Lithium ion battery SOC-OCV curve determination and voltage drop prediction method

Abstract

According to the lithium ion battery SOC-OCV curve determination and voltage drop prediction method provided by the invention, during curve determination, based on the test small current, a constant current intermittent titration method with standing time within 1-10 minutes is adopted to carry out charging and discharging test on the lithium ion battery, so that the existence of battery polarization in the test process is reduced to approach a battery equilibrium state; as the standing time of one minute contains data that the voltage of the battery is rapidly changed to be stable after the current is interrupted, the test time can be shortened by shortening the standing time. After actual measurement data generated in the testing process is obtained, data fitting is conducted on the actual measurement data based on a voltage-time relational expression representing the relation between the battery voltage and the standing time, the open-circuit voltage of the battery after long-time standing in multiple charge states is predicted according to the fitting result, and an SOC-OCV curve is drawn and generated. And meanwhile, the battery voltage drop prediction efficiency applied to the method can also be remarkably improved.

Description

Technical Field

[0001] The present application relates to the technical field of lithium-ion batteries, and in particular to a method for measuring a SOC-OCV curve and predicting a voltage drop of a lithium-ion battery. Background Art

[0002] Lithium-ion batteries have become an important energy supply method in many electronic fields due to their high energy density and good cycle performance. In these applications, there is a clear correspondence between the battery's state of charge SOC and open circuit voltage OCV; among them, SOC represents the ratio of the current amount of power that the battery can provide to the amount of power in the fully charged state, and OCV represents the terminal voltage of the battery when there is no external current passing through. The open circuit voltage is not affected by the charge and discharge current, and is related to the battery material and the state of charge. At a certain temperature, the battery's state of charge and open circuit voltage show a one-to-one correspondence, that is, the remaining battery power can be obtained by measuring the open circuit voltage. Therefore, the SOC-OCV curve obtained based on the state of charge and open circuit voltage is a basic task in formulating the battery SOC estimation strategy. At the same time, the SOC-OCV curve can also reflect the thermodynamic properties of lithium-ion battery materials and provide accurate kinetic parameters for applications such as simulation models.

[0003] However, the current method for determining the SOC-OCV curve requires measuring the OCV at regular intervals within the battery's SOC range and plotting the measurement results into a curve. But the problem is that before each measurement, the battery needs to be left at rest for a considerable period of time at the corresponding SOC state, usually 4 to 6 hours, to ensure the elimination of polarization effects and voltage stability. Therefore, the entire test process is time-consuming, especially when more dense SOC points need to be obtained, the test time will increase significantly, seriously affecting work efficiency. Summary of the invention

[0004] The purpose of the present application is to solve at least one of the above-mentioned technical defects, especially the technical defect that the SOC-OCV curve measurement method in the prior art is time-consuming, especially when more dense SOC points need to be obtained, the test time will be greatly increased, which seriously affects the work efficiency.

[0005] The present application provides a method for determining a SOC-OCV curve of a lithium-ion battery, the method comprising:

[0006] Obtaining a prefabricated lithium-ion battery and determining a small test current of the lithium-ion battery;

[0007] Based on the test small current, the lithium-ion battery is charged and discharged by a constant current intermittent titration method; wherein the charge and discharge time in the constant current intermittent titration method is in the range of [15min, 30min], and the standing time is in the range of [1min, 10min];

[0008] Reading measured data generated by the lithium-ion battery during the test process, and performing data fitting on the measured data based on a preset voltage-time relationship to obtain static voltage change curves of multiple charge states;

[0009] The battery voltage of the lithium-ion battery at the target rest time in each rest voltage variation curve is determined as the open circuit voltage, and a curve is drawn based on each state of charge and the open circuit voltage at each state of charge to obtain a SOC-OCV curve.

[0010] Optionally, determining the low test current of the lithium-ion battery includes:

[0011] Obtain electrochemical parameters of the lithium-ion battery, and determine a small test current of the lithium-ion battery according to the electrochemical parameters; the value range of the small test current is [0.01C, 1C].

[0012] Optionally, the charging and discharging test of the lithium-ion battery is performed based on the test low current by using a constant current intermittent titration method, comprising:

[0013] The lithium-ion battery is intermittently charged with the small test current until the battery voltage of the lithium-ion battery reaches a preset voltage upper limit, thereby terminating the charging test;

[0014] The lithium-ion battery is intermittently discharged using the small test current until the battery voltage of the lithium-ion battery reaches a preset voltage lower limit, thereby terminating the discharge test.

[0015] Optionally, the voltage-time relationship includes:

[0016]

[0017] In the formula, It represents the logarithmic relationship between the battery voltage E and the static time t; A and B are both constant terms; when the lithium-ion battery is in different charge states, the values ​​of A and B are different.

[0018] Optionally, the data fitting is performed on the measured data based on a preset voltage-time relationship to obtain static voltage variation curves of multiple charge states, including:

[0019] Extracting variation data between battery voltage and rest time at multiple charge states from the measured data;

[0020] For each state of charge, a preset voltage-time relationship is used to perform data fitting on the change data of the state of charge, and a constant term in the voltage-time relationship is predicted based on the fitting result;

[0021] The constant term is replaced in the voltage-time relationship, and a static voltage change curve of the charge state is obtained according to the replacement result.

[0022] Optionally, the step of plotting a curve based on each state of charge and the open circuit voltage at each state of charge to obtain a SOC-OCV curve includes:

[0023] Generating a SOC-OCV curve of the lithium-ion battery according to the open circuit voltage of the lithium-ion battery at each state of charge;

[0024] The SOC-OCV curve is fitted by regression analysis to obtain the SOC-OCV curve of the lithium-ion battery.

