Low frequency eis and constant current pulse based leakage current test method and system

Abstract

The application provides a leakage current test method and system based on low-frequency EIS and cross flow pulse, and relates to the technical field of leakage current test. The method comprises the following steps: firstly, determining the use information and low-frequency EIS test parameters of a target battery to be tested; then, using an electrochemical working device to test the battery according to the parameters, obtaining impedance data at different frequencies, and then obtaining first dynamic capacitance information. A power supply device is controlled to apply a preset cross flow pulse current to the battery, a voltage change amount cutoff condition is determined, and the voltage change amount is continuously monitored, and the test time change amount when the cutoff condition is reached is recorded. Finally, the leakage current value of the battery is determined by comprehensively considering the first dynamic capacitance information, the cross flow pulse current, the test time change amount and the voltage change amount. Compared with the traditional method, the leakage current value can be obtained by applying a direct current only once after the low-frequency EIS test, the test process is simplified, the test efficiency is improved, and the leakage current value can be more accurately determined.

Description

Technical Field

[0001] The present application relates to the technical field of leakage current testing, and in particular to a leakage current testing method and system based on low-frequency EIS and constant current pulses. Background Art

[0002] Battery leakage current is a small, persistent current that persists even when the battery is not supplying power to an external load. This current is typically caused by internal factors such as defects in the battery materials, electrolyte decomposition, self-discharge mechanisms, or small internal short circuits. Leakage current gradually depletes the battery over time, reducing its overall capacity and service life. Therefore, monitoring and minimizing leakage current is crucial to maintaining battery performance and extending its life.

[0003] In traditional battery testing, to obtain certain battery performance parameters, such as internal resistance and polarization resistance, multiple current pulse signals of varying magnitudes and durations are typically applied. The battery's voltage response to these pulses is measured, and the relationship between voltage and current changes is analyzed to obtain relevant performance parameters. Another method is the static self-discharge test, which indirectly calculates leakage current by leaving the battery at a specific temperature for a period of time and measuring the battery voltage drop.

[0004] However, the traditional method requires changing the current size and duration multiple times, making the testing process more complicated and time-consuming. Summary of the Invention

[0005] The present application provides a leakage current testing method and system based on low-frequency EIS and constant current pulses, which is used to efficiently and accurately measure battery leakage current, effectively solving the problems of cumbersome testing procedures, long test times, and inaccurate measurement results in traditional testing methods.

[0006] In the first aspect, the present application provides a leakage current testing method based on low-frequency EIS and constant current pulses, which is applied to a leakage current testing system, the method comprising: determining battery usage information of a target battery to be tested; determining test parameters of a low-frequency EIS based on the battery usage information; controlling an electrochemical working device to apply a test to the target battery to be tested according to the test parameters, and obtaining impedance data of the target battery to be tested at different frequencies; obtaining first dynamic capacitance information based on an imaginary impedance part corresponding to the impedance data and the test parameters; controlling a power supply device to apply a constant current pulse current of a preset size to the target battery to be tested, and determining a voltage change cutoff condition; continuously monitoring the voltage change of the target battery to be tested, and recording a test time change when the voltage change reaches the voltage change cutoff condition; determining a leakage current value based on the first dynamic capacitance information, the constant current pulse current, the test time change, and the voltage change.

[0007] By adopting the above technical solution, the battery usage information of the target battery to be tested is first determined, which provides basic data for subsequent tests. The low-frequency EIS test parameters are determined based on the battery usage information to make the test more targeted. The impedance data is obtained by parameter testing using electrochemical working equipment, and the first dynamic capacitance information is obtained by combining the imaginary part of the impedance and the test parameters. Then a constant current pulse current is applied and the voltage change and time change are monitored, and finally the leakage current value is determined by combining this information. The entire process combines low-frequency EIS and constant current pulse technology. Compared with traditional methods, there is no need to change the current size and duration multiple times. Only one DC current needs to be applied once after the low-frequency EIS test to obtain the leakage current value, which simplifies the test process, improves test efficiency, and can more accurately determine the leakage current value, providing a more reliable basis for battery performance evaluation.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, the step of obtaining the first dynamic capacitance information according to the imaginary impedance part corresponding to the impedance data and the test parameter specifically includes: determining the first dynamic capacitance information DNC1, f is the frequency in the test parameters of low-frequency EIS, Im(Z(f)) is the imaginary part of the impedance of the target battery to be tested, and Z(f) represents the impedance of the target battery to be tested when the frequency is f.

[0009] By adopting the above technical solution, based on the impedance characteristics of the battery at different frequencies, key information is directly extracted from the impedance data obtained from the test to calculate the dynamic capacitance, which can quickly and accurately reflect the dynamic capacitance characteristics of the battery, and provide important intermediate parameters for the subsequent calculation of leakage current, making the leakage current calculation more accurate and scientific, and helping to accurately evaluate battery performance.

[0010] In combination with some embodiments of the first aspect, in some embodiments, after the step of determining the first dynamic capacitance information DNC1, the method further includes: determining the second dynamic capacitance information DNC2, Wherein, I1 is the current value applied for the first time during the constant current pulse current test, I2 is the current value applied for the second time during the constant current pulse current test, ΔV1 is the voltage change at both ends of the battery corresponding to I1 within ΔT1, ΔV2 is the voltage change at both ends of the battery corresponding to I2 within ΔT2, ΔT1 is the time interval experienced during the voltage change process of ΔV1, and ΔT2 is the time interval experienced during the voltage change process of ΔV2; the battery cycle number of the target battery to be tested is obtained through the battery management system, and the current test environment temperature is obtained through the temperature sensor; the battery cycle number and the previous test environment temperature are compared. The temperature is input into the dynamic capacitance weight fusion model to determine the first weight of the first dynamic capacitance information DNC1 and the second weight of the second dynamic capacitance information DNC2 under the current test environment. The dynamic capacitance weight fusion model is obtained by machine learning training in advance based on battery sample data with different battery cycle numbers and different test environment temperatures. The battery sample data includes DNC1, DNC2, and weight combination labels under cycle number and temperature conditions; the first dynamic capacitance information DNC1 is adjusted according to the first dynamic capacitance information DNC1 and the first weight, the second dynamic capacitance information DNC2 and the second weight.

[0011] By adopting this technical solution, the number of battery cycles and the test environment temperature affect battery performance. The weighted fusion model, trained on a large amount of sample data, can reflect the relationship between these factors and the dynamic capacitance weight. Adjusting the first dynamic capacitance information based on the weight can more accurately consider the characteristics of the battery under different usage conditions and environments, making the first dynamic capacitance information more realistic, thereby improving the accuracy of subsequent leakage current calculations and providing strong support for more accurate assessment of battery leakage conditions.

