Lithium Battery Consistency Sorting Method, Device, Storage Medium and Computer Equipment

Abstract

The present application relates to the technical field of lithium batteries, and provides a method, device, storage medium and computer equipment for consistent sorting of lithium batteries. During battery sorting, test data of lithium batteries during the sudden application of a charging pulse test is collected to determine the open-circuit voltage and ohmic internal resistance, and to establish an equivalent circuit model to accurately reflect the polarization characteristics of lithium batteries. Then, based on the open-circuit voltage, ohmic internal resistance and equivalent circuit model, the time-domain response data of the polarization impedance on the relaxation time scale is determined, and then the distribution density function of the polarization resistance is solved, thereby enhancing the ability to explain batteries in different aging states. Subsequently, the fitting curve of the distribution density function is obtained by difference fitting, and the AC internal resistance eigenvalue is extracted from it to reduce data redundancy. Finally, a clustering algorithm is used to perform consistent sorting on the set of feature quantities obtained by combining various parameters, and then according to the sorting results, batteries with similar feature quantities are grouped together to reduce performance differences and improve sorting accuracy.

Description

Technical Field

[0001] This application relates to the technical field of lithium batteries, and particularly to a method and device for sorting lithium batteries for consistency, a storage medium, and a computer device. Background Art

[0002] With the rapid development of drone technology, large drones have shown great application potential in multiple fields such as aerial photography, logistics, and agriculture. To meet the high-power and long-endurance requirements of these drones, the battery pack is usually composed of multiple lithium battery cells connected in parallel to build a high-voltage or high-power battery system. However, under long-term operation and complex environmental conditions, the aging degrees of the battery cells in the battery pack will show significant differences, which will affect the energy utilization efficiency, safety, and overall health status of the battery pack.

[0003] Currently, in the method for sorting lithium batteries for consistency in drones, although the complete constant current and constant voltage charge and discharge test method can accurately obtain various performance indicators of the battery, its test process is time-consuming. For high-rate lithium batteries in drones that pursue rapid sorting, it is obviously not efficient enough. And the sorting method based on the battery test curve, although it improves the sorting accuracy to a certain extent, the high cost and complex data processing process limit 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 internal characteristic changes and consistency differences of lithium batteries under different environments and aging states.

[0004] All in all, the current methods for sorting lithium batteries for consistency have problems of low test efficiency and poor economy, and for high-rate lithium batteries in drones, there is a lack of an equivalent circuit model that can accurately reflect their complex internal characteristic changes and an effective model parameter identification method, resulting in poor accuracy in sorting lithium batteries for consistency. Summary of the Invention

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

[0006] This application provides a method for sorting lithium batteries for consistency, and the method includes:

[0007] Collect test data during the sudden application of a charging pulse test of the lithium battery, 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 on the 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 solve the distribution density function of the polarization resistance of the lithium battery based on the time-domain response data;

[0009] Interpolate and fit 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;

[0010] Generate a characteristic 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 perform consistency sorting on the characteristic quantity set to obtain the sorting result of the lithium battery.

[0011] Optionally, the acquisition of the test data during the sudden charging pulse test of the lithium battery 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 static time of the lithium battery reaches a preset static time, apply a sudden current to charge the lithium battery until the charging time reaches a preset charging time, and end the test;

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

[0015] Calculate the temperature change value of the lithium battery at 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.

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

[0017] Extract multiple sampling voltages of the lithium battery before applying the sudden current from the test data, and calculate the mean value of each sampling voltage to obtain the open-circuit voltage of the lithium battery;

[0018] 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.

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

[0020]

[0021] In the formula, represents the time-domain expression of the polarization resistance of the lithium battery on the relaxation time scale; represents the ohmic internal resistance of the lithium battery; represents the sampled current of the lithium battery; represents the open-circuit voltage of the lithium battery; represents the external voltage of the lithium battery.

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

[0023]

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

[0025] Optionally, the method for solving the 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 use the Fourier transform formula to convert the impedance frequency-domain expression into an impedance time-domain expression;

[0027] 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.

