Battery data processing method, battery health assessment method, battery data processing device, battery health assessment device, electronic device, detection device, computer readable medium, and program product

Abstract

The embodiment of the invention provides a battery data processing method, a battery health assessment method, a battery data processing device, a battery health assessment device, electronic equipment, a detection device, a computer readable medium and a program product. The battery data processing method comprises the following steps: acquiring capacity voltage curves of a plurality of retired batteries in a charging process; wherein each retired battery has a health degree value; acquiring a plurality of curvature maximum values according to the capacity voltage curve; and based on the capacity value corresponding to the curvature maximum value and the health degree value of the retired battery, obtaining a mapping relationship between the capacity and the health degree of the battery. According to the method, the mapping relation used for evaluating the battery health state can be quickly generated based on the curvature maximum value, the calculation amount in the battery health state evaluation process is reduced, and therefore the efficiency and accuracy of battery health evaluation are improved.

Description

Technical Field

[0001] The present application relates to the field of battery evaluation technology, and in particular to a battery data processing method, a battery health evaluation method, a battery data processing device, a battery health evaluation device, an electronic device, a detection device, a computer-readable medium, and a program product. Background Art

[0002] Lithium-ion batteries have become the most widely used power source for electric vehicles due to their green and environmentally friendly, long service life, large capacity, and high charge and discharge safety. When the capacity of lithium-ion batteries decays to about 80% of the rated capacity, lithium-ion batteries will face retirement. Retired lithium-ion batteries can be reused in cascades to fully utilize the remaining energy in retired power lithium-ion batteries. Assessing the health status of retired power lithium-ion batteries is crucial to whether they can be reused safely and efficiently.

[0003] In the related art, the estimation methods of SOH of retired power lithium-ion batteries can be mainly divided into direct measurement method and indirect estimation method. The direct measurement method is to conduct a complete discharge test on retired power lithium-ion batteries. This method requires very complete battery data, which will lead to a long time and low efficiency in SOH value estimation. The indirect estimation method mainly refers to the mechanism model-based method and the data-driven method. Such methods rely on the accuracy of model establishment, and have large calculation amount and long time consumption, resulting in poor applicability of SOH value estimation. Therefore, it is crucial to evaluate the health status of the battery more efficiently and accurately. Summary of the invention

[0004] The embodiments of the present application provide a battery data processing method, a battery health assessment method, a battery data processing device, a battery health assessment device, an electronic device, a detection device, a computer-readable medium and a program product, which are used to quickly generate a mapping relationship for assessing the battery health status based on the maximum curvature, reduce the amount of calculation in the battery health status assessment process, and thus improve the efficiency and accuracy of the battery health assessment.

[0005] In a first aspect, an embodiment of the present application provides a battery data processing method, comprising: obtaining capacity-voltage curves of multiple retired batteries during charging; wherein each of the retired batteries has a health value; according to the capacity-voltage curves, obtaining multiple curvature maxima; based on the capacity values ​​corresponding to the curvature maxima and the health values ​​of the retired batteries, obtaining a mapping relationship between the capacity and health of the battery.

[0006] In a possible implementation, obtaining a plurality of curvature maxima according to the capacity-voltage curve includes: calculating a plurality of curvature values ​​of the capacity-voltage curve according to the capacity-voltage curve; obtaining a plurality of capacity curvature curves of retired batteries based on the plurality of curvature values; wherein the capacity curvature curve represents a mapping relationship between a curvature of a retired battery and a capacity of the retired battery during charging; and obtaining a plurality of curvature maxima of the capacity curvature curve according to the capacity curvature curve.

[0007] In a possible implementation, calculating multiple curvature values ​​of the capacity-voltage curve according to the capacity-voltage curve includes: selecting multiple discrete points of the capacity-voltage curve according to the capacity-voltage curve to obtain capacity data and voltage data of the multiple discrete points; acquiring multiple capacity change values ​​and multiple voltage change values ​​of the retired battery according to the capacity data and voltage data of the multiple discrete points; and calculating multiple curvature values ​​of the capacity-voltage curve according to the multiple capacity change values ​​and the multiple voltage change values.

[0008] In a possible implementation, calculating the multiple curvature values ​​of the capacity-voltage curve according to the multiple capacity change values ​​and the multiple voltage change values ​​includes: smoothing some of the multiple capacity values ​​to obtain multiple smoothed capacity values; and calculating the multiple curvature values ​​of the capacity-voltage curve according to the multiple smoothed capacity values ​​and the multiple unsmoothed capacity values ​​that have not been smoothed.

[0009] In a possible implementation, smoothing some of the capacity values ​​among the multiple capacity values ​​to obtain multiple smoothed capacity values ​​includes: obtaining a midpoint capacity value of a preset capacity span range, wherein the midpoint capacity value is an average value of two end points of the capacity span range; determining multiple smoothing coefficients according to the midpoint capacity value, multiple capacity values ​​within the preset capacity span range, and the capacity span range; and calculating multiple curvature values ​​of the capacity-voltage curve according to the multiple smoothing coefficients and the multiple capacity values ​​within the preset capacity span range.

[0010] In a possible implementation, obtaining the capacity-voltage curves of multiple retired batteries during the charging process includes: obtaining health values ​​of multiple retired batteries of the same model from the same batch; obtaining capacity data and voltage data of multiple retired batteries within a preset health range during the charging process, wherein the multiple retired batteries are charged according to preset charging currents and charging start and end voltages, the capacity data include multiple capacity values ​​of the retired batteries, and the voltage data include multiple voltage values ​​of the retired batteries; generating the capacity-voltage curves of the multiple retired batteries during the charging process according to the capacity data and voltage data of the multiple retired batteries during the charging process.

[0011] In a possible implementation, the battery data processing method also includes: after obtaining the mapping relationship between the battery capacity and health, obtaining the correlation coefficient between the battery capacity and health; determining whether the correlation coefficient is within a preset correlation coefficient range; if the correlation coefficient is within the preset correlation coefficient range, using the mapping relationship as the mapping relationship between the battery capacity and health; if the correlation coefficient is outside the preset correlation coefficient range, re-obtaining the mapping relationship between the battery capacity and health.