[0012] In combination with some embodiments of the first aspect, in some embodiments, the step of determining the leakage current value according to the first dynamic capacitance information, the constant current pulse current, the test time variation, and the voltage variation specifically includes: determining the leakage current information I according to the leakage current formula sd , the leakage current formula is Where DNC1 is the first dynamic capacitance information, ΔT is the time change corresponding to the voltage change ΔV reaching the voltage change cutoff condition, ΔV is the voltage change at both ends of the battery during the test that matches the voltage change cutoff condition, and I is the pulse current value applied during the test.

[0013] By employing this technical solution, the previously acquired key information is integrated and calculated, directly deriving the leakage current value based on physical principles and mathematical relationships. This calculation method is scientific and rational, fully utilizing the data obtained from previous tests. It can intuitively and accurately reflect the battery's leakage current, providing a quantitative basis for determining whether the battery is leaking and the extent of the leakage.

[0014] In combination with some embodiments of the first aspect, in some embodiments, the step of determining the test parameters of the low-frequency EIS based on the battery usage information specifically includes: determining the environmental data of the current low-frequency EIS test; inputting the battery usage information and the environmental data into a preset frequency determination model to obtain a matching low-frequency range, the battery usage information includes the battery type, and the construction and training process of the frequency determination model is as follows: obtaining actual operating data of multiple different types of batteries under different environmental conditions, the actual operating data including impedance data under different frequency EIS tests; using the battery type as a classification label, and combining the actual operating data to obtain a frequency determination model through machine learning.

[0015] By adopting the above technical solution, the environmental data of the current low-frequency EIS test is first determined, and then the battery usage information and environmental data are input into the preset frequency determination model. The frequency determination model is trained based on the actual operating data of different types of batteries in different environments, with battery type as the classification label. This method combines battery type and test environment factors to find the most suitable low-frequency test range based on actual conditions. The appropriate test frequency can improve the accuracy of test results, avoid test data deviations caused by improper frequency selection, ensure that the impedance data obtained subsequently better reflects the true characteristics of the battery, and lay a good foundation for accurately calculating dynamic capacitance and leakage current.

[0016] In combination with some embodiments of the first aspect, in some embodiments, before the step of controlling the electrochemical workstation to apply the test to the target battery to be tested according to the test parameters, it also includes: obtaining temperature curve data of the target battery to be tested before the test; inputting the temperature curve data into a pre-trained temperature feature recognition model to obtain a temperature recognition output result, and the temperature feature recognition model is obtained by machine learning training through temperature change data of multiple normal batteries and leakage batteries; according to the temperature recognition output result, adjusting the test parameters of the low-frequency EIS in a dynamic adjustment manner, and the dynamic adjustment manner includes, when it is identified that the temperature curve data has leakage characteristics, reducing the test frequency according to the set value to improve the impedance measurement accuracy; when it is identified that the temperature curve data does not have leakage characteristics, increasing the test frequency according to the set value to shorten the test time.

[0017] By employing the above technical solution, the temperature curve data of the target battery under test is obtained before testing and input into a pre-trained temperature feature recognition model. This model, trained using temperature change data from both normal and leaking batteries, can identify whether the temperature curve exhibits leakage characteristics. Based on the identification results, the low-frequency EIS test parameters are dynamically adjusted. If leakage characteristics are present, the test frequency is lowered to improve impedance measurement accuracy; if not, the test frequency is increased to shorten test time. This ensures more accurate test data in the event of potential leakage while improving test efficiency under normal conditions. This enables intelligent and optimized testing processes and enhances the performance of the entire leakage current test system.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the step of determining the leakage current value based on the first dynamic capacitance information, the constant current pulse current, the test time change and the voltage change, it also includes: determining the leaking battery, and inputting the obtained remaining capacity, health status and usage environment data of the leaking battery into a pre-built intelligent leakage assessment model for risk quantification assessment to obtain a risk assessment result, and the intelligent leakage assessment model is established in advance through machine learning training based on multiple groups of battery leakage data with different remaining capacities, different health statuses, and different usage environment conditions and their corresponding risk level labels; according to the risk assessment result and the size of the leakage current value, the batteries are prioritized according to the level of risk to obtain a sorting result; according to the sorting result, the battery is given an electronic label containing the leakage degree and risk level information with different color marks; and the electronic label is sent to the visual end in the order of the sorting results.

[0019] By employing this technical solution, after leaking batteries are identified, their remaining capacity, health status, and operating environment data are fed into an intelligent leakage assessment model trained on a large amount of battery leakage data under various conditions. The model then outputs a risk assessment result, ranks the batteries by risk based on leakage current values, and creates and sends an electronic tag containing leakage level and risk level information to the visual terminal. This process enables a quantitative assessment and intuitive display of the risk of leaking batteries, allowing staff to quickly understand the battery leakage risk and promptly address high-risk batteries, such as repair or replacement, to ensure battery safety and reliability and improve the efficiency and quality of battery management.

[0020] In a second aspect, the present application provides a leakage current testing system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the leakage current testing system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, the present application provides a computer-readable storage medium comprising instructions, which, when executed on a leakage current testing system, causes the leakage current testing system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0022] In a fourth aspect, the present application provides a computer program product. When the computer program product is run on a leakage current testing system, the leakage current testing system executes the method described in the first aspect and any possible implementation of the first aspect.

[0023] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0024] 1. The present invention adopts a technical method of determining low-frequency EIS test parameters by combining battery usage information of the target battery to be tested, applying a constant current pulse current and monitoring relevant parameters, and finally comprehensively determining the leakage current value. Therefore, the technical problems of the leakage current test process being cumbersome, inefficient and inaccurate in the prior art are effectively solved, thereby achieving the technical effect of simplifying the test process, improving test efficiency and more accurately determining the leakage current value, thereby providing a reliable basis for battery performance evaluation.

[0025] 2. By adopting the technical means of determining the second dynamic capacitance information, combining the number of battery cycles and the test environment temperature, and adjusting the first dynamic capacitance information using the dynamic capacitance weight fusion model, the technical problem of inaccurate leakage current calculation caused by not fully considering the impact of battery usage status and environmental factors on dynamic capacitance in the existing technology is effectively solved. This makes the first dynamic capacitance information more in line with the actual situation, improves the accuracy of subsequent leakage current calculation, and provides strong support for accurately evaluating the battery leakage condition.