[0028] Optionally, the method for interpolating and fitting the distribution density function to obtain a fitting curve includes:

[0029] Use the 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;

[0030] 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 according to the summation result.

[0031] This application also provides a lithium battery consistency sorting device, including:

[0032] A pulse test module is used to collect test data of a lithium battery during a sudden charging pulse test, 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 on a relaxation time scale;

[0033] A data solving module is used to determine the time-domain response data of the polarization impedance of the lithium battery on the relaxation time scale according to the open-circuit voltage, the ohmic internal resistance and the equivalent circuit model of the lithium battery, 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 is used to perform interpolation fitting on the distribution density function to obtain a fitting curve, and extract the AC internal resistance eigenvalue of the lithium battery from the fitting curve;

[0035] A clustering and 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 eigenvalue, and perform consistency sorting on the feature quantity set to obtain the sorting result of the lithium battery.

[0036] This 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 are caused to execute the steps of the lithium battery consistency sorting method as described in any one of the above embodiments.

[0037] This application also provides a computer device, including: 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 as described in any one of the above embodiments are executed.

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

[0040] The lithium battery consistency sorting method, device, storage medium and computer device provided by the present application can automatically collect the test data of a lithium battery during the sudden charging pulse test when sorting the lithium battery, improving 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, improving the model accuracy and mechanism fitting degree. Next, the time-domain response data of the polarization impedance on the relaxation time scale can be determined according to the open-circuit voltage, ohmic internal resistance and equivalent circuit model, and the distribution density function of the polarization resistance of the lithium battery can be solved based on the time-domain response data, thereby enhancing the ability to explain batteries in different aging states. Subsequently, interpolation fitting can be performed on the distribution density function 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 the processing efficiency. Finally, a characteristic 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 characteristic quantity set to obtain the sorting result of the lithium battery. Furthermore, batteries with similar characteristic quantities can be grouped together according to the sorting result to reduce performance differences and improve the sorting accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0042] Figure 1 It is a schematic flowchart of a lithium battery consistency sorting method provided by an embodiment of the present application;

[0043] Figure 2 It is a schematic curve diagram of a fitting curve of a polarization resistance on the relaxation time scale provided by an embodiment of the present application;

[0044] Figure 3 It is a schematic logic diagram of a test data sampling process provided by an embodiment of the present application;

[0045] Figure 4 It is a schematic structural diagram of an equivalent circuit model provided by an embodiment of the present application;

[0046] Figure 5 It is a schematic structural diagram of a lithium battery consistency sorting device provided by an embodiment of the present application;

[0047] Figure 6Schematic diagram of the internal structure of a computer device provided by an embodiment of the present application. Detailed implementation manners

[0048] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0049] The current lithium battery consistency sorting method has problems of low test efficiency and poor economy. Moreover, for high-rate UAV lithium batteries, there is a lack of an equivalent circuit model that can accurately reflect the complex internal characteristic changes and an effective model parameter identification method, resulting in poor accuracy of lithium battery consistency sorting.

[0050] Based on this, the present application proposes the following technical solutions. For details, please refer to the following:

[0051] In one embodiment, as Figure 1 shown, Figure 1 is a flowchart of a lithium battery consistency sorting method provided by an embodiment of the present application; the present application provides a lithium battery consistency sorting method, which specifically includes the following:

[0052] S110: Collect the test data of the lithium battery during the sudden application of a 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 on the relaxation time scale.

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

[0054] Among them, the sudden application of a charging pulse test is a fast test method for lithium batteries. It suddenly applies a large current charging pulse in a short time and then monitors the voltage, current, and temperature responses of the battery to obtain the key characteristic parameters of the battery. Therefore, the present application can automatically collect the key characteristic parameters of the lithium battery during the test 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 them after forming the test data. During the test, since electrochemical reactions, charge transfer, and other changes occur in the lithium battery in a short period of time, the voltage shows dynamic changes and enters the polarization stage. Here, the computer device can extract the parameter change values in the test data, such as the voltage change value and the current change value, and then calculate the open-circuit voltage and ohmic internal resistance of the lithium battery to characterize the basic electrical conductivity characteristics 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 use 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 Thevenin model. It combines the polarization behavior of lithium batteries during pulse charging and can describe the dynamic characteristics of lithium batteries in more fine-grained detail.

