Evaluation method, device, equipment and medium for battery testing device

Abstract

The present invention discloses an evaluation method, device, equipment and medium for a battery testing device. The evaluation method for the battery testing device comprises: obtaining battery capacity sequence data, the battery capacity sequence data is obtained by the battery testing device performing capacity testing on the battery, the battery capacity sequence data represents the relationship between the battery capacity and the number of charge and discharge cycles; fitting the battery capacity sequence data to obtain fitting sequence data; based on the fitting sequence data, compensating the battery capacity sequence data to eliminate capacity attenuation information in the battery capacity sequence data to obtain compensated sequence data; based on the fluctuation information of the compensated sequence data, obtaining a fluctuation evaluation result of the battery testing device.

Description

Technical Field

[0001] The present application relates to technical fields such as energy storage and charging, and in particular to an evaluation method, device, equipment and medium for a battery testing device. Background Art

[0002] With the popularity of new energy vehicles, the capacity of battery cells has received more and more attention. The charger and discharger plays a very important role in the measurement of battery cell capacity. How to accurately evaluate the measurement fluctuation of the charger and discharger channel has become an important technical issue.

[0003] In the related art, a compensation model for capacity prediction residuals is proposed, which can effectively compensate for local capacity prediction errors and improve the accurate prediction of new energy vehicle battery capacity. However, it does not achieve the evaluation of measurement fluctuations in the charging and discharging motor channels. In another related art, during the process of testing the cell capacity of the charging and discharging motor, due to the attenuation of the cell, the capacity measurement value of the charging and discharging motor keeps decreasing. The continuously attenuated capacity measurement value cannot accurately evaluate the fluctuations in the charging and discharging motor channels. Summary of the invention

[0004] In view of the above problems, the present application provides an evaluation method, device, equipment and medium for a battery testing device, which can more accurately evaluate the measurement fluctuations of the charging and discharging motor channels.

[0005] In a first aspect, the present application provides an evaluation method for a battery testing device, and the fluctuation evaluation method for a battery testing device includes: obtaining battery capacity sequence data, the battery capacity sequence data is obtained by a battery testing device performing a capacity test on a battery, and the battery capacity sequence data represents the relationship between the battery capacity and the number of charge and discharge cycles; fitting the battery capacity sequence data to obtain fitting sequence data; based on the fitting sequence data, compensating the battery capacity sequence data to eliminate capacity attenuation information in the battery capacity sequence data to obtain compensated sequence data; based on the fluctuation information of the compensated sequence data, obtaining a fluctuation evaluation result of the battery testing device.

[0006] In the technical solution of the embodiment of the present application, the battery capacity data is fitted to obtain a benchmark for data compensation, and the battery capacity sequence data is compensated based on the fitted sequence data. The compensation can eliminate the attenuation of the battery during the capacity cycle, facilitate the analysis of the repeatability of the channel measurement, and evaluate the measurement fluctuations of the charging and discharging machine channels.

[0007] In some embodiments, based on the fitting sequence data, the battery capacity sequence data is compensated to eliminate the capacity attenuation information in the battery capacity sequence data to obtain the compensated sequence data, including: for any current data point in the battery capacity sequence data, determining a compensation value corresponding to the current data point based on the fitting sequence data; based on the compensation value, compensating the current data point in the battery capacity sequence data to obtain a compensated data point; based on the compensated data point, obtaining the compensated sequence data.

[0008] In the technical solution of the embodiment of the present application, the compensation value corresponding to the current data point is determined based on the fitting sequence data, and the current data point in the battery capacity sequence data is compensated to obtain the compensated sequence data. In the above method, the fitting sequence data can better reflect the attenuation trend of the battery during the charging cycle test, and the compensation value corresponding to the current data point is obtained by using the fitting sequence data as the reference data for compensation, thereby improving the compensation effect.

[0009] In some embodiments, each data point includes a battery capacity and a number of charge and discharge cycles; determining a compensation value corresponding to the current data point based on the fitting sequence data includes: determining a benchmark data point from the fitting sequence data; determining a difference between the battery capacity corresponding to the current data point and the battery capacity corresponding to the benchmark data point; and obtaining a compensation value corresponding to the current data point based on the difference.

[0010] In the technical solution of the embodiment of the present application, the difference between the battery capacity corresponding to the current data point and the battery capacity corresponding to the reference data point is determined by determining the reference data point from the fitting sequence data, thereby obtaining the compensation value corresponding to the current data point based on the difference. In the above method, the reference data point is determined from the fitting sequence data for compensation, which simplifies the calculation method while ensuring accuracy.

[0011] In some embodiments, based on the compensation value, the current data point in the battery capacity sequence data is compensated to obtain the compensated data point, including: adding the compensation value to the battery capacity corresponding to the current data point in the battery capacity sequence data to obtain the compensated data point.

[0012] In the technical solution of the embodiment of the present application, the battery capacity corresponding to the current data point in the battery capacity sequence data is added with a compensation value to obtain a compensated data point. The compensated data still retains the measurement fluctuation, and the compensated data has no attenuation phenomenon, which can more accurately evaluate the measurement fluctuation of the charging and discharging machine channel.

[0013] In some embodiments, the battery capacity sequence data includes at least one group of sequence data, and the battery capacity sequence data corresponds to at least one test process; fitting the battery capacity sequence data to obtain fitted sequence data includes: clustering the battery capacity sequence data to obtain at least one group of sequence data; fitting any one group of sequence data in at least one group of sequence data to obtain fitted sequence data.

