Lithium battery consistency sorting method and device, storage medium and computer equipment

Abstract

The invention relates to the technical field of lithium batteries, and provides a lithium battery consistency sorting method and device, a storage medium and computer equipment, during battery sorting, test data of a lithium battery in a sudden charging pulse test process is collected and used for determining open-circuit voltage and ohmic internal resistance, and an equivalent circuit model is established; the polarization characteristic of the lithium battery is accurately reflected; determining time domain response data of the polarization impedance under the relaxation time scale according to the open-circuit voltage, the ohmic internal resistance and an equivalent circuit model, and further solving to obtain a distribution density function of the polarization resistance, thereby enhancing the explanation capability of batteries in different aging states; performing difference value fitting to obtain a fitting curve of the distribution density function, extracting an AC internal resistance characteristic value from the fitting curve, and reducing data redundancy; and finally, a clustering algorithm is adopted to carry out consistency sorting on a characteristic quantity set obtained by combining the parameters, so that the batteries with similar characteristic quantities are gathered together according to a sorting result, the performance difference is reduced, and the sorting precision is improved.

Description

Technical Field

[0001] The present application relates to the field of lithium battery technology, and in particular to a lithium battery consistency sorting method, device, storage medium and computer equipment. Background Art

[0002] With the rapid development of drone technology, large drones have shown great application potential in many fields such as aerial photography, logistics, and agriculture. In order to meet the high power and long endurance requirements of these drones, battery packs are usually composed of multiple lithium battery cells connected in parallel to build high-voltage or high-power battery systems. However, under long-term operation and complex environmental conditions, the degree of aging of the cells in the battery pack will vary significantly, which in turn affects the energy utilization efficiency, safety, and overall health of the battery pack.

[0003] Currently, in the consistency sorting method of UAV lithium batteries, although the complete constant current and constant voltage charge and discharge test method can accurately obtain various performance indicators of the battery, its testing process is time-consuming and lengthy, which is obviously not efficient enough for high-rate UAV lithium batteries that pursue rapid sorting. Although the sorting method based on battery test curves has improved the accuracy of sorting to a certain extent, its high cost and complex data processing flow have limited its wide application. In addition, when using the traditional lithium battery equivalent circuit model to evaluate the consistency of the battery pack, due to the limitations of the model itself, it is difficult to accurately reflect the changes in the internal characteristics and consistency differences of lithium batteries under different environments and aging conditions.

[0004] In summary, the current lithium battery consistency sorting method has the problems of low testing efficiency and poor economy. In addition, for high-rate drone lithium batteries, there is a lack of equivalent circuit models and effective model parameter identification methods that can accurately reflect the changes in their complex internal characteristics, resulting in poor accuracy in lithium battery consistency sorting. Summary of the invention

[0005] The purpose of the present application is to solve at least one of the above-mentioned technical defects, especially the technical defect that the sorting method in the prior art lacks an equivalent circuit model that can accurately reflect the changes in its complex internal characteristics and an effective model parameter identification method, resulting in poor accuracy in the consistency sorting of lithium batteries.

[0006] The present application provides a lithium battery consistency sorting method, the method comprising:

[0007] Collecting test data of the lithium battery during a sudden charging pulse test, and determining the open circuit voltage and ohmic internal resistance of the lithium battery based on the test data, and establishing an equivalent circuit model of the lithium battery; wherein the test data includes a temperature change value of the lithium battery at a relaxation time scale;

[0008] Determine the time domain response data of the polarization impedance of the lithium battery at the relaxation time scale according to the open circuit voltage, the ohmic internal resistance and the equivalent circuit model, and obtain the distribution density function of the polarization resistance of the lithium battery by solving the time domain response data;

[0009] Performing interpolation fitting on the distribution density function to obtain a fitting curve, and extracting an AC internal resistance characteristic value of the lithium battery from the fitting curve;

[0010] A feature quantity set of the lithium battery is generated according to the temperature change value, the open circuit voltage, the ohmic internal resistance and the AC internal resistance characteristic value, and the feature quantity set is sorted for consistency to obtain a sorting result of the lithium battery.

[0011] Optionally, collecting test data of the lithium battery during a sudden charging pulse test includes:

[0012] Obtain a lithium battery in a fully charged state; the lithium battery is connected in parallel with a voltage sensor and in series with a current sensor and a temperature sensor;

[0013] After the rest time of the lithium battery reaches a preset rest time, applying a sudden current to the lithium battery for charging until the charging time reaches a preset charging time, and then the test is terminated;

[0014] Determine a sampling time sequence of the lithium battery during the test, and respectively collect a voltage sampling sequence, a current sampling sequence, and a temperature sampling sequence corresponding to the sampling time sequence through the voltage sensor, the current sensor, and the temperature sensor;

[0015] The temperature variation value of the lithium battery under the relaxation time scale is calculated based on the temperature sampling sequence, and the test data of the lithium battery is generated according to the temperature variation value, the current voltage sampling sequence, the current sampling sequence and the temperature sampling sequence.

[0016] Optionally, determining the open circuit voltage and ohmic internal resistance of the lithium battery based on the test data includes:

[0017] Extracting multiple sampled voltages of the lithium battery before the sudden current is applied from the test data, and performing mean calculation on each sampled voltage to obtain the open circuit voltage of the lithium battery;

[0018] The sudden current of the lithium battery during the test and the instantaneous voltage when the sudden current is applied are obtained, and the ohmic internal resistance of the lithium battery is calculated based on the sudden current, the instantaneous voltage and the open circuit voltage.