[0012] In a second aspect, an embodiment of the present application provides a battery health assessment method, comprising: obtaining a target capacity value of a battery to be tested; determining a target health value corresponding to the target capacity value of the battery to be tested based on a mapping relationship between the capacity and health of the battery obtained by using any of the methods described in the first aspect, wherein the target health value is used to characterize the health status of the battery to be tested.

[0013] In a possible implementation, obtaining the target capacity value of the battery to be tested includes: obtaining a capacity-voltage curve of the battery to be tested during a charging process; obtaining a maximum curvature value of the battery to be tested based on the capacity-voltage curve of the battery to be tested during a charging process; and obtaining a target capacity value corresponding to the maximum curvature value of the battery to be tested based on the maximum curvature value of the battery to be tested.

[0014] In a possible implementation, obtaining the maximum curvature value of the battery to be tested according to the capacity-voltage curve of the battery to be tested during the charging process includes: calculating multiple curvature values ​​of the battery to be tested according to the capacity-voltage curve of the battery to be tested during the charging process; obtaining the capacity curvature curve of the battery to be tested based on the multiple curvature values ​​of the battery to be tested; wherein the capacity curvature curve represents a mapping relationship between the curvature of the battery to be tested and the capacity of the battery to be tested during the charging process; and obtaining the maximum curvature value of the battery to be tested according to the capacity curvature curve of the battery to be tested.

[0015] In a third aspect, an embodiment of the present application provides a battery data processing device, comprising: a curve acquisition module, used to obtain capacity-voltage curves of multiple retired batteries during charging; wherein each of the retired batteries has a health value; an extreme value acquisition module, used to obtain multiple curvature maxima according to the capacity-voltage curve; and a mapping relationship acquisition module, used to obtain a mapping relationship between battery capacity and health based on the capacity value corresponding to the curvature maximum and the health value of the retired battery.

[0016] In a fourth aspect, an embodiment of the present application provides a battery health assessment device, comprising: a target capacity value acquisition module, used to obtain a target capacity value of a battery to be tested; a target health value determination module, used to determine a target health value corresponding to the target capacity value of the battery to be tested based on a mapping relationship between the capacity and health of the battery obtained by using any method described in the first aspect, wherein the target health value is used to characterize the health status of the battery to be tested.

[0017] In a fifth aspect, an embodiment of the present application provides an electronic device, comprising: a memory, a processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory, so that the processor executes the method described in any one of the first and second aspects.

[0018] In a sixth aspect, an embodiment of the present application provides a detection device, which includes an electronic device and a charge and discharge component, and the charge and discharge component is used to charge or discharge a battery to be tested and a plurality of retired batteries.

[0019] In a seventh aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the method described in any one of the first and second aspects.

[0020] In an eighth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the method described in any one of the first and second aspects.

[0021] The battery data processing method, battery health assessment method, battery data processing device, battery health assessment device, electronic device, detection device, computer-readable medium and program product provided in the embodiments of the present application obtain multiple curvature maxima by utilizing the capacity-voltage curves of multiple retired batteries during the charging process, and obtain the mapping relationship between the capacity and health of the battery based on the capacity values ​​corresponding to the curvature maxima and the health values ​​of the retired batteries. The mapping relationship for assessing the battery health status can be quickly generated based on the curvature maxima, thereby reducing the amount of calculation in the battery health status assessment process, thereby improving the efficiency and accuracy of the battery health assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0023] Figure 1 Schematic diagram of the battery data processing method provided in this application Figure 1 ;

[0024] Figure 2 Schematic diagram of the battery data processing method provided in this application Figure 2 ;

[0025] Figure 3 An overall schematic diagram of the capacity-voltage curve provided for this application;

[0026] Figure 4 A partially enlarged schematic diagram of the capacity-voltage curve provided in this application;

[0027] Figure 5 Schematic diagram of the battery data processing method provided in this application Figure 3 ;

[0028] Figure 6 Schematic diagram of the battery data processing method provided in this application Figure 4 ;

[0029] Figure 7 A schematic diagram of the mapping relationship between the capacity and health of the battery provided in this application;

[0030] Figure 8 Schematic diagram of the battery data processing method provided in this application Figure 5 ;

[0031] Fig. 9 A flowchart of a battery health assessment method provided for this application;

[0032] Fig.10 A schematic diagram of the structure of the battery data processing device provided in this application;

[0033] Fig.11 A schematic diagram of the structure of the battery health assessment device provided in this application;

[0034] Fig.12 A schematic diagram of the structure of the electronic device provided in this application.

[0035] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0036] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0037] The present application analyzes battery data during the charging process of multiple retired batteries and establishes a mapping relationship between battery capacity and health based on the maximum curvature, so that the mapping relationship can more truly and quickly reflect the changing pattern of battery data during the charging process of multiple retired batteries, reduce the amount of calculation in the battery health status assessment process, and improve the efficiency and accuracy of battery health assessment.

[0038] In a possible implementation, the execution subject of the above process may be a detection device. The detection device establishes a mapping relationship between battery capacity and health based on the collected battery data of multiple retired batteries, then obtains the target capacity value of the battery to be tested, and obtains the target health value of the battery to be tested based on the mapping relationship between the battery capacity and health.

[0039] Figure 1 Schematic diagram of the battery data processing method provided in this application Figure 1 .like Figure 1 As shown, the battery data processing method provided by this application includes:

[0040] Step S1: obtaining capacity-voltage curves of multiple retired batteries during charging; wherein each of the retired batteries has a health value;

[0041] In a possible implementation, the present application uses the ratio of the capacity that can be discharged by a retired battery to the nominal capacity of the battery to indicate the health of the battery. Each of the retired batteries has a health value, which is expressed in the form of a percentage as follows:

[0042]

[0043] Among them, SOH represents the health of retired batteries, which is an indicator of the health status of retired batteries; C represents the capacity released by the retired battery under the current discharge conditions when it is fully discharged at the standard current to the discharge cut-off voltage; C 标称 Indicates the nominal capacity of a new battery. When the health of a retired battery is 100%, it means that the health status of the retired battery is the same as that of a new battery.

