and the second weight;

[0042] Estimating the health state of the battery under test according to the second weighted value and the health state reference values of each health state interval.

[0043] In this embodiment, according to the second weighting value and the health state reference values of each health state interval, the health state of the battery to be tested is estimated without fully charging and discharging the battery to be tested, saving the time and cost of estimating the health state, and thus being applicable to the usage scenario of batch estimating the health state of the batteries to be tested.

[0044] In one of the embodiments, estimating the health state of the battery to be tested according to the second weighting value and the health state reference values of each health state interval includes:

[0045] Determining the absolute value of the difference between the second weighting value and the health state reference value of each health state interval;

[0046] Estimating the health state of the battery to be tested according to the health state interval corresponding to the smallest absolute value.

[0047] In this embodiment, estimating the health state of the battery to be tested according to the health state interval corresponding to the smallest absolute value does not require fully charging and discharging the battery to be tested, saving the time and cost of estimating the health state, and thus being applicable to the usage scenario of batch estimating the health state of the batteries to be tested.

[0048] In one of the embodiments, determining a specific frequency point based on the first Nyquist plot includes:

[0049] Determining a third frequency interval corresponding to the semi-circular region of the first Nyquist plot;

[0050] Determining specific frequency points with a preset number of frequency points from the third frequency interval; the preset number of frequency points is greater than 2.

[0051] In this embodiment, by determining specific frequency points with a preset number of frequency points from the third frequency interval corresponding to the semi-circular region of the first Nyquist plot, the specific frequency points are determined, thus laying a foundation for obtaining the first EIS data of each battery in each health state classification at each specific frequency point, without scanning each frequency point in the preset frequency band for each battery in each health state classification, thus saving the time required to obtain the first EIS data, and therefore being able to further improve the efficiency of health state estimation.

[0052] In one of the embodiments, determining specific frequency points from the third frequency interval includes:

[0053] According to the preset number of frequency points, the maximum frequency point value and the minimum frequency point value corresponding to the third frequency interval, using the logarithmic interval distribution function to determine specific frequency points with the preset number of frequency points.

[0054] In this embodiment, by using a logarithmic spacing distribution function to determine specific frequency points of a preset number of frequency points, only the first EIS data at the specific frequency points needs to be obtained, without obtaining the first EIS data at each frequency point in a preset frequency band. Therefore, the time for obtaining the first EIS data is saved, and further, the time required to determine a health state estimation strategy based on the first EIS data at the specific frequency points is saved, improving the efficiency of health state estimation.

[0055] In one embodiment, the health state classification includes health state values; determining third EIS data corresponding to each specific frequency point under the health state classification according to the first EIS data of each battery at each specific frequency point under the health state classification includes:

[0056] For each specific frequency point under the health state value, determining a third average value and a first standard deviation of the real part data of each first EIS data at the specific frequency point, and a fourth average value and a second standard deviation of the imaginary part data of each first EIS data at the specific frequency point;

[0057] Determining a first threshold according to the third average value and the first standard deviation at the specific frequency point, and determining a second threshold according to the fourth average value and the second standard deviation at the specific frequency point;

[0058] Determining fourth EIS data from the first EIS data at the specific frequency point according to the first threshold and the second threshold at the specific frequency point;

[0059] Based on the fourth EIS data corresponding to each specific frequency point under the health state value, determining third EIS data corresponding to each specific frequency point under the health state value.

[0060] In this embodiment, by determining the third EIS data corresponding to each specific frequency point under the health state value, a foundation is laid for determining a health state estimation strategy based on the third EIS data corresponding to each specific frequency point under the health state value, and since abnormal data is excluded, the accuracy of the determined health state estimation strategy can be improved.

[0061] In one embodiment, determining fourth EIS data from the first EIS data at the specific frequency point according to the first threshold and the second threshold at the specific frequency point includes:

[0062] Determining abnormal EIS data from the first EIS data at the specific frequency point; the abnormal EIS data includes first EIS data with a real part greater than the first threshold and / or first EIS data with an imaginary part greater than the second threshold;

[0063] Taking the first EIS data other than the abnormal EIS data as the fourth EIS data.

[0064] In this embodiment, the first EIS data except the abnormal EIS data is used as the fourth EIS data, so as to achieve the elimination of abnormal data, improve the accuracy of the obtained fourth EIS data, and thus improve the accuracy of the third EIS data corresponding to each specific frequency point under the health state value determined based on the fourth EIS data corresponding to each specific frequency point under the health state value.

[0065] In one embodiment, determining the third EIS data corresponding to each specific frequency point under the health state value based on the fourth EIS data corresponding to each specific frequency point under the health state value includes:

[0066] For each specific frequency point under the health state value, determine the third parameter of the real part data of the fourth EIS data at the specific frequency point, and determine the fourth parameter of the imaginary part data of the fourth EIS data at the specific frequency point; the third parameter includes the fifth average value or the third median, and the fourth parameter includes the sixth average value or the fourth median;

[0067] Take the third parameter as the real part data of the third EIS data at the specific frequency point, and take the fourth parameter as the imaginary part data of the third EIS data at the specific frequency point.

[0068] In this embodiment, by taking the third parameter as the real part data of the third EIS data at the specific frequency point and taking the fourth parameter as the imaginary part data of the third EIS data at the specific frequency point, the accuracy of the determined third EIS data is improved, and further the accuracy of the health state estimation strategy determined based on the third EIS data is improved.

[0069] In one embodiment, determining the health state estimation strategy according to the third EIS data corresponding to each specific frequency point under each health state classification includes:

[0070] For each health state value, determine the second Nyquist plot under the health state value according to the third EIS data corresponding to each specific frequency point under the health state value;

[0071] Determine the first radius of the semi-circle of the second Nyquist plot under each health state value;

[0072] Determine the health state estimation strategy according to each health state value and the first radius corresponding to the health state value.

[0073] In this embodiment, determining the health state estimation strategy according to each health state value and the first radius corresponding to the health state value lays a foundation for estimating the health state of the battery under test based on the health state estimation strategy, eliminates the need for full charge and full discharge of the battery under test, reduces the estimation time of the health state of the battery under test, and improves the estimation efficiency of the health state of the battery under test.

[0074] In one embodiment, determining a health state estimation strategy according to each health state value and the first radius corresponding to the health state value includes:

[0075] Fitting each health state value and the first radius corresponding to the health state value to obtain a fitting function;

[0076] Taking the fitting function as the health state estimation strategy.

[0077] In this embodiment, a fitting function is obtained by fitting each health state value and the first radius corresponding to the health state value, and the fitting function is used as the health state estimation strategy, thereby laying a foundation for estimating the health state of the battery under test based on the health state estimation strategy. It is not necessary to fully charge and fully discharge the battery under test, reducing the estimation time of the health state of the battery under test and improving the estimation efficiency of the health state of the battery under test.

[0078] In one embodiment, estimating the health state of the battery under test according to the second EIS data of the battery under test at a specific frequency point and the health state estimation strategy includes:

[0079] Determining a third Nyquist plot corresponding to the battery under test according to the second EIS data of the battery under test at a specific frequency point;

[0080] Determining the second radius of the semicircle of the third Nyquist plot;

[0081] Estimating the health state of the battery under test according to the second radius and the fitting function.

[0082] In this embodiment, the health state of the battery under test is estimated according to the second radius and the fitting function of the third Nyquist plot corresponding to the battery under test. It is not necessary to fully charge and fully discharge the battery under test, saving the estimation time and cost of the health state, and thus being applicable to the usage scenario of batch estimating the health state of the battery under test.

[0083] In a second aspect, the present application also provides a battery health state estimation device. The device includes:

[0084] A first acquisition module, configured to acquire first electrochemical impedance spectroscopy (EIS) data of batteries under multiple health state classifications at a specific frequency point;

[0085] A first determination module, configured to determine a health state estimation strategy according to the first EIS data of the battery at a specific frequency point;

[0086] An estimation module, configured to estimate the health state of the battery under test according to the second EIS data of the battery under test at a specific frequency point and the health state estimation strategy.

[0087] In a third aspect, the present application also provides a computer device. The computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the above method are implemented.

[0088] In a fourth aspect, the present application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0089] In a fifth aspect, the present application also provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0090] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented in accordance with the content of the description. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the following specifically illustrates the specific embodiments of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present application. And in all the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0092] Figure 1 is the internal structure diagram of the computer device provided by the embodiment of the present application;

[0093] Figure 2 is one of the flow diagrams of the battery health state estimation method provided by the embodiment of the present application;

[0094] Figure 3 is one of the flow diagrams of the health state estimation strategy determination method provided by the embodiment of the present application;

[0095] Figure 4 is one of the flow diagrams of the specific frequency point determination method provided by the embodiment of the present application;

[0096] Figure 5 is the second flow diagram of the specific frequency point determination method provided by the embodiment of the present application;

[0097] Figure 6 is a schematic diagram of a Nyquist plot provided by the embodiment of the present application;

[0098] Figure 7 is the second flow diagram of the specific frequency point determination method provided by the embodiment of the present application;

[0099] Figure 8 It is a schematic flow chart of a third EIS data determination method provided by an embodiment of the present application;

[0100] Figure 9 It is the second schematic flow chart of a health state estimation strategy determination method provided by an embodiment of the present application;

[0101] Figure 10 It is the third schematic flow chart of a health state estimation strategy determination method provided by an embodiment of the present application;

[0102] Figure 11 It is the second schematic flow chart of a health state estimation method provided by an embodiment of the present application;

[0103] Figure 12 It is the third schematic flow chart of a health state estimation method provided by an embodiment of the present application;

[0104] Figure 13 It is the third schematic flow chart of a specific frequency point determination method provided by an embodiment of the present application;

[0105] Figure 14 It is a schematic flow chart of another third EIS data determination method provided by an embodiment of the present application;

[0106] Figure 15 It is a schematic flow chart of a fourth EIS data determination method provided by an embodiment of the present application;

[0107] Figure 16 It is a schematic flow chart of a fourth EIS data determination method provided by an embodiment of the present application;

[0108] Figure 17 It is the fourth schematic flow chart of a health state estimation strategy determination method provided by an embodiment of the present application;

[0109] Figure 18 It is the fifth schematic flow chart of a health state estimation strategy determination method provided by an embodiment of the present application;

[0110] Figure 19 It is the fourth schematic flow chart of a health state estimation method provided by an embodiment of the present application;

[0111] Figure 20 It is the fifth schematic flow chart of a health state estimation method provided by an embodiment of the present application;

[0112] Figure 21 It is the sixth schematic flow chart of a health state estimation method provided by an embodiment of the present application;

[0113] Figure 22It is one of the structural block diagrams of the battery health state estimation device provided by the embodiments of the present application;

[0114] Figure 23 It is the structural block diagram of a first determination module provided by the embodiments of the present application;

[0115] Figure 24 It is the second of the structural block diagrams of the battery health state estimation device provided by the embodiments of the present application;

[0116] Figure 25 It is the structural block diagram of a second determination module provided by the embodiments of the present application;

[0117] Figure 26 It is the structural block diagram of a second determination sub-module provided by the embodiments of the present application;

[0118] Figure 27 It is the structural block diagram of an estimation module provided by the embodiments of the present application;

[0119] Figure 28 It is the structural block diagram of a first determination unit provided by the embodiments of the present application;

[0120] Figure 29 It is the structural block diagram of a second determination unit provided by the embodiments of the present application. Detailed implementation manners

[0121] Next, embodiments of the technical solutions of the present application will be described in detail with reference to the accompanying drawings. The following embodiments are only used to illustrate the technical solutions of the present application more clearly, so they are only examples and cannot be used to limit the protection scope of the present application.