[0019] Optionally, the calculation expression of the equivalent circuit model includes:

[0020]

[0021] In the formula, The time domain expression of the polarization resistance of lithium batteries in the relaxation time scale; Indicates the ohmic internal resistance of the lithium battery; Indicates the sampling current of the lithium battery; Indicates the open circuit voltage of the lithium battery; Indicates the external voltage of the lithium battery.

[0022] Optionally, the calculation expression of the time domain response data includes:

[0023]

[0024] In the formula, It represents the time domain expression of the polarization resistance of lithium battery in the relaxation time scale, and the calculation result is expressed as time domain response data; Indicates the external voltage of the lithium battery, Indicates the open circuit voltage of the lithium battery; Indicates the sudden current of lithium battery; Indicates the ohmic internal resistance of the lithium battery.

[0025] Optionally, obtaining a distribution density function of the polarization resistance of the lithium battery based on the time domain response data includes:

[0026] Determine the impedance frequency domain expression of the lithium battery, and convert the impedance frequency domain expression into an impedance time domain expression using a Fourier transform formula;

[0027] The time domain response data is input into the impedance time domain expression for solution to obtain a distribution density function of the polarization resistance of the lithium battery.

[0028] Optionally, performing interpolation fitting on the distribution density function to obtain a fitting curve includes:

[0029] The distribution density function is piecewise fitted using a piecewise linear interpolation method to obtain a Dirac distribution function corresponding to a plurality of characteristic times of the lithium battery under the relaxation time scale;

[0030] The Dirac distribution functions corresponding to the characteristic times are summed, and a fitting curve of the polarization resistance of the lithium battery at the relaxation time scale is reconstructed according to the summation result.

[0031] The present application also provides a lithium battery consistency sorting device, comprising:

[0032] A pulse test module, used to collect test data of the lithium battery during a sudden charging pulse test, and determine the open circuit voltage and ohmic internal resistance of the lithium battery based on the test data, and establish an equivalent circuit model of the lithium battery; wherein the test data includes the temperature change value of the lithium battery under the relaxation time scale;

[0033] A data solving module, used to determine the time domain response data of the polarization impedance of the lithium battery at the relaxation time scale according to the open circuit voltage, the ohmic internal resistance and the lithium battery equivalent circuit model, and solve the distribution density function of the polarization resistance of the lithium battery based on the time domain response data;

[0034] A feature extraction module, used to perform interpolation fitting on the distribution density function to obtain a fitting curve, and extract the AC internal resistance characteristic value of the lithium battery from the fitting curve;

[0035] A clustering sorting module is used to generate a feature quantity set of the lithium battery according to the temperature change value, the open circuit voltage, the ohmic internal resistance and the AC internal resistance characteristic value, and to perform consistency sorting on the feature quantity set to obtain a sorting result of the lithium battery.

[0036] The present application also provides a storage medium, in which computer-readable instructions are stored. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the lithium battery consistency sorting method as described in any of the above embodiments.

[0037] The present application also provides a computer device, comprising: one or more processors, and a memory;

[0038] The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the lithium battery consistency sorting method described in any one of the above embodiments are performed.

[0039] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0040] The lithium battery consistency sorting method, device, storage medium and computer equipment provided in the present application can automatically collect the test data of the lithium battery during the sudden charging pulse test when sorting the lithium battery for consistency, thereby improving the test efficiency. Then, the open circuit voltage and ohmic internal resistance of the lithium battery can be determined based on the test data, and an equivalent circuit model of the lithium battery can be established to accurately reflect the polarization characteristics of the high-rate lithium battery, thereby improving the model accuracy and mechanism fit. Then, the time domain response data of the polarization impedance at the relaxation time scale can be determined based on the open circuit voltage, ohmic internal resistance and equivalent circuit model, and the polarization resistance of the lithium battery can be obtained by solving the time domain response data. distribution density function, thereby enhancing the ability to interpret batteries in different aging states; then the distribution density function can be interpolated and fitted to obtain a fitting curve, and the AC internal resistance characteristic value of the lithium battery can be extracted from the fitting curve, which can reduce data redundancy and further improve processing efficiency; finally, a feature quantity set of the lithium battery can be generated according to the temperature change value, open circuit voltage, ohmic internal resistance and AC internal resistance characteristic value in the test data, and a clustering algorithm can be used to perform consistency sorting on the feature quantity set to obtain the sorting result of the lithium battery, and then batteries with similar feature quantities can be clustered together according to the sorting result, reducing performance differences and improving sorting accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0042] Figure 1 A schematic diagram of a lithium battery consistency sorting method provided in an embodiment of the present application;

[0043] Figure 2 A curve diagram of a fitting curve of a polarization resistance on a relaxation time scale provided in an embodiment of the present application;

[0044] Figure 3 A logical schematic diagram of a test data sampling process provided in an embodiment of the present application;

[0045] Figure 4 A schematic diagram of the structure of an equivalent circuit model provided in an embodiment of the present application;

[0046] Figure 5 A schematic diagram of the structure of a lithium battery consistency sorting device provided in an embodiment of the present application;

[0047] Figure 6A schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0049] The current lithium battery consistency sorting method has the problems of low testing efficiency and poor economy. In addition, for high-rate drone lithium batteries, there is a lack of equivalent circuit models and effective model parameter identification methods that can accurately reflect the changes in their complex internal characteristics, resulting in poor accuracy in lithium battery consistency sorting.

[0050] Based on this, this application proposes the following technical solutions, see below for details:

[0051] In one embodiment, Figure 1 As shown, Figure 1 A schematic diagram of a lithium battery consistency sorting method provided in an embodiment of the present application; the present application provides a lithium battery consistency sorting method, which specifically includes the following:

[0052] S110: Collect test data of the lithium battery during a sudden charging pulse test, and determine the open circuit voltage and ohmic internal resistance of the lithium battery based on the test data, and establish an equivalent circuit model of the lithium battery; wherein the test data includes a temperature change value of the lithium battery under a relaxation time scale.