[0057] S120: Determine the time-domain response data of the polarization impedance on the relaxation time scale according to 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.

[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 on the relaxation time scale according to 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 interpretability of batteries in different aging states.

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

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

[0061] More specifically, through the distribution density function, the computer device can further analyze the dynamic response characteristics of the lithium battery, especially the change trend under different aging states. Compared with traditional models, the present application can more accurately fit the polarization behavior of the battery and effectively distinguish the changes in polarization characteristics caused by battery aging. For example, for a battery in a better health state, the distribution of its polarization impedance on a short time scale is relatively concentrated, while for a more severely aged battery, its polarization impedance will show more obvious dispersion on a longer time scale.

[0062] S130: 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.

[0063] In this step, after generating the distribution density function through 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 can be understood 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, the computer device can use the interpolation fitting method to smooth the distribution density function to form a more accurate fitting curve, eliminating noise and abnormal fluctuations in the data while ensuring the data trend remains unchanged, improving calculation stability, and then more accurately depicting the change trend of the polarization impedance on different time scales, making the analysis of the polarization characteristics of the battery more refined and reliable.

[0065] Further, the computer device can extract the AC internal resistance eigenvalue from the fitting curve. The AC internal resistance here is an important indicator for measuring the ion transport resistance and polarization effect inside the battery, and is closely related to the health state and performance of the battery. Therefore, through the extracted AC internal resistance eigenvalue, the present application can effectively avoid the calculation deviation caused by data discreteness 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 process of feature extraction can avoid the influence of redundant data, making the calculation more efficient, reducing the large storage requirement for the original data, and thus reducing the complexity of data processing.

[0066] Schematically, as Figure 2 shown, Figure 2 is a schematic diagram of the fitting curve of the polarization resistance on the relaxation time scale provided by the embodiment of the present application; Figure 2 After feature extraction is performed on the fitting curve, the AC internal resistance eigenvalue , and can be obtained and used as the features for lithium battery sorting. In other words, since the fitting curve on the relaxation time scale has a strong correlation with the SOH (State of Health, battery capacity), that is, the current aging degree of the lithium battery, these three eigenvalues can be used as a reflection of the current capacity of the lithium battery.

[0067] S140: Generate a set of characteristic quantities of the lithium battery according to the temperature change value, open circuit voltage, ohmic internal resistance, and AC internal resistance eigenvalue, and perform consistency sorting on the set of characteristic quantities to obtain the sorting result of the lithium battery.

[0068] In this step, through step S130, the AC internal resistance eigenvalue of the lithium battery is obtained. The computer device can generate a set of characteristic quantities of the lithium battery according to the temperature change value, open circuit voltage, ohmic internal resistance, and AC internal resistance eigenvalue, and use a clustering algorithm to perform consistency sorting on the set of characteristic quantities to obtain the sorting result of the lithium battery. Furthermore, the batteries with similar characteristic quantities can be grouped together according to the sorting result, reducing the performance difference and improving the sorting accuracy.

[0069] Specifically, the computer device can use the temperature change value, open-circuit voltage, ohmic internal resistance, and AC internal resistance characteristic value of the lithium battery as core indicators to construct a characteristic quantity set of the lithium battery, so as to comprehensively characterize the electrochemical performance and health status of the battery. After the computer device constructs the characteristic quantity sets of all lithium batteries of the same type to be classified, it can use a clustering algorithm to perform consistency sorting on all lithium batteries to identify batteries with similar performance and classify them into the same group, so as to effectively gather batteries with similar characteristic quantities together, ensuring that the deviation of each group of batteries in key characteristics is minimized, thereby reducing the performance differences between batteries and improving the overall consistency of the battery pack. Compared with the traditional single-index screening method, the method based on multi-feature clustering can measure the consistency of the battery more comprehensively, making the sorting result 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 categories, and then calculate the Euclidean distance between each lithium battery characteristic quantity set and these cluster centers, and classify it 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 characteristic mean value of all lithium batteries in the current cluster as the new cluster center, and then repeat this process iteratively, continuously optimizing the classification boundary until all cluster centers converge or reach the preset number of iterations to obtain the sorting result.

