Battery impedance analysis methods, apparatus, storage media and computer equipment

Abstract

This application provides a battery impedance analysis method, apparatus, storage medium, and computer equipment. When analyzing battery impedance, an EIS test is performed on the target battery to capture various electrochemical behaviors of the battery during the test process through the dynamic characteristics in the test data. Therefore, the DRT spectrum generated by DRT analysis of the test data can intuitively display the impedance response time corresponding to different electrochemical processes in the time dimension. After determining the response range of each electrochemical process, standard charge-discharge cycles can be performed on the target battery. During the cycle, DCR tests are performed on the target battery for multiple selected cycles, and impedance decomposition is performed on the obtained DCR data based on each response range to achieve real-time subdivision of the overall impedance information. Finally, the impedance decomposition data obtained after subdivision for each selected cycle number are compared and analyzed to generate high-quality impedance analysis results.

Description

Technical Field

[0001] This application relates to the field of battery technology, and in particular to a battery impedance analysis method, apparatus, storage medium, and computer equipment. Background Technology

[0002] With the rapid development of energy storage and conversion technologies, batteries, as core components, are of paramount importance in terms of performance stability and reliability. However, batteries inevitably experience performance degradation and even failure during long-term use. To gain a deeper understanding of battery performance changes and improve battery quality and lifespan, battery failure analysis has become a crucial step, and impedance analysis, as an important analytical method, has attracted much attention.

[0003] Currently, in the field of battery impedance analysis, DCR (Direct Current Resistance) analysis and EIS (Electrical Impedance Scanning) analysis are mostly used. DCR analysis allows for online impedance analysis, and the obtained resistance value is strongly correlated with the battery's charge-discharge performance. However, DCR testing only provides an overall impedance value and cannot refine the impedance details. In contrast, EIS analysis is a quasi-steady-state impedance analysis method, but the resistance value it obtains can only be analyzed according to the changes in individual resistance values, and it cannot identify the impedance online. In summary, current battery impedance analysis methods have significant limitations, making it difficult to simultaneously achieve online real-time monitoring and high-precision impedance detailing, resulting in poor analytical performance. Summary of the Invention

[0004] The purpose of this application is to at least solve one of the above-mentioned technical defects, in particular the technical defect that the existing battery impedance analysis method has obvious limitations, making it difficult to take into account both online real-time monitoring and high-precision subdivision of impedance characteristics, resulting in poor analysis results.

[0005] In a first aspect, this application provides a battery impedance analysis method, the method comprising:

[0006] The battery to be tested is adjusted to the test state and used as the target battery. The target battery is then subjected to EIS testing to obtain EIS test data, which is impedance response data at different frequencies.

[0007] DRT analysis was performed on the EIS test data to generate DRT spectra, and multiple time response intervals were identified from the DRT spectra, with each time response interval corresponding to an electrochemical process.

[0008] The target battery is subjected to standard charge-discharge cycles, and during the cycle, the DCR test is performed on the target battery for multiple selected cycles to obtain the DCR data corresponding to each selected cycle.

[0009] Impedance decomposition is performed on each DCR data based on each time response interval to obtain the impedance decomposition data of the target battery at each selected cycle number. By comparing and analyzing the impedance decomposition data, the impedance analysis results of the target battery are generated.

[0010] Optionally, the number of test cycles in the EIS test is the same as the selected number of cycles in the DCR test for the target battery; adjusting the battery to be tested to the test state as the target battery includes:

[0011] The battery under test is subjected to at least two constant current charge-discharge tests using a preset test current. After the last constant current charge-discharge test is completed, the battery under test is discharged to 50% of its state of charge and used as the target battery.

[0012] Optionally, performing EIS testing on the target battery to obtain EIS test data includes:

[0013] Determine the initial test data; the initial test data includes the test room temperature, disturbance current, and frequency range;

[0014] Based on the test room temperature, the perturbation current is used to apply a current perturbation to the target battery within the frequency range, and the EIS test data generated by the current response is measured; the EIS test data includes frequency data, real part data, and imaginary part data.

[0015] Optionally, the step of performing DRT analysis on the EIS test data to generate a DRT map includes:

[0016] The EIS test data is input into the DRT processing program so that the DRT processing program can perform relaxation time distribution analysis on the EIS test data and generate a DRT map based on the analysis results.

[0017] Optionally, the step of identifying multiple time response intervals from the DRT map includes:

[0018] Electrochemical analysis of the DRT spectrum was performed to obtain the time response interval of the target battery for each electrochemical process during the EIS test.

[0019] In chronological order, the time response intervals are, in turn, the ohmic impedance contact impedance response interval, the negative electrode charge transfer response interval, the positive electrode charge transfer response interval, the liquid phase diffusion response interval, and the solid phase diffusion response interval.

[0020] Optionally, the impedance decomposition of each DCR data based on each time response interval to obtain the impedance decomposition data of the target battery at each selected cycle number includes:

[0021] For each selected number of revolutions, the voltage change value for each time response interval is extracted from the DCR data of that selected number of revolutions;

[0022] Obtain the DCR test current, and determine the impedance value corresponding to each time response interval based on the DCR test current and the voltage change value of each response interval;

[0023] The impedance source is determined based on the electrochemical process corresponding to each time response interval, and impedance decomposition data for the selected number of cycles is generated based on the impedance source and impedance value corresponding to each time response interval.