[0053] In this step, after determining the lithium battery to be sorted, the computer can perform a sudden charging pulse test on the lithium battery and automatically collect test data of the lithium battery during the test to improve the test efficiency. Then, based on the test data, the open circuit voltage and ohmic internal resistance of the lithium battery can be determined, and an equivalent circuit model of the lithium battery can be established to accurately reflect the polarization characteristics of the high-rate lithium battery and improve the model accuracy and mechanism fit.

[0054] Among them, the sudden charging pulse test is a fast testing method for lithium batteries. It obtains the key characteristic parameters of the battery by suddenly applying a large current charging pulse in a short period of time and then monitoring the voltage, current and temperature response of the battery. Therefore, the present application can automatically collect the key characteristic parameters of the lithium battery during the testing process to form test data.

[0055] Specifically, before testing the lithium battery, the computer device can check the initial state of the lithium battery, including voltage, temperature and power, to ensure that the battery is in a measurable state. After the test is started, the computer device can control the test device to apply a high-rate pulse current to the battery, and collect its key characteristic parameters at a certain frequency during the test, and store the test data after forming it. During the test, the lithium battery undergoes electrochemical reactions, charge transfer and other changes in a short period of time, causing the voltage to change dynamically and enter the polarization stage. Here, the computer device can extract parameter change values ​​in the test data, such as voltage change values ​​and current change values, and then calculate the open circuit voltage and ohmic internal resistance of the lithium battery, which are used to characterize the basic conductive properties of the lithium battery. At the same time, by recording the voltage recovery process after the pulse ends, the computer device can analyze the polarization impedance and relaxation effect of the battery, thereby constructing an equivalent circuit model of the battery.

[0056] It can be understood that in order to more accurately reflect the polarization characteristics of high-rate lithium batteries, the present application can adopt equivalent circuit modeling based on the relaxation time scale to obtain an equivalent circuit model. This model is different from the traditional Rint model or the Thevenin model. It combines the polarization behavior of lithium batteries during pulse charging and can describe the dynamic characteristics of lithium batteries in a more fine-grained manner.

[0057] S120: Determine the time domain response data of the polarization impedance at the relaxation time scale according to the open circuit voltage, the ohmic internal resistance and the equivalent circuit model, and solve the distribution density function of the polarization resistance of the lithium battery based on the time domain response data.

[0058] In this step, after determining the open circuit voltage, ohmic internal resistance and equivalent circuit model through step S110, the computer device can determine the time domain response data of the polarization impedance at the relaxation time scale based on the open circuit voltage, ohmic internal resistance and equivalent circuit model, and solve the distribution density function of the polarization resistance of the lithium battery based on the time domain response data, thereby enhancing the ability to interpret batteries in different aging states.

[0059] Among them, the time domain response data refers to the change data of the characteristics of the lithium battery such as voltage, current or impedance in the time domain, that is, the time dimension, over time after the sudden charging pulse test. Specifically, it describes the dynamic behavior of how the internal electrochemical process of the battery evolves over time after the current pulse input and finally returns to a stable state.

[0060] Specifically, the computer equipment can obtain the dynamic characteristics of the lithium battery after pulse charging through the analysis of open circuit voltage, ohmic internal resistance and equivalent circuit model, especially the time domain response of polarization impedance at the relaxation time scale. Here, the voltage recovery curve of the lithium battery can be modeled through the analysis results, and then the dynamic impedance changes of the lithium battery at different time scales can be extracted to obtain complete time domain response data. Then, the computer equipment can use mathematical transformation and optimization algorithms to extract the change trend of polarization impedance from the time domain response data, and solve the distribution density function of polarization resistance based on this trend, which is used to describe the contribution of polarization impedance at different time constants and reveal the deep mechanism of polarization effect inside lithium batteries.

[0061] More specifically, through the distribution density function, the computer equipment can further analyze the dynamic response characteristics of the lithium battery, especially the changing trends under different aging conditions. Compared with the traditional model, the present application can more accurately fit the polarization behavior of the battery and effectively distinguish the changes in the polarization characteristics of the battery caused by aging. For example, the polarization impedance of a battery in a better health state is more concentrated on a short time scale, while the polarization impedance of a battery with more severe aging will show more obvious dispersion on a longer time scale.

[0062] S130: performing interpolation fitting on the distribution density function to obtain a fitting curve, and extracting an AC internal resistance characteristic value of the lithium battery from the fitting curve.

[0063] In this step, after the distribution density function is generated in step S120, the computer device can perform interpolation fitting on the distribution density function to obtain a fitting curve, and extract the AC internal resistance characteristic value of the lithium battery from the fitting curve, which can reduce data redundancy and further improve processing efficiency.

[0064] It is understandable that since the distribution density function is obtained through discrete calculation of test data, there are problems such as measurement errors, data fluctuations and uneven distribution of discrete points. Therefore, computer equipment can use interpolation fitting methods to smooth the distribution density function to form a more accurate fitting curve. While ensuring that the data trend remains unchanged, it can eliminate noise and abnormal fluctuations in the data, improve calculation stability, and more accurately characterize the changing trend of polarization impedance on different time scales, making the analysis of the polarization characteristics of the battery more precise and reliable.