[0044] Step S2: obtaining a plurality of maximum curvature values ​​according to the capacity-voltage curve;

[0045] In a possible implementation, the maximum curvature value corresponds to a specific capacity span range, and the capacity span range may be, for example, a range of 0.1Ah-10Ah. Since there are multiple retired batteries, each retired battery may correspond to a maximum curvature value, and therefore there may be multiple maximum curvature values.

[0046] Step S3: Based on the capacity value corresponding to the maximum curvature value and the health value of the retired battery, a mapping relationship between the capacity and health of the battery is obtained.

[0047] In a possible implementation, the mapping relationship between the battery capacity and health can be used to evaluate the health status of the battery. It should be noted that the battery data processing method of the present application is applicable to various types of lithium-ion batteries, such as cylindrical batteries, square batteries, etc. It is understandable that the present application does not limit the specific type of retired batteries.

[0048] By utilizing the capacity-voltage curves of multiple retired batteries during the charging process to obtain multiple curvature maxima, and obtaining a mapping relationship between the capacity and health of the battery based on the capacity values ​​corresponding to the curvature maxima and the health values ​​of the retired batteries, the present application can quickly generate a mapping relationship for evaluating the battery health status based on the curvature maxima, reduce the amount of calculation in the battery health status evaluation process, and thus improve the efficiency and accuracy of the battery health evaluation.

[0049] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0050] Figure 2 Schematic diagram of the battery data processing method provided in this application Figure 2 ,like Figure 2 As shown, in this embodiment Figure 1 Based on the embodiment, the battery data processing method is described in detail, wherein the obtaining of the capacity-voltage curves of multiple retired batteries during the charging process includes:

[0051] Step S11: Obtain health values ​​of multiple retired batteries of the same model in the same batch;

[0052] In one possible implementation, the models and batches of multiple retired batteries of the present application are the same, so that the data of the retired batteries will not be affected by the models and batches, reducing interference factors in the process of establishing a mapping relationship between battery capacity and health.

[0053] Step S12: acquiring capacity data and voltage data of a plurality of retired batteries within a preset health range during charging, wherein the plurality of retired batteries are charged according to preset charging currents and charging start and end voltages, the capacity data includes a plurality of capacity values ​​of the retired batteries, and the voltage data includes a plurality of voltage values ​​of the retired batteries;

[0054] In a possible implementation, each of the retired batteries has a health value, and the health values ​​of the multiple retired batteries are different and are all within a preset health range. Exemplarily, the preset health range is 50% to 80%, and the health values ​​of the multiple retired batteries are discretely distributed within the range of 50% to 80%, for example, the health value of retired battery 1 is 60%, and the health value of retired battery 2 is 70%.

[0055] In a possible implementation, the multiple retired batteries are charged according to a preset charging current and charging start and end voltages. Exemplarily, the multiple retired batteries are charged at a constant current of 0.37C, the charging start voltage is 2.7V, and the charging end voltage is 3.8V, that is, the charging start and end voltages are 2.7V-3.8V.

[0056] Among them, the voltage of the present application may be the voltage between the positive and negative electrodes of the retired battery. Since the health status of each retired battery is different, at the initial moment, the voltage of each retired battery is also different. Therefore, in order to uniformly charge the multiple retired batteries according to the preset charging current and charging start and end voltage, the retired batteries whose voltage is not 2.7V may be pre-charged or pre-discharged first, so that the voltage of the retired battery reaches 2.7V, and then constant current charging is performed at a current of 0.37C. During the constant current charging process, the change in the health value of the retired battery is very small and can be ignored in the present application.

[0057] In a possible implementation, the capacity data includes multiple capacity values ​​during the charging process of the retired battery, and the voltage data includes multiple voltage values ​​during the charging process of the retired battery. During the charging process, the capacity value and voltage value of each retired battery during the charging process can be recorded according to a preset collection accuracy to obtain the capacity data and the voltage data, so that sufficient battery data can be collected to improve the accuracy of battery evaluation.

[0058] Step S13: generating capacity-voltage curves of the plurality of retired batteries during the charging process according to the capacity data and voltage data of the plurality of retired batteries during the charging process.

[0059] In a possible implementation, the capacity-voltage curves of the multiple retired batteries during charging are used to characterize the mapping relationship between the capacity and the voltage during charging of the retired batteries. Different retired batteries may have different capacity-voltage curves, so there may be multiple capacity-voltage curves.

[0060] Figure 3 The overall schematic diagram of the capacity-voltage curve provided in this application is Figure 4 This is a partial enlarged schematic diagram of the capacity-voltage curve provided in this application. Figure 3 and Figure 4As shown in the figure, the dotted line is the capacity-voltage curve of the retired battery. Figure 3 There are two capacity-voltage curves, among which the capacity-voltage curve of the retired battery with a health value of 76% is different from the capacity-voltage curve of the retired battery with a health value of 72%. Figure 3 It can be seen that for the two capacity-voltage curves, the voltage increases faster when the capacity of the retired battery is smaller, and the voltage increases slower when the capacity of the retired battery is larger.

[0061] Combination Figure 3 and Figure 4 As shown, it can be clearly observed that as the health of the retired battery decreases, the maximum point of its discrete curvature gradually shifts to the left, and the corresponding characteristic capacity value will gradually decrease. That is to say, the lower the health of the retired battery, the smaller its characteristic capacity value.

[0062] Figure 5 Schematic diagram of the battery data processing method provided in this application Figure 3 ,like Figure 5 As shown, in this embodiment Figure 2 Based on the embodiment, the battery data processing method is described in detail, wherein the step of obtaining a plurality of maximum curvature values ​​according to the capacity-voltage curve includes:

[0063] Step S21: calculating a plurality of curvature values ​​of the capacity-voltage curve according to the capacity-voltage curve;

[0064] In a possible implementation, multiple curvature values ​​of the capacity-voltage curve may be calculated based on the capacity-voltage curve, and the curvature values ​​are used to characterize the change trend of the capacity-voltage curve. Each retired battery may correspond to multiple curvature values.