[0071] In the above embodiment, when performing consistency sorting on the lithium battery, it is possible to automatically collect the test data of the lithium battery during the sudden charging pulse test, improve the test efficiency, and then 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 to accurately reflect the polarization characteristics of the high-rate lithium battery and improve the model accuracy and mechanism fitting degree; then, according to the open-circuit voltage, ohmic internal resistance, and equivalent circuit model, determine the time-domain response data of the polarization impedance on the relaxation time scale, and solve the distribution density function of the polarization resistance of the lithium battery based on the time-domain response data, so as to enhance the interpretability of batteries in different aging states; subsequently, 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 the processing efficiency; finally, generate a characteristic quantity set of the lithium battery according to the temperature change value, open-circuit voltage, ohmic internal resistance, and AC internal resistance characteristic value in the test data, and use a clustering algorithm to perform consistency sorting on the characteristic quantity set to obtain the sorting result of the lithium battery, and then gather batteries with similar characteristic quantities together according to the sorting result, reduce performance differences, and improve the 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: 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.

[0074] S112: After the standing time of the lithium battery reaches a preset standing time, apply a sudden current to charge the lithium battery until the charging time reaches a preset charging duration, and end the test.

[0075] S113: 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.

[0076] S114: Calculate the temperature change value of the lithium battery on 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, current voltage sampling sequence, current sampling sequence, and temperature sampling sequence.

[0077] In this embodiment, when the computer device tests the lithium battery, it can connect the lithium battery in a fully charged state in parallel with a voltage sensor and in series with a current sensor and a temperature sensor, and after the standing time of the lithium battery reaches a preset standing time, apply a sudden current to charge the lithium battery until the charging time reaches a preset charging duration, and end the test. 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 on the relaxation time scale based on the temperature sampling sequence. Finally, it can 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 increase current of the lithium battery during the test and the instantaneous voltage when the sudden increase current is applied, and calculate the ohmic internal resistance of the lithium battery based on the sudden increase 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 increase current is applied from the test data, calculate the mean value of each sampled voltage to obtain the open-circuit voltage of the lithium battery, and then obtain the sudden increase current of the lithium battery during the test and the instantaneous voltage when the sudden increase current is applied, and calculate the ohmic internal resistance of the lithium battery based on the sudden increase current, the instantaneous voltage, and the open-circuit voltage.

[0085] Specifically, since the test time of the lithium battery is short and the test has little impact on the lithium battery, because the state of the lithium battery does not change during this process, the open-circuit voltage of the lithium battery during the test and the ohmic internal resistance remain unchanged. At this time, the computer device can calculate the current sampling sequence and the voltage sampling sequence under the sampling time series, and take the mean value as the open-circuit voltage of the lithium battery , and use , and the sudden increase current I to calculate the ohmic internal resistance of the lithium battery. The formula is as follows:

[0086]

[0087]

[0088] In addition, the calculation process of the polarization resistance 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, represents the time-domain expression of the polarization resistance of the lithium battery on the relaxation time scale; represents the ohmic internal resistance of the lithium battery; represents the sampled current of the lithium battery; represents the open-circuit voltage of the lithium battery; represents the external voltage of the lithium battery.

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

[0094] Schematically, as Figure 4 shown, Figure 4 is a schematic structural diagram of an equivalent circuit model provided by an embodiment of the present application; Figure 4 mainly includes an open-circuit voltage , an ohmic internal resistance , and a series structure of multiple parallel resistor-capacitor (RC) networks, which is used to simulate the polarization characteristics and dynamic response behavior of the battery. In this model, the ohmic internal resistance reflects the internal DC resistance of the lithium battery and generates a voltage drop for instantaneous current changes. And an infinite number of polarization processes are equivalent to a set of finite RC networks, and each RC component corresponds to the polarization behavior at different time scales. Therefore, this model can more accurately describe the dynamic characteristics of the battery at different relaxation time scales, thereby improving the analytical ability of the polarization phenomenon of the lithium battery.