[0065] Furthermore, the computer device can extract the AC internal resistance characteristic value from the fitting curve. The AC internal resistance here is an important indicator to measure the internal ion transmission resistance and polarization effect of the battery, and is closely related to the health status and performance of the battery. Therefore, through the extracted AC internal resistance characteristic value, the present application can effectively avoid the calculation deviation caused by the discreteness of the data in the traditional method, making the characteristic parameter more stable and representative. In addition, since the fitting curve is constructed based on global data, the feature extraction process can avoid the influence of redundant data, making the calculation more efficient, reducing the large storage demand for raw data, and thus reducing the complexity of data processing.

[0066] Indicatively, Figure 2 As shown, Figure 2 A curve diagram of a fitting curve of a polarization resistance on a relaxation time scale provided in an embodiment of the present application; Figure 2 After extracting the features of the fitting curve, the AC internal resistance characteristic value can be obtained. , and , and used as features for lithium battery sorting. In other words, since the fitting curve at the relaxation time scale has a strong correlation with the SOH (State of Health) of the lithium battery, that is, the current degree of aging, these three characteristic values ​​can be used as a reflection of the current capacity of the lithium battery.

[0067] S140: generating a feature quantity set of the lithium battery according to the temperature change value, the open circuit voltage, the ohmic internal resistance and the AC internal resistance characteristic values, and performing consistency sorting on the feature quantity set to obtain a sorting result of the lithium battery.

[0068] In this step, the AC internal resistance characteristic value of the lithium battery is obtained through step S130, and the computer device can generate a feature quantity set of the lithium battery based on the temperature change value, open circuit voltage, ohmic internal resistance and AC internal resistance characteristic value, and use a clustering algorithm to perform consistency sorting on the feature quantity set to obtain the sorting result of the lithium battery, and then the batteries with similar feature quantities can be clustered together according to the sorting result to reduce performance differences and improve sorting accuracy.

[0069] Specifically, the computer device can use the temperature change value, open circuit voltage, ohmic internal resistance and AC internal resistance characteristic values ​​of the lithium battery as core indicators to construct a set of characteristic quantities of the lithium battery to comprehensively characterize the electrochemical performance and health status of the battery. After constructing a set of characteristic quantities of all lithium batteries of the same type to be classified, the computer device can use a clustering algorithm to sort all lithium batteries for consistency, so as to identify batteries with similar performance and classify them into the same group, so as to effectively cluster batteries with similar characteristic quantities together, thereby ensuring that the deviation of each group of batteries in key characteristics is minimal, thereby reducing the performance difference between batteries and improving the overall consistency of the battery pack. Compared with the traditional single indicator screening method, the method based on multi-feature clustering can more comprehensively measure the consistency of the battery, making the sorting results more stable and reliable.

[0070] More specifically, when classifying each lithium battery, the computer device can set an appropriate number of clusters according to actual needs, and then use clustering algorithms such as K-means to classify the data set. During the classification process, the computer device can randomly initialize several cluster centers, that is, the initial representatives of the lithium battery category, and then calculate the Euclidean distance between each lithium battery feature set and these cluster centers, and classify them into the cluster with the closest distance. After the initial classification is completed, the computer device can recalculate the center point of each cluster, that is, take the feature mean of all lithium batteries in the current cluster as the new cluster center, and then repeat the iterative process to continuously optimize the classification boundary until all cluster centers converge or reach a preset number of iterations to obtain the sorting result.

[0071] In the above embodiment, when the lithium battery is sorted for consistency, the test data of the lithium battery during the sudden charging pulse test can be automatically collected to improve the test efficiency. Then, the open circuit voltage and ohmic internal resistance of the lithium battery can be determined based on the test data and an equivalent circuit model of the lithium battery can be established to accurately reflect the polarization characteristics of the high-rate lithium battery and improve the model accuracy and mechanism fit. Then, the time domain response data of the polarization impedance at the relaxation time scale can be determined based on the open circuit voltage, ohmic internal resistance and the equivalent circuit model, and the distribution density function of the polarization resistance of the lithium battery can be obtained based on the time domain response data, thereby enhancing The ability to interpret batteries in different aging states; then the distribution density function can be interpolated and fitted to obtain a fitting curve, and the AC internal resistance characteristic value of the lithium battery can be extracted from the fitting curve, which can reduce data redundancy and further improve processing efficiency; finally, a set of lithium battery feature quantities can be generated based on the temperature change value, open circuit voltage, ohmic internal resistance and AC internal resistance characteristic values ​​in the test data, and a clustering algorithm can be used to perform consistency sorting on the feature quantity set to obtain the sorting results of the lithium batteries, and then batteries with similar feature quantities can be clustered together according to the sorting results to reduce performance differences and improve sorting accuracy.

[0072] In one embodiment, the process of collecting test data of the lithium battery during the sudden charging pulse test in step S110 may include:

[0073] S111: Acquire a fully charged lithium battery; the lithium battery is connected in parallel with a voltage sensor, and in series with a current sensor and a temperature sensor.

[0074] S112: After the resting time of the lithium battery reaches the preset resting time, a sudden current is applied to the lithium battery for charging until the charging time reaches the preset charging time, and the test is terminated.

[0075] S113: Determine a sampling time sequence of the lithium battery during the test, and respectively collect a voltage sampling sequence, a current sampling sequence, and a temperature sampling sequence corresponding to the sampling time sequence through a voltage sensor, a current sensor, and a temperature sensor.

[0076] S114: Calculate the temperature change value of the lithium battery under the relaxation time scale based on the temperature sampling sequence, and generate test data of the lithium battery according to the temperature change value, the current-voltage sampling sequence, the current sampling sequence and the temperature sampling sequence.

