Lithium ion battery SOC-OCV curve calibration method and device and medium

Abstract

The invention provides a calibration method and device for a lithium ion battery SOC-OCV curve and a medium, and relates to the technical field of lithium battery tests.According to the calibration method and device for the lithium ion battery SOC-OCV curve, by means of points with the large change rate in the SOC-OCV relation curve, it can be completed that the lithium battery waiting and standing time is shortened by nearly half, and data acquisition points are reduced by nearly half; according to the method, the working time can be saved by nearly 50%, the efficiency of drawing the SOC and OCV relation curve of the lithium battery at multiple temperatures is greatly improved, the function value, namely the OCV value, between all the test data points is predicted through the interpolation polynomial, the curve relation of all the test data points is generated through fitting, the total interpolation polynomial is constructed by using the value corresponding to the basis function weighting, and the calculation efficiency is improved. And all test data points can be positioned on the fitted curve, so that the accuracy of the SOC-OCV curve of the lithium ion battery is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium battery testing, and specifically to a calibration method, device, and medium for the SOC-OCV curve of a lithium-ion battery. Background Art

[0002] Due to characteristics such as high energy density, good safety, and long cycle life, lithium-ion batteries have become the main on-vehicle power source for electric vehicles. The state of charge of a lithium-ion battery reflects the remaining capacity of the battery and is a key indicator in the battery management system. In the management and monitoring of lithium-ion batteries, SOC and OCV are two very important terms. SOC represents the state of the current battery charge, expressed as a percentage. It indicates the proportion of the current battery charge relative to its rated capacity. OCV refers to the voltage measured when the battery has no load. It reflects the chemical state inside the battery. The SOC-OCV curve is an important tool in the management and monitoring of lithium-ion batteries, which reflects the relationship between the state of charge SOC and the open-circuit voltage OCV of the battery. The SOC-OCV curve shows how the open-circuit voltage of the battery changes when the state of charge of the battery changes. Generally, as the SOC increases, the OCV also increases accordingly, but this relationship is not linear, but shows a certain curve shape.

[0003] Generally, when calibrating the SOC-OCV curve, multiple discharge tests need to be performed on the lithium-ion battery. During the test process, it is also necessary to frequently leave it static for at least 1 hour to reach the thermal equilibrium state to ensure the test accuracy. At the same time, it is also necessary to conduct grouped tests on the lithium-ion battery at different temperatures, which consumes a large amount of time and energy, has high repeatability, and low work efficiency.

[0004] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure, and therefore it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0005] The purpose of the present invention is to provide a calibration method, device, and medium for the SOC-OCV curve of a lithium-ion battery to solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] A calibration method for the SOC-OCV curve of a lithium-ion battery, the specific steps including:

[0008] S1. Prepare a lithium-ion battery to be tested with 100% SOC, ensure it stands still at 25 degrees Celsius for at least 1 hour to reach thermal equilibrium, measure the OCV values of the battery at different SOCs using a voltmeter, record the data points, and plot the relationship curve between SOC and OCV at 25 degrees Celsius based on the data points;

[0009] S2. Use Matlab software to fit the relationship curve between SOC and OCV at 25 degrees Celsius with an eighth-order polynomial, output the expression of the eighth-order polynomial, calculate the curve slopes of the eighth-order polynomial when SOC is 0%, 5%, 10%, …, 100% respectively, select 8 points with the largest slopes without repetition in sequence as test points, and record the corresponding SOC values;

[0010] S3. Test the OCV data when SOC is 0%, 5%, 10%, …, 100% at other temperatures;

[0011] S4. Conduct a correlation analysis on the data measured in S3 to generate an interpolation polynomial, and the function image of the interpolation polynomial is used to reflect the fitted SOC and OCV curves, and the curve of the interpolation polynomial strictly passes through all test data points;

[0012] S5. Plot the relationship curve between SOC and OCV at other temperatures according to the interpolation polynomial.

[0013] Further, in S1, discharge the lithium battery at a discharge rate of 0.5C for 6 minutes to 95% SOC, measure its current OCV value after the battery stands still for 1 hour, repeat this process to make the lithium battery be at 90%, 85%, …, 0% SOC respectively, and measure their corresponding OCV values, and record a total of 21 groups of data points.