[0014] In the technical solution of the embodiment of the present application, the battery capacity sequence data is obtained by testing the battery capacity through a test process. When performing a capacity cycle test, different test processes lead to obvious differences in capacity measurement results. Through the clustering algorithm, the data of different test processes can be identified to ensure the accuracy of the measurement results.

[0015] In some embodiments, the battery capacity sequence data is clustered to obtain at least one group of sequence data, including: traversing each data point in the battery capacity sequence data, for the traversed current data point, based on the distance between the current data point and each subsequent data point in the multiple subsequent data points, determining a target data point from the multiple subsequent data points; adding the target data point to the group where the current data point is located, thereby obtaining at least one group of sequence data.

[0016] In the technical solution of the embodiment of the present application, since the data information of different test processes is quite different, the data points are grouped by calculating the distance between the current data point and multiple subsequent data points, and the data points obtained through the same test process during the capacity cycle are identified, which facilitates the analysis of data points belonging to the same test process and improves the accuracy of the analysis.

[0017] In some embodiments, each data point includes battery capacity and charge and discharge cycles, and the charge and discharge cycles corresponding to the subsequent data point are greater than the charge and discharge cycles corresponding to the current data point; the target data point includes the subsequent data point with the smallest distance from the current data point among multiple subsequent data points.

[0018] In the technical solution of the embodiment of the present application, the target data point is determined by calculating the subsequent data point with the smallest distance from the current data point, so that the probability that the determined target data point and the current data point belong to the same test process is greater, thereby improving the effect of data clustering, and the calculation method is simple and has high accuracy.

[0019] In some embodiments, fitting the battery capacity sequence data to obtain fitting sequence data includes: determining stationary subsequence data from the battery capacity sequence data; and fitting the stationary subsequence data to obtain fitting sequence data.

[0020] In the technical solution of the embodiment of the present application, due to the large variation in the measured values ​​of the first few processes during the capacity cycle, such as when a fresh battery cell is tested for the first time, there is an unstable data area, which will affect the fitting effect when used for fitting. Therefore, by determining the stationary subsequence data from the battery capacity sequence data, fitting the stationary subsequence data, and obtaining the fitting sequence data, this method can improve the fitting effect, thereby improving the accuracy of evaluating the measurement fluctuations of the charging and discharging machine channels.

[0021] In some embodiments, determining stationary subsequence data from battery capacity sequence data includes: traversing each data point in the battery capacity sequence data, for the traversed current data point, determining a preset number of subsequent data points corresponding to the current data point, fitting the current data point and the preset number of subsequent data points to obtain a fitting line, thereby obtaining multiple fitting lines; determining a target fitting line from the multiple fitting lines; and determining the current data point and the preset number of subsequent data points corresponding to the target fitting line as stationary subsequence data.

[0022] In the technical solution of the embodiment of the present application, by fitting the current data point and a preset number of subsequent data points, multiple fitting lines are obtained, and the most satisfactory target fitting line can be selected according to corresponding conditions to ensure that the data points corresponding to the target fitting line are more stable.

[0023] In some embodiments, determining a target fitting line from a plurality of fitting lines includes at least one of the following: for any one of the plurality of fitting lines, determining the distance values ​​between a current data point and a preset number of subsequent data points corresponding to the fitting line and the fitting line, and determining the target fitting line from the plurality of fitting lines based on the distance values ​​corresponding to each fitting line; determining the target fitting line from the plurality of fitting lines based on the goodness of fit of each fitting line.

[0024] In the technical solution of the embodiment of the present application, by determining the distance values ​​between the current data point corresponding to the fitting line and a preset number of subsequent data points and the fitting line, and / or determining the target fitting line based on the goodness of fit of each fitting line, the accuracy of selecting the target fitting line is improved, thereby ensuring that the data points corresponding to the target fitting line are more stable.

[0025] On the other hand, the present application provides an evaluation device for a battery testing device, and the fluctuation evaluation device for the battery testing device includes: an acquisition module, used to acquire battery capacity sequence data, the battery capacity sequence data is obtained by the battery testing device performing capacity testing on the battery, and the battery capacity sequence data represents the relationship between the battery capacity and the number of charge and discharge cycles; a fitting module, used to fit the battery capacity sequence data to obtain fitting sequence data; a compensation module, used to perform compensation processing on the battery capacity sequence data based on the fitting sequence data to eliminate the capacity attenuation information in the battery capacity sequence data to obtain compensated sequence data; an acquisition module, used to obtain a fluctuation evaluation result of the battery testing device based on the fluctuation information of the compensated sequence data.

[0026] On the other hand, the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method of any of the above-mentioned embodiments when executing the computer program.

[0027] On the other hand, the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the method of any of the above-mentioned embodiments are implemented.

[0028] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Moreover, the same reference numerals are used throughout the drawings to represent the same components. In the drawings:

[0030] Figure 1 A compensation model test diagram of a capacity prediction residual is shown;

[0031] Figure 2 A flow chart of an evaluation method for a battery testing device provided in an embodiment of the present application;

[0032] Figure 3 A cycle capacity test diagram of an embodiment of the present application is shown;

[0033] Figure 4 A multi-process capacity cycle test diagram of an embodiment of the present application is shown;

[0034] Figure 5A capacity cycle process clustering algorithm test diagram of an embodiment of the present application is shown;

[0035] Figure 6 A schematic diagram showing a process for determining a capacity cycle stability interval according to an embodiment of the present application is shown;

[0036] Figure 7 A schematic diagram showing a capacity cycle stability interval of an embodiment of the present application is shown;

[0037] Figure 8 A schematic diagram of capacity cycle test compensation simulation according to an embodiment of the present application is shown;

[0038] Fig. 9 A block diagram of an evaluation device of a battery testing device provided in another embodiment of the present application is shown. DETAILED DESCRIPTION

[0039] The following embodiments of the technical solution of the present application are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application, and are therefore only used as examples, and cannot be used to limit the scope of protection of the present application.