[0077] In this embodiment, when the computer device tests the lithium battery, the lithium battery in a fully charged state can be connected in parallel with a voltage sensor, and in series with a current sensor and a temperature sensor, and after the static time of the lithium battery reaches a preset static time, a sudden current is applied to the lithium battery for charging until the charging time reaches the preset charging time, and the test is terminated. Then the computer device can determine the sampling time sequence of the lithium battery during the test, and respectively collect the voltage sampling sequence, current sampling sequence and temperature sampling sequence corresponding to the sampling time sequence through the voltage sensor, current sensor and temperature sensor, and calculate the temperature change value of the lithium battery under the relaxation time scale based on the temperature sampling sequence, and finally generate the test data of the lithium battery according to the temperature change value, current voltage sampling sequence, current sampling sequence and temperature sampling sequence.

[0078] Specifically, the lithium battery to be tested needs to be left to stand for a period of time, such as more than 30 minutes, when it is fully charged, that is, when the SOC is 100%, to ensure that its electrochemical state is stable and reduce the interference of external factors on the test results. Then the computer device can connect the lithium battery to the test equipment to connect the positive and negative poles of the lithium battery in parallel with the voltage sensor, and connect the current sensor and the temperature sensor in series at the same time, so as to collect the voltage, current and temperature data of the battery in real time during the entire test process. After the test starts, the computer device can start timing and collect data at a certain frequency. When the timing reaches the preset standing time, the computer device can control the test equipment to apply a sudden current to the lithium battery, so that the battery enters a high-current charging state for a short time, and monitors the response of the battery in real time. The charging process continues until the preset charging time is reached, and the test equipment stops charging, so that the data of the entire test process can be fully recorded.

[0079] Indicatively, Figure 3 As shown, Figure 3 A logical schematic diagram of a test data sampling process provided in an embodiment of the present application; Figure 3 In the test, after the test starts, the computer equipment can collect various parameters of the lithium battery at a certain frequency, where the collection frequency can be 100Hz. When the static collection time reaches the preset static time, such as 5s, the test equipment can apply a 5C surge current to the lithium battery for charging until the charging time reaches the preset charging time, such as 30s, then stop charging and end the entire test process. Therefore, the sampling time sequence collected by the computer equipment during this period is , the current sampling data collected under the sampling time series is , the voltage sampling data is ;in, Indicates the moment when the sudden current is applied. and Indicates The sampling current and sampling voltage at the moment, and arrive The temperature change value under , that is, the temperature change value The calculation formula is as follows:

[0080]

[0081] In one embodiment, the process of determining the open circuit voltage and ohmic internal resistance of the lithium battery based on the test data in step S110 may include:

[0082] S115: extracting multiple sampled voltages of the lithium battery before the sudden current is applied from the test data, and calculating the average of each sampled voltage to obtain the open circuit voltage of the lithium battery.

[0083] S116: Obtain the sudden current of the lithium battery during the test and the instantaneous voltage when the sudden current is applied, and calculate the ohmic internal resistance of the lithium battery based on the sudden current, the instantaneous voltage and the open circuit voltage.

[0084] In this embodiment, after collecting the test data of the lithium battery, the computer device can extract multiple sampled voltages of the lithium battery before the sudden current is applied from the test data, and calculate the average of each sampled voltage to obtain the open circuit voltage of the lithium battery. Then, the sudden current of the lithium battery during the test process and the instantaneous voltage when the sudden current is applied can be obtained, and the ohmic internal resistance of the lithium battery can be calculated based on the sudden current, instantaneous voltage and open circuit voltage.

[0085] Specifically, since the test time of the lithium battery is short and the test impact on the lithium battery is small, because the state of the lithium battery does not change during the test, the open circuit voltage of the lithium battery during the test is and ohmic internal resistance At this time, the computer device can perform the current sampling sequence under the sampling time sequence. and voltage sampling sequence Calculate and take The mean value is the open circuit voltage of lithium battery , and using , The ohmic internal resistance of the lithium battery is calculated by the sudden current I , the formula is as follows:

[0086]

[0087]

[0088] In addition, the polarization resistance The calculation process can be expressed as follows:

[0089]

[0090] In one embodiment, the calculation expression of the equivalent circuit model in step S110 may include:

[0091]

[0092] In the formula, The time domain expression of the polarization resistance of lithium batteries in the relaxation time scale; Indicates the ohmic internal resistance of the lithium battery; Indicates the sampling current of the lithium battery; Indicates the open circuit voltage of the lithium battery; Indicates the external voltage of the lithium battery.

[0093] In this embodiment, it can be seen from the above formula that in the equivalent circuit model, the external voltage of the lithium battery is determined by the open circuit voltage, polarization resistance, ohmic internal resistance and current. The sampling current generates a voltage drop through the internal resistance and polarization impedance, reflecting the transient response characteristics of the battery. Therefore, the equivalent circuit model can effectively reflect the polarization behavior of the lithium battery during the pulse charging process, and can describe the voltage dynamic characteristics of the lithium battery in a more fine-grained manner.

[0094] Indicatively, Figure 4 As shown, Figure 4 A schematic diagram of the structure of an equivalent circuit model provided in an embodiment of the present application; Figure 4 Mainly includes open circuit voltage 、Ohm internal resistance And multiple parallel resistor-capacitor (RC) network series structures are used to simulate the polarization characteristics and dynamic response behavior of the battery. In this model, the ohmic internal resistance It reflects the internal DC resistance of the lithium battery and produces a voltage drop for instantaneous current changes. The infinite polarization process is equivalent to a finite set of RC networks, and each RC component Corresponding to the polarization behavior at different time scales. Therefore, the model can more accurately describe the dynamic characteristics of the battery at different relaxation time scales, thereby improving the ability to analyze the polarization phenomenon of lithium batteries.