[0122] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above drawings are intended to cover non-exclusive inclusion.

[0123] In the description of the embodiments of the present application, technical terms such as "first" and "second" are only used to distinguish different objects and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity, specific order or primary-secondary relationship of the indicated technical features. In the description of the embodiments of the present application, "a plurality" means more than two unless otherwise specifically defined.

[0124] Reference to "embodiment" in this document means that the specific features, structures, or characteristics described in connection with the embodiment may be included in at least one embodiment of the present application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments.

[0125] In the description of the embodiments of the present application, the term "and / or" is merely a description of the associated relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects before and after are in an "or" relationship.

[0126] In the description of the embodiments of the present application, the term "plurality" refers to two or more (including two). Similarly, "multiple groups" refers to two or more groups (including two groups), and "multiple pieces" refers to two or more pieces (including two pieces).

[0127] With the wide application of batteries in devices such as new energy vehicles and ships, their use safety has received increasing attention. The State Of Health (SOH) is an indication of the health life status of the battery. Therefore, by estimating the SOH of the battery, different disposals can be carried out on the battery according to the estimated SOH. For example, for some batteries with particularly severe SOH decay, if they are continued to be used in a cascaded manner, it may lead to serious safety accidents. For some batteries with not very severe SOH decay, they can continue to be used for other purposes.

[0128] The current method for estimating the health state requires full charge and discharge of each battery, resulting in a long time consumption problem.

[0129] To solve the above technical problems, the embodiments of the present application provide a method for estimating the health state of a battery, which can be applied to, for example Figure 1The computer device shown can be a terminal. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (Near Field Communication), or other technologies. The computer program, when executed by the processor, implements a method for estimating the battery health state. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or buttons, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0130] Those skilled in the art can understand that Figure 1 the structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0131] In one embodiment, as Figure 2 shown, Figure 2 is one of the schematic flowcharts of a method for estimating the battery health state provided by an embodiment of this application. Taking the application of this method to the Figure 1 computer device in as an example, it includes the following steps S201 - S203:

[0132] S201, obtain the first electrochemical impedance spectroscopy (EIS) data of the battery at specific frequency points under multiple health state classifications.

[0133] The state of health (SOH) classification, i.e., the SOH classification, may include an SOH range, an SOH value, etc. When the SOH classification includes an SOH range, the first Electrochemical Impedance Spectroscopy (EIS) data of the battery under the SOH range at a specific frequency point can be obtained; when the SOH classification includes an SOH value, the first EIS data of the battery at the SOH value at a specific frequency point can be obtained; when the SOH classification includes an SOH gear, the first EIS data of the battery at the SOH gear at a specific frequency point can be obtained. Among them, exemplarily, the SOH value greater than 90% and less than or equal to 100% can be regarded as the SOH range A, the SOH value greater than 85% and less than or equal to 90% can be regarded as the SOH range B, the SOH value greater than 80% and less than or equal to 85% can be regarded as the SOH range C, and the SOH value less than or equal to 80% can be regarded as the SOH range D. The SOH range A can be regarded as the SOH gear A, the SOH range B can be regarded as the SOH gear B, the SOH range C can be regarded as the SOH gear C, and the SOH range D can be regarded as the SOH gear D.

[0134] The number of batteries under the SOH classification can be multiple or one. When the number of batteries under the SOH classification is multiple, the first EIS data of multiple batteries at a specific frequency point under each SOH classification can be obtained. Exemplarily, taking the SOH classification including the SOH range A, the SOH range B, the SOH range C, and the SOH range D, the batteries under the SOH range A include battery A1 and battery A1, the batteries under the SOH range B include battery B1 and battery B2, the batteries under the SOH range C include battery C1 and battery C2, and the batteries under the SOH range D include battery D1 and battery D2 as an example, the first EIS data of battery A1 under the SOH range A at a specific frequency point can be obtained, and the first EIS data of battery A2 under the SOH range A at a specific frequency point can be obtained. The first EIS data of battery B1 under the SOH range B at a specific frequency point can be obtained, and the first EIS data of battery B2 under the SOH range B at a specific frequency point can be obtained. And so on, which will not be elaborated here, so as to obtain the first EIS data of multiple batteries at a specific frequency point under each SOH classification.

[0135] The number of specific frequency points can be multiple. When the number of specific frequency points is multiple and the number of batteries is multiple, the first EIS data of multiple batteries at multiple specific frequency points under each health state classification can be obtained. Exemplarily, taking the specific frequency points including specific frequency point 1 and specific frequency point 2 as an example, the first EIS data A11 of battery A1 at specific frequency point 1 in SOH interval A can be obtained, the first EIS data A12 of battery A1 at specific frequency point 2 in SOH interval A can be obtained, the first EIS data A21 of battery A2 at specific frequency point 1 in SOH interval A can be obtained, and the first EIS data A22 of battery A2 at specific frequency point 2 in SOH interval A can be obtained. The first EIS data B11 of battery B1 at specific frequency point 1 in SOH interval B can be obtained, the first EIS data B12 of battery B1 at specific frequency point 2 in SOH interval B can be obtained, the first EIS data B21 of battery B2 at specific frequency point 1 in SOH interval B can be obtained, and the first EIS data B22 of battery B2 at specific frequency point 2 in SOH interval B can be obtained. And so on, which will not be elaborated here, so as to obtain the first EIS data of multiple batteries at each specific frequency point under each health state classification.

[0136] It should be noted that when the number of batteries is 1 and the number of specific frequency points is multiple, the first EIS data of one battery at multiple specific frequency points under each health state classification can be obtained.

[0137] S202, determine a health state estimation strategy according to the first EIS data of the battery at the specific frequency point.

[0138] In a possible implementation manner, when the health state classification includes health state intervals, according to the first EIS data of multiple batteries at each specific frequency point under each health state interval, determine the health state reference values corresponding to each health state interval, and use the health state reference values corresponding to each health state interval as the health state estimation strategy. This health state estimation strategy is the health state estimation strategy corresponding to the case where the health state classification includes health state intervals.

[0139] In another possible implementation manner, when the health state classification includes health state values, according to the first EIS data of multiple batteries at each specific frequency point under each health state value, determine the radius of the Nyquist diagram corresponding to each health state value, perform fitting according to the radius of the Nyquist diagram corresponding to each health state value to obtain a fitting function, and use the fitting function as the health state estimation strategy. This health state estimation strategy is the health state estimation strategy corresponding to the case where the health state classification includes health state values.

[0140] S203, estimate the health state of the battery to be measured according to the second EIS data of the battery to be measured at the specific frequency point and the health state estimation strategy.

[0141] Any one of the health state estimation strategies introduced above can be used to estimate the health state of the battery under test. It should be noted that the method for determining the specific frequency points in the case where the health state classification includes health state intervals is different from that in the case where the health state classification includes health state values. Therefore, the determined specific frequency points are different. If the health state estimation strategy corresponding to the case where the health state classification includes health state intervals is adopted, the specific frequency points of the battery under test are consistent with the specific frequency points in the case where the health state classification includes health state intervals; if the health state estimation strategy corresponding to the case where the health state classification includes health state values is adopted, the specific frequency points of the battery under test are consistent with the specific frequency points in the case where the health state classification includes health state values.

[0142] In the above battery health state estimation method, based directly on the second EIS data of the battery under test at specific frequency points and the health state estimation strategy, the health state of the battery under test can be estimated. Therefore, when estimating the health state of the battery under test, it is not necessary to fully charge and discharge the battery under test, which reduces the time required for estimating the health state of the battery and the cost required for estimating the health state of the battery.

[0143] Moreover, with the large-scale use of devices such as new energy vehicles and ships, there are more and more retired batteries, and the detection of retired batteries has become a difficult problem. In particular, how to quickly obtain the SOH of retired batteries and then perform different disposals on the batteries according to different SOHs has become a difficult problem in the industry. If the traditional method is used to fully charge and discharge each battery module or cell, the cost of cascade utilization will increase. Through the method provided in this embodiment, for retired batteries, only by simply measuring the EIS data of the retired batteries at specific frequency points once, the SOH of the retired batteries can be obtained, which can reduce the cost of cascade utilization of the batteries.

[0144] In one embodiment, as Figure 3 shown, Figure 3 is one of the flow diagrams of the health state estimation strategy determination method provided by the embodiment of the present application. The number of batteries and specific frequency points in the embodiment of the present application is multiple, and the above S202 includes the following steps S301-S302:

[0145] S301, for each health state classification, determine the third EIS data corresponding to each specific frequency point under the health state classification according to the first EIS data of each battery at each specific frequency point under the health state classification.

[0146] Determine the third EIS data corresponding to the same specific frequency point under the health state classification based on the first EIS data of each battery at the same specific frequency point under the health state classification. Exemplarily, when the health state classification includes SOH interval A and SOH interval B, if the specific frequency points include specific frequency point 1 and specific frequency point 2, in combination with the above example, the third EIS data corresponding to specific frequency point 1 under SOH interval A can be determined according to the first EIS data A11 of battery A1 at specific frequency point 1 under SOH interval A and the first EIS data A21 of battery A2 at specific frequency point 1 under SOH interval A; the third EIS data corresponding to specific frequency point 2 under SOH interval A can be determined according to the first EIS data A12 of battery A1 at specific frequency point 2 under SOH interval A and the first EIS data A22 of battery A2 at specific frequency point 2 under SOH interval A.