[0026] 3. By adopting the technical means of obtaining the temperature curve data of the target battery before testing, inputting the temperature feature recognition model, and dynamically adjusting the low-frequency EIS test parameters according to the recognition results, the technical problem of the existing technology that the low-frequency EIS test frequency is fixed and cannot take into account both test accuracy and efficiency is effectively solved. In addition, the technical effect of improving the impedance measurement accuracy in the case of possible leakage, shortening the test time under normal circumstances, optimizing the test process, and improving the performance of the leakage current test system is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of a leakage current testing method based on low-frequency EIS and constant current pulses in an embodiment of the present application;

[0028] Figure 2 1 is another flow chart of the leakage current testing method based on low-frequency EIS and constant current pulse in an embodiment of the present application;

[0029] Figure 3 This is a schematic diagram of the structure of a physical device of a leakage current testing system in an embodiment of the present application. DETAILED DESCRIPTION

[0030] The terms used in the following examples of the present application are for the purpose of describing specific embodiments only and are not intended to limit the present application. As used in the specification and appended claims of the present application, the singular expressions "a," "an," "said," "above," "the," and "this" are intended to include plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to and encompasses any or all possible combinations of one or more of the listed items.

[0031] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of the technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more.

[0032] For ease of understanding, the following describes the process of the method provided by this implementation. Figure 1 , which is a flow chart of a leakage current testing method based on low-frequency EIS and constant current pulse in an embodiment of the present application.

[0033] S101, determining battery usage information of a target battery to be tested;

[0034] The leakage current test system first collects battery usage information from the target battery under test through various channels. The system then exchanges data with the battery management system (BMS), which monitors and manages the various battery states. This information contains a wealth of battery usage information, including charge and discharge cycles and battery type. For example, in electric vehicle battery management scenarios, the BMS monitors various battery status information in real time. Using the CAN protocol, the leakage current test system periodically sends data request commands to the BMS. Upon receiving the commands, the BMS packages the stored battery charge and discharge cycle data and sends it to the test system.

[0035] For battery type information, the leakage current test system determines the battery type by reading the battery identification code. During the production process, each battery is assigned a unique identification code that contains key information such as battery type and production batch.

[0036] S102, determining low-frequency EIS test parameters according to battery usage information;

[0037] When determining the test parameters for low-frequency EIS (electrochemical impedance spectroscopy), the leakage current test system must carefully consider multiple factors to ensure the accuracy and effectiveness of subsequent tests. The system first determines the environmental data for the current low-frequency EIS test using temperature and pressure sensors set within the set range of the battery used in the test experiment. This environmental data can include temperature and pressure data during the test. The reason for this is as follows: The chemical reaction rate within the battery is closely related to temperature. Increasing temperature accelerates chemical reactions. This is because increasing temperature increases the thermal motion energy of molecules, making it easier for reactants to overcome the activation energy of the reaction, thereby accelerating the reaction. For example, in lithium-ion batteries, increasing temperature accelerates the insertion and deintercalation of lithium ions into and out of the positive and negative electrode materials, reducing the battery's internal resistance and increasing the self-discharge rate, which in turn increases leakage current. Conversely, decreasing temperature slows the chemical reaction rate, increasing the battery's internal resistance and reducing leakage current. If temperature is not taken into account during the test, the leakage current test results may not truly reflect the battery's actual performance and may overestimate or underestimate the battery's leakage, affecting an accurate assessment of battery performance. For the electrode materials of the battery, their crystal structure and electronic conductivity will change at different temperatures. In a high temperature environment, the crystal structure of the electrode material may be distorted to a certain extent, resulting in changes in the diffusion path and diffusion rate of lithium ions, which in turn affects the charge and discharge performance and leakage current of the battery. In low-frequency EIS testing, temperature will affect the performance of the test equipment and the accuracy of the test data. Temperature changes may cause the performance of the electronic components of the test equipment to drift, causing deviations in the impedance data obtained from the test. If the temperature factor is not taken into account, the first dynamic capacitance information and the final leakage current value calculated based on these inaccurate impedance data will also have errors.

[0038] In addition, air pressure changes the partial pressure of gases within the battery. In some batteries that utilize gas to participate in reactions, such as metal-air batteries, oxygen enters the battery through the air to participate in chemical reactions. When air pressure changes, the partial pressure of oxygen changes, affecting the rate at which oxygen diffuses to the electrode surface. At high altitudes, where air pressure is lower, the oxygen diffusion rate decreases, and the battery's reaction rate also decreases, which in turn changes the battery's impedance characteristics. Considering air pressure when determining low-frequency EIS test parameters can more accurately reflect the battery's internal reactions under different air pressure environments, thereby obtaining more realistic impedance data.

[0039] The system inputs the battery usage information and environmental data into the preset frequency determination model to obtain the matching low-frequency range in the test parameters. The construction and training process of the frequency determination model is based on a large amount of actual operation data. The system retrieves the actual operation data of multiple different types of batteries under different environmental conditions from the database. These data cover common battery types such as lead-acid batteries, lithium-ion batteries, nickel-metal hydride batteries, and conditions under different temperatures (-20℃-60℃), humidity (20%RH-80%RH), and air pressure (90kPa-110kPa). The actual operation data includes impedance data under EIS tests at different frequencies. The frequency range is set to 0.01Hz-100Hz, and the test is performed in steps of 0.01Hz and the corresponding impedance data is recorded.

[0040] Using battery type as the classification label and combined with actual operating data, the system employs a machine learning algorithm for model training. During training, a support vector machine (SVM) algorithm is used to perform data classification and regression analysis. Battery type is used as the classification target, and environmental data (temperature, humidity, air pressure) and impedance data at different frequencies are input as feature vectors into the model. After multiple iterations of training and adjustment of model parameters, the model accurately predicts the optimal low-frequency test range based on the input battery usage information (battery type) and environmental data.

[0041] Assume that the battery usage information obtained by the system shows that the target battery to be tested is a certain model of lithium-ion battery, the current ambient temperature is 28°C, the humidity is 55% RH, and the air pressure is 102kPa. After inputting this information into the frequency determination model, the low-frequency test range output by the model is 0.1Hz-1Hz. Based on this result, the system determines the test frequency parameters of the low-frequency EIS and selects 0.1Hz, 0.3Hz, 0.5Hz, 0.7Hz, and 0.9Hz as specific test frequency points. At the same time, the amplitude of the AC excitation signal is determined to be 5mV to ensure that accurate impedance data is obtained without affecting the internal electrochemical process of the battery.

[0042] It should be noted that the test voltage of the low-frequency EIS test parameters can be selected from 1mv-10mv to avoid nonlinearity, the test frequency range can be selected from 10mHz-10kHz to extract dynamic capacitance in the low-frequency band, and the AC amplitude selected for the test can be selected from 1-10mV to avoid activating side reactions.