[0025] The present application also provides a lithium-ion battery voltage drop prediction method, the method comprising:

[0026] Obtaining the state of charge and initial open circuit voltage of the lithium-ion battery after formation, and a static voltage change curve corresponding to the state of charge;

[0027] Predicting the open circuit voltage of the lithium-ion battery at a preset standby time based on the static voltage change curve;

[0028] The K value calculation formula is used to calculate the initial open circuit voltage, the predicted open circuit voltage and the preset standby time to obtain the voltage drop of the lithium ion battery.

[0029] The present application also provides a lithium-ion battery SOC-OCV curve measuring device, comprising:

[0030] A current determination module, used to obtain a prefabricated lithium-ion battery and determine a small test current of the lithium-ion battery;

[0031] A battery testing module, used for performing a charge and discharge test on the lithium-ion battery by a constant current intermittent titration method based on the test small current; wherein the charge and discharge time in the constant current intermittent titration method has a value range of [15min, 30min], and a value range of the rest time is [1min, 10min];

[0032] A data fitting module, used for reading the measured data generated by the lithium-ion battery during the test process, and performing data fitting on the measured data based on a preset voltage-time relationship to obtain static voltage change curves of multiple charge states;

[0033] The curve drawing module is used to determine the battery voltage of the lithium-ion battery at the target rest time in each static voltage change curve as the open circuit voltage, and draw a curve based on each state of charge and the open circuit voltage at each state of charge to obtain a SOC-OCV curve.

[0034] The present application also provides a storage medium, in which computer-readable instructions are stored. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the lithium-ion battery SOC-OCV curve determination method and the lithium-ion battery voltage drop prediction method as described in any of the above embodiments.

[0035] The present application also provides a computer device, comprising: one or more processors, and a memory;

[0036] The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the lithium-ion battery SOC-OCV curve determination method and the lithium-ion battery voltage drop prediction method as described in any one of the above embodiments are performed.

[0037] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0038] The present application provides a method for measuring the SOC-OCV curve and predicting the voltage drop of a lithium-ion battery. When measuring the SOC-OCV curve of a lithium-ion battery, the small test current used in the test of the lithium-ion battery can be first determined to reduce the presence of battery polarization during the test to approach the battery equilibrium state, and then the lithium-ion battery can be charged and discharged by a constant current intermittent titration method based on the small test current; the range of charge and discharge time in the constant current intermittent titration method is [15min, 30min], and the range of standing time is [1min, 10min]. Since the measured data with a standing time of 10 minutes can predict the voltage change to a stable data, the present application can reduce the test time by shortening the standing time. After reading the measured data generated by the lithium-ion battery during the test, the measured data can be fitted based on the voltage-time relationship that is derived in advance to represent the relationship between the battery voltage and the rest time, and the rest voltage change curves of multiple charge states can be obtained. Then, the open circuit voltage of the lithium-ion battery at the target rest time after a long rest period can be predicted from the rest voltage change curve, and the SOC-OCV curve can be generated based on the predicted data. Based on this, the present application can derive the open circuit voltage of the battery under long-term rest in a shorter test time by reducing the rest time of the battery test and the voltage-time relationship, and then realize the determination of the SOC-OCV curve, so that the battery voltage drop prediction efficiency applied to this method can also be significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0040] Figure 1 A schematic diagram of a flow chart of a method for determining a SOC-OCV curve of a lithium-ion battery provided in an embodiment of the present application;

[0041] Figure 2 A curve display diagram of the data fitting under a state of charge provided in an embodiment of the present application;

[0042] Figure 3 A comparison diagram of the predicted and measured static voltage change curve under a charged state provided in an embodiment of the present application;

[0043] Figure 4 A comparison diagram of the predicted and measured SOC-OCV curve provided in the embodiment of the present application;

[0044] Figure 5 A schematic diagram of a flow chart of a lithium-ion battery voltage drop prediction method provided in an embodiment of the present application;

[0045] Figure 6 A schematic diagram of the structure of a lithium-ion battery SOC-OCV curve measuring device provided in an embodiment of the present application;

[0046] Figure 7 A schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0048] The current method for determining the SOC-OCV curve requires measuring the OCV at regular intervals within the battery's SOC range and plotting the measurement results into a curve. But the problem is that before each measurement, the battery needs to be left at rest for a considerable period of time at the corresponding SOC state, usually 4 to 6 hours, to ensure the elimination of polarization effects and voltage stability. Therefore, the entire test process is time-consuming, especially when more dense SOC points need to be obtained, the test time will increase significantly, seriously affecting work efficiency.

[0049] Based on this, this application proposes the following technical solutions, see below for details:

[0050] In one embodiment, Figure 1 As shown, Figure 1 A schematic diagram of a flow chart of a method for measuring the SOC-OCV curve of a lithium-ion battery provided in an embodiment of the present application; the present application provides a method for measuring the SOC-OCV curve of a lithium-ion battery, which specifically includes the following:

[0051] S110: Obtain a prefabricated lithium-ion battery and determine a small test current of the lithium-ion battery.

[0052] In this embodiment, when measuring the SOC-OCV curve of a lithium-ion battery, the computer device can first obtain a pre-made lithium-ion battery for testing, so that the lithium-ion battery can be tested using a small test current suitable for the lithium-ion battery. In this way, a smaller current can be used to reduce the presence of battery polarization during the test to approach the battery equilibrium state, thereby reducing the impact of polarization in subsequent tests and improving test efficiency.