[0065] Figure 6 Schematic diagram of the battery data processing method provided in this application Figure 4 ,like Figure 6 As shown, in this embodiment Figure 5 Based on the embodiment, the battery data processing method is described in detail, and the calculation of multiple curvature values ​​of the capacity-voltage curve according to the capacity-voltage curve includes:

[0066] Step S211: According to the capacity-voltage curve, multiple discrete points of the capacity-voltage curve are selected to obtain capacity data and voltage data of the multiple discrete points;

[0067] See also Figure 2During the charging process, the capacity value and voltage value of each retired battery in the charging process can be recorded according to a preset acquisition accuracy, thereby obtaining the capacity data and the voltage data, and then using the capacity data and the voltage data to generate the capacity-voltage curves of the multiple retired batteries during the charging process. In the process of calculating the curvature value, multiple discrete points can be selected from the capacity-voltage curve to establish a set A of discrete points on the retired battery charging capacity-voltage curve. n :

[0068] A n = {P1,P2,…,P n} i =(x i ,y i )

[0069] Among them, P1, P2, …, P n Indicates the capacity value and the corresponding voltage value of the retired battery during charging, which may be related to the data collection accuracy during charging, for example, the capacity value and voltage value are collected every 30 seconds. i x in i represents the capacity value of the retired battery at the i-th discrete point, P i The y in i Represents the voltage value of the retired battery at the i-th discrete point, where i is a positive integer.

[0070] In some embodiments, multiple discrete points of the capacity-voltage curve may be randomly selected from an existing capacity-voltage curve to obtain capacity data and voltage data of the multiple discrete points, instead of strictly selecting the multiple discrete points of the capacity-voltage curve according to the collection time, thereby expanding the collected sample data.

[0071] Step S212: acquiring a plurality of capacity change values ​​and a plurality of voltage change values ​​of the retired battery according to the capacity data and voltage data of the plurality of discrete points;

[0072] In a possible implementation, multiple capacity change values ​​and multiple voltage change values ​​of the retired battery are obtained by the following formula:

[0073]

[0074] Among them, x i+1 represents the capacity value of the retired battery at the i+1th discrete point, y i+1 represents the voltage value of the retired battery at the i+1th discrete point, x i-1 represents the capacity value of the retired battery at the i-1th discrete point, y i-1 Δx represents the voltage value of the retired battery at the i-1th discrete point. irepresents the first-order capacity change value of the retired battery, Δy i represents the first-order voltage change value of the retired battery; Δ 2 x i represents the second-order capacity change value of the retired battery, Δ 2 y i Indicates the second-order voltage change value of the retired battery. In the present application, the capacity change value may be a first-order capacity change value or a second-order capacity change value; the voltage change value may be a first-order voltage change value or a second-order voltage change value.

[0075] Step S213: Calculating a plurality of curvature values ​​of the capacity-voltage curve according to a plurality of capacity change values ​​and a plurality of voltage change values.

[0076] In a possible implementation, multiple curvature values ​​of the capacity-voltage curve are calculated according to multiple capacity change values ​​and multiple voltage change values ​​using the following formula:

[0077]

[0078] Among them, K i is the i-th curvature value, 1.5 is a preset parameter, and can be set as needed in practical applications.

[0079] Further, the calculating a plurality of curvature values ​​of the capacity-voltage curve according to a plurality of capacity change values ​​and a plurality of voltage change values ​​comprises:

[0080] Step S2131: smoothing some of the capacity values ​​to obtain a plurality of smoothed capacity values;

[0081] In a possible implementation, a local weighted least square method is used to smooth some of the capacity values ​​in the plurality of capacity values ​​to obtain a plurality of smoothed capacity values. It should be noted that in the present application, the object of smoothing may be some of the capacity values ​​in the plurality of capacity values. Figure 2 For example, the calibration range of the horizontal axis battery capacity is 0Ah to 210Ah, and the capacity values ​​of discrete points within the capacity span from 80Ah to 120Ah need to be smoothed, while the capacity values ​​of discrete points outside the capacity range from 80Ah to 120Ah can be directly calculated using the curvature value calculation formula in step S213. Through smoothing, the noise of the capacity value can be removed, further improving the accuracy of battery evaluation.

[0082] Step S2132: Calculating a plurality of curvature values ​​of the capacity-voltage curve according to the plurality of smoothed capacity values ​​and a plurality of unsmoothed capacity values ​​that have not been smoothed.

[0083] In a possible implementation, the smoothed capacity value and the unsmoothed capacity value may be processed separately, and the curvature value finally obtained may be a curvature value calculated based on the smoothed capacity value, or a curvature value calculated based on the unsmoothed capacity value.

[0084] Furthermore, the step of smoothing some of the capacity values ​​to obtain a plurality of smoothed capacity values ​​includes:

[0085] Step A: obtaining a midpoint capacity value of a preset capacity span range, wherein the midpoint capacity value is an average value of two end points of the capacity span range;

[0086] In one possible implementation, see Figure 2 , the preset capacity span range may be 80Ah to 120Ah, at which point the midpoint capacity value is (80Ah+120Ah) / 2=100Ah. In some embodiments, the preset capacity span range may also be 0.1Ah to 10Ah, where 0.1Ah to 10Ah is a capacity span range with a faster voltage increase.

[0087] Step B: determining a plurality of smoothing coefficients according to the midpoint capacity value, a plurality of capacity values ​​within a preset capacity span range, and the capacity span range;

[0088] In a possible implementation, multiple smoothing coefficients may be determined according to the midpoint capacity value, multiple capacity values ​​within a preset capacity span range, and the capacity span range by using the following formula:

[0089]

[0090] Where x is the midpoint capacity value, for example 100Ah. i is the adjacent capacity value within the span associated with the current midpoint capacity value within the preset capacity span, d i The maximum distance between the midpoint capacity value and the end point of the preset capacity span range, for example, 120Ah-100Ah=20Ah. i (x) is the smoothing coefficient. ω i The function of (x) is also called a tricubic weight function. By calculating the smoothing coefficient, the effect of noise removal can be improved, thereby improving the accuracy of battery evaluation.