[0043] S103, controlling the electrochemical working device to apply a test to the target battery to be tested according to the test parameters, and obtaining impedance data of the target battery to be tested at different frequencies;

[0044] The leakage current test system establishes a communication connection with the electrochemical working equipment and transmits the determined low-frequency EIS test parameters to the electrochemical working equipment. After receiving the test parameters, the electrochemical working equipment generates corresponding test signals based on these parameters. Taking a common electrochemical workstation as an example, its internal signal generator will generate a sinusoidal AC excitation signal with a frequency range of 0.1Hz-1Hz (assuming the low-frequency test range is determined by the frequency model) according to the set test frequency, and the signal amplitude is 5mV. The AC excitation signal is applied to the target battery to be tested through a three-electrode system consisting of a working electrode, a reference electrode, and a counter electrode. During the signal application process, the electrochemical working equipment will monitor the signal output in real time to ensure the stability and accuracy of the signal. If there is a signal fluctuation or abnormality, the device will automatically adjust or report an error, notifying the leakage current test system to recheck the test parameters or equipment connection.

[0045] In order to obtain the impedance data of the target battery under test at different frequencies, the electrochemical working equipment will test in sequence according to the set test frequency points. Each time it switches to a new test frequency, the equipment will wait for a period of time to allow the electrochemical process inside the battery to reach a stable state. The setting of this waiting time is based on the characteristics of the battery and related electrochemical theory, and is generally between a few seconds and tens of seconds. For example, for lithium-ion batteries, when testing at a low frequency, it may be necessary to wait for 10-20 seconds to ensure that the ion diffusion and charge transfer processes inside the battery are stable, so as to avoid inaccurate impedance data due to premature testing.

[0046] After stabilization at each test frequency point, the electrochemical working device will measure the current response and voltage response of the battery at that frequency. The impedance of the battery is calculated using Ohm's law and complex operations based on the measured voltage and current data. The specific calculation process is as follows: First, record the voltage amplitude V across the battery and the current amplitude I passing through the battery under the action of the AC excitation signal, as well as the phase difference between the voltage and current. Then, according to the definition of impedance Z = V / I, the impedance amplitude |Z| is calculated. At the same time, using the concept of complex numbers, the impedance is expressed as Where j is an imaginary unit. In this way, the impedance data of the battery at this frequency is obtained, including the real and imaginary parts of the impedance. During the entire test process, the electrochemical working equipment will transmit the measured impedance data to the leakage current test system in real time. After receiving the impedance data, the leakage current test system will perform preliminary filtering and denoising on the data to remove abnormal data caused by measurement noise or other interference factors. Common filtering methods include mean filtering, median filtering, etc. By performing statistical analysis on multiple measurement data, data points that significantly deviate from the normal range are removed to improve the quality and reliability of the data.

[0047] In some embodiments, before step S103, the leakage current test system may also utilize an array of internal or external high-precision temperature sensors to obtain pre-test temperature profile data for the target battery. These temperature sensors are tightly integrated at various locations within the battery, such as at the positive and negative terminals, and at the center and edges of the battery casing, ensuring comprehensive capture of temperature variations across all battery components. For example, an electric vehicle's power battery pack consists of multiple battery cells connected in series or parallel. The system installs temperature sensors at key locations within each cell to monitor the temperature distribution throughout the pack. The system samples temperature data at fixed intervals, and the sampling frequency can be flexibly adjusted based on the battery's characteristics and testing requirements. For batteries with rapidly changing temperatures, such as lithium-ion batteries during high-rate charge and discharge, the sampling frequency can be set to 1Hz, collecting temperature data once per second. For batteries with more slowly changing temperatures, the sampling frequency can be reduced to 0.1Hz. During the sampling process, the system records the temperature data and corresponding timestamps at each sampling point in real time, forming a temperature-time series data set. In the actual test, assuming that before the leakage current test of a mobile phone battery, the system starts collecting temperature data from time 0, and in the next 5 minutes, a total of 300 temperature data points are collected at a sampling frequency of 1Hz. These data points constitute the initial temperature curve data of the battery before the test.

[0048] The leakage current test system extracts pre-test temperature curve data from the target battery stored locally and preprocesses it according to the format required by the temperature feature recognition model. This preprocessing process includes data normalization, denoising, and feature extraction. After preprocessing, the system inputs the temperature curve data into a pre-trained temperature feature recognition model, which is trained using machine learning data from a large number of temperature change data from both healthy and leaking batteries. Taking the convolutional neural network (CNN) used in deep learning as an example, during the training process, thousands of sets of temperature curve data from different battery types (including lithium-ion, lead-acid, and nickel-metal hydride batteries) are collected, half of which is from healthy batteries and the other half from leaking batteries. This data is divided into training, validation, and test sets. The training set is used to train the model, the validation set is used to adjust the model's hyperparameters, and the test set is used to evaluate model performance. During training, the model automatically learns the characteristic patterns in the temperature curve data through components such as convolutional layers, pooling layers, and fully connected layers. The convolutional layer extracts local features from the data, while the pooling layer reduces the dimensionality of these features to reduce computational complexity. The fully connected layer comprehensively analyzes the extracted features and outputs classification results. After multiple iterations of training, the model gradually learns the differences between the temperature curves of healthy and leaking batteries, enabling it to accurately identify whether a temperature curve exhibits leakage characteristics.

[0049] When the system inputs the temperature curve data of the target battery under test into the model, the model calculates and analyzes it and outputs a temperature identification result. For example, if the model outputs "has leakage characteristics" for a certain temperature curve data, this indicates that the temperature change pattern of the battery is similar to the temperature curve characteristics of leaking batteries in the training set, indicating a high possibility of leakage. If the model outputs "does not have leakage characteristics," it means that the battery's temperature curve conforms to the characteristics of a normal battery. The system stores the temperature identification output locally and displays it in real time on the operation interface, allowing testers to promptly understand the battery's temperature status.

[0050] After obtaining the output of the temperature signature recognition model, the leakage current test system dynamically adjusts the low-frequency EIS test parameters based on the results. If the temperature curve data is identified as exhibiting leakage characteristics, the system will reduce the test frequency by the set value to improve impedance measurement accuracy. This is because leakage complicates the electrochemical processes within the battery, altering the impedance characteristics. Lower test frequencies provide deeper insight into the battery's internal impedance. For example, for a certain lithium-ion battery model, the normal low-frequency EIS test frequency range is 0.1Hz-1Hz. If the temperature signature recognition model determines that the battery's temperature curve exhibits leakage characteristics, the system adjusts the test frequency range to 0.01Hz-0.1Hz. During this frequency adjustment, the system also adjusts other relevant test parameters, such as the AC excitation signal amplitude. This ensures that the test does not cause additional damage to the battery while ensuring accurate impedance measurements. After reducing the test frequency, the system will correspondingly extend the test time at each frequency point. Because low-frequency testing requires longer time for the battery's internal electrochemical processes to reach a stable state, sufficient waiting time is required to obtain accurate impedance data. For example, during normal frequency testing, the test time for each frequency point is set to 10 seconds, but after reducing the frequency, the test time may be extended to 30 seconds. The system monitors the battery's impedance response at different frequencies in real time to ensure the accuracy and reliability of the test data.