[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians 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" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0041] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0042] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0043] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0044] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0045] In the description of the embodiments of the present application, the technical terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, which are only for the convenience of describing the embodiments of the present application and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the embodiments of the present application.

[0046] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0047] With the popularity of new energy vehicles, the capacity of battery cells has received more and more attention. The charger and discharger plays a very important role in the measurement of battery cell capacity. How to accurately evaluate the measurement fluctuation of the charger and discharger channel has become an important technical issue.

[0048] In the related art, a compensation model for capacity prediction residuals is proposed, which can effectively compensate for local capacity prediction errors and improve the accurate prediction of new energy vehicle battery capacity. However, it does not achieve the evaluation of measurement fluctuations in the charging and discharging motor channels. In another related art, during the process of testing the cell capacity of the charging and discharging motor, due to the attenuation of the cell, the capacity measurement value of the charging and discharging motor keeps decreasing. The continuously attenuated capacity measurement value cannot accurately evaluate the fluctuations in the charging and discharging motor channels.

[0049] Figure 1 A compensation model test diagram of capacity prediction residual is shown.

[0050] like Figure 1 The compensation model for the capacity prediction residual shown can effectively compensate for the local capacity prediction error, improve the accurate prediction of the battery capacity of new energy vehicles, and thus improve the performance and reliability of new energy vehicles. First, using the sequence-to-sequence neural network, with the early (first 20%-50%) label capacity and feature data as input, and the future label capacity sequence as output, a power battery capacity attenuation trajectory prediction model is established, and the prediction error of the model is defined as △y. Then, using the Gaussian process regression algorithm, with the month, the highest cell temperature and the lowest cell temperature as input, and the prediction error △y of the sequence-to-sequence model as output, a compensation model for the power battery capacity prediction residual is established. This regression model can only perform single-point estimation. In order to predict the future capacity residual, it is necessary to know the future highest cell temperature and the lowest cell temperature. In actual operation, it can be set according to the temperature data of historical years. The final capacity attenuation prediction result is equal to the result of the sequence-to-sequence model plus the result of the Gaussian process regression model.

[0051] like Figure 1 As shown, the horizontal axis is the number of months and the vertical axis is the capacity (Ah). Figure 1 The circle in represents the actual reference value, and the star represents the final predicted value, that is, the final capacity decay prediction result mentioned above.

[0052] The above method fails to evaluate the measurement fluctuation of the charging and discharging motor channel, and during the process of testing the battery cell capacity, the capacity measurement value of the charging and discharging motor keeps decreasing due to the battery cell attenuation. The continuously attenuated capacity measurement value cannot accurately evaluate the fluctuation of the charging and discharging motor channel. Therefore, the present application provides a fluctuation evaluation method for a battery testing device, proposes an algorithm for seeking a stable capacity attenuation area, and then proposes a method for compensating for the stable attenuation area, clustering the capacities of different cycle numbers into the same cycle, and one cycle number usually corresponds to one test process. Therefore, the battery capacity measurement values ​​of different test processes can be distinguished by clustering, and then the repeatability of the capacity measurement (that is, the repeatability of the charging and discharging motor channel) is calculated to evaluate the measurement fluctuation of the charging and discharging motor channel.

[0053] Figure 2 A flow chart of an evaluation method for a battery testing device provided in an embodiment of the present application.

[0054] like Figure 2 As shown, the evaluation method 200 of the battery testing device provided in the embodiment of the present application includes, for example, steps S210 - S240 .

[0055] Step S210, obtaining battery capacity sequence data, the battery capacity sequence data is obtained by a battery testing device performing a capacity test on a battery, and the battery capacity sequence data represents the relationship between the battery capacity and the number of charge and discharge cycles.

[0056] For example, the battery testing device includes a charger and a discharger, and the battery testing device can perform multiple charge and discharge tests on the battery to obtain battery capacity sequence data. Taking the charging test as an example, each charging test can generate a data point, and multiple data points are obtained after multiple cycle charging tests on the battery, each data point including the battery capacity and the number of charge and discharge cycles. Multiple data points constitute the battery capacity sequence data.

[0057] Step S220, fitting the battery capacity sequence data to obtain fitting sequence data.

[0058] Exemplarily, the battery capacity sequence data includes a plurality of discrete data points, and the plurality of discrete data points may be fitted to obtain fitted sequence data, wherein the fitting includes straight line fitting, curve fitting, and the like.

[0059] Step S230 , based on the fitting sequence data, compensation processing is performed on the battery capacity sequence data to eliminate capacity decay information in the battery capacity sequence data, and compensated sequence data is obtained.

[0060] Exemplarily, the battery capacity in the battery capacity sequence data generally shows a decay trend as the number of charge and discharge cycles changes. For example, the battery capacity generally decreases as the number of charge and discharge cycles increases. By performing compensation processing on the battery capacity sequence data, the capacity decay information in the battery capacity sequence data can be eliminated. For example, the fitting sequence data reflects the capacity decay trend or capacity decay information. The battery capacity sequence data can be processed with the fitting sequence data as a reference benchmark to remove the capacity decay information in the battery capacity sequence data and obtain the compensated sequence data.