[0091] In practical applications, a second-order polynomial may also be used for regression processing, and this application does not limit the regression process.

[0092] Step C: calculating a plurality of curvature values ​​of the capacity-voltage curve according to the plurality of smoothing coefficients and a plurality of capacity values ​​within a preset capacity span.

[0093] After obtaining the smoothing coefficient, the smoothing coefficient can be multiplied by the capacity value of the discrete point within the preset capacity span range, and then substituted into the formula of step S213 to calculate the curvature value. In other words, when the capacity value of the discrete point within the preset capacity span range is needed in the formula of step S213, a smoothing coefficient is first assigned to the capacity value of the discrete point within the preset capacity span range, and then the curvature value is calculated, so that the noise of the curvature can be reduced.

[0094] Step S22: acquiring capacity curvature curves of a plurality of retired batteries based on the plurality of curvature values; wherein the capacity curvature curve represents a mapping relationship between the curvature of the retired battery and the capacity of the retired battery during charging;

[0095] In one possible implementation, see Figure 3 , the capacity values ​​of multiple retired batteries during charging can be used as the values ​​on the x-axis, and the multiple curvature values ​​can be used as the values ​​on the y-axis to draw multiple capacity curvature curves, that is, Figure 3 The solid curve in Figure 3 There are two capacity curvature curves. The capacity curvature curve of retired batteries with a health value of 76% is different from the capacity curvature curve of retired batteries with a health value of 72%, but the two have similar changing trends.

[0096] like Figure 3 As shown, the capacity curvature curve and the capacity voltage curve can be plotted in the same graph to form a double y-axis graph. The double y-axis graph can clearly observe the changing trend of the charging voltage and the discrete curvature during the charging process of the retired battery, and further identify the characteristic areas that affect the health assessment of the retired battery.

[0097] Step S23: According to the capacity curvature curve, a plurality of curvature maxima of the capacity curvature curve are obtained.

[0098] In one possible implementation, see Figure 3 , each capacity curvature curve has a maximum curvature value, and the maximum curvature value exists in the capacity span range of 0.1Ah-10Ah. The area surrounded by the capacity span range where the maximum curvature value exists and the curvature span range corresponding to the capacity span range is called a characteristic area, and the characteristic area includes multiple curvature maxima of the capacity curvature curve. Since the changing trend of the charging process of retired batteries is concentrated in this characteristic area, the health of retired batteries can be evaluated by using this characteristic area, thereby improving the efficiency and accuracy of battery evaluation.

[0099] In a possible implementation, multiple curvature maxima of the capacity curvature curve are obtained according to the capacity curvature curve, which can be performed by derivation. It can be understood that other mathematical methods can also be used to obtain multiple curvature maxima of the capacity curvature curve, and this application is not limited to this.

[0100] Step S3: Based on the capacity value corresponding to the maximum curvature value and the health value of the retired battery, a mapping relationship between the capacity and health of the battery is obtained.

[0101] Figure 7 A schematic diagram of the mapping relationship between the battery capacity and health provided in this application. Figure 7 As shown, the horizontal axis represents the health value of the retired battery, and the vertical axis represents the capacity value corresponding to the maximum curvature value. The mapping relationship between the capacity and health of the battery is a linear relationship, and the intercept and slope may vary depending on different application situations. Since the mapping relationship model is a simple linear model, the applicability and operability are simple and convenient, and can be widely promoted.

[0102] Figure 8 Schematic diagram of the battery data processing method provided in this application Figure 5 ,like Figure 8 As shown, in this embodiment Figure 1 Based on the embodiment, the battery data processing method is described in detail, and the battery data processing method also includes:

[0103] Step S41: after obtaining the mapping relationship between the battery capacity and the health, obtaining the correlation coefficient between the battery capacity and the health;

[0104] In a possible implementation, the correlation coefficient may be a Pearson correlation coefficient. The Pearson correlation coefficient is also called the Pearson product-moment correlation coefficient, which can be used to measure the correlation between two variables and . The value of the Pearson correlation coefficient is between -1 and 1.

[0105] In a possible implementation, the capacity value corresponding to the maximum value of the curvature can be read and recorded as the characteristic capacity value. Since there are multiple retired batteries, multiple characteristic capacity values ​​of retired batteries with different health levels can be read, and then multiple health levels and multiple characteristic capacity values ​​can be linearly fitted to obtain a mapping relationship between the capacity and health of the battery, and the correlation coefficient between the capacity and health of the battery can be obtained based on the mapping relationship.

[0106] Step S42: determining whether the correlation coefficient is within a preset correlation coefficient range;

[0107] In a possible implementation, the preset correlation coefficient range may be [0.99, 1]. The preset correlation coefficient range may be set as required, and the present application does not limit this.

[0108] Step S43: if the correlation coefficient is within the preset correlation coefficient range, the mapping relationship is used as the mapping relationship between the battery capacity and the health; if the correlation coefficient is outside the preset correlation coefficient range, the mapping relationship between the battery capacity and the health is re-acquired.

[0109] In a possible implementation, if the correlation coefficient is within the preset correlation coefficient range, for example, the Pearson correlation coefficient between the health of the retired battery and the characteristic capacity is 0.99041, which is close to 1, indicating that the health of the retired battery is highly correlated with the characteristic capacity, and the mapping relationship can be used as the mapping relationship between the battery capacity and health to evaluate the health of the battery to be tested; if the correlation coefficient is outside the preset correlation coefficient range, indicating that the health of the retired battery is weakly correlated with the characteristic capacity, the mapping relationship cannot be used as the mapping relationship between the battery capacity and health at this time, and the mapping relationship between the battery capacity and health needs to be obtained based on new battery data. This can ensure that the established mapping relationship more closely fits the actual health status of the retired battery, thereby improving the accuracy of battery evaluation.