[0051] When the system detects that the temperature curve data does not indicate leakage, it increases the test frequency according to the set value to shorten the test time. In this case, since the battery is in a normal state, the higher test frequency can obtain sufficient impedance information in a shorter time, improving test efficiency. This ensures more accurate test data in cases where leakage is possible, while also improving test efficiency under normal conditions. This makes the test process intelligent and optimized, and improves the performance of the entire leakage current test system.

[0052] S104, obtaining first dynamic capacitance information according to the impedance imaginary part corresponding to the impedance data and the test parameter;

[0053] After receiving the impedance data at different frequencies transmitted by the electrochemical working device, the leakage current test system extracts the imaginary part of the impedance from these data. The system will associate and organize the imaginary part of the impedance data corresponding to each frequency point according to the previously set test frequency parameters. For example, if the test frequency points are 0.1Hz, 0.3Hz, 0.5Hz, 0.7Hz, and 0.9Hz, the system will extract the imaginary part of the battery impedance at these frequencies respectively, and according to the formula For each test frequency point, the system will substitute the frequency value f and the corresponding impedance imaginary part value Im(Z(f)) into the formula for calculation.

[0054] S105, controlling the power supply device to apply a constant current pulse of a preset magnitude to the target battery to be tested, and determining a voltage change cutoff condition;

[0055] The leakage current test system connects to the power supply device via a control interface and sends commands to the device, instructing it to apply a preset constant current pulse to the target battery under test. Before issuing the command, the system checks the preset constant current pulse to ensure it meets the test requirements and the battery's safe operating range. For example, for a certain lithium-ion battery model, the preset constant current pulse may be set to 100mA based on its specification and testing experience. After receiving the system's command, the power supply device generates a stable constant current pulse signal. The current control circuit within the power supply device uses a feedback mechanism to precisely control the current level and pulse width. For example, a PID (proportional-integral-differential) control algorithm is used to monitor the output current in real time and compare it with a preset value. The output current is adjusted based on the deviation to ensure the stability of the constant current pulse within ±1%. During the constant current pulse generation process, the power supply device monitors its operating status, such as temperature and voltage. If any abnormality occurs, an alarm is promptly sent to the leakage current test system to prevent damage to the battery.

[0056] While applying a constant pulse current, the leakage current test system needs to determine the voltage change cutoff condition. This cutoff condition is determined based on the characteristics of the battery and the purpose of the test. First, the system will refer to the battery specification and relevant industry standards to obtain the voltage change range of the battery under normal working conditions. In this application, the voltage change needs to be greater than 0.5mV (signal-to-noise ratio) and less than 10mV (linear region). The voltage change cutoff condition in this application can be 2mv. In order to monitor the voltage change and voltage change rate in real time, the leakage current test system will use the data acquisition module to sample the voltage at both ends of the battery in real time. The sampling frequency of the data acquisition module can be set according to the test requirements.

[0057] In some embodiments, to more accurately determine the constant pulse current applied to the target battery under test, a current determination model can be constructed using a machine learning-based approach. Specifically, the system collects a large amount of battery test data from pre-trained devices, collecting response data for different types of batteries (such as lithium-ion batteries, lead-acid batteries, and nickel-metal hydride batteries) under different current stimuli. This response data includes various parameters such as voltage changes across the battery terminals, internal resistance changes, and temperature changes. Each set of data is labeled with a corresponding battery type to distinguish between different battery types. A machine learning algorithm is then used to train the collected battery response data with different type labels. For example, a neural network algorithm uses the collected battery response data as input layer data, and the battery type labels as output layer data. During the training process, the neural network continuously adjusts its internal weights and biases. Through multiple iterations, the network's output battery type predictions are ensured to match the actual labels as closely as possible. After training on this large amount of data, the neural network gradually learns the characteristic patterns of the response data of different battery types under different current stimuli, thereby constructing a current determination model that understands the relationship between the characteristics of different battery types and their responses to current stimuli. When a leakage current test is required for the target battery to be tested, the type of the target battery to be tested is input into the trained current determination model. The model outputs a constant current pulse current value suitable for this type of battery based on the previously learned characteristic pattern. For example, if the target battery to be tested is a certain type of lithium-ion battery, the current determination model will give a suitable constant current pulse current value, such as 80mA, based on the learned lithium-ion battery-related characteristics. This current value is obtained by comprehensively considering the characteristics of this type of battery and the optimal stimulation current in previous test data. Compared with the traditional experience-based current value, it is more scientific and accurate.

[0058] S106, continuously monitoring the voltage variation of the target battery to be tested, and recording the test time variation when the voltage variation reaches the voltage variation cutoff condition;

[0059] The leakage current test system uses a data acquisition module to perform high-frequency, real-time sampling of the voltage across the target battery under test. The sampling frequency is set based on the battery characteristics and test accuracy requirements. The system analyzes the collected voltage data in real time and calculates the voltage difference between adjacent sampling points to determine the voltage change. When the monitored voltage change approaches the preset voltage change cutoff condition, the system activates the high-precision timing module. The timing module continuously records the time until the voltage change reaches the cutoff condition. The time obtained at this point is the test time change.

[0060] S107 . Determine a leakage current value according to the first dynamic capacitance information, the constant current pulse current, the test time variation, and the voltage variation.

[0061] Specifically, according to the leakage current formula, To determine the leakage information I sd ,in f is the frequency in the low-frequency EIS test parameters, Z(f) represents the impedance of the target battery to be tested at frequency f, and Im(Z(f)) is the imaginary part of the impedance of the target battery to be tested. During the test, the electrochemical working equipment applies the test to the battery according to the set parameters to obtain impedance data at different frequencies. The system extracts the imaginary part of the impedance from this data and substitutes it into the formula to calculate DNC1, which reflects the dynamic capacitance characteristics of the battery and provides an important parameter for leakage current calculation. Among them, ΔT is the time change corresponding to the voltage change ΔV reaching the voltage change cutoff condition, ΔV is the voltage change at both ends of the battery during the test that matches the voltage change cutoff condition, and I is the pulse current value applied during the test.

[0062] In an embodiment of the present application, the low-frequency EIS test parameters are specifically determined by determining the battery usage information of the target battery to be tested, the electrochemical working equipment and the power supply equipment are controlled to perform the test and obtain relevant data, and the leakage current value is determined by comprehensively integrating the first dynamic capacitance information, the constant current pulse current, the test time change and the voltage change. This not only simplifies the test process, avoids the complex operation of repeatedly changing the current size and duration in the traditional method, and improves the test efficiency, but also fully considers the battery characteristics and test environment factors, can more accurately determine the leakage current value, and provides a reliable basis for battery performance evaluation.