[0061] Step S240 , obtaining a fluctuation evaluation result of the battery testing device based on the fluctuation information of the compensated sequence data.

[0062] Exemplarily, the fluctuation information of the compensated sequence data includes, for example, the degree of change of the data, the fluctuation information of the data, the frequency of change of the data, etc., and the fluctuation evaluation result of the battery testing device can be obtained based on the degree of change of the data, the fluctuation information of the data, the frequency of change of the data, etc. For example, when the degree of change of the data is drastic, the fluctuation of the data is obvious, and the frequency of change of the data is large, it means that the fluctuation of the battery testing device is serious.

[0063] In the technical solution of the embodiment of the present application, the battery capacity data is fitted to obtain a benchmark for data compensation, and the battery capacity sequence data is compensated based on the fitted sequence data. The compensation can eliminate the attenuation of the battery during the capacity cycle, facilitate the analysis of the repeatability of the channel measurement, and evaluate the measurement fluctuations of the charging and discharging machine channels.

[0064] Figure 3 A cycle capacity test diagram of an embodiment of the present application is shown.

[0065] like Figure 3 As shown, Figure 3 The horizontal axis is the number of cycles (i.e., the number of battery charge and discharge cycles, which is also called the number of charge and discharge cycles), and the vertical axis is the battery capacity obtained by the test. After multiple cycle charging tests, the battery capacity will be attenuated, and the battery capacity sequence containing attenuation information is difficult to directly reflect the measurement fluctuations of the battery testing device. Therefore, the battery capacity sequence data is compared with the fitting sequence data, and the battery capacity sequence data is compensated according to certain calculation rules to remove the battery attenuation information in the battery capacity sequence data and obtain the compensated sequence data.

[0066] Exemplarily, after compensating the battery capacity sequence data, the obtained sequence data does not have the attenuation phenomenon of the battery, and based on the fluctuation information of the compensated sequence data, the fluctuation evaluation result of the battery testing device is obtained.

[0067] Figure 4 A multi-process capacity cycle test diagram of an embodiment of the present application is shown. Figure 5 A capacity cycle process clustering algorithm test diagram of an embodiment of the present application is shown.

[0068] The battery testing device includes multiple test processes. For example, when the battery testing device includes a charger and discharger, the charger and discharger may include multiple test channels, and each test channel may implement a test process. In the process of cycle capacity testing of the battery, multiple test processes are often alternated, and the test control logic of each test process may be different. Therefore, each test process (test channel) of the battery testing device can be evaluated for fluctuations. The battery capacity data obtained through different test processes cannot be directly compensated and evaluated for repeatability, so it is necessary to first identify the capacity data of the same test process.

[0069] When testing the number of charge and discharge cycles each time, a test process may be randomly selected for testing. Therefore, the battery capacity sequence data obtained after multiple charging cycle tests includes data points obtained based on multiple test processes. For each data point, each subsequent data point is traversed, and its distance on the two-dimensional plane is calculated to obtain the data point closest to the data point, and it is determined to be a data point of the same test process, thereby achieving a clustering effect.

[0070] The battery capacity sequence data includes at least one group of sequence data, and the battery capacity sequence data corresponds to at least one test process; fitting the battery capacity sequence data to obtain fitting sequence data includes: clustering the battery capacity sequence data to obtain at least one group of sequence data; fitting any one group of sequence data in at least one group of sequence data to obtain fitting sequence data.

[0071] In one example, at least one set of sequence data is obtained by performing a capacity test on a battery through at least one test process in a one-to-one correspondence, that is, each set of sequence data corresponds to one test process.

[0072] For example, Figure 4 As shown, the horizontal axis represents the number of charge and discharge cycles (i.e., the number of charge and discharge cycles), and the vertical axis represents the battery capacity. Figure 4 A plurality of discrete data points are shown in FIG. 1 , where the plurality of data points are obtained through a plurality of test processes, and the plurality of discrete data points constitute battery capacity sequence data. Figure 4 Each charge and discharge cycle number (i.e., the number of charge and discharge cycles) corresponding to the horizontal axis usually corresponds to only one data point, indicating that each charging test randomly or alternately selects a test process from multiple test processes for testing.

[0073] Take two test processes as an example. Figure 4 The data point corresponding to the number of charge and discharge cycles (i.e., the number of charge and discharge cycles) is 1 is located at Figure 4 At the point where the data points above are clustered, the data point corresponding to the number of charge and discharge cycles (i.e., the number of charge and discharge cycles) is 2 is located at Figure 4 The data points below are clustered. Therefore, the battery capacity sequence data channels obtained by the test are for multiple test processes, so it is necessary to cluster the battery capacity sequence data in order to distinguish the data points corresponding to different test processes. The data points clustered above and the data points clustered below are obtained from two test processes respectively. Figure 4 For example, it is necessary to divide the multiple data points in the battery capacity sequence into two groups, each group corresponds to a test process, and the capacity test results are different, so it is necessary to use an algorithm (clustering algorithm) to identify the tests of different processes. The clustering algorithm can be used to obtain Figure 5 , multiple data points in the battery capacity sequence data are divided into two groups corresponding to the two test processes.

[0074] For ease of understanding, Figure 4 and Figure 5 The data of different test processes are quite different. However, in some cases, if the test logic of different test processes is not very different, the data points obtained from different test processes may be merged and difficult to distinguish. Therefore, it is very necessary to distinguish them based on clustering algorithms.