[0024] Optionally, each of the selected laps includes an initial lap, a middle lap, and a lap before the inflection point;

[0025] The step of generating impedance analysis results for the target battery through comparative analysis of the various impedance decomposition data includes:

[0026] Impedance growth analysis is performed on the impedance values ​​of each impedance source in the impedance decomposition data of the initial loop and the intermediate loop to obtain the initial loop analysis results.

[0027] Impedance growth analysis is performed on the impedance values ​​of each impedance source in the impedance decomposition data of the intermediate phase and the phase before the inflection point to obtain the aging rate analysis results.

[0028] Impedance analysis results for the target battery are generated based on the initial cycle analysis results and the aging rate analysis results.

[0029] Secondly, this application also provides a battery impedance analysis device, comprising:

[0030] The EIS test module is used to adjust the battery under test to the test state, which serves as the target battery, and to perform EIS testing on the target battery to obtain EIS test data, which is impedance response data at different frequencies.

[0031] The DRT analysis module is used to perform DRT analysis on the EIS test data, generate DRT spectra, and identify multiple time response intervals from the DRT spectra, with each time response interval corresponding to an electrochemical process.

[0032] The DCR test module is used to perform standard charge-discharge cycles on the target battery and, during the cycle, perform DCR tests on the target battery for multiple selected cycles to obtain DCR data corresponding to each selected cycle.

[0033] The impedance analysis module is used to perform impedance decomposition on each of the DCR data based on each time response interval, to obtain the impedance decomposition data of the target battery at each selected number of cycles, and to generate the impedance analysis result of the target battery by comparing and analyzing the impedance decomposition data.

[0034] Thirdly, this application also provides a storage medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the battery impedance analysis method described in the first aspect above.

[0035] Fourthly, this application also provides a computer device, including: one or more processors, and memory;

[0036] The memory stores computer-readable instructions, which, when executed by the one or more processors, perform the steps of the battery impedance analysis method as described in the first aspect above.

[0037] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:

[0038] This application provides a battery impedance analysis method, apparatus, storage medium, and computer equipment. When analyzing battery impedance, the battery under test can be adjusted to the test state as the target battery, and EIS testing can be performed on the target battery to obtain EIS test data. This comprehensively reflects the dynamic characteristics of the battery and captures various electrochemical behaviors of the battery during the test. Therefore, the DRT spectrum generated after DRT analysis of the EIS test data can more intuitively show the impedance response time of different electrochemical processes in the time dimension. After identifying the time response interval of each electrochemical process from the DRT spectrum, the target battery can be subjected to standard charge-discharge cycles. During the cycle, DCR testing can be performed on the target battery for multiple selected cycles to obtain DCR data corresponding to each selected cycle. This allows for real-time acquisition of the overall impedance information of the battery at different stages, reflecting the actual performance changes of the battery under different operating conditions. For each selected cycle, impedance decomposition can be performed on its DCR data based on each time response interval to achieve real-time subdivision of the overall impedance information. Therefore, comparative analysis of the impedance decomposition data after subdivision of each selected cycle can generate high-quality impedance analysis results, thereby providing a deeper understanding of the performance changes of the battery at different charge-discharge cycle stages. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 A schematic flowchart illustrating a battery impedance analysis method provided in an embodiment of this application;

[0041] Figure 2 This is a schematic diagram of the structure of a DRT map provided in an embodiment of this application;

[0042] Figure 3 A schematic diagram of a battery charge-discharge cycle process provided in an embodiment of this application;

[0043] Figure 4 A schematic diagram of DCR data provided in an embodiment of this application;

[0044] Figure 5 This is a schematic flowchart of a battery impedance analysis device provided in an embodiment of this application;

[0045] Figure 6 This is a schematic diagram of the internal structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0046] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0047] Currently, in the field of battery impedance analysis, DCR (Direct Current Resistance) analysis and EIS (Electrical Impedance Scanning) analysis are mostly used. DCR analysis allows for online impedance analysis, and the obtained resistance value is strongly correlated with the battery's charge-discharge performance. However, DCR testing only provides an overall impedance value and cannot refine the impedance details. In contrast, EIS analysis is a quasi-steady-state impedance analysis method, but the resistance value it obtains can only be analyzed according to the changes in individual resistance values, and it cannot identify the impedance online. In summary, current battery impedance analysis methods have significant limitations, making it difficult to simultaneously achieve online real-time monitoring and high-precision impedance detailing, resulting in poor analytical performance.

[0048] Based on this, this application proposes the following technical solution, as detailed below:

[0049] In one embodiment, such as Figure 1 As shown, Figure 1 This is a flowchart illustrating a battery impedance analysis method provided in an embodiment of this application. The application provides a battery impedance analysis method, specifically including the following:

[0050] S110: Adjust the battery to be tested to the test state, use it as the target battery, and perform EIS testing on the target battery to obtain EIS test data. The EIS test data is impedance response data at different frequencies.

[0051] In this step, when analyzing battery impedance, the computer equipment can first adjust the battery under test to the test state as the target battery, and perform EIS testing on the target battery to obtain EIS test data containing impedance response data at different frequencies, so as to comprehensively reflect the dynamic characteristics of the battery and capture various electrochemical behaviors of the battery during the test process.

[0052] Understandably, in the process of analyzing battery impedance, the computer equipment first needs to charge and discharge the battery under test to bring it to a specific test state, such as 50% state of charge or other working states set according to test requirements, in order to ensure that the battery has stable initial conditions during the test, so that the internal state of the battery can be as balanced as possible and the impact of external disturbances on the test results can be reduced.