[0063] In some embodiments, once a battery is determined to be leaking, a series of operations are performed to assess the leakage risk, categorize and manage the batteries, and present them intuitively to the operator. Specifically, leaking batteries are first identified using the previously described leakage current testing method based on a set leakage current threshold. When the leakage current exceeds the threshold, the battery is considered leaking. For these leaking batteries, data on their remaining capacity, health status, and usage environment is collected. The remaining capacity can be obtained through the battery management system, while the health status can be determined based on a combination of parameters such as the number of charge and discharge cycles and changes in internal resistance. Environmental data, such as temperature, humidity, and air pressure, is collected through appropriate sensors. This data is then input into a pre-built intelligent leakage assessment model. This model is built based on extensive experimental data and machine learning techniques. During the model training phase, multiple sets of battery leakage data are collected under varying residual capacity, health status, and usage environment conditions. Each set of data is labeled with a corresponding risk level, which can be categorized as low, medium, or high risk. This data is then used to train the model using machine learning algorithms such as decision trees and neural networks. During training, the model continuously learns the characteristics and patterns in the data, adjusts its parameters, and ultimately acquires the ability to assess leakage risk based on the input battery data. When fed with data related to leaky batteries, the model outputs the corresponding risk assessment results.

[0064] After obtaining the risk assessment results, the leaking batteries are prioritized based on the leakage current values ​​obtained from previous tests. This ranking is based on a comprehensive consideration of the risk assessment results and leakage current values. Batteries with a high risk assessment result and a high leakage current value are prioritized, while batteries with a low risk assessment result and a low leakage current value are prioritized. In this way, all leaking batteries are sorted by risk level, forming a ranking result. This ranking helps quickly identify the risk level of different batteries, facilitating targeted follow-up measures. Based on the ranking results, an electronic label is created for each battery. The electronic label contains leakage level information, which can be categorized as mild, moderate, or severe based on the leakage current value. It also includes color-coded risk level information, such as green for low risk, yellow for medium risk, and red for high risk. This intuitive color coding and leakage level identification allows operators to quickly understand the battery's risk status. The system sends the prepared electronic tags in sorted order to a visual terminal, such as a computer screen, mobile device display, or dedicated industrial monitoring screen. On this terminal, operators can clearly see the electronic tag information of all leaking batteries, arranged in order of risk. This allows operators to clearly understand the battery leakage situation and take timely measures for high-risk batteries, such as repair, replacement, or further testing, thereby effectively managing batteries and ensuring their safety and stability.

[0065] In some embodiments, after confirming the first dynamic capacitance information and before confirming the leakage current value, the first dynamic capacitance information can be corrected in advance. The following is a detailed description of the process of this aspect. In combination with the above content, the following is a further and more specific description of the process of the method provided by this embodiment. Figure 2 , is another flow chart of the leakage current testing method based on low-frequency EIS and constant current pulse in an embodiment of the present application.

[0066] S201, determining first dynamic capacitance information DNC1;

[0067] After obtaining the impedance data of the target battery under test at different frequencies transmitted by the electrochemical working device, the leakage current test system begins to determine the first dynamic capacitance information DNC1. The system accurately extracts the imaginary impedance information from these impedance data and, based on the test frequency parameters, orderly associates and organizes the imaginary impedance data corresponding to each frequency point. More specifically, f is the frequency in the low-frequency EIS test parameters, and Im(Z(f)) is the imaginary impedance part of the target battery under test. Taking a certain type of ternary lithium battery as an example, assuming that the test frequency points are set to 0.05Hz, 0.1Hz, 0.2Hz, 0.5Hz and 1Hz. The system first finds the corresponding imaginary impedance value Im(Z(0.05Hz)) from the impedance data Z(f) for the frequency point of 0.05Hz. Then, according to the formula Substitute the frequency f = 0.05 Hz and the imaginary part of the impedance at this frequency into the formula for calculation.

[0068] In order to ensure the reliability of the calculation, the system will check the calculation process multiple times. On the one hand, the system will check whether the input frequency value and the imaginary part of the impedance are accurate and whether they are within a reasonable measurement range. If the imaginary part of the impedance at a certain frequency point is found to fluctuate abnormally, such as if the deviation from the imaginary part of the impedance of the same type of battery under similar test conditions is too large, the system will automatically mark the data point and recheck the measurement process of the electrochemical working equipment at this frequency. This may involve checking the stability of the test signal, whether the connection between the electrode and the battery is good, etc. On the other hand, the system will use different calculation methods to verify the results. For example, in addition to calculating directly according to the formula, the system will also use a numerical approximation algorithm for approximate calculations, compare the two calculation results, and if the deviation between the two is within the allowable error range (such as 0.5%), the calculation result is considered reliable.

[0069] S202, determining second dynamic capacitance information DNC2;

[0070] When the leakage current test system determines the second dynamic capacitance information DNC2, it needs to obtain relevant data during the constant current pulse current test. First, the system extracts the current value I1 applied for the first time and the current value I2 applied for the second time during the constant current pulse current application from the test record. For example, in a test on a lithium iron phosphate battery, the current value I1 applied for the first time is set to 50mA, and the current value I2 applied for the second time is set to 80mA. At the same time, the system obtains the voltage change ΔV1 corresponding to I1 at both ends of the battery in ΔT1, and the voltage change ΔV2 corresponding to I2 at both ends of the battery in ΔT2, where ΔV1 and ΔV2 both match the pre-set voltage change cutoff condition. In this application, the voltage change cutoff condition is 2mv, so both ΔV1 and ΔV2 can be 2mv.

[0071] Then pass The second dynamic capacitance information is obtained, where I1 is the current value applied for the first time during the constant current pulse current test, I2 is the current value applied for the second time during the constant current pulse current test, ΔV1 is the voltage change at both ends of the battery corresponding to I1 within ΔT1, ΔV2 is the voltage change at both ends of the battery corresponding to I2 within ΔT2, ΔT1 is the time interval corresponding to the ΔV1 voltage change process, ΔT2 is the time interval corresponding to the ΔV2 voltage change process, where ΔV1 and ΔV2 meet the voltage change cutoff condition.

[0072] S203, obtaining the battery cycle count of the target battery to be tested through the battery management system, and obtaining the current test environment temperature through the temperature sensor;

[0073] The leakage current test system establishes a stable data transmission link with the battery management system (BMS) through standard communication protocols, such as the Controller Area Network (CAN) protocol. In electric vehicle battery management scenarios, the BMS is responsible for real-time monitoring of key information such as the power battery's charge and discharge status and remaining power, including the battery cycle count. The system sends a request data packet containing a specific instruction code to the BMS. Upon receiving the request, the BMS reads the battery cycle count data from its internal storage unit and encapsulates the data into a response data packet according to the protocol format, transmitting it back to the leakage current test system.