[0075] In the technical solution of the embodiment of the present application, the battery capacity sequence data is obtained by testing the battery capacity through a test process. When performing a capacity cycle test, different test processes lead to obvious differences in capacity measurement results. Through the clustering algorithm, the data of different test processes can be identified to ensure the accuracy of the measurement results.

[0076] Exemplarily, clustering the battery capacity sequence data to obtain at least one set of sequence data includes: traversing each data point in the battery capacity sequence data, and for the traversed current data point, determining a target data point from multiple subsequent data points based on the distance between the current data point and each subsequent data point in multiple subsequent data points; adding the target data point to the group where the current data point is located, thereby obtaining at least one set of sequence data. Each data point includes a battery capacity and a number of charge and discharge cycles, and the number of charge and discharge cycles corresponding to the subsequent data point is greater than the number of charge and discharge cycles corresponding to the current data point; the target data point includes the subsequent data point with the smallest distance from the current data point among the multiple subsequent data points.

[0077] For example, for multiple data points included in the battery capacity sequence data, traverse from the first data point based on the number of charge and discharge cycles, and the first data point is, for example, the point with the smallest number of charge and discharge cycles. data point (current data point), The coordinates of the data points on the two-dimensional plane are , at the data point According to the distance formula between two points , find the data points one by one The distance to each subsequent data point is obtained , data points For example, the data point corresponding to the maximum number of charging cycles, that is, the last point. For a data point (current data point), determine all data points whose charge and discharge cycles are greater than the charge and discharge cycles corresponding to the current data point. For example, all data points include and calculate the current data point and The distance between each data point is obtained .

[0078] Next, from Determine the data point with the smallest distance, assuming it is the data point ,Right now Minimum, the data point Add to Data Point The group they belong to, for example, each group is stored in a list, then the data points Add to Data Point At the end of the list, repeat the above steps until the last point, and then you can get the list , for the data points skipped in the above steps, traverse again according to this method to get the list Finally, for example, two tables are obtained, each representing two test processes, and the points in each table are a group. Figure 4 The data points corresponding to the two test processes in , after clustering algorithm, can be obtained Figure 5 , Figure 5 The data points clustered in the upper middle area are one group, and the data points clustered in the lower middle area are another group.

[0079] The above clustering algorithm is improved compared with the clustering algorithm of the related art. The clustering algorithm is suitable for time series data and clusters scattered points towards a curve. The clustering method of the related art usually clusters scattered points towards a central point, which is not suitable for time series data.

[0080] In the technical solution of the embodiment of the present application, since the data information of different test processes is quite different, the data points are grouped by calculating the distance between the current data point and multiple subsequent data points, and the data points obtained through the same test process during the capacity cycle are identified, which facilitates the analysis of data points belonging to the same test process and improves the accuracy of the analysis.

[0081] During the capacity cycle, such as when a fresh battery cell is tested for the first time, the measured values ​​of the first few processes vary greatly and there is an unstable data area. This unstable data area will affect the fitting effect when used for fitting. Therefore, by determining the stable subsequence data from the battery capacity sequence data and fitting the stable subsequence data to obtain the fitting sequence data, this method can improve the fitting effect and thus improve the accuracy of the measurement fluctuation in evaluating the charging and discharging machine channels.

[0082] Figure 6 A schematic diagram of a capacity cycle stability interval determination process according to an embodiment of the present application is shown.

[0083] Fitting the battery capacity sequence data to obtain fitting sequence data includes: determining stationary subsequence data from the battery capacity sequence data; fitting the stationary subsequence data to obtain fitting sequence data. Exemplarily, after clustering the battery capacity sequence data to obtain multiple groups, any one group can be selected from the multiple groups to calculate the stationary subsequence data of the group, or the stationary subsequence data can be calculated for the multiple groups respectively, and subsequent compensation can also be performed for each group.

[0084] Exemplarily, determining stationary subsequence data from battery capacity sequence data includes: traversing each data point in the battery capacity sequence data, for a current data point traversed, determining a preset number of subsequent data points corresponding to the current data point, fitting the current data point and the preset number of subsequent data points to obtain a fitting line, thereby obtaining multiple fitting lines; determining a target fitting line from the multiple fitting lines; and determining the current data point and the preset number of subsequent data points corresponding to the target fitting line as stationary subsequence data.

[0085] For example, Figure 6 As shown, Figure 6 The horizontal axis is the number of charge and discharge cycles (i.e. the number of charge and discharge cycles), and the vertical axis is the battery capacity. Figure 6 The fitting line and the battery capacity sequence data are shown respectively. For multiple data points in the battery capacity sequence data, traverse from the first data point based on the number of charge and discharge cycles. The first data point is, for example, the point with the smallest number of charge and discharge cycles. When there are data points (current data points), take the subsequent consecutive points ( is the preset number, m is the algorithm's hyperparameter, which can be adjusted according to the actual effect), the m data points including the current data point are fitted by the least square method to obtain a fitting line, and the fitting line is taken as the straight line For example, for each current data point, a corresponding fitting line is obtained, thereby obtaining multiple fitting lines.

[0086] Next, a target fitting line is determined from the multiple fitting lines. There are multiple ways to determine the target fitting line, and the present application embodiment uses two ways as examples for description.

[0087] The first method is to determine the distance values ​​between the current data point and a preset number of subsequent data points corresponding to any one of the multiple fitting lines and the fitting line, and determine the target fitting line from the multiple fitting lines based on the distance values ​​corresponding to each fitting line.