[0095] In one embodiment, the calculation expression of the time domain response data in step S120 may include:

[0096]

[0097] In the formula, It represents the time domain response data of the polarization resistance of the lithium battery in the relaxation time scale, and the calculation results are expressed as time domain response data; Indicates the external voltage of the lithium battery, Indicates the open circuit voltage of the lithium battery; Indicates the sudden current of lithium battery; Indicates the ohmic internal resistance of the lithium battery.

[0098] In this embodiment, it can be seen from the above formula that by measuring the external voltage and open circuit voltage of the battery, and combining the sudden current and the ohmic internal resistance, the computer device can calculate the instantaneous value of the polarization impedance. Therefore, using this formula, the computer device can calculate the time domain response data under the relaxation time scale, and characterize the dynamic changes of the internal polarization effect of the battery under different time scales.

[0099] In one embodiment, the process of solving the distribution density function of the polarization resistance of the lithium battery based on the time domain response data in step S120 may include:

[0100] S121: Determine the impedance frequency domain expression of the lithium battery, and use the Fourier transform formula to convert the impedance frequency domain expression into an impedance time domain expression.

[0101] S122: Input the time domain response data into the impedance time domain expression for solution to obtain the distribution density function of the polarization resistance of the lithium battery.

[0102] In this embodiment, when solving the distribution density function of the polarization resistance, the computer device can first determine the impedance frequency domain expression of the lithium battery, and use the Fourier transform formula to convert the impedance frequency domain expression into an impedance time domain expression, and then the time domain response data can be input into the impedance time domain expression for solution to obtain the distribution density function of the polarization resistance of the lithium battery.

[0103] Specifically, from the DRT (Distribution of Relaxation Times) method, it can be seen that the impedance characteristics in the battery equivalent circuit are not determined by a single time constant, but are the result of the superposition of polarization processes on multiple time scales. Therefore, the impedance of a lithium battery can be expressed as follows:

[0104]

[0105] In the formula, Indicates ohmic internal resistance; Indicates plural units; Indicates frequency; represents the relaxation time; represents the frequency domain expression of impedance; Represents the polarization resistance in the time constant logarithmic domain The distribution density function within .

[0106] Therefore, the Fourier transform formula is used to perform Fourier transform on the impedance frequency domain expression, and the obtained impedance time domain expression is expressed as follows:

[0107]

[0108] At this time, the computer equipment will pre-calculate the time domain response data Substitute it into the impedance time domain expression for solution, and we can get the distribution density function .

[0109] In one embodiment, the process of performing interpolation fitting on the distribution density function to obtain a fitting curve in step S130 may include:

[0110] S131: Using a piecewise linear interpolation method to perform piecewise fitting on the distribution density function, obtain the Dirac distribution function corresponding to multiple characteristic times of the lithium battery on the relaxation time scale.

[0111] S132: summing the Dirac distribution functions corresponding to the characteristic times, and reconstructing a fitting curve of the polarization resistance of the lithium battery on the relaxation time scale according to the summation result.

[0112] In this embodiment, after calculating the distribution density function, the computer device can use a piecewise linear interpolation method to perform piecewise fitting on the distribution density function to obtain the Dirac distribution functions corresponding to multiple characteristic times of the lithium battery on the relaxation time scale, and then sum the Dirac distribution functions corresponding to each characteristic time, and reconstruct the fitting curve of the polarization resistance of the lithium battery on the relaxation time scale based on the summation result.

[0113] Specifically, in the process of piecewise fitting, the computer device can sample the distribution density function, determine a series of key characteristic time points, and construct the corresponding Dirac distribution function at these time points to discretize the polarization impedance characteristics of the lithium battery. Subsequently, by weighted summing the Dirac distribution functions corresponding to all characteristic time points, the computer device can effectively reconstruct the polarization impedance characteristics under the entire relaxation time scale, ensuring that the fitting curve can accurately reflect the polarization behavior of the lithium battery. Finally, based on the results obtained by the summation, the computer device can generate a fitted polarization impedance curve, thereby fully characterizing the dynamic characteristics of the lithium battery on the relaxation time scale.

[0114] The lithium battery consistency sorting device provided in the embodiment of the present application is described below. The lithium battery consistency sorting device described below and the lithium battery consistency sorting method described above can be referenced to each other.

[0115] In one embodiment, Figure 5 As shown, Figure 5 A schematic diagram of the structure of a lithium battery consistency sorting device provided in an embodiment of the present application; the present application also provides a lithium battery consistency sorting device, including a pulse testing module 210, a data solving module 220, a feature extraction module 230 and a cluster sorting module 240, specifically including the following:

[0116] The pulse test module 210 is used to collect test data of the lithium battery during the sudden charging pulse test, and determine the open circuit voltage and ohmic internal resistance of the lithium battery based on the test data, and establish an equivalent circuit model of the lithium battery; wherein the test data includes the temperature change value of the lithium battery under the relaxation time scale.

[0117] The data solving module 220 is used to determine the time domain response data of the polarization impedance of the lithium battery at the relaxation time scale according to the open circuit voltage, the ohmic internal resistance and the lithium battery equivalent circuit model, and to solve the distribution density function of the polarization resistance of the lithium battery based on the time domain response data.

[0118] The feature extraction module 230 is used to perform interpolation fitting on the distribution density function to obtain a fitting curve, and extract the AC internal resistance characteristic value of the lithium battery from the fitting curve.

[0119] The clustering sorting module 240 is used to generate a feature quantity set of the lithium battery according to the temperature change value, open circuit voltage, ohmic internal resistance and AC internal resistance characteristic values, and to perform consistency sorting on the feature quantity set to obtain a sorting result of the lithium battery.