[0053] It is understandable that the computer equipment can select a lithium-ion battery that is pre-made and suitable for testing, ensuring that the state of the lithium-ion battery is consistent with the actual application conditions, and the test results have a high degree of accuracy. At the same time, in order to improve the test efficiency, the computer equipment can also select a corresponding test small current for the relevant performance of the lithium-ion battery to perform a charge and discharge test on it; in detail, by using a smaller test small current, the present application can effectively reduce the polarization phenomenon of the battery during the test, thereby eliminating the voltage deviation and capacity estimation error caused by the polarization effect, so that the battery gradually approaches the electrochemical equilibrium state, and provides more stable conditions for subsequent tests without increasing the test time.

[0054] Furthermore, when manufacturing the lithium-ion battery of the present application, the pole piece used for battery manufacturing can be dried first to ensure that there is no excess moisture or impurities on the surface of the pole piece, reducing the risk of electrolyte decomposition during subsequent battery assembly and use. After the pole piece is dried, the present application can assemble it with other battery components in a standardized order to obtain a lithium-ion battery; for example, the battery shell, pole piece, diaphragm, electrolyte, lithium sheet, shrapnel, and gasket are assembled, and finally the battery shell is encapsulated to complete the assembly of the button battery.

[0055] It should be noted that the lithium-ion battery used in this application has strong applicability and flexibility. It can be applied not only to button batteries, but also to other types of battery cells, such as card batteries, soft-pack batteries, square shell batteries and laminated batteries, etc. There is no restriction here, as long as it is consistent with the actual battery materials and other information.

[0056] Furthermore, after the lithium-ion battery is assembled, it needs to be left to stand for 12 hours so that the electrolyte can fully infiltrate the electrodes and diaphragms to improve the uniformity and activity inside the battery; after standing, the present application also needs to activate the lithium-ion battery. In a specific implementation, when the computer device activates the lithium-ion battery, it can use a small current of 0.05C to charge it with a constant current, and the upper limit of the charging voltage is set to 3.75V. When the battery is fully charged, it is discharged and tested, and the discharge cut-off voltage is 2.0V, so that the internal materials of the battery are fully contacted and fully reacted, so as to achieve the best initial performance state, thereby improving the accuracy of subsequent lithium-ion battery tests.

[0057] S120: Based on the test small current, the lithium-ion battery is charged and discharged by using the constant current intermittent titration method; wherein the charge and discharge time in the constant current intermittent titration method ranges from [15min, 30min], and the standing time ranges from [1min, 10min].

[0058] In this step, after the test low current for testing is determined in step S110, the computer device can use a constant current intermittent titration method to perform a charge and discharge test on the lithium-ion battery based on the test low current to obtain relevant data during the test to determine the SOC-OCV curve of the lithium-ion battery.

[0059] Among them, the Galvanostatic Intermittent Titration Technique (GITT) is a test method used to analyze the performance of electrochemical materials. It is used to apply a constant current to the measurement system under a specific environment and cut off the current after a period of time, and then observe the change of the system potential over time during the applied current period and the voltage reached after standing. Specifically, a complete GITT test needs to be composed of multiple "current step" units, each of which includes three steps of "pulse + constant current + relaxation (standing)". Through the intermittent constant current charging and discharging process, combined with the measurement of the battery voltage change during the standing stage, the test results of this method can be used to analyze the diffusion kinetics, chemical diffusion coefficient and reversibility of the electrode reaction of the material.

[0060] It is understandable that in order to obtain accurate voltage data, the prior art needs to stand for a long enough time after charging and discharging to achieve the purpose of depolarization. Compared with the long standing time in the prior art, the present application shortens the standing time to 10min, which can effectively save time and test resources. At the same time, the standing time of the present application cannot be less than 1min, because the internal resistance is included within 10s at the moment of battery cut-off, and standing is a depolarization process, and a reasonable standing time voltage data is required as the original data for prediction. However, it should be noted that in the present application, standing for 1min can reflect the electrode potential of the battery caused by ohmic internal resistance, reaction impedance and diffusion internal resistance after the current is interrupted. Therefore, in the present application, the lithium-ion battery can be left standing for a longer time. Based on this, when the present application performs GITT testing on lithium-ion batteries, the range of the charging and discharging time is [15min, 30min], and the range of the standing time is [1min, 10min], which can greatly reduce the standing time during the test and improve the overall test efficiency.

[0061] It should be noted that the duration of each charge and discharge should be kept consistent to ensure the consistency of SOC growth in each charging or discharging process. The charge and discharge duration can be set as needed. For example, set 30 minutes to charge 5% SOC at a rate of 0.01C, or set 15 minutes to charge 2.5% SOC at a rate of 0.01C. The choice of SOC here can also be adjusted according to your own needs. The smaller the SOC growth, the denser the value points of the SOC-OCV curve, and the more accurate the curve; at the same time, the rest time each time can be the same or different, and can be set as needed, but the difference between the charge and discharge time and the rest time must be greater than or equal to 15 minutes to avoid the charge and discharge time and the rest time being too close, resulting in reduced data accuracy.

[0062] S130: reading measured data generated by the lithium-ion battery during the test process, and performing data fitting on the measured data based on a preset voltage-time relationship to obtain static voltage variation curves of multiple charge states.

[0063] In this step, when the lithium-ion battery is subjected to the GITT test in step S120, the computer device can read the relevant data generated during the test in real time and summarize it into measured data after the test is completed. After obtaining the measured data of the lithium-ion battery, the computer device can perform data fitting on the measured data based on a preset voltage-time relationship to obtain static voltage change curves of multiple charge states.

[0064] It can be understood that the voltage-time relationship refers to the logarithmic relationship between the battery voltage and the rest time. Based on this, the computer equipment can input the test data obtained in the GITT test in a shorter rest time into the voltage-time relationship for fitting, and obtain the relationship between the battery voltage and the rest time in the lithium-ion battery in accordance with the logarithm, and then the static voltage change curve of the battery voltage under multiple charge states for a long time can be calculated and plotted based on the logarithm of the relationship. In summary, the present application can derive the change of the battery voltage under long-term rest from the measured data of short-term rest through the voltage-time relationship, thereby effectively reducing the test cost and improving the test efficiency.