[0088] For example, calculate the distances between the m data points and the fitting line, obtain m distances, and use these The distances are added together and recorded as The distances and corresponding to the data points are used Continue to traverse points, and get the distance and , until you reach point( is the last point), confirm The smallest value among all values, assumed to be ,but The corresponding fitting line is the target fitting line, and the obtained capacity cycle stable area is from point Start continuous points, and the m data points corresponding to the target fitting line are determined as stationary subsequence data.

[0089] The second method is to determine the target fitting line from multiple fitting lines based on the goodness of fit of each fitting line. In addition to selecting the most stable region by sum of distances, the goodness of fit of each fitting line can also be output, and the fitting line with the highest goodness of fit can be selected as the target line based on the goodness of fit, and the m data points corresponding to the target fitting line can be determined as the stable subsequence data.

[0090] Figure 7 A schematic diagram of the capacity cycle stability interval of an embodiment of the present application is shown.

[0091] like Figure 7 As shown, taking m=30 as an example, the 30 data points in the middle part are stationary subsequence data.

[0092] In the technical solution of the embodiment of the present application, by determining the stable subsequence data from the battery capacity sequence data, fitting the stable subsequence data, and obtaining the fitting sequence data, this method can improve the fitting effect, thereby improving the accuracy of the measurement fluctuation of the evaluation of the charging and discharging machine channel. By fitting the current data point and the preset number of subsequent data points, a plurality of fitting lines can be obtained, and the target fitting line that best meets the conditions can be selected according to the corresponding conditions to ensure that the data points corresponding to the target fitting line have a higher stability. And by determining the distance value between the current data point and the preset number of subsequent data points corresponding to the fitting line and the fitting line, and / or determining the target fitting line based on the goodness of fit of each fitting line, the selection accuracy of the target fitting line is improved, thereby ensuring that the data points corresponding to the target fitting line have a higher stability. Since the charging and discharging machine has a capacity decay phenomenon when testing the cycle capacity, the cell capacity value decays continuously as the number of cycles increases, and the repeatability analysis of the charging and discharging machine channel cannot be directly performed, so it is necessary to compensate the battery capacity sequence data. For example, the obtained stable subsequence data can be compensated.

[0093] Figure 8 A schematic diagram of capacity cycle test compensation simulation according to an embodiment of the present application is shown.

[0094] For example, Figure 8 The compensation can be performed only on the stationary subsequence data, and of course, can also be performed on the original fitting sequence data. Specifically, based on the fitting sequence data, the battery capacity sequence data is compensated to eliminate the capacity decay information in the battery capacity sequence data to obtain the compensated sequence data, including: for any current data point in the battery capacity sequence data, the compensation value corresponding to the current data point is determined based on the fitting sequence data.

[0095] Exemplarily, each data point includes a battery capacity and a number of charge and discharge cycles; determining a compensation value corresponding to the current data point based on the fitting sequence data includes: determining a benchmark data point from the fitting sequence data; determining a difference between the battery capacity corresponding to the current data point and the battery capacity corresponding to the benchmark data point; and obtaining a compensation value corresponding to the current data point based on the difference.

[0096] For example, Figure 8 As shown, Figure 8 The fitted line, battery capacity series and compensation value data are shown respectively. The orange curve is the line connecting the orange points, and the orange points are the actual observed capacity coordinates (stable area part). The coordinates of the data points are calculated using It can be seen that it shows an overall attenuation, but there is a certain fluctuation trend, which is the actual observed cycle curve. The blue straight line is the fitting line of the observed capacity (based on the point fitting in the stable area), that is, the fitting line of the orange point. Assume that its equation is , and the corresponding data points on the straight line are , , … Indicates that the points on the blue line have the same Coordinates, yes .for Figure 8 The fitting straight line in the figure shows that the capacity value of the first cycle is The difference between the test capacity values The calculation process is shown in formula (1). Based on the difference, the The compensation value corresponding to each data point.

[0097]

[0098]

[0099]

[0100]

[0101] In the technical solution of the embodiment of the present application, the difference between the battery capacity corresponding to the current data point and the battery capacity corresponding to the reference data point is determined by determining the reference data point from the fitting sequence data, thereby obtaining the compensation value corresponding to the current data point based on the difference. In the above method, the reference data point is determined from the fitting sequence data for compensation, which simplifies the calculation method while ensuring accuracy.

[0102] Based on the compensation value, the current data point in the battery capacity sequence data is compensated to obtain a compensated data point.

[0103] Exemplarily, based on the compensation value, compensating the current data point in the battery capacity sequence data to obtain the compensated data point includes: adding the compensation value to the battery capacity corresponding to the current data point in the battery capacity sequence data to obtain the compensated data point.

[0104] For example, Figure 8 As shown in the figure, determine the benchmark data point from the fitting sequence data. For example, take the first data point on the fitting line (the number of charge and discharge cycles is 1) as the benchmark data point. Of course, other data points on the fitting line can also be used as benchmark data points. It is necessary to convert all the capacity test values ​​of the battery capacity sequence data (on the orange line) to the benchmark data point, so the original observation value needs to be converted to the original observation value. (orange point), each point plus the compensation value corresponding to the data point , we can get the attenuation-corrected curve. Assume that the coordinates of the corrected data points are , , … express, That is the compensated data point, which can be expressed as formula (2):

[0105]

[0106] (2)

[0107]

[0108] In the technical solution of the embodiment of the present application, the battery capacity corresponding to the current data point in the battery capacity sequence data is added with a compensation value to obtain a compensated data point. The compensated data still retains the measurement fluctuation, and the compensated data has no attenuation phenomenon, which can more accurately evaluate the measurement fluctuation of the charging and discharging machine channel.