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

[0096]

[0097] In the formula, represents the time-domain response data of the polarization resistance of the lithium battery at the relaxation time scale, and the calculation result is expressed as the time-domain response data; represents the external voltage of the lithium battery, represents the open-circuit voltage of the lithium battery; represents the sudden applied current of the lithium battery; represents the ohmic internal resistance of the lithium battery.

[0098] In this embodiment, as can be seen from the above formula, by measuring the external voltage and open-circuit voltage of the battery and combining the sudden applied current and ohmic internal resistance, the computer device can calculate the instantaneous value of the polarization impedance. Therefore, by using this formula, the computer device can calculate the time-domain response data at the relaxation time scale, characterizing the dynamic changes of the internal polarization effect of the battery at 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, use the Fourier transform formula to convert the impedance frequency-domain expression into an impedance time-domain expression, and then 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.

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

[0104]

[0105] In the formula, represents the ohmic internal resistance; represents the imaginary unit; represents the frequency; represents the relaxation time; represents the impedance frequency-domain expression; represents the distribution density function of the polarization resistance in the logarithmic domain of the time constant within.

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

[0107]

[0108] At this time, the computer device substitutes the pre-computed time-domain response data into the impedance time-domain expression for solution, and the distribution density function can be obtained.

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

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

[0111] S132: 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 according to the summation result.

[0112] In this embodiment, after the computer device calculates the distribution density function, it can use the 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 according to the summation result.

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

[0114] The lithium battery consistency sorting device provided by the embodiments of the present application will be described below. The lithium battery consistency sorting device described below can be correspondingly referred to the lithium battery consistency sorting method described above.

[0115] In one embodiment, as Figure 5 shown, Figure 5 is a schematic structural diagram of a lithium battery consistency sorting device provided by an embodiment of the present application; the present application also provides a lithium battery consistency sorting device, including a pulse test module 210, a data solving module 220, a feature extraction module 230, and a clustering and sorting module 240, specifically including the following:

[0116] The pulse test module 210 is configured to collect test data during the sudden application of a charging pulse test of the lithium battery, 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 on the relaxation time scale.

[0117] The data solving module 220 is configured to determine the time-domain response data of the polarization impedance of the lithium battery on the relaxation time scale according to the open-circuit voltage, ohmic internal resistance, and the equivalent circuit model of the lithium battery, and 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 configured to perform interpolation fitting on the distribution density function to obtain a fitting curve, and extract the AC internal resistance eigenvalue of the lithium battery from the fitting curve.

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

[0120] In the above embodiment, when performing consistency sorting on the lithium battery, the test data of the lithium battery during the sudden application of a 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, improving the model accuracy and the degree of fitting with the mechanism. Next, the time-domain response data of the polarization impedance on the relaxation time scale can be determined according to the open-circuit voltage, ohmic internal resistance, and equivalent circuit model, and the distribution density function of the polarization resistance of the lithium battery can be solved based on the time-domain response data, thereby enhancing the interpretability of batteries in different aging states. Subsequently, interpolation fitting can be performed on the distribution density function to obtain a fitting curve, and the AC internal resistance eigenvalue of the lithium battery can be extracted from the fitting curve, which can reduce data redundancy and further improve the processing efficiency. Finally, a set of characteristic quantities of the lithium battery can be generated according to the temperature change value, open-circuit voltage, ohmic internal resistance, and AC internal resistance eigenvalue in the test data, and a clustering algorithm can be used to perform consistency sorting on the set of characteristic quantities to obtain the sorting result of the lithium battery. Furthermore, batteries with similar characteristic quantities can be grouped together according to the sorting result to reduce performance differences and improve the sorting accuracy.

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

[0122] The battery acquisition sub-module is configured 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 sub-module is configured to apply a sudden current to charge the lithium battery until the charging time reaches a preset charging duration after the static time of the lithium battery reaches a preset static time, and end the test.

[0124] The data acquisition sub-module is configured to determine the sampling time series of the lithium battery during the test, and respectively acquire the voltage sampling series, current sampling series, and temperature sampling series corresponding to the sampling time series through the voltage sensor, current sensor, and temperature sensor.