[0065] Specifically, in the GITT test of lithium-ion batteries, the computer equipment can monitor and record the key parameters of the entire test process in real time through the data monitoring equipment, including the changes in the battery's voltage, current, capacity, etc. over time. These data cover the dynamic behavior of the battery in the constant current charging and discharging stages, as well as the changes in the voltage gradually tending to equilibrium over time during the static stage. To elaborate, in the constant current charging stage, after the computer equipment uses the test small current to charge the lithium-ion battery with constant current, it can use the data monitoring equipment to record in real time the gradual rise of the battery voltage, the accumulation of charging capacity and the stable output of the current during the charging process, as well as the static stage after each charging stage. Monitor the change of the battery voltage with the static time in real time, record the voltage data that gradually tends to the equilibrium state from the non-equilibrium state, so as to form complete charging data after the constant current charging is completed; similarly, the computer equipment can also use this method to record the complete discharge data of the lithium-ion battery in the constant current discharge stage; finally, the computer equipment can form measured data based on the charging data and discharge data.

[0066] Furthermore, during the entire charging and discharging process, the lithium-ion battery will cover the charge state from fully discharged (SOC=0%) to fully charged (SOC=100%). Based on this, the computer equipment can obtain the ratio of the current battery power to the power in the fully charged state in each static stage of the GITT test as the state of charge SOC in the static stage, and obtain the measured data of the static stage for fitting to obtain the static voltage change curve of the charge state. Specifically, the current can be integrated by the current integration method to obtain the battery power change, thereby determining the state of charge SOC.

[0067] S140: Determine the battery voltage of the lithium-ion battery at the target rest time in each rest voltage change curve as the open circuit voltage, and draw a curve based on each state of charge and the open circuit voltage at each state of charge to obtain a SOC-OCV curve.

[0068] In this step, after obtaining the static voltage change curves of the lithium-ion battery under multiple charge states through step S130, the computer device can determine the battery voltage of the lithium-ion battery at the target static time in each static voltage change curve as the open circuit voltage, and draw a curve based on each charge state and the open circuit voltage under each charge state to obtain a SOC-OCV curve.

[0069] It is understood that the target rest time here refers to the time point required for the lithium-ion battery to be left to rest in order to restore the equilibrium state after the constant current charge and discharge phase ends; at this time point, the battery voltage of the battery can be considered to be close to the equilibrium voltage, that is, the open circuit voltage OCV. In this application, the target rest time of the lithium-ion battery can be set to 6 hours.

[0070] Furthermore, the computer device can determine the battery voltage at the target rest time from the rest voltage change curve corresponding to each state of charge SOC according to the set target rest time, and regard it as the open circuit voltage of the lithium-ion battery at the corresponding state of charge, and then draw the SOC-OCV curve of the lithium-ion battery.

[0071] In the above embodiment, when determining the SOC-OCV curve of the lithium-ion battery, the small test current used in the test of the lithium-ion battery can be determined first to reduce the presence of battery polarization during the test to approach the battery equilibrium state, and then the constant current intermittent titration method can be used to perform charge and discharge tests on the lithium-ion battery based on the small test current; the range of charge and discharge time in the constant current intermittent titration method is [15min, 30min], and the range of standing time is [1min, 10min]. Since the measured data within 10 minutes of standing time can predict the voltage change to stable data, the present application can reduce the test time by shortening the standing time. After reading the measured data generated by the lithium-ion battery during the test, the measured data can be fitted based on the voltage-time relationship formula derived in advance to represent the relationship between the battery voltage and the standing time, and the static voltage change curves of multiple charge states can be obtained, and then the open circuit voltage of the lithium-ion battery after a long period of standing at the target standing time can be predicted from the static voltage change curve, and the SOC-OCV curve can be generated by drawing according to the predicted data. Based on this, the present application can derive the open circuit voltage of the battery under long-term static conditions in a shorter test time by reducing the static time of the battery test and the voltage-time relationship, thereby realizing the determination of the SOC-OCV curve.

[0072] In one embodiment, the process of determining the low test current of the lithium-ion battery in step S110 may include:

[0073] S111: Obtain electrochemical parameters of the lithium-ion battery, and determine a small test current of the lithium-ion battery according to the electrochemical parameters; the value range of the small test current is [0.01C, 1C].

[0074] In this embodiment, after determining the lithium-ion battery for testing, the computer device can obtain the electrochemical parameters of the lithium-ion battery, and then determine the electrochemical parameters suitable for the lithium-ion battery based on the electrochemical parameters. Here, the small test current can be taken in the range of [0.01C, 1C], thereby reducing the existence of battery polarization during the test to approach the battery equilibrium state.

[0075] It is understood that electrochemical parameters refer to the key physical and chemical properties used to describe the electrochemical behavior of lithium-ion batteries, which may include parameters such as internal resistance, open circuit voltage, polarization voltage, diffusion coefficient, etc. Based on the acquired electrochemical parameters, the computer equipment can further analyze the response characteristics of the lithium-ion battery under different test conditions, and then select a small test current suitable for the lithium-ion battery in the range of [0.01C, 1C]; where C represents the rated charge rate of the battery.

[0076] It should be noted that the current value in the range of [0.01C, 1C] is small enough, so it can effectively reduce the errors caused by ohmic polarization and concentration polarization during the test process, improve the accuracy of the test results, and simplify the analysis process in the subsequent data processing and fitting process to ensure the reliability of the fitting results.