[0147] Similarly, the third EIS data corresponding to specific frequency point 1 under SOH interval B can be determined according to the first EIS data B11 of battery B1 at specific frequency point 1 under SOH interval B and the first EIS data B21 of battery B2 at specific frequency point 1 under SOH interval B; the third EIS data corresponding to specific frequency point 2 under SOH interval B can be determined according to the first EIS data B12 of battery B1 at specific frequency point 2 under SOH interval B and the first EIS data B22 of battery B2 at specific frequency point 2 under SOH interval B.

[0148] In a possible implementation, the average value of the first EIS data of each battery at the same specific frequency point under the health state classification can be used as the third EIS data corresponding to the same specific frequency point.

[0149] In another possible implementation, the median of the first EIS data of each battery at the same specific frequency point under the health state classification is used as the third EIS data corresponding to the same specific frequency point.

[0150] In yet another possible implementation, the abnormal data in the first EIS data of each battery at the same specific frequency point under the health state classification can be removed, and the average value of the first EIS data of the same specific frequency point after removing the abnormal data is used as the third EIS data corresponding to the same specific frequency point. Among them, the first EIS data greater than the preset threshold can be used as abnormal data, and the preset threshold can be the average value or median of the first EIS data of each battery at the same specific frequency point under the health state classification before removing the abnormal data, and the preset threshold can also be set according to actual experience.

[0151] S302. Determine the health state estimation strategy according to the third EIS data corresponding to each specific frequency point under each health state classification.

[0152] In a possible implementation, when the health state classification includes health state intervals, based on the third EIS data corresponding to each specific frequency point in each health state interval, determine the health state reference value corresponding to each health state interval, and use the health state reference value corresponding to each health state interval as the health state estimation strategy.

[0153] In another possible implementation, when the health state classification includes health state values, based on the third EIS data corresponding to each specific frequency point in each health state value, determine the radius of the Nyquist plot corresponding to each health state value, perform fitting based on the radius of the Nyquist plot corresponding to each health state value to obtain a fitting function, and use the fitting function as the health state estimation strategy.

[0154] In this embodiment, by determining the health state estimation strategy, it is possible to estimate the health state of the battery under test based on the health state estimation strategy, reducing the time and cost required for health state estimation.

[0155] In one embodiment, as Figure 4 shown, Figure 4 is one of the flow diagrams of the specific frequency point determination method provided by the embodiment of the present application. The method includes the following steps S401 - S402:

[0156] S401, obtain the EIS data of the battery sample in the preset frequency band.

[0157] The preset frequency band can be a frequency band greater than or equal to the first preset frequency point and less than or equal to the second preset frequency point. The first preset frequency point is, for example, set to a value close to 0 such as 0.01 Hz, 0.012 Hz, etc., and the second preset frequency point is set to a frequency point of 20 KHz or a frequency point close to 20 KHz. A new battery can be used as the battery sample, and an electrochemical workstation can be used to scan the battery sample within the preset frequency band to obtain the EIS data of the preset frequency band.

[0158] S402, determine the specific frequency point according to the EIS data of the preset frequency band.

[0159] The EIS data includes real - part EIS data and imaginary - part EIS data. It is possible to determine the coordinate points corresponding to the real - part EIS data and the imaginary - part EIS data of the preset frequency band. In one implementation, when the health state classification includes health state intervals, a coordinate point in the middle - frequency band can be selected, and the frequency point corresponding to this coordinate point is used as a specific frequency point. At the same time, a coordinate point in the low - frequency band is selected, and this coordinate point is used as a specific frequency point.

[0160] In another implementation, when the health state classification includes health state values, multiple coordinate points in the middle - frequency band can be selected, and the frequency points corresponding to the multiple coordinate points are used as specific frequency points.

[0161] In this embodiment, by determining specific frequency points based on the EIS data of a preset frequency band, a foundation is laid for obtaining the first EIS data of each battery in each health state classification at each specific frequency point, without having to scan each frequency point of each battery in the preset frequency band under the health state classification, thus saving the time required to obtain the first EIS data, and therefore being able to further improve the health state estimation efficiency.

[0162] In one embodiment, as Figure 5 shown, Figure 5 FIG. 2 is a second flowchart of the specific frequency point determination method provided by the embodiment of the present application. The above S402 may include the following steps S501-S502:

[0163] S501, obtain a first Nyquist plot corresponding to the EIS data of the preset frequency band.

[0164] Referring to Figure 6 , Figure 6 FIG. 3 is a schematic diagram of a Nyquist plot provided by the embodiment of the present application. An EIS data includes real part data and imaginary part data. Figure 6 The horizontal axis in FIG. 3 represents the real part data of the EIS data, and the vertical axis represents the imaginary part data of the EIS data. A pair of real part data and imaginary part data corresponds to a coordinate point on the Nyquist plot. The frequency points on the horizontal axis decrease sequentially from left to right, that is, the frequency points corresponding to the coordinate points closer to the origin of the coordinate system are higher than the frequency points corresponding to the coordinate points farther from the origin of the coordinate system.

[0165] S502, determine specific frequency points based on the first Nyquist plot.

[0166] Figure 6 The Nyquist plot in FIG. 3 includes a semicircle region 601 and a linear region 602. In a possible implementation manner, specific frequency points on the medium frequency band corresponding to the semicircle region 601 and specific frequency points on the medium frequency band corresponding to the linear region 602 can be determined based on the first Nyquist plot. Mechanistically speaking, the real part EIS data corresponding to the frequency points in the medium frequency band usually represents ACR, that is, the conventional AC impedance, and the imaginary part EIS data in the low frequency band represents the impedance change caused by material attenuation. Therefore, selecting the frequency points in these two key frequency bands takes into account both the measurement efficiency and the accuracy of SOH estimation. Among them, the range of the medium frequency band is, for example, greater than or equal to 1000 Hz and less than or equal to 5000 Hz. The range of the low frequency band is, for example, greater than or equal to 1 Hz and less than or equal to 50 Hz.

[0167] As Figure 6As shown, the semicircle region and the linear region of the first Nyquist plot can be determined, the vertex A of the semicircle region is determined, and the frequency point corresponding to the vertex A is used as a specific frequency point of an intermediate frequency band, or any coordinate point whose distance from the vertex A is less than a preset distance is used as a specific frequency point of the intermediate frequency band. The center frequency point in the frequency interval corresponding to the linear region is used as the specific frequency point of the low frequency band, thereby determining the specific frequency point of the intermediate frequency band and the specific frequency point of the low frequency band.

[0168] In another possible implementation, based on the first Nyquist plot, multiple specific frequency points corresponding to the semicircle region of the first Nyquist plot can be determined. Mechanistically speaking, since the radius of the semicircle of the Nyquist plot can characterize the EIS characteristics, multiple specific frequency points corresponding to the semicircle region are all points that are particularly sensitive to EIS and can better restore the semicircle of EIS. Characterizing the EIS characteristics based on the radius will not reduce the accuracy of SOH estimation.

[0169] In this embodiment, determining specific frequency points based on the first Nyquist plot can more intuitively determine specific frequency points based on the first Nyquist plot, improving the determination efficiency of specific frequency points. Moreover, since it is only necessary to scan a preset frequency band for the battery sample once to obtain the EIS data of the battery sample in the preset frequency band, and then determine the specific frequency points, and subsequently only need to obtain the first EIS data of the battery under multiple health state classifications at the specific frequency points, without obtaining the first EIS data of the battery under multiple health state classifications at each frequency point in the preset frequency band, therefore, the acquisition time of the first EIS data is saved, thereby improving the efficiency of determining the health state estimation strategy based on the first EIS data and the estimation efficiency of the health state of the battery to be tested.

[0170] In one embodiment, as Figure 7 shown, Figure 7 FIG. 2 is a second flowchart of the specific frequency point determination method provided by the embodiment of the present application. This embodiment relates to a possible implementation of how to determine specific frequency points based on the first Nyquist plot. On the basis of the above embodiment, the above S502 may include the following steps S701-S702:

[0171] S701, determining a first frequency interval corresponding to the semicircle region of the first Nyquist plot and a second frequency interval corresponding to the linear region; the preset frequency band includes the first frequency interval and the second frequency interval.

[0172] S702, determining a first frequency point from the first frequency interval and a second frequency point from the second frequency interval; the specific frequency points include the first frequency point and the second frequency point.

[0173] Exemplarily, the frequency point corresponding to the vertex of the semi-circular region can be used as the first frequency point, or any coordinate point whose distance from the vertex is less than a preset distance can be used as the first frequency point. The central frequency point in the frequency interval corresponding to the linear region is used as the second frequency point. The intermediate frequency band can be selected from the first frequency interval corresponding to the semi-circular region, and the first frequency point is selected from the frequency points corresponding to the intermediate frequency band. The second frequency interval corresponding to the linear region is the low frequency band. Therefore, the first frequency point of the intermediate frequency band and the second frequency point of the low frequency band are determined. Among them, the frequency point corresponding to the vertex of the semi-circular region is, for example, 500 Hz, and the central frequency point in the frequency interval corresponding to the linear region is, for example, 13 Hz. The frequency interval range of the intermediate frequency band can be set to a frequency band greater than or equal to 1000 Hz and less than or equal to 5000 Hz. The frequency interval range of the low frequency band can be set to a frequency band greater than or equal to 1 Hz and less than or equal to 50 Hz.

[0174] In this embodiment, by determining the first frequency point from the first frequency interval and the second frequency point from the second frequency interval, specific frequency points are determined, laying a foundation for obtaining the first EIS data of each battery at each specific frequency point under the health state classification. There is no need to scan each frequency point of each battery under the health state classification in the preset frequency band, thus saving the time required to obtain the first EIS data, and therefore the health state estimation efficiency can be further improved.

[0175] In one embodiment, for the above S702, determining the first frequency point from the first frequency interval and the second frequency point from the second frequency interval can be implemented in the following manner:

[0176] The frequency point corresponding to the vertex of the semi-circular region in the first frequency interval is used as the first frequency point, and the central frequency point of the second frequency interval is used as the second frequency point.

[0177] In this embodiment, using the frequency point corresponding to the vertex of the semi-circular region in the first frequency interval as the first frequency point and the central frequency point of the second frequency interval as the second frequency point makes the real part EIS data corresponding to the determined first frequency point more accurately reflect the AC impedance, and the imaginary part EIS data corresponding to the second frequency point more accurately represents the impedance change caused by material attenuation, further improving the measurement efficiency and the accuracy of SOH estimation.