[0125] A data combination sub-module, configured to calculate the temperature change value of the lithium battery on a relaxation time scale based on a temperature sampling sequence, and generate test data of the lithium battery according to the temperature change value, a current voltage sampling sequence, a current sampling sequence, and the temperature sampling sequence.

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

[0127] A voltage calculation sub-module, configured to extract a plurality of sampled voltages of the lithium battery before the sudden current is applied from the test data, and perform an average calculation on each sampled voltage to obtain the open-circuit voltage of the lithium battery.

[0128] An internal resistance calculation sub-module, configured 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, the instantaneous voltage, and the 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, represents the time-domain expression of the polarization resistance of the lithium battery on a relaxation time scale; represents the ohmic internal resistance of the lithium battery; represents the sampled current of the lithium battery; represents the open-circuit voltage of the lithium battery; represents 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, represents the time-domain expression of the polarization resistance of the lithium battery on a relaxation time scale, and the calculation result is represented as time-domain response data; represents the external voltage of the lithium battery, represents the open-circuit voltage of the lithium battery; represents the sudden current of the lithium battery; represents the ohmic internal resistance of the lithium battery.

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

[0136] An expression conversion sub-module, configured to determine the impedance frequency-domain expression of the lithium battery, and convert the impedance frequency-domain expression into an impedance time-domain expression by using the Fourier transform formula.

[0137] A function generation sub-module, configured to input time-domain response data into an impedance time-domain expression for solution to obtain a distribution density function of the polarization resistance of the lithium battery.

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

[0139] A piecewise fitting sub-module, configured to perform piecewise fitting on the distribution density function by using a piecewise linear interpolation method to obtain a Dirac distribution function corresponding to multiple characteristic times of the lithium battery under a relaxation time scale.

[0140] A curve reconstruction sub-module, configured to sum the Dirac distribution functions corresponding to each characteristic time and reconstruct a fitting curve of the polarization resistance of the lithium battery under the relaxation time scale according to the summation result.

[0141] In one embodiment, the present application further provides a storage medium storing computer-readable instructions, which when executed by one or more processors, cause the one or more processors to execute the steps of the lithium battery consistency sorting method according to any one of the above embodiments.

[0142] In one embodiment, the present application further provides a computer device storing computer-readable instructions, which when executed by one or more processors, cause the one or more processors to execute the steps of the lithium battery consistency sorting method according to any one of the above embodiments.

[0143] Schematically, as Figure 6 shown, Figure 6 is an internal structural schematic diagram of a computer device provided by an embodiment of the present application. The computer device 300 may be provided as a server. Referring to Figure 6 , the computer device 300 includes a processing component 302, which further includes one or more processors, and memory resources represented by a memory 301 for storing instructions executable by the processing component 302, such as application programs. The application programs 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 XTM, Unix TM, Linux TM, Free BSDTM, or the like.

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

[0146] Finally, it should also be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is 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 expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the element.

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

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

Claims

1. A method for sorting the consistency of lithium batteries, characterized in that, The method includes: Collecting test data of a lithium battery during a sudden charging pulse test, 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 the temperature change value of the lithium battery on a relaxation time scale; Determining the time-domain response data of the polarization impedance of the lithium battery on the relaxation time scale according to the open-circuit voltage, the ohmic internal resistance, and the equivalent circuit model, and solving to obtain the distribution density function of the polarization resistance of the lithium battery based on the time-domain response data; Performing interpolation fitting on the distribution density function to obtain a fitting curve, and extracting the AC internal resistance characteristic value of the lithium battery from the fitting curve; Generating a characteristic 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 performing consistency sorting on the characteristic quantity set to obtain the sorting result of the lithium battery; Wherein, the solving to obtain the distribution density function of the polarization resistance of the lithium battery based on the time-domain response data includes: Determining the impedance frequency-domain expression of the lithium battery, and converting the impedance frequency-domain expression into an impedance time-domain expression by using the Fourier transform formula; Inputting the time-domain response data into the impedance time-domain expression for solving to obtain the distribution density function of the polarization resistance of the lithium battery; The impedance time-domain expression is expressed as follows: ； In the formula, represents the impedance time-domain expression of the lithium battery; represents the ohmic internal resistance; represents the imaginary unit; represents the relaxation time; represents the distribution density function of the polarization resistance in the logarithmic domain of the time constant within; represents the angular frequency.