[0077] In one embodiment, the process of performing a charge and discharge test on a lithium-ion battery using a constant current intermittent titration method based on a small test current in step S120 may include:

[0078] S121: intermittently charge the lithium-ion battery with a small test current until the battery voltage of the lithium-ion battery reaches a preset voltage upper limit, and the charging test is terminated.

[0079] S122: intermittently discharge the lithium-ion battery using a small test current until the battery voltage of the lithium-ion battery reaches a preset voltage lower limit, thereby terminating the discharge test.

[0080] In this embodiment, when the computer device performs the GITT test on the lithium-ion battery, the computer device can first use a small test current to intermittently charge the lithium-ion battery until the battery voltage of the lithium-ion battery reaches a preset voltage upper limit, and then end the charging test; then, use a small test current to intermittently discharge the lithium-ion battery until the battery voltage of the lithium-ion battery reaches a preset voltage lower limit, and then end the discharge test. In this way, a complete lithium-ion battery test process can be completed.

[0081] In a specific implementation, during the constant current charging stage, the computer device can use a pulse current of 0.1C to perform constant current charging on the lithium-ion battery for 30 minutes, then interrupt the current for 10 minutes, and detect the battery voltage of the lithium-ion battery. If the battery voltage is less than or equal to the preset upper limit voltage of 3.75V, the process of constant current charging for 30 minutes and interrupting the current for 10 minutes is repeated until the detected battery voltage is greater than 3.75V, the constant current charging stage is terminated, and the constant current discharge stage is entered. Similarly, during the constant current discharge stage, the computer device can use a pulse current of 0.1C to perform constant current discharge on the lithium-ion battery for 30 minutes, then interrupt the current for 10 minutes, and detect the battery voltage of the lithium-ion battery. If the battery voltage is greater than or equal to the preset lower limit voltage of 2.0V, the process of constant current discharge for 30 minutes and interrupting the current for 10 minutes is repeated until the detected battery voltage is less than 2.0V, the constant current charging stage is terminated, and the entire GITT test of the lithium-ion battery is completed.

[0082] In one embodiment, the voltage-time relationship in step S130 may include:

[0083]

[0084] In the formula, It represents the logarithmic relationship between the battery voltage E and the static time t; A and B are both constant terms; the values ​​of A and B are different for lithium-ion batteries under different charge states.

[0085] Specifically, during the GITT test, the expression of the battery voltage change over time of a lithium-ion battery after charging / discharging is as follows:

[0086] (1)

[0087] In the formula, Indicates the battery voltage when the rest time is t; Represents ohmic resistance R and reaction impedance R ct The sum of the voltages; Indicates the diffusion resistance when the standing time is t voltage.

[0088] The diffusion resistance After the current is interrupted, it changes with the change of lithium concentration in the lithium-ion battery. The lithium concentration changes with the static time and thus affects the diffusion internal resistance. , where the diffusion part follows Fick's second law, as shown below:

[0089] (2)

[0090] In the formula, D represents the diffusion coefficient; c(x, t) represents the lithium concentration at position x and time t; for the non-steady-state diffusion of the planar electrode, it can be assumed that the particle concentration at each point on the page parallel to the electrode is the same and the particles diffuse only in one dimension along the x direction perpendicular to the electrode surface. It should be noted that it is assumed here that the liquid volume of a small electrode area in the planar electrode is much larger than the electrode area. The specific conditions are as follows;

[0091] The initial conditions are: when t=0, (3)

[0092] The boundary conditions are: hour, (4)

[0093] The formula is processed in the direction of the electrode thickness. The lithium concentration at x=L / 2 is considered to be the concentration of the entire electrode. L is the thickness of the electrode, and L / 2 is 1 / 2 of the thickness of the electrode. It is considered to be the center, and its boundary conditions are: (5)

[0094] By performing Laplace transformation on equation (2), the special solution of Fick's second law is obtained as follows:

[0095] (6)

[0096] Where ref represents the symbol of Gaussian error function.

[0097] Differentiating equation (6) with respect to x yields the following equation:

[0098] (7)

[0099] Substituting x=0 into equation (7), we can obtain the following equation:

[0100] (8)

[0101] The diffusion current density can be expressed as follows:

[0102] (9)

[0103] Where j represents the diffusion current density; n represents the amount of charge involved in the diffusion; F represents the Faraday constant, which represents the amount of charge carried by each mole of electrons.

[0104] Substituting equation (8) into equation (9) yields the diffusion current density when x = 0, which is specifically expressed as follows:

[0105] (10)

[0106] At this time, the current expression formula can be shown as follows:

[0107] (11)

[0108] Where, I represents the current; A represents the research electrode area of ​​the lithium-ion battery;

[0109] Therefore, the formula of formula (2) can be expressed as follows:

[0110] (12)

[0111] At this time, the formula (1) can be expressed as follows:

[0112] (13)

[0113] Where M represents the constant term, which is the ohmic resistance R and the reaction impedance R ct The voltage setting value.

[0114] After processing equation (13), the relationship between battery voltage and rest time can be generated, which is specifically expressed as follows:

[0115] (14)

[0116] in, , .

[0117] In one embodiment, the process of performing data fitting on the measured data based on the preset voltage-time relationship in step S130 to obtain static voltage variation curves of multiple charge states may include:

[0118] S131: Extracting variation data between battery voltage and rest time under multiple charge states from measured data.

[0119] S132: For each state of charge, a preset voltage-time relationship is used to perform data fitting on the change data of the state of charge, and constant terms in the voltage-time relationship, namely A and B, are predicted based on the fitting results.

[0120] S133: Substituting the constant term into the voltage-time relationship equation, and plotting a static voltage variation curve of the charge state according to the substitution result.