[0053] Furthermore, EIS testing can obtain EIS test data characterizing frequency-impedance properties by applying small-amplitude AC perturbations of different frequencies to the battery and recording its voltage response. Through EIS testing, computer equipment can comprehensively capture the electrochemical behavior of the battery, including ohmic impedance, contact impedance, charge transfer reactions, diffusion reactions, and other behaviors. These can reflect the different dynamic performance of the battery at high and low frequencies, providing high-quality basic data for subsequent impedance analysis.

[0054] S120: Perform DRT analysis on EIS test data, generate DRT spectra, and identify multiple time response intervals from the DRT spectra. Each time response interval corresponds to an electrochemical process.

[0055] In this step, after obtaining the EIS test data through step S110, the computer equipment can perform DRT analysis on the EIS test data and generate DRT spectrum. This allows the impedance response time of different electrochemical processes to be intuitively divided in the time dimension based on the response rate of each electrochemical reaction, thereby obtaining the time response interval of each electrochemical process.

[0056] Understandably, electrochemical reactions in EIS testing are primarily manifested as impedance changes at different frequencies. Distributed Time-Relaxation (DRT) analysis, using inversion algorithms, converts EIS frequency domain data into a distribution function in the time constant domain, mapping impedance frequency domain information to the time constant domain. This allows for high-resolution separation of overlapping polarization processes (such as charge transfer, diffusion, and double-layer effects). Therefore, DRT spectra generated through DRT analysis enable computer equipment to acquire high-resolution dynamic characteristics of different processes in complex electrochemical reactions, effectively avoiding information obscuring or misjudgment.

[0057] S130: Perform standard charge-discharge cycles on the target battery, and during the cycle, perform DCR tests on the target battery for multiple selected cycles to obtain DCR data corresponding to each selected cycle.

[0058] In this step, after determining the battery's time response time in each electrochemical process through step S120, the computer equipment can also perform standard charge-discharge cycles on the target battery. During the cycle, DCR tests are performed on multiple target batteries with selected number of cycles to obtain DCR data corresponding to each selected number of cycles. This allows for the real-time acquisition of the battery's overall impedance information at different stages, which can be used to reflect the actual performance changes of the battery under different operating conditions.

[0059] Specifically, during standard charge-discharge cycles, the computer equipment can perform a complete charge-discharge cycle on the battery according to set parameters such as charging current, voltage upper and lower limits, and discharge rate, and accurately record the number of cycles for each cycle. During this process, the computer equipment can select multiple key cycle numbers based on battery characteristics and perform DCR tests on the target battery at these selected cycle numbers. The DCR test is performed by applying a constant current pulse and measuring the resulting voltage change, and then calculating the overall impedance value of the battery in the current state according to Ohm's law. Through the test results of each selected cycle number, the computer equipment can promptly understand the changing trend of the battery's overall impedance as the usage period increases.

[0060] S140: Based on each time response interval, impedance decomposition is performed on each DCR data to obtain the impedance decomposition data of the target battery at each selected cycle number. Through comparative analysis of each impedance decomposition data, the impedance analysis results of the target battery are generated.

[0061] In this step, after obtaining the DCR data of the target battery at each selected number of cycles through step S130, the computer device can perform impedance decomposition on the DCR data based on each time response interval for each selected number of cycles, realizing real-time subdivision of the overall impedance information. Therefore, by comparing and analyzing the impedance decomposition data after subdivision at each selected number of cycles, high-quality impedance analysis results can be generated, thereby gaining a deeper understanding of the performance changes of the battery at different charge and discharge cycle stages.

[0062] Understandably, the DCR data obtained from the DCR test can reflect the overall impedance information of the battery during use. Therefore, the computer device can decompose the impedance-related information in the DCR data into multiple dimensions based on the response time corresponding to each electrochemical process obtained in step S120, so as to map it to the impedance components corresponding to different electrochemical processes, thereby determining the impedance analysis results of the battery during use.

[0063] Specifically, computer equipment can use algorithmic models to proportionally fit or reconstruct DCR data according to the contributions of electrochemical processes such as ohmic impedance, contact impedance, charge transfer reaction, and diffusion reaction, thereby achieving a refined expression of DCR at the microscopic mechanism level. In this process, the computer equipment can store the decomposition results for each selected number of cycles in the form of vectors or parameter sets, and construct an impedance decomposition dataset along the cycle dimension. This allows subsequent impedance analysis processes to perform longitudinal comparisons and trend analyses of the various impedance components at different cycle numbers.

[0064] In the above embodiments, when analyzing battery impedance, the battery under test can be adjusted to the test state as the target battery, and EIS testing can be performed on the target battery to obtain EIS test data, which comprehensively reflects the dynamic characteristics of the battery and captures various electrochemical behaviors of the battery during the test. Therefore, the DRT spectrum generated after DRT analysis of the EIS test data can more intuitively show the impedance response time of different electrochemical processes in the time dimension. After identifying the time response interval of each electrochemical process from the DRT spectrum, standard charge-discharge cycles can be performed on the target battery, and DCR testing can be performed on the target battery for multiple selected cycles during the cycle to obtain DCR data corresponding to each selected cycle. This allows for real-time acquisition of the overall impedance information of the battery at different stages, which reflects the actual performance changes of the battery under different operating conditions. For each selected cycle, impedance decomposition can be performed on its DCR data based on each time response interval to achieve real-time subdivision of the overall impedance information. Therefore, comparative analysis of the impedance decomposition data after subdivision of each selected cycle can generate high-quality impedance analysis results, thereby providing a deeper understanding of the performance changes of the battery at different charge-discharge cycle stages.