[0178] In one embodiment, as Figure 8 shown, Figure 8 is a schematic flowchart of a method for determining the third EIS data provided by an embodiment of the present application. In the embodiment of the present application, the health state classification includes health state intervals, and the above S301 includes the following steps S801 - S802:

[0179] S801. Determine the first parameter corresponding to the first frequency point in the health state interval according to the real part data of each first EIS data at the first frequency point in the health state interval, and determine the second parameter corresponding to the second frequency point in the health state interval according to the imaginary part data of each first EIS data at the second frequency point in the health state interval.

[0180] Among them, the first parameter includes the first average value or the first median, and the second parameter includes the second average value or the second parameter.

[0181] In this embodiment, the first frequency point is the specific frequency point 1, the second frequency point is the specific frequency point 2, the health state interval includes the SOH interval A and the SOH interval B. The batteries in the SOH interval A include battery A1, battery A2,..., battery A10, and the batteries in the SOH interval B include battery B1, battery B2,..., battery B10. The first EIS data of battery A1 at the specific frequency point 1 in the SOH interval A is A11, the first EIS data of battery A2 at the specific frequency point 1 in the SOH interval A is A21, and so on. The first EIS data of battery A10 at the specific frequency point 1 in the SOH interval A is A101. That is, the first EIS data of each first frequency point in the SOH interval A includes A11, A21,..., A101. Then, the first parameter of the real part data of A11, A21,..., A101 can be determined, and this first parameter is the first parameter corresponding to the specific frequency point 1 in the SOH interval A. The first parameter can be the average value or the median of the real part data of A11, A21,..., A101.

[0182] It is also possible to first determine the abnormal data in the real part data of A11, A21,..., A101, and use the first parameter of the real part data of the first EIS data after removing the abnormal data as the first parameter corresponding to the specific frequency point 1 in the SOH interval A. Exemplarily, if the real part data of A11 is abnormal data, then the first parameter of the real part data of A21,..., A101 can be determined, and this first parameter is used as the first parameter corresponding to the specific frequency point 1 in the SOH interval A. The first parameter can be the average value or the median of the real part data of A21,..., A101. Among them, the abnormal data can be the real part data of the first EIS data greater than the preset real part data.

[0183] Similarly, similar to the process of determining the first parameter corresponding to the specific frequency point 1 in the SOH interval A, the second parameter of the imaginary part data of the first EIS data of batteries A1 to A10 in the SOH interval A at the specific frequency point 2 can be determined, that is, the second parameter of the imaginary part data of A11, A21,..., A101 is determined, and this second parameter is used as the second parameter corresponding to the specific frequency point 2 in the SOH interval A.

[0184] It should be noted that if the real part data or the imaginary part data of a certain first EIS data is abnormal data, the first EIS data can be excluded, and when calculating the first parameter and the second parameter, the abnormal first EIS data does not need to be considered.

[0185] Similar to the process of determining the first parameter corresponding to the specific frequency point 1 and the second parameter corresponding to the specific frequency point 2 in the SOH interval A, the first parameter of the real part data of the first EIS data of the batteries B1 to B10 at the specific frequency point 1 in the SOH interval B can be determined, that is, the first parameter corresponding to the specific frequency point 1 in the SOH interval B is determined, and the second parameter of the imaginary part data of the first EIS data of the batteries B1 to B10 at the specific frequency point 2 in the SOH interval B is determined, that is, the second parameter corresponding to the specific frequency point 2 in the SOH interval B is determined.

[0186] S802, taking the first parameter as the third EIS data corresponding to the first frequency point in the health state interval, and taking the second parameter as the third EIS data corresponding to the second frequency point in the health state interval.

[0187] For the SOH interval A, taking the first parameter corresponding to the specific frequency point 1 in the SOH interval A as the third EIS data corresponding to the specific frequency point 1 in the SOH interval A, and taking the second parameter corresponding to the specific frequency point 2 in the SOH interval A as the third EIS data corresponding to the specific frequency point 2 in the SOH interval A.

[0188] For the SOH interval B, taking the first parameter corresponding to the specific frequency point 1 in the SOH interval B as the third EIS data corresponding to the specific frequency point 1 in the SOH interval B, and taking the second parameter corresponding to the specific frequency point 2 in the SOH interval B as the third EIS data corresponding to the specific frequency point 2 in the SOH interval B.

[0189] In this embodiment, by determining the average value or the median value, the third EIS data corresponding to the first frequency point and the third EIS data corresponding to the second frequency point in the health state interval are determined, which improves the accuracy of the determined third EIS data, and further improves the accuracy of the health state estimation strategy determined according to the third EIS data corresponding to each specific frequency point under each health state classification.

[0190] In one embodiment, as Figure 9 shown, Figure 9 is the second flow chart of the method for determining the health state estimation strategy provided by the embodiment of the present application. This embodiment relates to a possible implementation manner of how to determine the health state estimation strategy according to the third EIS data corresponding to each specific frequency point under each health state classification. The health state classification in this embodiment includes the health state interval. On the basis of the above embodiment, the above S302 includes the following steps S901-S902:

[0191] S901. For each health state interval, determine the first weighted value corresponding to the health state interval according to the real part data and the first weight of the third EIS data corresponding to the first frequency point in the health state interval, and the imaginary part data and the second weight of the third EIS data corresponding to the second frequency point; the first weight is less than the second weight.

[0192] Taking the number of the first frequency points as one and the number of the second frequency points as one as an example, if the real part data of the third EIS data corresponding to the first frequency point in the health state interval is denoted as x1, the imaginary part data of the third EIS data corresponding to the second frequency point in the health state interval is denoted as x2, the first weight is denoted as A1, and the second weight is denoted as A2, then the first weighted value y corresponding to the health state interval can be calculated by the following formula:

[0193] y = A1×x1 + A2×x2

[0194] Among them, A1 < 0.5, A2 > 0.5, and the sum of A1 and A2 is equal to 1.

[0195] In some embodiments, if the number of the first frequency points is multiple, the third EIS data corresponding to each first frequency point in the health state interval can be determined. There is a preset weight corresponding to the third EIS data corresponding to each first frequency point. Calculate the product of the real part data of the third EIS data corresponding to each first frequency point and the preset weight corresponding to the real part data, determine the summation result A of each product, and take the sum of the summation result A and A2×x2 as the first weighted value corresponding to the health state interval. Similarly, if the number of the second frequency points is multiple, the product of the imaginary part data of the third EIS data corresponding to each second frequency point and the preset weight corresponding to the imaginary part data can be calculated, determine the summation result B of each product, and take the sum of the summation result B and A1×x1 as the first weighted value corresponding to the health state interval. If the number of the first frequency points is multiple and the number of the second frequency points is multiple, the sum of the summation result A and the summation result B can be taken as the first weighted value corresponding to the health state interval.

[0196] The real part data of the third EIS data at the first frequency point in the medium frequency band represents the ohmic impedance, and the imaginary part data of the third EIS data at the second frequency point in the low frequency band represents the polarization impedance. Therefore, by performing weighted calculation on the real part data of the third EIS data at the first frequency point in the medium frequency band and the imaginary part data of the third EIS data at the second frequency point in the low frequency band, the first weighted value is obtained, so that the first weighted value can more comprehensively represent the capacitance value and improve the accuracy of the determined first weighted value.

[0197] S902. Determine the health state estimation strategy according to the first weighted value corresponding to each health state interval.

[0198] In a possible implementation, the first weighted value corresponding to the health state interval can be used as the health state reference value of the health state interval, and the health state reference values of each health state interval can be used as the health state estimation strategy.

[0199] In another possible implementation, the first weighted value corresponding to each health state interval can also be corrected to obtain the corrected first weighted value, and the corrected first weighted value can be used as the health state reference value of the health state interval, and the health state reference values of each health state interval can be used as the health state estimation strategy. Among them, the corrected first weighted value can be obtained by multiplying the first weighted value by a preset coefficient.

[0200] In this embodiment, by determining the health state estimation strategy according to the first weighted value corresponding to each health state interval, the accuracy of the determined health state estimation strategy is improved, so that a more accurate health state of the battery under test can be obtained based on a more accurate health state estimation strategy.

[0201] In one embodiment, as Figure 10 shown, Figure 10 FIG. 3 is a third flowchart of the method for determining the health state estimation strategy provided by the embodiment of the present application. This embodiment relates to a possible implementation of how to determine the health state estimation strategy according to the first weighted value corresponding to each health state interval. On the basis of the above embodiment, the above S902 includes the following steps S1001-S1002:

[0202] S1001. For each health state interval, the first weighted value corresponding to the health state interval is used as the health state reference value of the health state interval.

[0203] S1002. The health state reference values of each health state interval are used as the health state estimation strategy.

[0204] In this embodiment, by using the first weighted value corresponding to the health state interval as the health state reference value of the health state interval and using the health state reference values of each health state interval as the health state estimation strategy, the accuracy of the determined health state estimation strategy is improved, so that a more accurate health state of the battery under test can be obtained based on a more accurate health state estimation strategy.

[0205] In one embodiment, as Figure 11 shown, Figure 11 FIG. 2 is a second flowchart of the health state estimation method provided by the embodiment of the present application. This embodiment relates to a possible implementation of how to estimate the health state of the battery under test according to the second EIS data of the battery under test at a specific frequency point and the health state estimation strategy. On the basis of the above embodiment, the above S203 includes the following steps S1101-S1102:

[0206] S1101, determine a second weighted value based on the real - part data of the second EIS data of the battery under test at the first frequency point, the first weight, the imaginary - part data of the second EIS data of the battery under test at the second frequency point, and the second weight.

[0207] Utilize Figure 9 and Figure 10 In the case of the health - state estimation strategy of the corresponding embodiment, it is necessary to scan the battery under test at the first frequency point and the second frequency point to obtain the real - part data a of the second EIS data at the first frequency point and the imaginary - part data b of the second EIS data at the second frequency point. When the number of the first frequency point and the second frequency point is both one, the result of A1×a + A2×b can be calculated and taken as the second weighted value. When the number of the first frequency point and the second frequency point is multiple, the process of calculating the second weighted value is similar to the process of calculating the first weighted value, which will not be elaborated here.

[0208] S1102, estimate the health state of the battery under test according to the second weighted value and the health - state reference values of each health - state interval.

[0209] The ratio of the second weighted value to the health - state reference values of each health - state interval can be determined. When the ratios are all greater than 0 and less than or equal to 1, the maximum ratio is determined, and the health - state interval corresponding to the maximum ratio is taken as the target health - state interval, and it is determined that the health state of the battery under test is within the target health - state interval, that is, the health state of the battery under test belongs to the SOH gear corresponding to the target health - state interval.