[0121] In this embodiment, when the computer device performs data fitting on the measured data, it can first extract the change data between the battery voltage and the static time under multiple charge states from the measured data. Then, for each charge state, the computer device can use a preset voltage-time relationship to perform data fitting on the change data under the charge state, and predict the constant term in the voltage-time relationship based on the fitting result, and then replace the predicted constant term into the voltage-time relationship to obtain the fitting formula corresponding to the charge state, so that the static voltage change curve of the charge state can be drawn based on the fitting formula. Through the above method, the voltage corresponding to the ohmic resistance, reaction impedance and diffusion internal resistance can be fitted without directly calculating the ohmic resistance, reaction impedance and diffusion internal resistance, thereby facilitating the subsequent prediction of the open circuit voltage.

[0122] For example, if Figure 2 As shown, Figure 2 A curve display diagram of the data fitting under a state of charge provided in an embodiment of the present application, namely, a static voltage change curve; Figure 2 The example is the fitting result of the change data of a certain state of charge SOC in the measured data after standing for 500s. After the computer device extracts the change data, it can use origin to fit the data, and then deduce the constant term based on the fitted curve to obtain the fitting formula of the state of charge, which is specifically expressed as follows:

[0123]

[0124] Furthermore, if Figure 3 As shown, Figure 3 A comparison diagram of the predicted and measured static voltage change curve under a charged state provided in an embodiment of the present application; Figure 3 The example in the figure shows that under the same state of charge, the static voltage change curve is predicted by fitting the data obtained by short-time static state using the voltage-time relationship, and the static voltage change curve is directly drawn according to the data measured under long-time static state. By comparing the two curves, it can be seen that the static voltage change curve obtained by fitting the voltage-time relationship has a higher accuracy. Therefore, the present application can derive the open circuit voltage of the battery under long-time static state by the voltage-time relationship, thereby improving the test efficiency and reducing the test cost.

[0125] In one embodiment, the process of obtaining the SOC-OCV curve by plotting curves based on the various states of charge and the open circuit voltage at each state of charge in step S140 may include:

[0126] S141: Generate a SOC-OCV curve of the lithium-ion battery according to the open circuit voltage of the lithium-ion battery at each state of charge.

[0127] S142: Fitting the SOC-OCV curve by regression analysis to obtain the SOC-OCV curve of the lithium-ion battery.

[0128] In this embodiment, the computer device can use the various states of charge SOC of the lithium-ion battery from full discharge (SOC=0%) to full charge (SOC=100%) as the horizontal coordinate, and the open circuit voltage OCV corresponding to each state of charge as the vertical coordinate to draw the SOC-OCV curve of the lithium-ion battery, and then use regression analysis to fit the SOC-OCV curve to obtain the SOC-OCV curve. Of course, the present application can also fit the SOC-OCV curve in other ways, which can be selected according to the actual situation and are not limited here.

[0129] Indicatively, Figure 4 As shown, Figure 4 A comparison diagram of the predicted and measured SOC-OCV curve provided in the embodiment of the present application; Figure 4 The two curves in the example are test results obtained from the same lithium-ion battery. They are the predicted SOC-OCV curve derived from the formula using the measured data after a short period of rest, and the measured SOC-OCV curve directly drawn from the measured data after a long period of rest. Figure 4 It can be seen that the two curves overlap approximately, therefore, the predicted SOC-OCV curve obtained by the present application has a higher accuracy.

[0130] In one embodiment, Figure 5 As shown, Figure 5 A schematic diagram of a lithium-ion battery voltage drop prediction method provided in an embodiment of the present application; the present application also provides a lithium-ion battery voltage drop prediction method, which specifically includes the following:

[0131] S150: Obtaining the state of charge and initial open circuit voltage of the lithium-ion battery after formation, and a static voltage change curve corresponding to the state of charge.

[0132] S160: Predicting the open circuit voltage of the lithium-ion battery at a preset standby time based on the static voltage change curve.

[0133] S170: Calculate the initial open circuit voltage, the predicted open circuit voltage, and the preset standby time using a K value calculation formula to obtain a voltage drop of the lithium-ion battery.

[0134] In this embodiment, after the computer device obtains the static voltage change curve of each state of charge by fitting the voltage-time relationship, the static voltage change curve can also be used to predict the K value of the lithium-ion battery, that is, the voltage drop, to provide a simple and easy method for obtaining the voltage drop. In detail, the computer device can obtain the state of charge and initial open circuit voltage of the lithium-ion battery after formation, as well as the static voltage change curve corresponding to the state of charge, and then predict the predicted open circuit voltage of the lithium-ion battery under the preset shelf time based on the static voltage change curve, and then the K value calculation formula can be used to calculate the initial open circuit voltage, the predicted open circuit voltage and the preset shelf time to obtain the voltage drop of the lithium-ion battery.

[0135] It is understandable that the K value refers to the voltage drop of a lithium-ion battery per unit time, usually expressed in mV / d, and is an indicator used to measure the self-discharge rate of a lithium battery. Factors affecting the K value include the constituent materials of the battery, storage time, temperature and humidity of the storage environment, self-consumption of the battery protection board, and the starting voltage of the test. Therefore, the K value test can be used to assist in screening cells with poor performance. In the traditional K value test method, after the lithium-ion battery cell completes the capacity test, it needs to be stored for a few days at a specified SOC before the voltage is measured. The test time is relatively time-consuming. Therefore, the present application can use the static voltage change curve predicted by the voltage-time relationship to improve the prediction efficiency of the K value.

[0136] Specifically, the K value calculation formula is as follows:

[0137]

[0138] In the formula, OCV1 represents the initial open circuit voltage of the lithium-ion battery after formation; OCV2 represents the battery's voltage after a preset shelf time. The predicted open circuit voltage after .