[0120] In the above embodiment, when the lithium battery is sorted for consistency, the test data of the lithium battery during the sudden charging pulse test can be automatically collected to improve the test efficiency. Then, the open circuit voltage and ohmic internal resistance of the lithium battery can be determined based on the test data and an equivalent circuit model of the lithium battery can be established to accurately reflect the polarization characteristics of the high-rate lithium battery and improve the model accuracy and mechanism fit. Then, the time domain response data of the polarization impedance at the relaxation time scale can be determined based on the open circuit voltage, ohmic internal resistance and the equivalent circuit model, and the distribution density function of the polarization resistance of the lithium battery can be obtained based on the time domain response data, thereby enhancing The ability to interpret batteries in different aging states; then the distribution density function can be interpolated and fitted to obtain a fitting curve, and the AC internal resistance characteristic value of the lithium battery can be extracted from the fitting curve, which can reduce data redundancy and further improve processing efficiency; finally, a set of lithium battery feature quantities can be generated based on the temperature change value, open circuit voltage, ohmic internal resistance and AC internal resistance characteristic values ​​in the test data, and a clustering algorithm can be used to perform consistency sorting on the feature quantity set to obtain the sorting results of the lithium batteries, and then batteries with similar feature quantities can be clustered together according to the sorting results to reduce performance differences and improve sorting accuracy.

[0121] In one embodiment, the pulse test module 210 may include:

[0122] The battery acquisition submodule is used to acquire a lithium battery in a fully charged state; the lithium battery is connected in parallel with a voltage sensor and in series with a current sensor and a temperature sensor.

[0123] The battery test submodule is used to apply a sudden current to the lithium battery for charging after the rest time of the lithium battery reaches a preset rest time, until the charging time reaches a preset charging time, and then the test ends.

[0124] The data sampling submodule is used to determine the sampling time sequence of the lithium battery during the test process, and collect the voltage sampling sequence, current sampling sequence and temperature sampling sequence corresponding to the sampling time sequence through the voltage sensor, current sensor and temperature sensor respectively.

[0125] The data combination submodule is used to calculate the temperature change value of the lithium battery under the relaxation time scale based on the temperature sampling sequence, and generate the test data of the lithium battery according to the temperature change value, the current voltage sampling sequence, the current sampling sequence and the temperature sampling sequence.

[0126] In one embodiment, the pulse test module 210 may further include:

[0127] The voltage calculation submodule is used to extract multiple sampled voltages of the lithium battery before the sudden current is applied from the test data, and to calculate the average of each sampled voltage to obtain the open circuit voltage of the lithium battery.

[0128] The internal resistance calculation submodule is used to obtain the sudden current of the lithium battery during the test and the instantaneous voltage when the sudden current is applied, and calculate the ohmic internal resistance of the lithium battery based on the sudden current, instantaneous voltage and open circuit voltage.

[0129] In one embodiment, the calculation expression of the equivalent circuit model in the pulse test module 210 may include:

[0130]

[0131] In the formula, The time domain expression of the polarization resistance of lithium batteries in the relaxation time scale; Indicates the ohmic internal resistance of the lithium battery; Indicates the sampling current of the lithium battery; Indicates the open circuit voltage of the lithium battery; Indicates the external voltage of the lithium battery.

[0132] In one embodiment, the calculation expression of the time domain response data in the data solving module 220 may include:

[0133]

[0134] In the formula, It represents the time domain expression of the polarization resistance of lithium battery in the relaxation time scale, and the calculation result is expressed as time domain response data; Indicates the external voltage of the lithium battery, Indicates the open circuit voltage of the lithium battery; Indicates the sudden current of lithium battery; Indicates the ohmic internal resistance of the lithium battery.

[0135] In one embodiment, the data solving module 220 may include:

[0136] The expression conversion submodule is used to determine the impedance frequency domain expression of the lithium battery and convert the impedance frequency domain expression into the impedance time domain expression using the Fourier transform formula.

[0137] The function generation submodule is used to input the time domain response data into the impedance time domain expression for solution to obtain the distribution density function of the polarization resistance of the lithium battery.

[0138] In one embodiment, the feature extraction module 230 may include:

[0139] The piecewise fitting submodule is used to perform piecewise fitting on the distribution density function using a piecewise linear interpolation method to obtain the Dirac distribution function corresponding to multiple characteristic times of the lithium battery on the relaxation time scale.

[0140] The curve reconstruction submodule is used to sum the Dirac distribution functions corresponding to each characteristic time, and reconstruct the fitting curve of the polarization resistance of the lithium battery on the relaxation time scale based on the summation result.

[0141] In one embodiment, the present application also provides a storage medium, in which computer-readable instructions are stored. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the lithium battery consistency sorting method as described in any of the above embodiments.

[0142] In one embodiment, the present application also provides a computer device having computer-readable instructions stored therein. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the lithium battery consistency sorting method as described in any one of the above embodiments.

[0143] Indicatively, Figure 6 As shown, Figure 6 This is a schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. The computer device 300 may be provided as a server. Figure 6 The computer device 300 includes a processing component 302, which further includes one or more processors, and a memory resource represented by a memory 301 for storing instructions executable by the processing component 302, such as an application. The application stored in the memory 301 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 302 is configured to execute instructions to perform the lithium battery consistency sorting method of any of the above embodiments.

[0144] The computer device 300 may further include a power supply component 303 configured to perform power management of the computer device 300, a wired or wireless network interface 304 configured to connect the computer device 300 to a network, and an input / output (I / O) interface 305. The computer device 300 may operate based on an operating system stored in the memory 301, such as Windows Server TM, Mac OS X TM, Unix TM, Linux TM, Free BSD TM, or the like.