[0110] Fig. 9 A flowchart of the battery health assessment method provided in this application. Fig. 9 As shown, the present application also provides a battery health assessment method, including:

[0111] Step S51: obtaining a target capacity value of the battery to be tested;

[0112] In a possible implementation, the target capacity value of the battery to be tested is the capacity value corresponding to the maximum curvature value of the battery to be tested. To obtain the target capacity value of the battery to be tested, the battery data of the battery to be tested can be processed according to the process of step S1 to step S3 to obtain the target capacity value of the battery to be tested.

[0113] Further, obtaining the target capacity value of the battery to be tested includes:

[0114] Step S511: obtaining a capacity-voltage curve of the battery to be tested during charging;

[0115] In a possible implementation, the battery to be tested can be charged according to a preset charging current and charging starting voltage. Exemplarily, the battery to be tested is charged with a constant current of 0.37C, and the charging starting voltage value is 2.7V. On this basis, the battery to be tested is charged with a state of charge (SOC) of 3 to 8%. For example, when the starting voltage value of the battery to be tested is 2.7V, the battery to be tested can be charged with a constant current of 0.37C, so that the state of charge of the battery to be tested increases by 6%, and then the charging is stopped.

[0116] Step S512: obtaining a maximum curvature value of the battery to be tested according to a capacity-voltage curve of the battery to be tested during charging;

[0117] Since the process of obtaining the capacity-voltage curve of the battery to be tested during charging is similar to the process of obtaining the capacity-voltage curves of multiple retired batteries, the maximum curvature value can be quickly determined, thereby improving the efficiency of battery evaluation.

[0118] Further, obtaining the maximum curvature value of the battery to be tested according to the capacity-voltage curve of the battery to be tested during charging includes:

[0119] Step S5121: Calculating multiple curvature values ​​of the battery to be tested according to the capacity-voltage curve of the battery to be tested during charging;

[0120] In a possible implementation, multiple curvature values ​​of the battery to be tested can be calculated based on the capacity-voltage curve of the battery to be tested, and the curvature values ​​are used to characterize the change trend of the capacity-voltage curve of the battery to be tested. The battery to be tested may correspond to multiple curvature values.

[0121] Step S5122: obtaining a capacity curvature curve of the battery to be tested based on multiple curvature values ​​of the battery to be tested; wherein the capacity curvature curve represents a mapping relationship between the curvature of the battery to be tested and the capacity of the battery to be tested during charging;

[0122] In a possible implementation, a capacity curvature curve of the battery to be tested can be obtained based on multiple curvature values ​​of the battery to be tested with reference to step S22. The horizontal axis of the capacity curvature curve of the battery to be tested represents the capacity of the battery to be tested during charging, and the vertical axis of the capacity curvature curve of the battery to be tested represents the curvature of the battery to be tested during charging.

[0123] Step S5123: obtaining a maximum curvature value of the battery to be tested according to the capacity curvature curve of the battery to be tested.

[0124] In a possible implementation, the maximum curvature value of the battery to be tested is obtained according to the capacity curvature curve of the battery to be tested, which can be performed by derivation. It can be understood that other mathematical methods can also be used to obtain the maximum curvature value of the battery to be tested, and this application does not limit this.

[0125] Step S513: according to the maximum value of the curvature of the battery to be tested, obtaining a target capacity value corresponding to the maximum value of the curvature of the battery to be tested.

[0126] In a possible implementation, the target capacity value corresponding to the curvature maximum value of the battery to be tested can be obtained based on the capacity curvature curve of the battery to be tested. There can be one target capacity value. When there are multiple batteries to be tested, there can also be multiple target capacity values. Since the process of obtaining the curvature maximum value of the battery to be tested is similar to the process of obtaining multiple curvature maximum values ​​of multiple retired batteries, the target capacity value corresponding to the curvature maximum value can be quickly determined, thereby improving the efficiency of battery evaluation.

[0127] Step S52: determining a target health value corresponding to a target capacity value of the battery to be tested based on a mapping relationship between battery capacity and health, wherein the target health value is used to characterize a health state of the battery to be tested.

[0128] In a possible implementation, since the mapping relationship between the battery capacity and health has been determined, when the target capacity value corresponding to the curvature maximum value of the battery to be tested is known, the target capacity value can be substituted into the mapping relationship between the battery capacity and health, thereby obtaining the target health value corresponding to the target capacity value of the battery to be tested, and the target health value can be used to evaluate the health status of the battery to be tested. In this way, the time used to obtain the target health value is extremely short, and the operation is simple and convenient.

[0129] This application performs functional analysis on the battery's state of health SOH and the characteristic capacity during the charging process to obtain the correlation between SOH and the characteristic capacity value during the charging process. This characteristic capacity value appears in the initial stage of charging the retired battery (within 5% SOC), which means that the retired battery only needs to be charged to 5% SOC to accurately evaluate the SOH of the retired battery, truly achieving the low-cost goal of cascade utilization of retired batteries. In addition, the method proposed in this application is low-cost, and its application and promotion are simple and convenient. Moreover, the original data used only needs to collect the charging capacity-voltage data of the retired battery, and no additional equipment is required for other electrochemical performance tests. Compared with existing technologies, both the R&D cost and the application cost are greatly reduced.

[0130] also, Fig.10 A schematic diagram of the structure of the battery data processing device provided in this application, such as Fig.10As shown, an embodiment of the present application provides a battery data processing device, which includes: a curve acquisition module 101, used to obtain capacity-voltage curves of multiple retired batteries during charging; wherein each of the retired batteries has a health value; an extreme value acquisition module 102, used to obtain multiple curvature maximum values ​​according to the capacity-voltage curve; a mapping relationship acquisition module 103, used to obtain a mapping relationship between the capacity and health of the battery based on the capacity value corresponding to the curvature maximum value and the health value of the retired battery.

[0131] In a possible implementation, the curve acquisition module includes: a curvature value calculation module, used to calculate multiple curvature values ​​of the capacity-voltage curve according to the capacity-voltage curve; a first capacity curvature curve acquisition module, used to acquire capacity curvature curves of multiple retired batteries based on the multiple curvature values; wherein the capacity curvature curve represents a mapping relationship between the curvature of the retired battery and the capacity of the retired battery during charging; and a first curvature maximum value acquisition module, used to acquire multiple curvature maximum values ​​of the capacity curvature curve according to the capacity curvature curve.