[0139] The following is a description of a lithium-ion battery SOC-OCV curve measuring device provided in an embodiment of the present application. The lithium-ion battery SOC-OCV curve measuring device described below and the lithium-ion battery SOC-OCV curve measuring method described above can be referenced to each other.

[0140] In one embodiment, Figure 6 As shown, Figure 6 A schematic diagram of a lithium-ion battery SOC-OCV curve measuring device provided in an embodiment of the present application; the present application also provides a lithium-ion battery SOC-OCV curve measuring device, including a flow determination module 210, a battery test module 220, a data fitting module 230 and a curve drawing module 240, specifically including the following:

[0141] The current determination module 210 is used to obtain a pre-fabricated lithium-ion battery and determine a small test current of the lithium-ion battery.

[0142] The battery testing module 220 is used to perform charge and discharge tests on lithium electronic batteries based on a small test current using a constant current intermittent titration method; wherein the charge and discharge time in the constant current intermittent titration method ranges from [15min, 30min] and the rest time ranges from [1min, 10min].

[0143] The data fitting module 230 is used to read the measured data generated by the lithium-ion battery during the test process, and perform data fitting on the measured data based on a preset voltage-time relationship to obtain static voltage variation curves of multiple charge states.

[0144] The curve drawing module 240 is used to determine the battery voltage of the lithium electronic battery at the target rest time in each static voltage change curve as the open circuit voltage, and draw a curve based on each state of charge and the open circuit voltage at each state of charge to obtain a SOC-OCV curve.

[0145] In the above embodiment, when determining the SOC-OCV curve of the lithium-ion battery, the small test current used in the test of the lithium-ion battery can be determined first to reduce the presence of battery polarization during the test to approach the battery equilibrium state, and then the constant current intermittent titration method can be used to perform charge and discharge tests on the lithium-ion battery based on the small test current; the range of charge and discharge time in the constant current intermittent titration method is [15min, 30min], and the range of standing time is [1min, 10min]. Since the measured data within 10 minutes of standing time can predict the voltage change to stable data, the present application can reduce the test time by shortening the standing time. After reading the measured data generated by the lithium-ion battery during the test, the measured data can be fitted based on the voltage-time relationship formula derived in advance to represent the relationship between the battery voltage and the standing time, and the static voltage change curves of multiple charge states can be obtained, and then the open circuit voltage of the lithium-ion battery after a long period of standing at the target standing time can be predicted from the static voltage change curve, and the SOC-OCV curve can be generated by drawing according to the predicted data. Based on this, the present application can derive the open circuit voltage of the battery under long-term static conditions in a shorter test time by reducing the static time of the battery test and the voltage-time relationship, thereby realizing the determination of the SOC-OCV curve.

[0146] In one embodiment, the current determination module 210 may include:

[0147] The parameter acquisition submodule is used to obtain the electrochemical parameters of the lithium-ion battery and determine the small test current of the lithium-ion battery according to the electrochemical parameters; the value range of the small test current is [0.01C, 1C].

[0148] In one embodiment, the battery test module 220 may include:

[0149] The charging test submodule is used to intermittently charge the lithium-ion battery with a small test current until the battery voltage of the lithium-ion battery reaches a preset voltage upper limit, thereby terminating the charging test.

[0150] The discharge test submodule is used to intermittently discharge the lithium-ion battery using a small test current until the battery voltage of the lithium-ion battery reaches a preset voltage lower limit, thereby terminating the discharge test.

[0151] In one embodiment, the voltage-time relationship used in the data fitting module 230 may include:

[0152]

[0153] In the formula, It represents the logarithmic relationship between the battery voltage E and the static time t; A and B are both constant terms; the values ​​of A and B are different for lithium-ion batteries under different charge states.

[0154] In one embodiment, the data fitting module 230 may include:

[0155] The data extraction submodule is used to extract the variation data between the battery voltage and the rest time under multiple charge states from the measured data.

[0156] The data fitting submodule is used to fit the changing data of each state of charge using a preset voltage-time relationship for each state of charge, and predict the constant term in the voltage-time relationship based on the fitting result.

[0157] The curve drawing submodule is used to replace the constant term into the voltage-time relationship, and draw the static voltage change curve of the charge state according to the replacement result.

[0158] In one embodiment, the curve drawing module 240 may include:

[0159] The broken line drawing submodule is used to generate the SOC-OCV broken line of the lithium-ion battery according to the open circuit voltage of the lithium-ion battery at each state of charge.

[0160] The curve generation submodule is used to fit the SOC-OCV broken line by regression analysis to obtain the SOC-OCV curve of the lithium-ion battery.

[0161] In one embodiment, the present application also provides a lithium-ion battery voltage drop prediction device, which specifically includes the following:

[0162] The data acquisition module is used to obtain the state of charge and initial open circuit voltage of the lithium-ion battery after formation, as well as the static voltage change curve corresponding to the state of charge.

[0163] The voltage prediction module is used to predict the open circuit voltage of the lithium-ion battery under a preset shelf time based on the static voltage change curve.

[0164] The K value calculation module is used to calculate the initial open circuit voltage, the predicted open circuit voltage and the preset shelf time using the K value calculation formula to obtain the voltage drop of the lithium-ion battery.

[0165] In one embodiment, the present application also provides a storage medium, which stores computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the lithium-ion battery SOC-OCV curve measurement and lithium-ion battery voltage drop prediction method as described in any of the above embodiments.

[0166] In one embodiment, the present application also provides a computer device having computer-readable instructions stored therein. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the method for determining the SOC-OCV curve of a lithium-ion battery and predicting the voltage drop of a lithium-ion battery as described in any of the above embodiments.