2. The method for sorting the consistency of lithium batteries according to claim 1, wherein The collecting the test data of the lithium battery during a sudden charging pulse test includes: Obtaining 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 static time of the lithium battery reaches a preset static time, applying a sudden current to charge the lithium battery until the charging time reaches a preset charging time, and ending the test; Determining the sampling time sequence of the lithium battery during the test, and respectively collecting the voltage sampling sequence, the current sampling sequence, and the temperature sampling sequence corresponding to the sampling time sequence through the voltage sensor, the current sensor, and the temperature sensor; Calculating the temperature change value of the lithium battery on a relaxation time scale based on the temperature sampling sequence, and generating 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.

3. The method for sorting the consistency of lithium batteries according to claim 1, characterized in that, The determining the open-circuit voltage and ohmic internal resistance of the lithium battery based on the test data includes: Extracting multiple sampling voltages of the lithium battery before applying the sudden current from the test data, and calculating the mean value of each sampling voltage to obtain the open-circuit voltage of the lithium battery; Obtaining the sudden current of the lithium battery during the test and the instantaneous voltage when applying the sudden current, and calculating the ohmic internal resistance of the lithium battery based on the sudden current, the instantaneous voltage, and the open-circuit voltage.

4. The method for sorting the consistency of lithium batteries according to claim 1, wherein The calculation expression of the equivalent circuit model includes: ； In the formula, represents the time-domain expression of the polarization resistance of the lithium battery on the relaxation time scale; represents the ohmic internal resistance of the lithium battery; represents the sampled current of the lithium battery; represents the open-circuit voltage of the lithium battery; represents the external voltage of the lithium battery.

5. The method for sorting the consistency of lithium batteries according to claim 1, wherein, The calculation expression of the time-domain response data includes: ； In the formula, represents the time-domain expression of the polarization resistance of the lithium battery on the relaxation time scale, and the calculation result is expressed as time-domain response data; represents the external voltage of the lithium battery, represents the open-circuit voltage of the lithium battery; represents the suddenly applied current of the lithium battery; represents the ohmic internal resistance of the lithium battery.

6. The method for sorting the consistency of lithium batteries according to claim 1, characterized in that The performing interpolation fitting on the distribution density function to obtain a fitting curve includes: The piecewise linear interpolation method is used to perform piecewise fitting on the distribution density function to obtain the Dirac distribution function corresponding to multiple characteristic times of the lithium battery on the relaxation time scale; The Dirac distribution functions corresponding to each characteristic time are summed, and the fitting curve of the polarization resistance of the lithium battery on the relaxation time scale is reconstructed according to the summation result.

7. A lithium battery consistency sorting device, characterized in that, It includes: A pulse test module, configured to collect test data during the sudden application of a charging pulse test of the lithium battery, 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 on the relaxation time scale; A data solving module, configured to determine the time-domain response data of the polarization impedance of the lithium battery on 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, configured 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 and sorting module, configured 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 perform consistency sorting on the feature quantity set to obtain the sorting result of the lithium battery; Wherein, the data solving module includes: 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; 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; The impedance time-domain expression is expressed as follows: ； In the formula, represents the impedance time-domain expression of the lithium battery; represents the ohmic internal resistance; represents the complex unit; represents the relaxation time; represents the polarization resistance in the logarithmic domain of the time constant and the distribution density function within; represents the angular frequency.

8. A storage medium, characterized in that: Computer-readable instructions are stored in the storage medium, 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 according to any one of claims 1 to 6.

9. A computer device, characterized in that, It includes: One or more processors, and a memory; Computer-readable instructions are stored in the memory, and when the computer-readable instructions are executed by the one or more processors, the steps of the lithium battery consistency sorting method according to any one of claims 1 to 6 are executed.

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