[0210] Exemplarily, if the health - state reference value corresponding to the health - state interval A is 0.8, the health - state reference value corresponding to the health - state interval B is 0.6, the health - state reference value corresponding to the health - state interval C is 0.9, and the calculated second weighted value is equal to 0.6, then the ratio of 0.6 to 0.8 is equal to 0.75, the ratio of 0.6 to 0.6 is equal to 1, the ratio of 0.6 to 0.9 is approximately equal to 0.67, and the ratio equal to 1 is the maximum ratio, so the health - state interval B corresponding to the ratio equal to 1 is taken as the target health - state interval.

[0211] In this embodiment, estimating the health state of the battery under test according to the second weighted value and the health - state reference values of each health - state interval does not require fully charging and discharging the battery under test, saving the time and cost of health - state estimation, and thus being applicable to the usage scenario of batch - wise health - state estimation of the battery under test.

[0212] In one embodiment, as Figure 12 shown, Figure 12It is the third schematic flow chart of the health state estimation method provided by the embodiments of the present application. This embodiment relates to a possible implementation of how to estimate the health state of a battery under test according to the second weighting value and the health state reference values of each health state interval. On the basis of the above embodiment, the above S1102 includes the following steps S1201 - S1202:

[0213] S1201, determine the absolute value of the difference between the second weighting value and the health state reference values of each health state interval.

[0214] Combined with the above examples, it can be determined that the absolute value of the difference between 0.6 and 0.8 is equal to 0.2, and the absolute value of the difference between 0.6 and 0.6 is equal to 0; the absolute value of the difference between 0.6 and 0.9 is equal to 0.3.

[0215] S1202, estimate the health state of the battery under test according to the health state interval corresponding to the smallest absolute value.

[0216] The smallest absolute value is equal to 0, and the health state interval corresponding to this absolute value is health state interval B. Take health state interval B as the target health state interval. The health state of the battery under test is within this target health state interval, that is, the health state of the battery under test belongs to the SOH gear corresponding to this target health state interval.

[0217] In this embodiment, the health state of the battery under test is estimated according to the health state interval corresponding to the smallest absolute value, without fully charging and discharging the battery under test, saving the time and cost of health state estimation, and thus being applicable to the usage scenario of batch health state estimation of batteries under test.

[0218] In one embodiment, as Figure 13 shown, Figure 13 It is the third schematic flow chart of the specific frequency point determination method provided by the embodiments of the present application. This embodiment relates to a possible implementation of how to determine a specific frequency point based on the first Nyquist diagram. On the basis of the above embodiment, the above S502 may include the following steps S1301 - S1302:

[0219] S1301, determine the third frequency interval corresponding to the semicircular region of the first Nyquist diagram.

[0220] Exemplarily, taking the Figure 6 shown Nyquist diagram as the first Nyquist diagram, the frequency span interval corresponding to the semicircular region 601 can be determined, and this frequency span interval is the third frequency interval.

[0221] S1302, determine a specific number of preset frequency points from the third frequency interval; the number of preset frequency points is greater than 2.

[0222] If the number of preset frequency points is 3, 3 specific frequency points can be selected from the third frequency range. If the number of preset frequency points is 4, 4 specific frequency points can be selected from the third frequency range. The specific frequency points of the preset number of frequency points can be selected at equal intervals, or any frequency points of the preset number of frequency points can be selected from the third frequency range, and the selected frequency points are used as specific frequency points.

[0223] In this embodiment, by determining the specific frequency points of the preset number of frequency points from the third frequency range corresponding to the semi-circular region of the first Nyquist diagram, the specific frequency points are determined, thereby laying a foundation for obtaining the first EIS data of each battery in each specific frequency point under the healthy state classification, without scanning each frequency point in the preset frequency band of each battery under the healthy state classification, thus saving the time required to obtain the first EIS data, and therefore the efficiency of healthy state estimation can be further improved.

[0224] In one embodiment, S1302, determining the specific frequency points from the third frequency range can be achieved by the following method:

[0225] According to the number of preset frequency points, the maximum frequency point value and the minimum frequency point value corresponding to the third frequency range, the specific frequency points of the preset number of frequency points are determined by using the logarithmic interval distribution function.

[0226] Exemplarily, if the third frequency range is not less than 50 Hz and not greater than 1000 Hz, the maximum frequency point value corresponding to the third frequency range is equal to 1000 Hz, the minimum frequency point value corresponding to the third frequency range is equal to 50 Hz. If the number of preset frequency points is equal to 4, 4 specific frequency points can be determined by using the logarithmic interval distribution function, and the 4 determined specific frequency points include 50 Hz, 136 Hz, 376 Hz, and 1000 Hz.

[0227] In this embodiment, by using the logarithmic interval distribution function to determine the specific frequency points of the preset number of frequency points, only the first EIS data at the specific frequency points needs to be obtained, and the first EIS data at each frequency point in the preset frequency band does not need to be obtained. Therefore, the time for obtaining the first EIS data is saved, and further the time required to determine the healthy state estimation strategy based on the first EIS data at the specific frequency points is saved, and the efficiency of healthy state estimation is improved.

[0228] In one embodiment, as Figure 14 shown Figure 14It is a schematic flowchart of another method for determining the third EIS data provided by an embodiment of the present application. In the embodiments of the present application, the health state classification includes health state values. This embodiment relates to a possible implementation manner of determining the third EIS data corresponding to each specific frequency point under the health state classification according to the first EIS data of each battery at each specific frequency point under the health state classification. On the basis of the above embodiment, the above S301 includes the following steps S1401-S1404:

[0229] S1401. For each specific frequency point under the health state value, determine the third average value and the first standard deviation of the real part data of each first EIS data at the specific frequency point, and the fourth average value and the second standard deviation of the imaginary part data of each first EIS data at the specific frequency point.

[0230] Taking the specific frequency points including specific frequency point 1, specific frequency point 2, specific frequency point 3, specific frequency point 4, the health state values including 100% SOH, 95% SOH, 90% SOH, 85% SOH, 80% SOH, 75% SOH, 70% SOH as an example, the batteries under 100% SOH include battery A1-battery A10, the batteries under 95% SOH include battery B1-battery B10. The batteries under other health state values can also include 10 batteries, which will not be elaborated one by one here. It is possible to obtain the real part data of 10 first EIS data of battery A1 to battery A10 under 100% SOH at specific frequency point 1, determine the third average value and the first standard deviation of the real part data of these 10 first EIS data, and similarly, it is possible to determine the fourth average value and the second standard deviation of the imaginary part data of these 10 first EIS data.

[0231] The same as obtaining the third average value and the first standard deviation of the real part data of 10 first EIS data at specific frequency point 1, it is possible to obtain the third average value and the first standard deviation of the real part data of 10 first EIS data of battery A1 to battery A10 under 100% SOH at specific frequency point 2, and obtain the fourth average value and the second standard deviation of the imaginary part data of 10 first EIS data of battery A1 to battery A10 under 100% SOH at specific frequency point 2.

[0232] Similarly, the third average value and the first standard deviation of the real part data of the 10 first EIS data of Battery A1 to Battery A10 at 100% SOH at a specific frequency point 3 can be obtained, and the fourth average value and the second standard deviation of the imaginary part data of the 10 first EIS data of Battery A1 to Battery A10 at 100% SOH at a specific frequency point 3 can be obtained. The third average value and the first standard deviation of the real part data of the 10 first EIS data of Battery A1 to Battery A10 at 100% SOH at a specific frequency point 4 can be obtained, and the fourth average value and the second standard deviation of the imaginary part data of the 10 first EIS data of Battery A1 to Battery A10 at 100% SOH at a specific frequency point 4 can be obtained.

[0233] Similar to obtaining the third average value and the first standard deviation of the real part data of each first EIS data at each specific frequency point at 100% SOH, and the fourth average value and the second standard deviation of the imaginary part data of each first EIS data at a specific frequency point, the third average value and the first standard deviation of the real part data of each first EIS data at each specific frequency point at other SOH values can be obtained, and the fourth average value and the second standard deviation of the imaginary part data of each first EIS data at a specific frequency point can be obtained, which will not be elaborated here.

[0234] S1402. Determine a first threshold according to the third average value and the first standard deviation at a specific frequency point, and determine a second threshold according to the fourth average value and the second standard deviation at a specific frequency point.

[0235] Exemplarily, the summation result between the third average value of the real part data of the 10 first EIS data of Battery A1 to Battery A10 at 100% SOH at a specific frequency point 1 and the first standard deviation multiplied by a preset multiple can be determined, and this summation result is used as the first threshold. If the preset multiple is equal to 3, then the summation result between the third average value and 3 times the first standard deviation can be used as the first threshold corresponding to the specific frequency point 1 at 100% SOH. Similarly, the summation result between the fourth average value at the specific frequency point 1 at 100% SOH and 3 times the second standard deviation can be determined, and this summation result is used as the second threshold corresponding to the specific frequency point 1 at 100% SOH.

[0236] Similarly, the first threshold and the second threshold corresponding to each specific frequency point at each SOH value can be determined.

[0237] S1403. Determine the fourth EIS data from the first EIS data at a specific frequency point according to the first threshold and the second threshold at the specific frequency point.

[0238] Taking the 10 first EIS data of Battery A1 to Battery A10 at 100% SOH at a specific frequency point 1 as an example, if there is real part data greater than the first threshold among the real part data of the 10 first EIS data, then remove the first EIS data corresponding to the real part data greater than the first threshold, and use the removed first EIS data as the fourth EIS data. Exemplarily, if the real part data of the first EIS data of Battery A1 at the specific frequency point 1 is greater than the first threshold, then remove the first EIS data, and use the 9 first EIS data of Battery A2 to Battery A10 at the specific frequency point 1 as the fourth EIS data.

[0239] Similar to determining the fourth EIS data corresponding to the specific frequency point 1 at 100% SOH, the fourth EIS data corresponding to other specific frequency points at 100% SOH can be determined, and the fourth EIS data corresponding to each specific frequency point at other SOH values can also be determined.

[0240] S1404, Based on the fourth EIS data corresponding to each specific frequency point under the state of health value, determine the third EIS data corresponding to each specific frequency point under the state of health value.