[0065] In one embodiment, the number of test cycles for the EIS test in step S110 is the same as the selected number of cycles for the target battery in the DCR test; wherein, the process of adjusting the battery to be tested to the test state as the target battery may include:

[0066] S111: Perform at least two constant current charge-discharge tests on the battery under test using a preset test current, and after the last constant current charge-discharge test is completed, discharge the battery under test to 50% state of charge, and use it as the target battery.

[0067] In this embodiment, when adjusting the state of the battery under test, the computer device can use a preset test current to perform at least two constant current charge-discharge tests on the battery under test. In addition, during the last constant current charge-discharge test, the computer device can discharge the battery under test to 50% of its state of charge and use it as the target battery.

[0068] It should be noted that the number of test cycles in the EIS test is the same as the number of cycles selected in the DCR test for the target battery. This allows for a comprehensive analysis of the battery's electrochemical characteristics and actual performance at different cycling stages. The consistent number of cycles facilitates a comparison of the results of the EIS test and the DCR test, thereby more accurately identifying the source of impedance growth.

[0069] Specifically, during the first constant-current charging process, the battery, in its fully charged state, automatically enters the constant-current discharging phase, releasing its charge to a preset lower voltage limit. Then, the battery is slowly charged from its low-charge state to a preset upper voltage limit by a constant current, thus completing a full constant-current charge-discharge cycle. After completing the first round of testing, the computer equipment will continue with the second round of constant-current charge-discharge operations, allowing the battery to experience charge-discharge cycles under different states of charge. This further mitigates initial uncertainties such as internal resistance instability or polarization buildup, improving test accuracy. During the final constant-current charge-discharge process, the computer equipment can precisely control the discharge cutoff point based on real-time monitoring of the discharge capacity and voltage curves, ensuring the battery stably reaches a 50% state of charge. At this point, the battery is neither in a high-voltage stress state nor a low-voltage extreme state, making it the most suitable neutral point for subsequent test standard samples. Therefore, the computer equipment can identify the battery in this state as the target battery.

[0070] For example, a 50Ah LFP-Gr battery can be used as the test battery for a computer device, with voltage limits of 2.5V and 3.65V respectively. When adjusting the battery while it is fully charged, the computer device can first discharge it at a constant current of 1C to 2.5V and then let it rest for 30 minutes. Then, it can charge the battery at a constant current of 1C to 3.65V, then maintain a constant voltage at 0.05C, and let it rest for another 30 minutes, thus completing one constant current charge-discharge test cycle. After two such constant current charge-discharge tests, the computer device can calibrate the battery's discharge capacity as C0, and then discharge the battery at a constant current of 1C to 0.5C0, at which point the battery reaches a state of charge of 50%.

[0071] In one embodiment, the process of performing EIS testing on the target battery and obtaining EIS test data in step S110 may include:

[0072] S112: Determine the initial test data; the initial test data includes the test room temperature, disturbance current, and frequency range.

[0073] S113: Based on the test room temperature, a perturbation current is applied to the target battery within the frequency range, and the EIS test data generated by the current response is measured; the EIS test data includes frequency data, real part data and imaginary part data.

[0074] In this embodiment, when performing EIS testing on the target battery, the computer device can first determine the initial test data, including the test room temperature, the disturbance current, and the frequency range. Based on the test room temperature, the computer can apply a current disturbance to the target battery within the frequency range using the disturbance current and measure the EIS test data generated by the current response. The EIS test data here includes frequency data, real part data, and imaginary part data.

[0075] Specifically, setting the test room temperature ensures environmental consistency throughout the testing process. After setting the test room temperature, the computer equipment can control the test system to apply a perturbation current to the target battery within a frequency range. This perturbation current is a small-amplitude sinusoidal signal, and the frequency is scanned point-by-point in a logarithmic manner within the frequency range, for example, from a few millihertz to tens of kilohertz, to cover the response range of different electrochemical processes in the battery. At each frequency point, the computer equipment can record the steady-state voltage response of the target battery under this perturbation, and extract the phase difference and amplitude ratio of voltage and current through Fourier transform, thereby calculating the EIS test data.

[0076] It is understandable that EIS test data includes frequency data, as well as the corresponding real and imaginary parts of the frequency data. The frequency data refers to the scanning frequency points during the test; the real part refers to the ohmic components in the battery, such as the conductivity of the electrode material and the electrolyte resistance; and the imaginary part refers to the effects of capacitance and polarization processes, such as interfacial charge accumulation and ion diffusion delay.

[0077] In one embodiment, the process of performing DRT analysis on the EIS test data and generating a DRT map in step S120 may include:

[0078] S121: Input the EIS test data into the DRT processing program so that the DRT processing program can perform relaxation time distribution analysis on the EIS test data and generate a DRT spectrum based on the analysis results.

[0079] In this embodiment, when performing DRT analysis on EIS test data, the computer device can input the EIS test data into the DRT processing program so that the DRT processing program can perform relaxation time distribution analysis on the EIS test data and generate a DRT map based on the analysis results.