[0074] To obtain the current test environment temperature, the leakage current test system uses a high-precision thermistor temperature sensor. The resistance value of this sensor will change precisely with the ambient temperature, and its sensitivity can reach 0.01°C. The sensor is installed close to the target battery to ensure that the actual temperature of the battery environment can be accurately measured.

[0075] S204: Input the battery cycle number and the previous test environment temperature into a dynamic capacitance weight fusion model to determine a first weight of the first dynamic capacitance information DNC1 and a second weight of the second dynamic capacitance information DNC2 under the current test environment. The dynamic capacitance weight fusion model is obtained by machine learning training based on battery sample data with different battery cycle numbers and different test environment temperatures. The battery sample data includes DNC1, DNC2, and weight combination labels under cycle number and temperature conditions.

[0076] After obtaining the battery cycle count and current test environment temperature of the target battery to be tested, the leakage current test system organizes these two sets of data into a specific input format and inputs them into the dynamic capacitance weight fusion model. This model is based on a large number of battery sample data with different battery cycle counts and different test environment temperatures, and is trained through a machine learning algorithm. Taking the support vector machine (SVM) algorithm training model as an example, during the training phase, thousands of sets of sample data of different types of batteries (such as lithium-ion batteries, lead-acid batteries, nickel-metal hydride batteries, etc.) were collected under various cycle counts (from 0 cycles for new batteries to 800 cycles near the end of their service life) and test environment temperatures (-20°C-60°C). Each set of sample data contains the corresponding first dynamic capacitance information DNC1, the second dynamic capacitance information DNC2, and a weight combination label with cycle counts and temperature conditions determined in advance based on experiments and experience.

[0077] When the system inputs the current battery cycle count and test environment temperature into the model, the model's internal algorithm extracts and analyzes the input data. For example, the model analyzes the cycle count's impact on the battery's internal structure and performance; and considers the temperature's impact on the battery's chemical reaction rate and capacitance characteristics. Through these analyses, the model outputs a first weight for the first dynamic capacitance information DNC1 and a second weight for the second dynamic capacitance information DNC2 under the current test environment.

[0078] Assume that when testing a new lithium-ion battery, the battery cycle count is 250 times, and the test environment temperature is 28°C. After inputting this data into the dynamic capacitance weight fusion model, the model outputs the first dynamic capacitance information DNC1 with a first weight of 0.6, and the second dynamic capacitance information DNC2 with a second weight of 0.4. This indicates that under the current battery usage status and test environment, the first dynamic capacitance information is relatively more important in subsequent calculations, but the second dynamic capacitance information cannot be ignored.

[0079] The system verifies the rationality of the weights output by the model. On the one hand, it references historical weight data for similar batteries under similar cycle times and temperature conditions to check whether the current weights are within a reasonable fluctuation range. On the other hand, it changes the input data by a small amount (such as increasing or decreasing the number of cycles by 10 times, or changing the temperature by 1°C), re-enters the model, and observes the changes in weights. If the weight changes follow the expected trend and the amplitude of the changes is small, the weights output by the model are considered reliable. If the weights change abnormally, the system will re-check the input data and model training, and retrain or adjust the model if necessary.

[0080] S205 , adjust the first dynamic capacitance information DNC1 according to the first dynamic capacitance information DNC1 and the first weight, the second dynamic capacitance information DNC2 and the second weight.

[0081] After obtaining the first dynamic capacitance information DNC1, the first weight, the second dynamic capacitance information DNC2, and the second weight, the leakage current test system begins adjusting the first dynamic capacitance information DNC1. The purpose of this adjustment is to comprehensively consider the impact of various factors on the dynamic capacitance, so that the first dynamic capacitance information can more accurately reflect the actual characteristics of the battery in its current state, thereby improving the accuracy of subsequent leakage current calculations.

[0082] The adjustment formula is: Adjusted DNC1 = Initial DNC1 × First Weight + DNC2 × Second Weight. For a laptop battery, for example, the calculated first dynamic capacitance DNC1 is 20 μF, the first weight derived from the dynamic capacitance weight fusion model is 0.7, and the second dynamic capacitance DNC2 is 15 μF, with a second weight of 0.3. According to the adjustment formula, Adjusted DNC1 = 20 μF × 0.7 + 15 μF × 0.3 = 14 μF + 4.5 μF = 18.5 μF.

[0083] The system records and compares the first dynamic capacitance information before and after the adjustment. By drawing a chart, the changes in DNC1 before and after the adjustment are intuitively displayed, allowing technicians to understand the impact of different factors on dynamic capacitance. If the adjusted DNC1 changes significantly compared to before the adjustment, the system will further analyze the cause. This may be due to significant differences in the actual battery usage and test environment compared to previously expected, or there may be errors in the acquisition of DNC1, DNC2, or weight data.

[0084] In an embodiment of the present application, a technical solution is provided for determining first dynamic capacitance information, second dynamic capacitance information, and adjusting the first dynamic capacitance information in combination with the number of battery cycles and the test environment temperature. By accurately calculating the dynamic capacitance information, obtaining key battery data, and using a trained model to determine weights, a more accurate reflection of the capacitance characteristics of the battery in the current state is achieved. This not only improves the accuracy of subsequent leakage current calculations, but also effectively solves the problem in the prior art of not fully considering the impact of battery usage status and environmental factors on dynamic capacitance, resulting in inaccurate leakage current calculations. This provides strong support for accurately evaluating battery leakage conditions, and can also more reliably evaluate battery performance based on more accurate leakage current data, thereby improving the safety and stability of battery management and use.

[0085] The following describes the leakage current test system in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , is a schematic diagram of the physical device structure of the leakage current testing system in an embodiment of the present application.

[0086] It should be noted that Figure 3 The structure of the leakage current testing system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0087] like Figure 3 As shown, the leakage current test system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 into the random access memory (RAM) 303, such as the method described in the above embodiment. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, ROM 302 and RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0088] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a push button switch, and the like; an output section 307 including a liquid crystal display (LCD), an audio output device, an indicator light, and the like; a storage section 308 including a hard disk and the like; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. Removable media 311, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 310 as needed, so that computer programs read therefrom can be installed into the storage section 308 as needed.

[0089] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product that includes a computer program carried on a computer-readable medium, the computer program including a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 309 and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, the various functions defined in the present invention are performed.

[0090] It should be noted that specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0091] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings.