[0241] Taking the fourth EIS data corresponding to the specific frequency point 1 at 100% SOH as an example, if the fourth EIS data corresponding to the specific frequency point 1 at 100% SOH includes the 9 first EIS data of Battery A2 to Battery A10 at the specific frequency point 1, then the average or median of the 9 first EIS data can be used as the third EIS data corresponding to the specific frequency point 1 at 100% SOH. If the fourth EIS data corresponding to the specific frequency point 1 at 100% SOH includes the 10 first EIS data of Battery A1 to Battery A10 at the specific frequency point 1, then the average or median of the 10 first EIS data can be used as the third EIS data corresponding to the specific frequency point 1 at 100% SOH.

[0242] Similarly, the third EIS data corresponding to each specific frequency point under each state of health value can be determined.

[0243] In this embodiment, by determining the third EIS data corresponding to each specific frequency point under the state of health value, it lays a foundation for determining the health state estimation strategy based on the third EIS data corresponding to each specific frequency point under the state of health value, and since the abnormal data is excluded, the accuracy of the determined health state estimation strategy can be improved.

[0244] In one embodiment, as Figure 15 shown, Figure 15 is a schematic flow chart of the fourth EIS data determination method provided by the embodiment of the present application. In the embodiment of the present application, the above S1403 includes the following steps S1501 - S1502:

[0245] S1501. Determine abnormal EIS data from the first EIS data at a specific frequency point; the abnormal EIS data includes the first EIS data with a real part greater than a first threshold and / or the first EIS data with an imaginary part greater than a second threshold.

[0246] Combined with the above example, among the 10 first EIS data of cells A1 to A10 at a specific frequency point 1 under 100% SOH, if the real part data of the first EIS data corresponding to cell A1 is greater than the first threshold, then determine the first EIS data corresponding to cell A1 as abnormal data. If the imaginary part data of the first EIS data corresponding to cell A2 is greater than the second threshold, then determine the first EIS data corresponding to cell A2 as abnormal data. That is, two abnormal data are determined among the 10 first EIS data.

[0247] S1502. Use the first EIS data other than the abnormal EIS data as the fourth EIS data.

[0248] If two abnormal data are determined among the 10 first EIS data of cells A1 to A10 at a specific frequency point 1 under 100% SOH, and the two abnormal data are the first EIS data corresponding to cell A1 and the first EIS data corresponding to cell A2, then the fourth EIS data includes 8 first EIS data of cells A3 to A10 at the specific frequency point 1.

[0249] In this embodiment, using the first EIS data other than the abnormal EIS data as the fourth EIS data can eliminate abnormal data, improve the accuracy of the obtained fourth EIS data, and thus improve the accuracy of the third EIS data corresponding to each specific frequency point under the health state value determined based on the fourth EIS data corresponding to each specific frequency point under the health state value.

[0250] In one embodiment, as Figure 16 shown, Figure 16 is a schematic flowchart of the method for determining the fourth EIS data provided by an embodiment of the present application. In the embodiment of the present application, the above S1404 includes the following steps S1601 - S1602:

[0251] S1601. For each specific frequency point under the health state value, determine a third parameter of the real part data of the fourth EIS data at the specific frequency point, and determine a fourth parameter of the imaginary part data of the fourth EIS data at the specific frequency point; the third parameter includes a fifth average value or a third median value, and the fourth parameter includes a sixth average value or a fourth median value.

[0252] Taking the fourth EIS data at 100% SOH, which includes eight first EIS data of battery A3 to battery A10 at a specific frequency point 1, as an example, the average or median of the real part data of the eight first EIS data can be determined. This average is the fifth average, and this median is the third median. And the average or median of the imaginary part data of the eight first EIS data is determined. This average is the sixth average, and this median is the fourth median.

[0253] Similarly, the third parameter of the real part data of the fourth EIS data at each specific frequency point under other state of health values can be determined, as well as the fourth parameter of the imaginary part data, which will not be elaborated here.

[0254] S1602, taking the third parameter as the real part data of the third EIS data at a specific frequency point, and taking the fourth parameter as the imaginary part data of the third EIS data at a specific frequency point.

[0255] Taking the average or median of the real part data of the above eight first EIS data as the real part data A of the third EIS data at a specific frequency point 1 under 100% SOH. And taking the average or median of the imaginary part data of the above eight first EIS data as the imaginary part data B of the third EIS data at a specific frequency point 1 under 100% SOH, that is, the third EIS data at a specific frequency point 1 under 100% SOH is determined. The third EIS data includes the real part data A and the imaginary part data B.

[0256] Similarly, the real part data and the imaginary part data of the third EIS data at each specific frequency point under each SOH value can be determined.

[0257] In this embodiment, by taking the third parameter as the real part data of the third EIS data at a specific frequency point and taking the fourth parameter as the imaginary part data of the third EIS data at a specific frequency point, the accuracy of the determined third EIS data is improved, and further the accuracy of the state of health estimation strategy determined based on the third EIS data is improved.

[0258] In one embodiment, as Figure 17 shown, Figure 17 is the fourth flowchart of the state of health estimation strategy determination method provided by the embodiment of the present application. This embodiment relates to a possible implementation manner of how to determine the state of health estimation strategy according to the third EIS data corresponding to each specific frequency point under each state of health classification. The state of health classification in this embodiment includes the state of health value. On the basis of the above embodiment, the above S302 includes the following steps S1701 - S1703:

[0259] S1701, for each state of health value, according to the third EIS data corresponding to each specific frequency point under the state of health value, determine the second Nyquist plot under the state of health value.

[0260] Taking the combination of the above specific frequency points including 4 specific frequency points as an example, the second Nyquist plot under the health state value can be determined according to the third EIS data corresponding to the 4 specific frequency points under the health state value. Exemplarily, taking the third EIS data corresponding to 4 specific frequency points under 100% SOH as an example, the second Nyquist plot under 100% SOH can be determined based on the 4 third EIS data corresponding to the 4 specific frequency points. Similarly, the second Nyquist plots under other SOH values can be determined.

[0261] S1702. Determine the first radius of the semi-circle of the second Nyquist plot under each health state value.

[0262] S1703. Determine the health state estimation strategy according to each health state value and the first radius corresponding to the health state value.

[0263] In a possible implementation manner, if the health state values include 7 health state values of 100% SOH, 95% SOH, 90% SOH, 85% SOH, 80% SOH, 75% SOH, and 70% SOH, a total of 7 first radii corresponding to the 7 health state values are determined, that is, a total of 7 groups of data are determined. One group of data includes a health state value and the first radius corresponding to the health state value. The 7 groups of data can be fitted to obtain a fitting function, and the fitting function is used as the health state estimation strategy.

[0264] In another possible implementation manner, if the health state values include multiple health state values such as 100% SOH, 99.5% SOH, 99% SOH, 98.5% SOH, 98% SOH, 97.5% SOH, and 97% SOH, and the difference between every two adjacent health state values is 0.5% SOH, and the smallest health state value is equal to 70% SOH, more groups of data can be determined, and the determined groups of data are used as the health state estimation strategy.

[0265] In this embodiment, the health state estimation strategy is determined according to each health state value and the first radius corresponding to the health state value, thereby laying a foundation for estimating the health state of the battery under test based on the health state estimation strategy, without performing full charge and full discharge on the battery under test, reducing the estimation time of the health state of the battery under test, and improving the estimation efficiency of the health state of the battery under test.

[0266] In one embodiment, as Figure 18 shown, Figure 18 FIG. 5 is a schematic flowchart of the method for determining the health state estimation strategy provided by the embodiment of the present application. The health state classification in this embodiment includes health state values. On the basis of the above embodiment, the above S1703 includes the following steps S1801 - S1802:

[0267] S1801. Fit the first radius corresponding to each health state value and the health state value to obtain a fitting function.

[0268] The fitting method may include fitting methods such as exponential fitting and second-order fitting. The fitting method is used to fit the first radius corresponding to each health state value and the health state value to obtain a fitting function. The fitting degree between the actual value and the fitting value needs to be at least above 0.9, so as to improve the fitting accuracy of the obtained fitting function, and further improve the accuracy of the obtained health state estimation strategy.

[0269] In this embodiment, the first radius of the semi-circle of the second Nyquist diagram under each health state value is associated with the SOH to determine the health state estimation strategy. Since the first radius corresponding to each health state value and the health state value are fitted, the number of the first radius corresponding to each health state value and the health state value is reduced, thereby reducing the risk brought by overfitting.

[0270] S1802. Use the fitting function as the health state estimation strategy.

[0271] The fitting function is a function used to characterize the relationship between the radius of the Nyquist diagram and the health state. In practical applications, if the radius of the Nyquist diagram of the battery to be tested is determined, substituting the radius into the fitting function can determine the health state of the battery to be tested.

[0272] In this embodiment, a fitting function is obtained by fitting the first radius corresponding to each health state value and the health state value, and the fitting function is used as the health state estimation strategy, thereby laying a foundation for estimating the health state of the battery to be tested based on the health state estimation strategy, without the need for full charge and full discharge of the battery to be tested, reducing the estimation time of the health state of the battery to be tested, and improving the estimation efficiency of the health state of the battery to be tested.

[0273] In one embodiment, as Figure 19 shown, Figure 19 is the fourth flowchart of the health state estimation method provided by the embodiment of the present application. This embodiment relates to a possible implementation manner of how to estimate the health state of the battery to be tested according to the second EIS data of the battery to be tested at a specific frequency point and the health state estimation strategy. On the basis of the above embodiment, the above S203 includes the following steps S1901-S1903:

[0274] S1901. Determine the third Nyquist diagram corresponding to the battery to be tested according to the second EIS data of the battery to be tested at a specific frequency point.

[0275] When estimating the health state of a battery under test, it is not necessary to fully charge and discharge the battery under test. Instead, only the battery under test needs to be scanned at specific frequency points to obtain the second EIS data of the battery under test at the specific frequency points. Therefore, the time for obtaining the second EIS data can be saved.

[0276] S1902. Determine the second radius of the semicircle of the third Nyquist plot.

[0277] S1903. Estimate the health state of the battery under test according to the second radius and the fitting function.

[0278] Substitute the second radius into the fitting function to obtain a calculation result, and use this calculation result as the health state of the battery under test. It is also possible to correct the second radius to obtain a corrected second radius, substitute the corrected second radius into the fitting function to obtain a calculation result, and use this calculation result as the health state of the battery under test.

[0279] In this embodiment, the health state of the battery under test is estimated according to the second radius and the fitting function of the third Nyquist plot corresponding to the battery under test. It is not necessary to fully charge and discharge the battery under test, saving the time and cost for estimating the health state, and thus being applicable to the usage scenario of batch estimating the health state of batteries under test.