[0167] Indicatively, Figure 7 As shown, Figure 7 This is a schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. The computer device 300 may be provided as a server. Figure 7 The computer device 300 includes a processing component 302, which further includes one or more processors, and a memory resource represented by a memory 301 for storing instructions executable by the processing component 302, such as an application. The application stored in the memory 301 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 302 is configured to execute instructions to perform the lithium-ion battery SOC-OCV curve determination and lithium-ion battery voltage drop prediction method of any of the above embodiments.

[0168] The computer device 300 may further include a power supply component 303 configured to perform power management of the computer device 300, a wired or wireless network interface 304 configured to connect the computer device 300 to a network, and an input / output (I / O) interface 305. The computer device 300 may operate based on an operating system stored in the memory 301, such as Windows Server TM, Mac OS X TM, Unix TM, Linux TM, Free BSD TM, or the like.

[0169] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0170] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0171] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can refer to each other.

[0172] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for measuring the SOC-OCV curve of a lithium-ion battery, characterized in that: The method comprises: Obtaining a prefabricated lithium-ion battery and determining a small test current of the lithium-ion battery; Based on the test small current, the lithium-ion battery is charged and discharged by a constant current intermittent titration method; wherein the charge and discharge time in the constant current intermittent titration method is in the range of [15min, 30min], and the standing time is in the range of [1min, 10min]; Reading measured data generated by the lithium-ion battery during the test process, and performing data fitting on the measured data based on a preset voltage-time relationship to obtain static voltage change curves of multiple charge states; The battery voltage of the lithium-ion battery at the target rest time in each rest voltage variation curve is determined as the open circuit voltage, and a curve is drawn based on each state of charge and the open circuit voltage at each state of charge to obtain a SOC-OCV curve.

2. The method for measuring the SOC-OCV curve of a lithium-ion battery according to claim 1, characterized in that: The step of determining the small test current of the lithium-ion battery comprises: Obtain electrochemical parameters of the lithium-ion battery, and determine a small test current of the lithium-ion battery according to the electrochemical parameters; the value range of the small test current is [0.01C, 1C].

3. The method for measuring the SOC-OCV curve of a lithium-ion battery according to claim 1, characterized in that: The method of performing a charge and discharge test on the lithium-ion battery based on the small test current by using a constant current intermittent titration method comprises: The lithium-ion battery is intermittently charged with the small test current until the battery voltage of the lithium-ion battery reaches a preset voltage upper limit, thereby terminating the charging test; The lithium-ion battery is intermittently discharged using the small test current until the battery voltage of the lithium-ion battery reaches a preset voltage lower limit, thereby terminating the discharge test.

4. The method for measuring the SOC-OCV curve of a lithium-ion battery according to claim 1, characterized in that: The voltage-time relationship includes: In the formula, It represents the logarithmic relationship between the battery voltage E and the static time t; A and B are both constant terms; when the lithium-ion battery is in different charge states, the values ​​of A and B are different.

5. The method for measuring the SOC-OCV curve of a lithium-ion battery according to claim 1, characterized in that: The measured data is fitted based on a preset voltage-time relationship to obtain static voltage change curves of multiple charge states, including: Extracting variation data between battery voltage and rest time at multiple charge states from the measured data; For each state of charge, a preset voltage-time relationship is used to perform data fitting on the change data of the state of charge, and a constant term in the voltage-time relationship is predicted based on the fitting result; The constant term is replaced in the voltage-time relationship, and a static voltage change curve of the charge state is obtained according to the replacement result.

6. The method for measuring the SOC-OCV curve of a lithium-ion battery according to claim 1, characterized in that: The SOC-OCV curve is obtained by plotting curves based on each state of charge and the open circuit voltage under each state of charge, including: Generating a SOC-OCV curve of the lithium-ion battery according to the open circuit voltage of the lithium-ion battery at each state of charge; The SOC-OCV curve is fitted by regression analysis to obtain the SOC-OCV curve of the lithium-ion battery.

7. A method for predicting voltage drop of a lithium-ion battery, characterized in that: The method comprises: Obtaining the state of charge and initial open circuit voltage of the lithium-ion battery after formation, and a static voltage change curve corresponding to the state of charge; Predicting the open circuit voltage of the lithium-ion battery at a preset standby time based on the static voltage change curve; The K value calculation formula is used to calculate the initial open circuit voltage, the predicted open circuit voltage and the preset standby time to obtain the voltage drop of the lithium ion battery.

8. A lithium-ion battery SOC-OCV curve measuring device, characterized in that: include: A current determination module, used to obtain a prefabricated lithium-ion battery and determine a small test current of the lithium-ion battery; A battery testing module, used for performing a charge and discharge test on the lithium-ion battery by a constant current intermittent titration method based on the test small current; wherein the charge and discharge time in the constant current intermittent titration method has a value range of [15min, 30min], and a value range of the rest time is [1min, 10min]; A data fitting module, used for reading the measured data generated by the lithium-ion battery during the test process, and performing data fitting on the measured data based on a preset voltage-time relationship to obtain static voltage change curves of multiple charge states; The curve drawing module is used to determine the battery voltage of the lithium-ion battery at the target rest time in each static voltage change curve as the open circuit voltage, and draw a curve based on each state of charge and the open circuit voltage at each state of charge to obtain a SOC-OCV curve.

9. A storage medium, characterized in that: The storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the lithium-ion battery SOC-OCV curve determination method according to any one of claims 1 to 6, and the lithium-ion battery voltage drop prediction method according to claim 7.

10. A computer device, characterized in that: include: one or more processors, and memory; The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the lithium-ion battery SOC-OCV curve determination method according to any one of claims 1 to 6 and the lithium-ion battery voltage drop prediction method according to claim 7 are performed.

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