[0092] Specifically, the leakage current testing system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the leakage current testing method based on low-frequency EIS and constant current pulse provided in the above embodiment is implemented.

[0093] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the leakage current test system described in the above embodiments, or may exist independently and not be incorporated into the leakage current test system. The storage medium carries one or more computer programs, which, when executed by a processor of the leakage current test system, enable the leakage current test system to implement the leakage current test method based on low-frequency EIS and constant current pulses provided in the above embodiments.

[0094] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0095] As used in the above embodiments, the term “when…” may be interpreted to mean “if…” or “after…” or “in response to determining…” or “in response to detecting…”, depending on the context. Similarly, the phrases “upon determining…” or “if (stated condition or event) is detected” may be interpreted to mean “if determining…” or “in response to determining…” or “upon detecting (stated condition or event)” or “in response to detecting (stated condition or event)”, depending on the context.

[0096] Those skilled in the art will appreciate that all or part of the process steps in the above-described method embodiments can be implemented by a computer program instructing the relevant hardware. The program can be stored in a computer-readable storage medium, and when executed, the program can include the process steps in the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A leakage current testing method based on low-frequency EIS and constant current pulse, applied to a leakage current testing system, characterized in that: The method comprises: Determine battery usage information of the target battery to be tested; Determine low-frequency EIS test parameters based on battery usage information; Controlling the electrochemical working device to apply the test to the target battery to be tested according to the test parameters to obtain impedance data of the target battery to be tested at different frequencies; Obtaining first dynamic capacitance information according to the impedance imaginary part corresponding to the impedance data and the test parameter; Controlling the power supply device to apply a constant current pulse of a preset magnitude to the target battery to be tested, and determining a voltage change cutoff condition; Continuously monitoring the voltage change of the target battery to be tested, and recording the test time change when the voltage change reaches the voltage change cutoff condition; Determine a leakage current value according to the first dynamic capacitance information, the constant current pulse current, the test time variation, and the voltage variation; The step of determining the leakage current value according to the first dynamic capacitance information, the constant current pulse current, the test time variation, and the voltage variation specifically includes: Determine the leakage information I according to the leakage current formula sd , the leakage current formula is Wherein DNC1 is the first dynamic capacitance information, ΔT is the time change corresponding to the voltage change ΔV reaching the voltage change cutoff condition, ΔV is the voltage change at both ends of the battery during the test that matches the voltage change cutoff condition, and I is the pulse current value applied during the test.

2. The method according to claim 1, characterized in that The step of obtaining first dynamic capacitance information according to the imaginary impedance part corresponding to the impedance data and the test parameter specifically includes: Determine first dynamic capacitance information DNC1, f is the frequency in the test parameters of low-frequency EIS, Im(Z(f)) is the imaginary part of the impedance of the target battery to be tested, and Z(f) represents the impedance of the target battery to be tested when the frequency is f.

3. The method according to claim 2, characterized in that After the step of determining the first dynamic capacitance information DNC1, the method further includes: Determine the second dynamic capacitance information DNC2, Where I1 is the current value applied for the first time during the constant current pulse current test, I2 is the current value applied for the second time during the constant current pulse current test, ΔV1 is the voltage change across the battery corresponding to I1 within ΔT1, ΔV2 is the voltage change across the battery corresponding to I2 within ΔT2, ΔT1 is the time interval experienced during the voltage change process corresponding to ΔV1, and ΔT2 is the time interval experienced during the voltage change process corresponding to ΔV2; The battery management system is used to obtain the battery cycle count of the target battery to be tested, and the temperature sensor is used to obtain the current test environment temperature; Inputting the battery cycle number and the previous test environment temperature into a dynamic capacitance weight fusion model to determine a first weight of the first dynamic capacitance information DNC1 and a second weight of the second dynamic capacitance information DNC2 under the current test environment, wherein the dynamic capacitance weight fusion model is obtained by machine learning training based on battery sample data with different battery cycle numbers and different test environment temperatures, wherein the battery sample data includes DNC1, DNC2, and weight combination labels under cycle number and temperature conditions; The first dynamic capacitance information DNC1 is adjusted according to the first dynamic capacitance information DNC1 and the first weight, the second dynamic capacitance information DNC2 and the second weight.

4. The method according to claim 1, wherein The steps of determining the low-frequency EIS test parameters based on the battery usage information specifically include: Determine the environmental data for the current low-frequency EIS test; The battery usage information and the environmental data are input into a preset frequency determination model to obtain a matching low-frequency range. The battery usage information includes the battery type. The frequency determination model is constructed and trained as follows: Acquire actual operating data of multiple different types of batteries under different environmental conditions, the actual operating data including impedance data under EIS tests at different frequencies; The frequency determination model is obtained by machine learning using the battery type as a classification label and combining the actual operation data.

5. The method according to claim 1, wherein Before the step of controlling the electrochemical workstation to apply the test to the target battery according to the test parameters, the method further includes: Obtain temperature curve data of the target battery before testing; Inputting the temperature curve data into a pre-trained temperature feature recognition model to obtain a temperature recognition output result, wherein the temperature feature recognition model is obtained by machine learning training based on temperature change data of multiple normal batteries and leaking batteries; Based on the temperature identification output result, the low-frequency EIS test parameters are adjusted in a dynamic adjustment manner. The dynamic adjustment manner includes, when it is identified that the temperature curve data has leakage characteristics, reducing the test frequency according to a set value to improve the impedance measurement accuracy; when it is identified that the temperature curve data does not have leakage characteristics, increasing the test frequency according to a set value to shorten the test time.

6. The method according to claim 1, characterized in that After the step of determining the leakage current value according to the first dynamic capacitance information, the constant current pulse current, the test time variation, and the voltage variation, the method further includes: Identify leaking batteries and input the remaining capacity, health status, and usage environment data of the leaking batteries into a pre-built intelligent leakage assessment model for risk quantification assessment to obtain a risk assessment result. The intelligent leakage assessment model is previously established through machine learning training based on multiple sets of battery leakage data with different remaining capacity, different health status, and different usage environment conditions and their corresponding risk level labels; Prioritize the batteries according to risk level based on the risk assessment result and the leakage current value to obtain a ranking result; According to the ranking results, electronic labels containing leakage degree and risk level information with different color codes are applied to the batteries; The electronic tags are sent to the visual terminal in the order of the sorting results.

7. A leakage current testing system, characterized in that: The leakage current testing system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the leakage current testing system to execute the method described in any one of claims 1-6.

8. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on a leakage current testing system, the leakage current testing system is caused to execute the method according to any one of claims 1 to 6.

9. A computer program product, characterized in that When the computer program product is run on a leakage current testing system, the leakage current testing system is enabled to perform the method according to any one of claims 1 to 6.

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