[0280] In order to introduce the embodiments including the health state intervals of the above-introduced health state classification more clearly, the following is combined with Figure 20 for illustration. Figure 20 is the fifth flowchart of the health state estimation method provided by the embodiment of the present application.

[0281] S2001. Obtain the EIS data of the battery sample in the preset frequency band.

[0282] S2002. Obtain the first Nyquist plot corresponding to the EIS data in the preset frequency band.

[0283] S2003. Determine the specific frequency points based on the first Nyquist plot.

[0284] S2004. Obtain the first EIS data of the batteries in multiple health state intervals at the specific frequency points.

[0285] S2005. Determine the first parameter corresponding to the first frequency point in the health state interval according to the real part data of the first EIS data at the first frequency point in the health state interval, and determine the second parameter corresponding to the second frequency point in the health state interval according to the imaginary part data of the first EIS data at the second frequency point in the health state interval.

[0286] In S2006, use the first parameter as the third EIS data corresponding to the first frequency point in the healthy state interval, and use the second parameter as the third EIS data corresponding to the second frequency point in the healthy state interval.

[0287] In S2007, for each healthy state interval, determine the first weighted value corresponding to the healthy state interval according to the third EIS data corresponding to the first frequency point in the healthy state interval and the first weight, and the third EIS data corresponding to the second frequency point and the second weight; the first weight is less than the second weight.

[0288] In S2008, for each healthy state interval, use the first weighted value corresponding to the healthy state interval as the healthy state reference value of the healthy state interval.

[0289] In S2009, use the healthy state reference values of each healthy state interval as the healthy state estimation strategy.

[0290] In S2010, determine the second weighted value according to the second EIS data of the real part at the first frequency point of the battery to be tested, the first weight, the second EIS data of the imaginary part at the second frequency point, and the second weight.

[0291] In S2011, determine the absolute value of the difference between the second weighted value and the healthy state reference value of each healthy state interval.

[0292] In S2012, estimate the healthy state of the battery to be tested according to the healthy state interval corresponding to the smallest absolute value.

[0293] To introduce the embodiments corresponding to the healthy state values included in the above-mentioned healthy state classification more clearly, the following is combined Figure 21 for illustration. Figure 21 It is the sixth flowchart of the healthy state estimation method provided by the embodiments of the present application.

[0294] In S2101, obtain the EIS data of the battery sample in the preset frequency band.

[0295] In S2102, obtain the first Nyquist plot corresponding to the EIS data in the preset frequency band.

[0296] In S2103, determine the third frequency interval corresponding to the semicircle region of the first Nyquist plot.

[0297] In S2104, determine specific frequency points with a preset number of frequency points from the third frequency interval.

[0298] In S2105, according to the first EIS data of each battery at each specific frequency point under the healthy state classification, determine the third EIS data corresponding to each specific frequency point under the healthy state classification.

[0299] S2106. For each health status value, determine the second Nyquist plot under the health status value according to the third EIS data corresponding to each specific frequency point under the health status value.

[0300] S2107. Determine the first radius of the semi-circle of the second Nyquist plot under each health status value.

[0301] S2108. Fit the fitting function for each health status value and the first radius corresponding to the health status value.

[0302] S2109. Use the fitting function as the health status estimation strategy.

[0303] S2110. Determine the third Nyquist plot corresponding to the battery under test according to the second EIS data of the battery under test at specific frequency points.

[0304] S2111. Determine the second radius of the semi-circle of the third Nyquist plot.

[0305] S2112. Estimate the health status of the battery under test according to the second radius and the fitting function.

[0306] It should be understood that although the steps in the flowcharts involved in the above embodiments are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0307] Based on the same inventive concept, the embodiments of the present application also provide a battery health status estimation device for implementing the battery health status estimation method involved above. The implementation solutions provided by this device to solve problems are similar to the implementation solutions described in the above method. Therefore, the specific limitations in one or more embodiments of the battery health status estimation device provided below can refer to the limitations on the battery health status estimation method in the above text, and will not be repeated here.

[0308] In one embodiment, as Figure 22 shown, Figure 22 is one of the structural block diagrams of the battery health status estimation device provided by the embodiments of the present application. The device 22 includes:

[0309] The first acquisition module 221 is configured to acquire first Electrochemical Impedance Spectroscopy (EIS) data of batteries under multiple health state classifications at specific frequencies;

[0310] The first determination module 222 is configured to determine a health state estimation strategy according to the first EIS data of the battery at specific frequencies;

[0311] The estimation module 223 is configured to estimate the health state of the battery to be tested according to the second EIS data of the battery to be tested at specific frequencies and the health state estimation strategy.

[0312] In one embodiment, the number of batteries and specific frequencies is multiple, as Figure 23 shown, Figure 23 is a structural block diagram of a first determination module provided by an embodiment of the present application. The first determination module 222 includes:

[0313] The first determination unit 2221 is configured to, for each health state classification, determine third EIS data corresponding to each specific frequency under the health state classification according to the first EIS data of each battery at each specific frequency under the health state classification;

[0314] The second determination unit 2222 is configured to determine a health state estimation strategy according to the third EIS data corresponding to each specific frequency under each health state classification.

[0315] In one embodiment, as Figure 24 shown, Figure 24 is a second structural block diagram of a battery health state estimation device provided by an embodiment of the present application. The device 24 includes:

[0316] The second acquisition module 241 is configured to acquire EIS data of a battery sample in a preset frequency band;

[0317] The second determination module 242 is configured to determine a specific frequency according to the EIS data in the preset frequency band.

[0318] In one embodiment, as Figure 25 shown, Figure 25 is a structural block diagram of a second determination module provided by an embodiment of the present application. The second determination module 242 includes:

[0319] The acquisition unit 2421 is configured to acquire a first Nyquist plot corresponding to the EIS data in the preset frequency band;

[0320] The determination unit 2422 is configured to determine a specific frequency based on the first Nyquist plot.

[0321] In one embodiment, the determining unit 2422 is specifically configured to determine a first frequency interval corresponding to the semicircular region of the first Nyquist plot and a second frequency interval corresponding to the linear region; the preset frequency band includes the first frequency interval and the second frequency interval; determine a first frequency point from the first frequency interval and a second frequency point from the second frequency interval; the specific frequency points include the first frequency point and the second frequency point.

[0322] In one embodiment, the determining unit 2422 is specifically configured to use the frequency point corresponding to the vertex of the semicircular region in the first frequency interval as the first frequency point and use the center frequency point of the second frequency interval as the second frequency point.

[0323] In one embodiment, the health status classification includes a health status interval; the first determining unit 2221 is specifically configured to determine a first parameter corresponding to the first frequency point in the health status interval according to the real part data of each first EIS data of the first frequency point in the health status interval, and determine a second parameter corresponding to the second frequency point in the health status interval according to the imaginary part data of each first EIS data of the second frequency point in the health status interval; the first parameter includes a first average value or a first median, and the second parameter includes a second average value or a second parameter; use the first parameter as the third EIS data corresponding to the first frequency point in the health status interval and use the second parameter as the third EIS data corresponding to the second frequency point in the health status interval.

[0324] In one embodiment, as Figure 26 shown, Figure 26 is a structural block diagram of a second determining sub-module provided by an embodiment of the present application. The second determining unit 2222 includes:

[0325] The first determining subunit 22221 is configured to, for each health status interval, determine a first weighted value corresponding to the health status interval according to the real part data of the third EIS data corresponding to the first frequency point in the health status interval and a first weight, and the imaginary part data of the third EIS data corresponding to the second frequency point in the health status interval and a second weight; the first weight is less than the second weight;

[0326] The second determining subunit 22222 is configured to determine a health status estimation strategy according to the first weighted values corresponding to each health status interval.

[0327] In one embodiment, the second determining subunit 22222 is specifically configured to, for each health status interval, use the first weighted value corresponding to the health status interval as the health status reference value of the health status interval; use the health status reference values of each health status interval as the health status estimation strategy.

[0328] In one embodiment, as Figure 27 shown, Figure 27It is a structural block diagram of an estimation module provided by an embodiment of the present application. The estimation module 223 includes:

[0329] A third determination unit 2231, configured to determine a second weighted value according to the second EIS data of the real part at the first frequency point of the battery to be measured, the first weight, the second EIS data of the imaginary part at the second frequency point, and the second weight;

[0330] An estimation unit 2232, configured to estimate the health state of the battery to be measured according to the second weighted value and the health state reference values of each health state interval.

[0331] In one embodiment, the estimation unit 2232 is specifically configured to determine the absolute value of the difference between the second weighted value and the health state reference values of each health state interval; and estimate the health state of the battery to be measured according to the health state interval corresponding to the smallest absolute value.

[0332] In one embodiment, the determination unit 2422 is specifically configured to determine a third frequency interval corresponding to the semicircular region of the first Nyquist plot; determine a specific number of preset frequency points from the third frequency interval; the number of preset frequency points is greater than 2.

[0333] In one embodiment, the determination unit 2422 is specifically configured to determine a specific number of preset frequency points by using a logarithmic interval distribution function according to the number of preset frequency points, the maximum frequency point value and the minimum frequency point value corresponding to the third frequency interval.

[0334] In one embodiment, the health state classification includes health state values; as Figure 28 shown, Figure 28 It is a structural block diagram of a first determination unit provided by an embodiment of the present application. The first determination unit 2221 includes:

[0335] A first determination subunit 22211, configured to determine a third average value and a first standard deviation of the real part data of each first EIS data at a specific frequency point, and a fourth average value and a second standard deviation of the imaginary part data of each first EIS data at the specific frequency point for each specific frequency point under the health state value;

[0336] A second determination subunit 22212, configured to determine a first threshold according to the third average value and the first standard deviation at the specific frequency point, and determine a second threshold according to the fourth average value and the second standard deviation at the specific frequency point;

[0337] A third determination subunit 22213, configured to determine fourth EIS data from the first EIS data at the specific frequency point according to the first threshold and the second threshold at the specific frequency point;

[0338] The fourth determination subunit 22214 is configured to determine the third EIS data corresponding to each specific frequency point under the health state value based on the fourth EIS data corresponding to each specific frequency point under the health state value.

[0339] In one embodiment, the third determination subunit 22213 is specifically configured to determine abnormal EIS data from the first EIS data of a specific frequency point; the abnormal EIS data includes the first EIS data with a real part greater than a first threshold and / or the first EIS data with an imaginary part greater than a second threshold; and use the first EIS data other than the abnormal EIS data as the fourth EIS data.