[0145] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0146] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0147] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can refer to each other.

[0148] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A lithium battery consistency sorting method, characterized in that: The method comprises: Collecting test data of the lithium battery during a sudden charging pulse test, and determining the open circuit voltage and ohmic internal resistance of the lithium battery based on the test data, and establishing an equivalent circuit model of the lithium battery; wherein the test data includes a temperature change value of the lithium battery at a relaxation time scale; Determine the time domain response data of the polarization impedance of the lithium battery at the relaxation time scale according to the open circuit voltage, the ohmic internal resistance and the equivalent circuit model, and obtain the distribution density function of the polarization resistance of the lithium battery by solving the time domain response data; Performing interpolation fitting on the distribution density function to obtain a fitting curve, and extracting an AC internal resistance characteristic value of the lithium battery from the fitting curve; A feature quantity set of the lithium battery is generated according to the temperature change value, the open circuit voltage, the ohmic internal resistance and the AC internal resistance characteristic value, and the feature quantity set is sorted for consistency to obtain a sorting result of the lithium battery.

2. The lithium battery consistency sorting method according to claim 1, characterized in that: The collecting of test data of the lithium battery during the sudden charging pulse test includes: Obtain a lithium battery in a fully charged state; the lithium battery is connected in parallel with a voltage sensor and in series with a current sensor and a temperature sensor; After the rest time of the lithium battery reaches a preset rest time, applying a sudden current to the lithium battery for charging until the charging time reaches a preset charging time, and then the test is terminated; Determine a sampling time sequence of the lithium battery during the test, and respectively collect a voltage sampling sequence, a current sampling sequence, and a temperature sampling sequence corresponding to the sampling time sequence through the voltage sensor, the current sensor, and the temperature sensor; The temperature variation value of the lithium battery under the relaxation time scale is calculated based on the temperature sampling sequence, and the test data of the lithium battery is generated according to the temperature variation value, the current voltage sampling sequence, the current sampling sequence and the temperature sampling sequence.

3. The lithium battery consistency sorting method according to claim 1, characterized in that: The step of determining the open circuit voltage and the ohmic internal resistance of the lithium battery based on the test data comprises: Extracting multiple sampled voltages of the lithium battery before the sudden current is applied from the test data, and performing mean calculation on each sampled voltage to obtain the open circuit voltage of the lithium battery; The sudden current of the lithium battery during the test and the instantaneous voltage when the sudden current is applied are obtained, and the ohmic internal resistance of the lithium battery is calculated based on the sudden current, the instantaneous voltage and the open circuit voltage.

4. The lithium battery consistency sorting method according to claim 1, characterized in that: The calculation expression of the equivalent circuit model includes: In the formula, The time domain expression of the polarization resistance of lithium batteries in the relaxation time scale; Indicates the ohmic internal resistance of the lithium battery; Indicates the sampling current of the lithium battery; Indicates the open circuit voltage of the lithium battery; Indicates the external voltage of the lithium battery.

5. The lithium battery consistency sorting method according to claim 1, characterized in that: The calculation expression of the time domain response data includes: In the formula, It represents the time domain expression of the polarization resistance of lithium battery in the relaxation time scale, and the calculation result is expressed as time domain response data; Indicates the external voltage of the lithium battery, Indicates the open circuit voltage of the lithium battery; Indicates the sudden current of lithium battery; Indicates the ohmic internal resistance of the lithium battery.

6. The lithium battery consistency sorting method according to claim 1, characterized in that: The step of solving the distribution density function of the polarization resistance of the lithium battery based on the time domain response data includes: Determine the impedance frequency domain expression of the lithium battery, and convert the impedance frequency domain expression into an impedance time domain expression using a Fourier transform formula; The time domain response data is input into the impedance time domain expression for solution to obtain a distribution density function of the polarization resistance of the lithium battery.

7. The lithium battery consistency sorting method according to claim 1, characterized in that: The interpolation fitting of the distribution density function to obtain a fitting curve includes: The distribution density function is piecewise fitted using a piecewise linear interpolation method to obtain a Dirac distribution function corresponding to a plurality of characteristic times of the lithium battery under the relaxation time scale; The Dirac distribution functions corresponding to the characteristic times are summed, and a fitting curve of the polarization resistance of the lithium battery at the relaxation time scale is reconstructed according to the summation result.

8. A lithium battery consistency sorting device, characterized in that: include: A pulse test module, used to collect test data of the lithium battery during a sudden charging pulse test, and determine the open circuit voltage and ohmic internal resistance of the lithium battery based on the test data, and establish an equivalent circuit model of the lithium battery; wherein the test data includes the temperature change value of the lithium battery under the relaxation time scale; A data solving module, used to determine the time domain response data of the polarization impedance of the lithium battery at the relaxation time scale according to the open circuit voltage, the ohmic internal resistance and the lithium battery equivalent circuit model, and solve the distribution density function of the polarization resistance of the lithium battery based on the time domain response data; A feature extraction module, used to perform interpolation fitting on the distribution density function to obtain a fitting curve, and extract the AC internal resistance characteristic value of the lithium battery from the fitting curve; A clustering sorting module is used to generate a feature quantity set of the lithium battery according to the temperature change value, the open circuit voltage, the ohmic internal resistance and the AC internal resistance characteristic value, and to perform consistency sorting on the feature quantity set to obtain a sorting result of the lithium battery.

9. A storage medium, characterized in that: The storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the lithium battery consistency sorting method as described in any one of claims 1 to 7.

10. A computer device, characterized in that: include: one or more processors, and memory; The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the lithium battery consistency sorting method as described in any one of claims 1 to 7 are performed.

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