[0080] Specifically, the DRT processing program integrates mathematical modeling and inversion algorithm modules, enabling it to perform relaxation time distribution analysis on the input EIS test data. During the analysis, the DRT processing program can perform integral deconvolution calculations on the EIS test data to solve for the response intensity distribution of the electrochemical system under different time constants, that is, to transform the complex impedance Z(f) from the frequency domain to the relaxation time constant domain. Finally, the DRT processing program can plot the response intensity corresponding to each time constant as a distribution map, generating the final DRT spectrum.

[0081] Indicatively, such as Figure 2 As shown, Figure 2 This is a schematic diagram of the structure of a DRT map provided in an embodiment of this application; Figure 2The example is a DRT spectrum of a sample labeled 2.39 / Ah-A -3533. Each peak in this DRT spectrum corresponds to a specific electrochemical process, and the horizontal axis represents the time constant. The vertical axis represents the contribution intensity of the process. Furthermore, the range from 1E−04 to 1E+03 can cover all electrochemical reaction ranges from high frequency to low frequency, while 0.01, 0.12, 0.92, and 7.07 are the dividing points between various electrochemical processes.

[0082] In one embodiment, the process of identifying multiple time response intervals from the DRT map in step S120 may include:

[0083] S122: Perform electrochemical analysis on the DRT spectrum to obtain the time response interval of the target battery for each electrochemical process during EIS testing.

[0084] In this embodiment, the computer device can perform electrochemical analysis on the DRT spectrum. During the analysis, it can accurately distinguish the time response intervals of various electrochemical reactions based on the distribution of different peaks on the time axis, and sequentially extract the ohmic impedance contact impedance response interval, negative electrode charge transfer response interval, positive electrode charge transfer response interval, liquid phase diffusion response interval, and solid phase diffusion response interval of the target battery during the EIS test.

[0085] Understandably, analyzing the DRT spectrum from left to right, the impedance peaks within the smallest time constant range reflect the ohmic impedance contact impedance response range inside the battery. This is mainly affected by the material conductivity, electrolyte conductivity, and electrode contact quality, exhibiting extreme response times and high frequencies. Subsequently, within a slightly larger time constant range, two relatively independent impedance peaks exist. Their order and amplitude characteristics are related to the charge transfer reaction processes of the negative and positive electrodes. The negative electrode charge transfer response range appears in the mid-to-high frequency region, corresponding to the charge exchange capacity of the negative electrode surface, while the positive electrode charge transfer response range appears towards the end of the mid-frequency region, corresponding to the charge transfer kinetics of the positive electrode. Continuing the analysis to the right, the liquid-phase diffusion response range can be extracted in the higher time constant region. This reflects the restricted ion transport characteristics in the electrolyte, typically having a significant impact and closely related to temperature and concentration gradients. Finally, in the low-frequency time constant region, the solid-phase diffusion response range can be identified. This mainly involves the lithium-ion insertion and extraction rates within the active material, directly related to electrode particle size, microstructure integrity, and aging degree.

[0086] For example, such as Figure 2 As shown, by analyzing Figure 2By identifying the DRT spectrum, we can determine that 0-0.01s is the ohmic impedance contact impedance response range, 0.01-0.12s is the negative electrode charge transfer response range, 0.12-0.92s is the positive electrode charge transfer response range, 0.92-7.07s is the liquid phase diffusion response range, and 7.07-30s is the solid phase diffusion response range. Therefore, different response ranges correspond to different impedance sources.

[0087] In one embodiment, step S140, which involves performing impedance decomposition on each DCR data based on each time response interval to obtain impedance decomposition data of the target battery at each selected cycle number, may include:

[0088] S141: For each selected number of revolutions, extract the voltage change value for each time response interval from the DCR data of that selected number of revolutions.

[0089] S142: Obtain the DCR test current and determine the impedance value corresponding to each time response interval based on the DCR test current and the voltage change value of each time response interval.

[0090] S143: Determine the impedance source based on the electrochemical process corresponding to each time response interval, and generate impedance decomposition data for the selected number of cycles based on the impedance source and impedance value corresponding to each time response interval.

[0091] In this embodiment, for each selected number of revolutions, the computer device can extract the voltage change value of each time response interval from the DCR data of that selected number of revolutions, then obtain the DCR test current, and determine the impedance value corresponding to each time response interval based on the DCR test current and the voltage change value of each time response interval. At the same time, the impedance source can be determined based on the electrochemical process corresponding to each time response interval, and then the impedance decomposition data of that selected number of revolutions can be generated based on the impedance source and impedance value corresponding to each time response interval.

[0092] Specifically, after acquiring DCR data, the computer equipment can divide the DCR data into multiple intervals based on the time response intervals of each electrochemical process. Then, the voltage change value ΔV of each interval can be extracted and correlated with the real-time recorded DCR test current ΔI. Using Ohm's law R=ΔV / ΔI, the impedance value of each time response interval can be calculated. Since these time response intervals have been mapped to specific electrochemical processes in the time dimension through previous DRT analysis, including the ohmic impedance contact impedance response interval, negative electrode charge transfer response interval, positive electrode charge transfer response interval, liquid phase diffusion response interval, and solid phase diffusion response interval, the computer equipment can accurately determine the impedance source corresponding to the impedance value by combining the time position of each time response interval with the electrochemical reaction. This allows for a one-to-one correspondence between the impedance value of each time response interval and its electrochemical process, forming structured impedance decomposition data. This achieves fine-grained decomposition of DCR data on a time scale, thus solving the "black box" problem of traditional DCR data, realizing the physical interpretability of impedance sources, and enabling interpretable tracing and prediction of battery performance degradation.