[0340] In one embodiment, the fourth determination subunit 22214 is specifically configured to, for each specific frequency point under the health state value, determine a third parameter of the real part data of the fourth EIS data at the specific frequency point and determine a fourth parameter of the imaginary part data of the fourth EIS data at the specific frequency point; the third parameter includes a fifth average value or a third median, and the fourth parameter includes a sixth average value or a fourth median; use the third parameter as the real part data of the third EIS data at the specific frequency point and use the fourth parameter as the imaginary part data of the third EIS data at the specific frequency point.

[0341] In one embodiment, as Figure 29 shown, Figure 29 FIG. is a structural block diagram of a second determination unit provided by an embodiment of the present application. The second determination unit 2222 includes:

[0342] The fifth determination subunit 22221 is configured to, for each health state value, determine a second Nyquist plot under the health state value according to the third EIS data corresponding to each specific frequency point under the health state value.

[0343] The sixth determination subunit 22222 is configured to determine a first radius of a semicircle of the second Nyquist plot under each health state value.

[0344] The seventh determination subunit 22223 is configured to determine a health state estimation strategy according to each health state value and the first radius corresponding to the health state value.

[0345] In one embodiment, the seventh determination subunit 22223 is specifically configured to perform fitting on each health state value and the first radius corresponding to the health state value to obtain a fitting function; and use the fitting function as the health state estimation strategy.

[0346] In one embodiment, the estimation module 223 is specifically configured to determine a third Nyquist plot corresponding to a battery under test according to the second EIS data of the battery under test at a specific frequency point; determine a second radius of a semicircle of the third Nyquist plot; and estimate the health state of the battery under test according to the second radius and the fitting function.

[0347] Each module in the above battery health state estimation device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above modules can be embedded in the processor of the computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form so that the processor can call and execute the operations corresponding to each of the above modules.

[0348] In one embodiment, a computer device is provided, which includes a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps of the method provided in the above embodiment are implemented.

[0349] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, the steps of the method provided in the above embodiment are implemented.

[0350] In one embodiment, a computer program product is provided, which includes a computer program. When the computer program is executed by the processor, the steps of the method provided in the above embodiment are implemented.

[0351] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data that have been authorized by the user or fully authorized by all parties.

[0352] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0353] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0354] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for estimating the state of health of a battery, characterized in that, The method includes: Obtaining first Electrochemical Impedance Spectroscopy (EIS) data of batteries under multiple health state classifications at specific frequency points; Determining a health state estimation strategy based on the first EIS data of the batteries at the specific frequency points; Estimating the health state of the battery under test based on the second EIS data of the battery under test at the specific frequency point and the health state estimation strategy.

2. The method according to claim 1, characterized in that The number of the batteries and the specific frequency points is multiple. The step of determining a health state estimation strategy based on the first EIS data of the batteries at the specific frequency points includes: For each of the health state classifications, determining third EIS data corresponding to each of the specific frequency points under the health state classification according to the first EIS data of each of the batteries under the health state classification at each of the specific frequency points; Determining the health state estimation strategy according to the third EIS data corresponding to each of the specific frequency points under each of the health state classifications.

3. The method according to claim 2, wherein The method further includes: Obtaining EIS data of a battery sample in a preset frequency band; Determining the specific frequency points according to the EIS data in the preset frequency band.

4. The method according to claim 3, characterized in that, The step of determining the specific frequency points according to the EIS data in the preset frequency band includes: Obtaining a first Nyquist plot corresponding to the EIS data in the preset frequency band; Determining the specific frequency points based on the first Nyquist plot.

5. The method according to claim 4, wherein The step of determining the specific frequency points based on the first Nyquist plot includes: Determining a first frequency interval corresponding to the semicircle region of the first Nyquist plot and a second frequency interval corresponding to the linear region; the preset frequency band includes the first frequency interval and the second frequency interval; Determining a first frequency point from the first frequency interval and a second frequency point from the second frequency interval; the specific frequency points include the first frequency point and the second frequency point.

6. The method according to claim 5, characterized in that, The step of determining a first frequency point from the first frequency interval and a second frequency point from the second frequency interval includes: Taking the frequency point corresponding to the vertex of the semicircle region in the first frequency interval as the first frequency point, and taking the center frequency point of the second frequency interval as the second frequency point.

7. The method according to claim 5 or 6, characterized in that The health state classifications include health state intervals. The step of determining third EIS data corresponding to each of the specific frequency points under the health state classification according to the first EIS data of each of the batteries under the health state classification at each of the specific frequency points includes: Determining a first parameter corresponding to the first frequency point under the health state interval according to the real part data of the first EIS data of the first frequency point under the health state interval, and determining a second parameter corresponding to the second frequency point under the health state interval according to the imaginary part data of the first EIS data of the second frequency point under the health state interval; the first parameter includes a first average value or a first median, and the second parameter includes a second average value or a second parameter; Taking the first parameter as the third EIS data corresponding to the first frequency point under the health state interval, and taking the second parameter as the third EIS data corresponding to the second frequency point under the health state interval.

8. The method according to claim 7, wherein Determining the health state estimation strategy based on the third EIS data corresponding to each specific frequency point under each health state classification includes: For each health state interval, determine a first weighted value corresponding to the health state interval according to the real part data and the first weight of the third EIS data corresponding to the first frequency point under the health state interval, and the imaginary part data and the second weight of the third EIS data corresponding to the second frequency point; the first weight is less than the second weight; Determine the health state estimation strategy according to the first weighted value corresponding to each health state interval.

9. The method according to claim 8, wherein The determining the health state estimation strategy according to the first weighted value corresponding to each health state interval includes: For each health state interval, use the first weighted value corresponding to the health state interval as the health state reference value of the health state interval; Use the health state reference values of each health state interval as the health state estimation strategy.

10. The method according to claim 9, characterized in that, Estimating the health state of the battery under test according to the second EIS data of the battery under test at the specific frequency point and the health state estimation strategy includes: Determine a second weighted value according to the real part of the second EIS data of the battery under test at the first frequency point, the first weight, the imaginary part of the second EIS data of the battery under test at the second frequency point, and the second weight; Estimate the health state of the battery under test according to the second weighted value and the health state reference values of each health state interval.

11. The method according to claim 10, wherein The estimating the health state of the battery under test according to the second weighted value and the health state reference values of each health state interval includes: Determine the absolute value of the difference between the second weighted value and the health state reference values of each health state interval; Estimate the health state of the battery under test according to the health state interval corresponding to the smallest absolute value.

12. The method according to claim 4, wherein The determining the specific frequency point based on the first Nyquist plot includes: Determine a third frequency interval corresponding to the semicircle region of the first Nyquist plot; Determine the specific frequency points of the preset number of frequency points from the third frequency interval; the preset number of frequency points is greater than 2.

13. The method according to claim 12, wherein The determining the specific frequency points from the third frequency interval includes: According to the preset number of frequency points, the maximum frequency point value and the minimum frequency point value corresponding to the third frequency interval, use a logarithmic interval distribution function to determine the specific frequency points of the preset number of frequency points.

14. The method according to claim 12 or 13, characterized in that, The health state classification includes health state values; the determining the third EIS data corresponding to each specific frequency point under each health state classification according to the first EIS data of each battery at each specific frequency point under the health state classification includes: For each specific frequency point under the health state value, determine the third average value and the first standard deviation of the real part data of each first EIS data at the specific frequency point, and the fourth average value and the second standard deviation of the imaginary part data of each first EIS data at the specific frequency point; Determine a first threshold according to the third average value and the first standard deviation at the specific frequency point, and determine a second threshold according to the fourth average value and the second standard deviation at the specific frequency point; Determine fourth EIS data from the first EIS data at the specific frequency point according to the first threshold and the second threshold at the specific frequency point; Based on the fourth EIS data corresponding to each specific frequency point at the health state value, determine third EIS data corresponding to each specific frequency point at the health state value.

15. The method according to claim 14, wherein The determining the fourth EIS data from the first EIS data at the specific frequency point according to the first threshold and the second threshold at the specific frequency point includes: Determine abnormal EIS data from the first EIS data at the specific frequency point; the abnormal EIS data includes first EIS data with a real part greater than the first threshold and / or first EIS data with an imaginary part greater than the second threshold; Use the first EIS data other than the abnormal EIS data as the fourth EIS data.

16. The method according to claim 14 or 15, characterized in that The determining the third EIS data corresponding to each specific frequency point at the health state value based on the fourth EIS data corresponding to each specific frequency point at the health state value includes: For each specific frequency point at the health state value, determine a third parameter of the real part data of the fourth EIS data at the specific frequency point and determine a fourth parameter of the imaginary part data of the fourth EIS data at the specific frequency point; the third parameter includes a fifth average value or a third median, and the fourth parameter includes a sixth average value or a fourth median; Use the third parameter as the real part data of the third EIS data at the specific frequency point and use the fourth parameter as the imaginary part data of the third EIS data at the specific frequency point.

17. The method according to any one of claims 14 - 16, characterized in that, The determining the health state estimation strategy according to the third EIS data corresponding to each specific frequency point under each health state classification includes: For each health state value, determine a second Nyquist plot at the health state value according to the third EIS data corresponding to each specific frequency point at the health state value; Determine a first radius of the semicircle of the second Nyquist plot at each health state value; Determine the health state estimation strategy according to each health state value and the first radius corresponding to the health state value.

18. The method according to claim 17, wherein The determining the health state estimation strategy according to each health state value and the first radius corresponding to the health state value includes: Perform fitting on each health state value and the first radius corresponding to the health state value to obtain a fitting function; Use the fitting function as the health state estimation strategy.

19. The method according to claim 18, wherein The estimating the health state of the battery under test according to the second EIS data of the battery under test at the specific frequency point and the health state estimation strategy includes: Determine a third Nyquist plot corresponding to the battery under test according to the second EIS data of the battery under test at the specific frequency point; Determine a second radius of the semicircle of the third Nyquist plot; Estimate the health state of the battery under test according to the second radius and the fitting function.

20. A battery state of health estimation device, characterized in that, The device includes: A first acquisition module, configured to acquire first electrochemical impedance spectroscopy (EIS) data of batteries under multiple health state classifications at a specific frequency point; A first determination module, configured to determine a health state estimation strategy according to first EIS data of the battery at a specific frequency point; An estimation module, configured to estimate the health state of the battery under test according to second EIS data of the battery under test at the specific frequency point and the health state estimation strategy.

21. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 19 are implemented.

22. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 19 are implemented.

23. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 19 are implemented.

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