[0132] In a possible implementation, the first curvature maximum value acquisition module includes: a discrete point selection module, used to select multiple discrete points of the capacity-voltage curve according to the capacity-voltage curve, and obtain capacity data and voltage data of the multiple discrete points; a change value acquisition module, used to obtain multiple capacity change values ​​and multiple voltage change values ​​of the retired battery according to the capacity data and voltage data of the multiple discrete points; a first curvature value calculation module, used to calculate multiple curvature values ​​of the capacity-voltage curve according to the multiple capacity change values ​​and the multiple voltage change values.

[0133] In a possible implementation, the first curvature value calculation module includes: a smoothing processing module, used to smooth some of the multiple capacity values ​​to obtain multiple smoothed capacity values; a first curvature value calculation submodule, used to calculate multiple curvature values ​​of the capacity-voltage curve according to the multiple smoothed capacity values ​​and multiple unsmoothed capacity values ​​that have not been smoothed.

[0134] In a possible implementation, the smoothing processing module includes: a midpoint capacity value acquisition module, used to obtain the midpoint capacity value of a preset capacity span range, wherein the midpoint capacity value is the average value of the two end points of the capacity span range; a smoothing coefficient determination module, used to determine multiple smoothing coefficients according to the midpoint capacity value, multiple capacity values ​​within the preset capacity span range, and the capacity span range; and a second curvature value calculation submodule, used to calculate multiple curvature values ​​of the capacity-voltage curve according to the multiple smoothing coefficients and multiple capacity values ​​within the preset capacity span range.

[0135] In a possible implementation, the curve acquisition module includes: a health acquisition module, used to obtain health values ​​of multiple retired batteries of the same model in the same batch; a capacity data and voltage data acquisition module, used to obtain capacity data and voltage data of multiple retired batteries within a preset health range during a charging process, wherein the multiple retired batteries are charged according to a preset charging current and charging start and end voltages, the capacity data includes multiple capacity values ​​of the retired batteries, and the voltage data includes multiple voltage values ​​of the retired batteries; a generation module, used to generate capacity-voltage curves of the multiple retired batteries during the charging process according to the capacity data and voltage data of the multiple retired batteries during the charging process.

[0136] In a possible implementation, the battery data processing device also includes: a correlation coefficient acquisition module, which is used to obtain the correlation coefficient between the battery capacity and health after obtaining the mapping relationship between the battery capacity and health; a judgment module, which is used to judge whether the correlation coefficient is within a preset correlation coefficient range; a judgment output module, which is used to use the mapping relationship as the mapping relationship between the battery capacity and health if the correlation coefficient is within the preset correlation coefficient range; if the correlation coefficient is outside the preset correlation coefficient range, re-acquire the mapping relationship between the battery capacity and health.

[0137] Fig.11 A schematic diagram of the structure of the battery data processing device provided in this application, such as Fig.11 As shown, an embodiment of the present application also provides a battery health assessment device, including: a target capacity value acquisition module 111, used to obtain a target capacity value of a battery to be tested; a target health value determination module 112, used to determine a target health value corresponding to the target capacity value of the battery to be tested based on a mapping relationship between the capacity and health of the battery, wherein the target health value is used to characterize the health status of the battery to be tested.

[0138] In a possible implementation, the target capacity value acquisition module includes: a capacity-voltage curve acquisition module, used to obtain the capacity-voltage curve of the battery to be tested during the charging process; a second curvature maximum value acquisition module, used to obtain the curvature maximum value of the battery to be tested according to the capacity-voltage curve of the battery to be tested during the charging process; a target capacity value acquisition submodule, used to obtain the target capacity value corresponding to the curvature maximum value of the battery to be tested according to the curvature maximum value of the battery to be tested.

[0139] In a possible implementation, the second curvature maximum value acquisition module includes: a second curvature value calculation module, used to calculate multiple curvature values ​​of the battery to be tested according to the capacity-voltage curve of the battery to be tested during the charging process; a second capacity curvature curve acquisition module, used to obtain the capacity curvature curve of the battery to be tested based on the multiple curvature values ​​of the battery to be tested; wherein the capacity curvature curve represents the mapping relationship between the curvature of the battery to be tested and the capacity of the battery to be tested during the charging process; a second curvature maximum value acquisition submodule, used to obtain the curvature maximum value of the battery to be tested according to the capacity curvature curve of the battery to be tested.

[0140] The present application also provides a detection device, which includes an electronic device. In practical applications, the detection device may also include a charge-discharge component, which can charge or discharge the battery to be tested and multiple retired batteries.

[0141] The electronic device provided by the embodiment of the present application includes: a memory and a processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory, so that the processor executes the described method.

[0142] Fig.12 This is a schematic diagram of the structure of the electronic device provided in this application. Fig.12 As shown, the electronic device 100 provided in this embodiment includes: at least one processor 1001 and a memory 1002. Optionally, the device 100 further includes a communication component 1003. The processor 1001, the memory 1002 and the communication component 1003 are connected via a bus 1004.

[0143] In a specific implementation process, at least one processor 1001 executes the computer-executable instructions stored in the memory 1002, so that at least one processor 1001 executes the above method.

[0144] The specific implementation process of the processor 1001 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0145] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0146] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-volatile Memory, NVM), such as at least one disk memory.

[0147] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0148] The present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor. Specifically, an embodiment of the present application provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0149] The present application also provides a computer-readable storage medium, in which a computer-executable instruction is stored, and when a processor executes the computer-executable instruction, the above method is implemented. Specifically, an embodiment of the present application provides a computer-readable storage medium, in which a computer-executable instruction is stored, and when the computer-executable instruction is executed by a processor, the method is implemented.

[0150] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0151] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0152] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0153] The units described as segmented components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0154] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0155] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0156] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0157] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A battery data processing method, characterized in that: include: Acquire capacity-voltage curves of multiple retired batteries during charging; wherein each of the retired batteries has a health value; According to the capacity-voltage curve, a plurality of curvature maxima are obtained; Based on the capacity value corresponding to the maximum curvature value and the health value of the retired battery, a mapping relationship between the capacity and health of the battery is obtained.