[0093] In one embodiment, the selected number of revolutions in step S140 may include an initial revolution, a middle revolution, and a revolution before the inflection point; wherein, the process of generating the impedance analysis result of the target battery through comparative analysis of various impedance decomposition data may include:

[0094] S141: Perform impedance growth analysis on the impedance values ​​of each impedance source in the impedance decomposition data of the initial and intermediate loops to obtain the initial loop analysis results.

[0095] S142: Perform impedance growth analysis on the impedance values ​​of each impedance source in the impedance decomposition data of the intermediate and pre-inflection point rings to obtain the aging rate analysis results.

[0096] S143: Generate impedance analysis results for the target battery based on the initial cycle analysis results and aging rate analysis results.

[0097] In this embodiment, the present application can perform DCR testing on the initial cycle, intermediate cycle, and pre-inflection point cycle. When performing data comparison and analysis on each selected cycle number, the computer device can first perform impedance growth analysis on the impedance values ​​of each impedance source in the impedance decomposition data of the initial and intermediate cycles to obtain the initial cycle analysis results, such as identifying the early degradation dominant mechanism. Then, it can perform impedance growth analysis on the impedance values ​​of each impedance source in the impedance decomposition data of the intermediate and pre-inflection point cycles to obtain the aging rate analysis results, such as identifying the late accelerated degradation mechanism. Furthermore, it can generate the impedance analysis results of the target battery based on the initial cycle analysis results and the aging rate analysis results, such as comparing the initial cycle analysis results and the aging rate analysis results to analyze the battery failure path.

[0098] It is understandable that, such as Figure 3 As shown, Figure 3 A schematic diagram of a battery charge-discharge cycle process provided in an embodiment of this application; Figure 3 In this process, the first circle can be selected as the initial circle. Since the changing trend of circles 1-100 is relatively small, there is no need to analyze it. Therefore, an intermediate circle can be selected from circles 100-300. This also avoids missing key features in the analysis process due to an excessive number of circles. Here, this application can select circle 200 as the intermediate circle; from Figure 3 As can be seen, the battery reaches an inflection point after 500 cycles. The failure mechanisms before and after this inflection point are different. Therefore, this application can use the data before the inflection point for early failure analysis. For example, the 400th cycle can be selected as the cycle before the inflection point.

[0099] Specifically, such as Figure 4 As shown, Figure 4 A schematic diagram of DCR data provided in an embodiment of this application; Figure 4 In the process of impedance decomposition and subdivision of DCR data, the DCR data of the 200th cycle can be used as an intermediate evolution. If the trend of the 1st to 200th cycles is consistent with the growth trend of the 200th to 400th cycles, the main factors affecting the cell impedance can be determined. If the trends are different, such as the change in ohmic impedance and contact impedance in the early stage of the 200th cycle, while the change in charge transfer impedance or diffusion impedance in the later stage of the 400th cycle, it can be considered that the material loss is small in the early stage and there is obvious material loss in the later stage.

[0100] In detail, cycle 200 can serve as a mid-term performance reference point. At this stage, battery performance changes are already quite noticeable, but have not yet reached severe aging. Therefore, the data from cycle 200 can serve as an important reference for evaluating the battery's mid-term performance. By analyzing the impedance data at cycle 200, it can be determined whether the battery has maintained good performance stability during mid-term cycling. For example, if the changes in each impedance component are small, it indicates that the battery's performance is relatively stable during mid-term cycling; if the ohmic impedance and contact impedance increase significantly, it may indicate a decrease in the conductivity of the electrolyte or obstruction of the electronic conduction path of the electrode material; if the charge transfer impedance increases, it may indicate a reduction in active sites on the electrode surface or a thickening of the SEI film; if the diffusion impedance increases, it may indicate a deterioration in the diffusion performance of the electrolyte or blockage of the pore structure of the active material. All of the above situations may indicate that the battery has begun to show signs of aging. Through impedance analysis, accurate attribution can be achieved.

[0101] Furthermore, data from the 200th cycle can serve as an important basis for predicting the long-term performance trend of the battery. By comparing the data from the 200th and 400th cycles, the rate of change of the impedance component can be observed, thereby predicting the performance changes of the battery over longer cycles. For example, if the impedance increases rapidly at the 200th cycle, it may indicate that the battery will age faster in subsequent cycles; conversely, if the impedance change is small at the 200th cycle, it indicates that the battery's performance is relatively stable in the mid-term cycles and may have a longer service life. Therefore, by analyzing the impedance data at the 200th cycle, potential problems in the battery design can be identified; for example, if the negative electrode charge transfer impedance increases significantly, it may indicate that the selection of the negative electrode material or surface treatment needs improvement.

[0102] For example, after performing impedance decomposition analysis on the DCR data of a target battery, the following table of data can be obtained:

[0103]

[0104] As shown in the table above, comparing the data from cycle 1 to cycle 200 reveals the changes in the battery during the initial cycling phase. Comparing the data from cycle 200 to cycle 400 reveals the rate of impedance change, thus indicating whether the battery's aging process is accelerating. For example, at cycle 200, the ohmic contact impedance, negative electrode charge transfer impedance, liquid phase diffusion impedance, and solid phase diffusion impedance have already increased to some extent, but the magnitude of the change is relatively small. From cycle 200 to cycle 400, the rate of increase of these impedance components accelerates, especially the liquid phase diffusion impedance and solid phase diffusion impedance, indicating that the battery's aging process accelerates in the later stages.