[0109] Next, based on the compensated data points, compensated sequence data are obtained.

[0110] For example, Figure 8As shown, after the above calculation, we can get Figure 8 The red curve in the figure, the data points on the red curve constitute the compensated sequence data, that is, the capacity curve after attenuation correction. It can be seen that the red curve has the same fluctuation as the orange curve, but there is no attenuation phenomenon.

[0111] Finally, based on the fluctuation information of the compensated sequence data, the fluctuation evaluation result of the battery testing device is obtained.

[0112] In one example, the fluctuation information of the compensated sequence data can be characterized by the data deviation information of the compensated sequence data, and the data deviation information may include standard deviation, variance, etc. Taking the standard deviation as an example, for example, the larger the standard deviation, the more obvious the data fluctuation. Furthermore, the fluctuation of the battery testing device can also be evaluated based on the standard deviation, for example, the larger the standard deviation, the greater the fluctuation of the battery testing device. The standard deviation can also be further used as the fluctuation evaluation result of the battery testing device.

[0113] For example, Figure 8 As shown, according to the compensated capacity curve (i.e. Figure 8 The red curve in the figure shows the standard deviation of the corrected capacity values ​​on the red curve. , the standard deviation It can be used to evaluate the test fluctuation of the battery test device. The test fluctuation of the battery test device can represent the error or fluctuation when the battery test device is used to repeatedly test the same battery.

[0114] In the embodiments of the present application, the capacity values ​​of different cycle numbers are compensated to the same cycle number, so as to evaluate the measurement fluctuation of the charging and discharging machine channel, and then obtain the capacity repeatability performance of the charging and discharging machine channel.

[0115] In the technical solution of the embodiment of the present application, the compensation value corresponding to the current data point is determined based on the fitting sequence data, and the current data point in the battery capacity sequence data is compensated to obtain the compensated sequence data. In the above method, the fitting sequence data can better reflect the attenuation trend of the battery during the charging cycle test, and the compensation value corresponding to the current data point is obtained by using the fitting sequence data as the reference data for compensation, thereby improving the compensation effect.

[0116] The present application embodiment provides an evaluation device 900 for a battery testing device. Fig. 9 , the evaluation device 900 of the battery testing device comprises:

[0117] An acquisition module 910 is used to acquire battery capacity sequence data, wherein the battery capacity sequence data is obtained by a battery testing device performing a capacity test on a battery, and the battery capacity sequence data represents a relationship between the battery capacity and the number of charge and discharge cycles;

[0118] A fitting module 920 is used to fit the battery capacity sequence data to obtain fitting sequence data;

[0119] A compensation module 930 is used to perform compensation processing on the battery capacity sequence data based on the fitting sequence data to eliminate the capacity decay information in the battery capacity sequence data, and obtain compensated sequence data;

[0120] The obtaining module 940 is used to obtain the fluctuation evaluation result of the battery testing device based on the fluctuation information of the compensated sequence data.

[0121] It can be understood that for the detailed description of the evaluation device 900 for the battery testing device, reference can be made to the above description of the evaluation method for the battery testing device.

[0122] Exemplarily, the compensation module 930 is also used to: for any current data point in the battery capacity sequence data, determine the compensation value corresponding to the current data point based on the fitting sequence data; based on the compensation value, compensate the current data point in the battery capacity sequence data to obtain a compensated data point; based on the compensated data point, obtain compensated sequence data.

[0123] Exemplarily, the compensation module 930 is also used to: determine a reference data point from the fitting sequence data; determine the difference between the battery capacity corresponding to the current data point and the battery capacity corresponding to the reference data point; and obtain a compensation value corresponding to the current data point based on the difference.

[0124] Exemplarily, the compensation module 930 is further configured to: add the compensation value to the battery capacity corresponding to the current data point in the battery capacity sequence data to obtain a compensated data point.

[0125] Exemplarily, the battery capacity sequence data includes at least one group of sequence data, and the battery capacity sequence data corresponds to at least one test process; the fitting module 920 is also used to: cluster the battery capacity sequence data to obtain at least one group of sequence data; fit any one group of sequence data in the at least one group of sequence data to obtain fitted sequence data.

[0126] Exemplarily, the fitting module 920 is also used to: traverse each data point in the battery capacity sequence data, and for the traversed current data point, determine a target data point from multiple subsequent data points based on the distance between the current data point and each subsequent data point in the multiple subsequent data points; add the target data point to the group where the current data point is located, so as to obtain at least one group of sequence data.

[0127] Exemplarily, each data point includes battery capacity and charge and discharge cycles, and the charge and discharge cycles corresponding to the subsequent data point are greater than the charge and discharge cycles corresponding to the current data point; the target data point includes the subsequent data point with the smallest distance from the current data point among the multiple subsequent data points.

[0128] Exemplarily, the fitting module 920 is further used to: determine stationary subsequence data from the battery capacity sequence data; and fit the stationary subsequence data to obtain fitting sequence data.

[0129] Exemplarily, the fitting module 920 is also used to: traverse each data point in the battery capacity sequence data, for the traversed current data point, determine a preset number of subsequent data points corresponding to the current data point, fit the current data point and the preset number of subsequent data points to obtain a fitting line, thereby obtaining multiple fitting lines; determine a target fitting line from the multiple fitting lines; and determine the current data point and the preset number of subsequent data points corresponding to the target fitting line as stationary subsequence data.