2. The method according to claim 1, characterized in that: The step of obtaining a plurality of maximum curvature values ​​according to the capacity-voltage curve comprises: According to the capacity-voltage curve, calculating a plurality of curvature values ​​of the capacity-voltage curve; Acquire a plurality of capacity curvature curves of retired batteries based on the plurality of curvature values; wherein the capacity curvature curve represents a mapping relationship between the curvature of the retired battery and the capacity of the retired battery during charging; According to the capacity curvature curve, a plurality of curvature maxima of the capacity curvature curve are obtained.

3. The battery data processing method according to claim 2, characterized in that: Calculating a plurality of curvature values ​​of the capacity-voltage curve according to the capacity-voltage curve includes: According to the capacity-voltage curve, multiple discrete points of the capacity-voltage curve are selected to obtain capacity data and voltage data of the multiple discrete points; Acquire a plurality of capacity change values ​​and a plurality of voltage change values ​​of the retired battery according to the capacity data and the voltage data of the plurality of discrete points; A plurality of curvature values ​​of the capacity-voltage curve are calculated according to the plurality of capacity change values ​​and the plurality of voltage change values.

4. The battery data processing method according to claim 3, characterized in that: Calculating a plurality of curvature values ​​of the capacity-voltage curve according to a plurality of capacity change values ​​and a plurality of voltage change values ​​comprises: Smoothing some of the capacity values ​​among the plurality of capacity values ​​to obtain a plurality of smoothed capacity values; A plurality of curvature values ​​of the capacity-voltage curve are calculated according to the plurality of smoothed capacity values ​​and a plurality of unsmoothed capacity values ​​that have not been smoothed.

5. The battery data processing method according to claim 4, characterized in that: The smoothing of some capacity values ​​among the plurality of capacity values ​​to obtain a plurality of smoothed capacity values ​​comprises: Obtaining a midpoint capacity value of a preset capacity span range, wherein the midpoint capacity value is an average value of two end points of the capacity span range; Determining a plurality of smoothing coefficients according to the midpoint capacity value, a plurality of capacity values ​​within a preset capacity span range, and the capacity span range; A plurality of curvature values ​​of the capacity-voltage curve are calculated according to the plurality of smoothing coefficients and a plurality of capacity values ​​within a preset capacity span.

6. The battery data processing method according to claim 1, characterized in that: The step of obtaining the capacity-voltage curves of the multiple retired batteries during the charging process includes: Get the health values ​​of multiple retired batteries of the same model from the same batch; Acquiring capacity data and voltage data of a plurality of retired batteries within a preset health range during a charging process, wherein the plurality of retired batteries are charged according to preset charging currents and charging start and end voltages, the capacity data includes a plurality of capacity values ​​of the retired batteries, and the voltage data includes a plurality of voltage values ​​of the retired batteries; Capacity-voltage curves of the plurality of retired batteries during the charging process are generated according to the capacity data and voltage data of the plurality of retired batteries during the charging process.

7. The battery data processing method according to claim 1, characterized in that: The battery data processing method further includes: After obtaining the mapping relationship between the battery capacity and health, obtain the correlation coefficient between the battery capacity and health; Determining whether the correlation coefficient is within a preset correlation coefficient range; If the correlation coefficient is within a preset correlation coefficient range, the mapping relationship is used as a mapping relationship between the battery capacity and health; if the correlation coefficient is outside the preset correlation coefficient range, the mapping relationship between the battery capacity and health is re-obtained.

8. A battery health assessment method, characterized in that: include: Get the target capacity value of the battery to be tested; Based on the mapping relationship between the capacity and health of the battery obtained by the method according to any one of claims 1 to 7, a target health value corresponding to the target capacity value of the battery to be tested is determined, and the target health value is used to characterize the health status of the battery to be tested.

9. The battery health assessment method according to claim 8, characterized in that: The step of obtaining the target capacity value of the battery to be tested comprises: Obtain the capacity-voltage curve of the battery under test during the charging process; According to the capacity-voltage curve of the battery to be tested during the charging process, the maximum curvature value of the battery to be tested is obtained; According to the maximum value of the curvature of the battery to be tested, a target capacity value corresponding to the maximum value of the curvature of the battery to be tested is obtained.

10. The battery health assessment method according to claim 9, characterized in that: The step of obtaining the maximum curvature value of the battery to be tested according to the capacity-voltage curve of the battery to be tested during the charging process includes: Calculating multiple curvature values ​​of the battery under test according to the capacity-voltage curve of the battery under test during charging; Acquire a capacity curvature curve of the battery to be tested based on multiple curvature values ​​of the battery to be tested; wherein the capacity curvature curve represents a mapping relationship between the curvature of the battery to be tested and the capacity of the battery to be tested during charging; According to the capacity curvature curve of the battery to be tested, a maximum curvature value of the battery to be tested is obtained.

11. A battery data processing device, characterized in that: The battery data processing device comprises: A curve acquisition module, used to acquire capacity-voltage curves of multiple retired batteries during charging; wherein each of the retired batteries has a health value; An extreme value acquisition module, used for acquiring a plurality of curvature maximum values ​​according to the capacity-voltage curve; A mapping relationship acquisition module is used to acquire a mapping relationship between the capacity and health of the battery based on the capacity value corresponding to the maximum curvature value and the health value of the retired battery.

12. A battery health assessment device, characterized in that: include: A target capacity value acquisition module is used to obtain the target capacity value of the battery to be tested; A target health value determination module is used to determine a target health value corresponding to the target capacity value of the battery to be tested based on a mapping relationship between the capacity and health of the battery obtained by the method as described in any one of claims 1 to 7, wherein the target health value is used to characterize the health status of the battery to be tested.

13. An electronic device, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 10.

14. A detection device, characterized in that: The detection device comprises the electronic device as shown in claim 13 and a charge-discharge component, wherein the charge-discharge component is used to charge or discharge the battery to be tested and a plurality of retired batteries.

15. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 10 when executed by a processor.

16. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 10.

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