[0105] The battery impedance analysis device provided in the embodiments of this application is described below. The battery impedance analysis device described below can be referred to in correspondence with the battery impedance analysis method described above.

[0106] In one embodiment, such as Figure 5 As shown, Figure 5 This is a flowchart illustrating a battery impedance analysis device provided in an embodiment of this application. This application also provides a battery impedance analysis device, including an EIS testing module 210, a DRT analysis module 220, a DCR testing module 230, and an impedance analysis module 240, specifically comprising the following:

[0107] EIS test module 210 is used to adjust the battery under test to the test state as the target battery, and to perform EIS test on the target battery to obtain EIS test data, wherein the EIS test data is impedance response data at different frequencies.

[0108] The DRT analysis module 220 is used to perform DRT analysis on EIS test data, generate DRT spectra, and identify multiple time response intervals from the DRT spectra. Each time response interval corresponds to an electrochemical process.

[0109] The DCR test module 230 is used to perform standard charge-discharge cycles on the target battery and, during the cycle, perform DCR tests on the target battery for multiple selected cycles to obtain DCR data corresponding to each selected cycle.

[0110] Impedance analysis module 240 is used to perform impedance decomposition on each DCR data based on each time response interval to obtain impedance decomposition data of the target battery at each selected cycle number. Through comparative analysis of each impedance decomposition data, impedance analysis results of the target battery are generated.

[0111] In one embodiment, when analyzing battery impedance, the battery under test can be adjusted to the test state as the target battery, and EIS testing can be performed on the target battery to obtain EIS test data, which comprehensively reflects the dynamic characteristics of the battery and captures various electrochemical behaviors of the battery during the test. Therefore, the DRT spectrum generated after DRT analysis of the EIS test data can more intuitively show the impedance response time of different electrochemical processes in the time dimension. After identifying the time response interval of each electrochemical process from the DRT spectrum, the target battery can be subjected to standard charge-discharge cycles, and DCR testing can be performed on the target battery for multiple selected cycles during the cycle to obtain DCR data corresponding to each selected cycle. This allows for real-time acquisition of the overall impedance information of the battery at different stages, reflecting the actual performance changes of the battery under different operating conditions. For each selected cycle, impedance decomposition can be performed on its DCR data based on each time response interval to achieve real-time subdivision of the overall impedance information. Therefore, comparative analysis of the impedance decomposition data after subdivision of each selected cycle can generate high-quality impedance analysis results, thereby providing an in-depth understanding of the performance changes of the battery at different charge-discharge cycle stages and providing an effective technical means for analyzing battery performance changes.

[0112] In one embodiment, the EIS test module 210 may include:

[0113] The battery determination submodule is used to perform at least two constant current charge-discharge tests on the battery under test using a preset test current, and to discharge the battery under test to 50% of its state of charge during the last constant current charge-discharge test, thus identifying it as the target battery.

[0114] In one embodiment, the EIS test module 210 may further include:

[0115] The data initialization submodule is used to determine the initial test data; the initial test data includes the test room temperature, disturbance current, and frequency range.

[0116] The EIS test submodule is used to apply a current perturbation to the target battery within a frequency range based on the test room temperature, and measure the EIS test data generated by the current response; the EIS test data includes frequency data, real part data and imaginary part data.

[0117] In one embodiment, the DRT analysis module 220 may include:

[0118] The graph generation submodule is used to input EIS test data into the DRT processing program, so that the DRT processing program can perform relaxation time distribution analysis on the EIS test data and generate DRT graphs based on the analysis results.

[0119] In one embodiment, the DRT analysis module 220 may further include:

[0120] The spectral analysis submodule is used to perform electrochemical analysis on the DRT spectrum to obtain the time response interval of each electrochemical process of the target battery during EIS testing.

[0121] In chronological order, the time response intervals are, in turn, the ohmic impedance contact impedance response interval, the negative electrode charge transfer response interval, the positive electrode charge transfer response interval, the liquid phase diffusion response interval, and the solid phase diffusion response interval.

[0122] In one embodiment, the impedance analysis module 240 may include:

[0123] The voltage value extraction submodule is used to extract the voltage change value for each time response interval from the DCR data of each selected number of revolutions.

[0124] The impedance value determination submodule is used to obtain the DCR test current and determine the impedance value corresponding to each time response interval based on the DCR test current and the voltage change value of each time response interval.

[0125] The data generation submodule is used to determine the impedance source based on the electrochemical process corresponding to each time response interval, and to generate impedance decomposition data for the selected number of cycles based on the impedance source and impedance value corresponding to each time response interval.

[0126] In one embodiment, each selected number of turns in the impedance analysis module 240 may include an initial turn, a middle turn, and a turn before the inflection point; the impedance analysis module 240 may also include:

[0127] The first impedance analysis submodule is used to perform impedance growth analysis on the impedance values ​​of each impedance source in the impedance decomposition data of the initial and intermediate loops to obtain the initial loop analysis results.

[0128] The second impedance analysis submodule is used to perform impedance growth analysis on the impedance values ​​of each impedance source in the impedance decomposition data of the intermediate loop and the loop before the inflection point, and obtain the aging rate analysis results.

[0129] The analysis results generation submodule is used to generate impedance analysis results for the target battery based on the initial cycle analysis results and aging rate analysis results.

[0130] In one embodiment, this application also provides a storage medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the battery impedance analysis method as described in any of the above embodiments.

[0131] In one embodiment, this application also provides a computer device storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the battery impedance analysis method as described in any of the above embodiments.