[0130] Exemplarily, the fitting module 920 is also used to: for any one of the multiple fitting lines, determine the distance values ​​between the current data point and a preset number of subsequent data points corresponding to the fitting line and the fitting line, and determine a target fitting line from the multiple fitting lines based on the distance values ​​corresponding to each fitting line; and determine the target fitting line from the multiple fitting lines based on the goodness of fit of each fitting line.

[0131] An embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method of any of the above embodiments are implemented.

[0132] On the other hand, the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the method of any of the above-mentioned embodiments are implemented.

[0133] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this application, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and editable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or otherwise processing in a suitable manner if necessary, and then stored in a computer memory.

[0134] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0135] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should all be included in the scope of the claims and specification of the present application. In particular, as long as there is no structural conflict, the various technical features mentioned in the various embodiments can be combined in any way. The present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions that fall within the scope of the claims.

Claims

1. A method for evaluating a battery testing device, characterized in that: The method comprises: Acquire battery capacity sequence data, wherein the battery capacity sequence data is obtained by the battery testing device performing a capacity test on the battery, and the battery capacity sequence data represents a relationship between the battery capacity and the number of charge and discharge cycles; Fitting the battery capacity sequence data to obtain fitting sequence data; Determine a reference data point from the fitting sequence data, determine a difference between a battery capacity corresponding to any current data point in the battery capacity sequence data and a battery capacity corresponding to the reference data point, and obtain a compensation value corresponding to the current data point based on the difference; Based on the compensation value, performing compensation processing on the current data point in the battery capacity sequence data to eliminate capacity decay information in the battery capacity sequence data, thereby obtaining compensated sequence data; Based on the fluctuation information of the compensated sequence data, a fluctuation evaluation result of the battery testing device is obtained.

2. The method according to claim 1, characterized in that Each data point includes the battery capacity and the number of charge and discharge cycles.

3. The method according to claim 1, characterized in that The method of performing compensation processing on the current data point in the battery capacity sequence data based on the compensation value to eliminate capacity attenuation information in the battery capacity sequence data to obtain compensated sequence data includes: The battery capacity corresponding to the current data point in the battery capacity sequence data is added with the compensation value to obtain a compensated data point, and based on the compensated data point, the compensated sequence data is obtained.

4. The method according to any one of claims 1 to 3, characterized in that: The battery capacity sequence data includes at least one set of sequence data, and the battery capacity sequence data corresponds to at least one test process; The step of fitting the battery capacity sequence data to obtain fitting sequence data includes: Clustering the battery capacity sequence data to obtain the at least one group of sequence data; Fitting is performed on any one set of sequence data among the at least one set of sequence data to obtain the fitted sequence data.

5. The method according to claim 4, characterized in that The clustering of the battery capacity sequence data to obtain the at least one set of sequence data includes: Traversing each data point in the battery capacity sequence data, for a current data point traversed, based on a distance between the current data point and each subsequent data point in a plurality of subsequent data points, determining a target data point from the plurality of subsequent data points; The target data point is added to the group where the current data point is located, thereby obtaining at least one group of sequence data.

6. The method according to claim 5, characterized in that Each data point includes a battery capacity and a number of charge and discharge cycles, and the number of charge and discharge cycles corresponding to a subsequent data point is greater than the number of charge and discharge cycles corresponding to a current data point; the target data point includes a subsequent data point with the smallest distance from the current data point among the multiple subsequent data points.

7. The method according to any one of claims 5 to 6, characterized in that: The step of fitting the battery capacity sequence data to obtain fitting sequence data includes: Determining stable subsequence data from the battery capacity sequence data; The stationary subsequence data is fitted to obtain the fitted sequence data.

8. The method according to claim 7, characterized in that The step of determining the stationary subsequence data from the battery capacity sequence data comprises: Traversing each data point in the battery capacity sequence data, for the traversed current data point, determining a preset number of subsequent data points corresponding to the current data point, fitting the current data point and the preset number of subsequent data points to obtain a fitting line, thereby obtaining multiple fitting lines; determining a target fitting line from the plurality of fitting lines; The current data point and a preset number of subsequent data points corresponding to the target fitting line are determined as the stationary subsequence data.

9. The method according to claim 8, characterized in that The step of determining a target fitting line from the plurality of fitting lines comprises at least one of the following: For any one of the multiple fitting lines, determine the distance values ​​between a current data point and a preset number of subsequent data points corresponding to the fitting line and the fitting line, and determine a target fitting line from the multiple fitting lines based on the distance values ​​corresponding to each fitting line; Based on the goodness of fit of each fitting line, a target fitting line is determined from the plurality of fitting lines.

10. An evaluation device for a battery testing device, characterized in that: The device comprises: An acquisition module, used for acquiring battery capacity sequence data, wherein the battery capacity sequence data is obtained by the battery testing device performing a capacity test on the battery, and the battery capacity sequence data represents the relationship between the battery capacity and the number of charge and discharge cycles; A fitting module, used for fitting the battery capacity sequence data to obtain fitting sequence data; a compensation module, configured to determine a reference data point from the fitting sequence data, determine a difference between a battery capacity corresponding to any current data point in the battery capacity sequence data and a battery capacity corresponding to the reference data point, obtain a compensation value corresponding to the current data point based on the difference, and perform compensation processing on the current data point in the battery capacity sequence data based on the compensation value to eliminate capacity attenuation information in the battery capacity sequence data, thereby obtaining compensated sequence data; The obtaining module is used to obtain the fluctuation evaluation result of the battery testing device based on the fluctuation information of the compensated sequence data.

11. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

12. 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 9 are implemented.

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