[0132] Indicatively, such as Figure 6 As shown, Figure 6 This is a schematic diagram of the internal structure of a computer device 300 provided in an embodiment of this application. The computer device 300 can be provided as a server. (Refer to...) Figure 6 The computer device 300 includes a processing component 302, which further includes one or more processors, and memory resources represented by memory 301 for storing instructions, such as application programs, that can be executed by the processing component 302. The application programs stored in memory 301 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 302 is configured to execute instructions to perform the battery impedance analysis method of any of the above embodiments.

[0133] The computer device 300 may also 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 on an operating system stored in memory 301, such as Windows Server™, Mac OS X™, Unix™, Linux™, Free BSD™, or similar.

[0134] Those skilled in the art will understand that Figure 6The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0135] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0136] 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.

[0137] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method of battery impedance analysis, characterized by, The method includes: The battery to be tested is adjusted to the test state and used as the target battery. The target battery is then subjected to EIS testing to obtain EIS test data, which is impedance response data at different frequencies. DRT analysis was performed on the EIS test data to generate DRT spectra, and multiple time response intervals were identified from the DRT spectra, with each time response interval corresponding to an electrochemical process. The target battery is subjected to standard charge-discharge cycles, and during the cycle, the DCR test is performed on the target battery for multiple selected cycles to obtain the DCR data corresponding to each selected cycle. Impedance decomposition is performed on each DCR data based on each time response interval to obtain the impedance decomposition data of the target battery at each selected cycle number. By comparing and analyzing the impedance decomposition data, the impedance analysis results of the target battery are generated.

2. The battery impedance analysis method according to claim 1, characterized in that, The number of test cycles in the EIS test is the same as the number of selected cycles in the DCR test for the target battery; The step of adjusting the battery to be tested to the test state, as the target battery, includes: The battery under test is subjected to at least two constant current charge-discharge tests using a preset test current. After the last constant current charge-discharge test is completed, the battery under test is discharged to 50% of its state of charge and used as the target battery.

3. The battery impedance analysis method according to claim 1, characterized in that, The EIS test on the target battery to obtain EIS test data includes: Determine the initial test data; the initial test data includes the test room temperature, disturbance current, and frequency range; Based on the test room temperature, the perturbation current is used to apply a current perturbation to the target battery within the frequency range, and the EIS test data generated by the current response is measured; the EIS test data includes frequency data, real part data, and imaginary part data.

4. The battery impedance analysis method according to claim 1, characterized in that, The step of performing DRT analysis on the EIS test data to generate a DRT map includes: The EIS test data is input into the DRT processing program so that the DRT processing program can perform relaxation time distribution analysis on the EIS test data and generate a DRT map based on the analysis results.

5. The battery impedance analysis method according to claim 1, characterized in that, The process of identifying multiple time response intervals from the DRT map includes: Electrochemical analysis of the DRT spectrum was performed to obtain the time response interval of the target battery for each electrochemical process during the EIS test. In chronological order, the time response intervals are, in turn, the ohmic impedance contact impedance response interval, the negative electrode charge transfer response interval, the positive electrode charge transfer response interval, the liquid phase diffusion response interval, and the solid phase diffusion response interval.

6. The battery impedance analysis method according to claim 1, characterized in that, The impedance decomposition of each DCR data point based on each time response interval to obtain impedance decomposition data of the target battery at each selected cycle number includes: For each selected number of revolutions, the voltage change value for each time response interval is extracted from the DCR data of that selected number of revolutions; Obtain the DCR test current, and determine the impedance value corresponding to each time response interval based on the DCR test current and the voltage change value of each time response interval; The impedance source is determined based on the electrochemical process corresponding to each time response interval, and impedance decomposition data for the selected number of cycles is generated based on the impedance source and impedance value corresponding to each time response interval.

7. The battery impedance analysis method according to claim 1, characterized in that, Each of the selected laps includes the initial lap, the middle lap, and the lap before the inflection point; The step of generating impedance analysis results for the target battery through comparative analysis of the various impedance decomposition data includes: Impedance growth analysis is performed on the impedance values ​​of each impedance source in the impedance decomposition data of the initial loop and the intermediate loop to obtain the initial loop analysis results. Impedance growth analysis is performed on the impedance values ​​of each impedance source in the impedance decomposition data of the intermediate phase and the phase before the inflection point to obtain the aging rate analysis results. Impedance analysis results for the target battery are generated based on the initial cycle analysis results and the aging rate analysis results.

8. A battery impedance analysis device, characterized in that, include: The EIS test module is used to adjust the battery under test to the test state, which serves as the target battery, and to perform EIS testing on the target battery to obtain EIS test data, which is impedance response data at different frequencies. The DRT analysis module is used to perform DRT analysis on the EIS test data, generate DRT spectra, and identify multiple time response intervals from the DRT spectra, with each time response interval corresponding to an electrochemical process. The DCR test module is used to perform standard charge-discharge cycles on the target battery and, during the cycle, perform DCR tests on the target battery for multiple selected cycles to obtain DCR data corresponding to each selected cycle. The impedance analysis module is used to perform impedance decomposition on each of the DCR data based on each time response interval, to obtain the impedance decomposition data of the target battery at each selected number of cycles, and to generate the impedance analysis result of the target battery by comparing and analyzing the impedance decomposition data.

9. A storage medium, characterized in that: The storage medium stores computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the battery impedance analysis method as described in any one of claims 1 to 7.

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

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