[0014] Further, use a third-order RC equivalent circuit model to measure the OCV value, where the OCV value is output by measuring the terminal voltage and load current.

[0015] Further, in the data points (SOC i , OCV i ), the subscript i is used to index 21 groups of data points, where (SOC1, OCV1) represents the corresponding OCV value when the lithium battery is at 0% SOC, (SOC2, OCV2) represents the corresponding OCV value when the lithium battery is at 5% SOC, (SOC3, OCV3) represents the corresponding OCV value when the lithium battery is at 10% SOC, …, (SOC 21 , OCV 21 ) represents the corresponding OCV value when the lithium battery is at 100% SOC, and plot the relationship curve between SOC and OCV at 25 degrees Celsius based on the data points.

[0016] Further, in S1, the data points are identified by the lsqcurvefit fitting function of Matlab software, and the relationship curve between SOC and OCV at 25 °C is plotted, where SOC is the abscissa and OCV is the ordinate.

[0017] Further, Matlab software is used to fit the relationship curve between SOC and OCV at 25 °C with an eighth-order polynomial, and the expression of the eighth-order polynomial is output. The curve slopes when the SOC of the eighth-order polynomial is 0%, 5%, 10%, …, 100% are calculated. By taking the derivative of the output eighth-order polynomial, the derivatives when the SOC values are 0%, 5%, 10%, …, 100% are generated. The derivatives are used to reflect the curve slopes. Eight points with the largest slopes are selected in sequence without repetition as test points, and they are arranged in ascending order of the SOC value. The corresponding SOC values are recorded. The test points are used to reflect the points with a higher rate of change in the relationship curve between SOC and OCV at 25 °C.

[0018] Further, in S3, a lithium-ion battery to be tested with 100% SOC is prepared, and it is ensured to stand still for at least 1 hour at other temperatures to achieve thermal equilibrium. A voltmeter is used to measure the OCV value of the lithium battery at the SOC corresponding to the test point, and the end point values, that is, the OCV values when the SOC values are 0 and 1, are measured, and the test data points P j , and P j =(x j ,y j ) are recorded. In the test data point P j (x j ,y j ), x j is the SOC value corresponding to the test point of the lithium battery, and y j is the OCV value of the lithium battery at the SOC corresponding to the test point. The value of j is an integer between 0 and 9. Among them, the test data point P0(x0,y0) is (0, OCV0), and P9(x9,y9) is (1, OCV1). The abscissas of other test data points are SOC values, and the ordinates are their corresponding OCV values.

[0019] Further, correlation analysis is performed on the data measured in S3 to generate an interpolation polynomial P(x). The formula based on is:

[0020]

[0021] where L j (x) is the jth basis function, defined as:

[0022]

[0023] where \(P(x)\) is the interpolation polynomial, \(x\) k and \(x\) j The subscripts \(k\) and \(j\) are both used to index the test data points. The value range of \(k\) is an integer between 0 and 9, which is used to predict the function values between each test data point \(P\) j The basis function \(L\) j (x) is used to ensure that the basis function output is 1 at a specific test data point \(P\) j That is, \(L\) j (x)=1, while at other test data points \(x\) k , (\(k\neq j\)) the basis function output is 0, that is, \(L\) j (x)=0, which is used to weight the corresponding value \(y\) j to construct the total interpolation polynomial \(P(x)\). According to the interpolation polynomial, the relationship curves between SOC and OCV at other temperatures are drawn.

[0024] The present invention provides a calibration device for the SOC-OCV curve of a lithium-ion battery, which is used to execute the calibration method of the SOC-OCV curve of a lithium-ion battery, including:

[0025] A normal temperature test module, which is used to prepare a lithium-ion battery to be tested with 100% SOC, ensure that it stands still at 25 degrees Celsius for at least 1 hour to reach thermal equilibrium, measure the OCV values of the battery at different SOCs using a voltmeter, record the data points, and draw the relationship curve between SOC and OCV at 25 degrees Celsius according to the data points;

[0026] A test point selection module, which is used to fit the relationship curve between SOC and OCV at 25 degrees Celsius by using an eighth-order polynomial through Matlab software, output the expression of the eighth-order polynomial, calculate the curve slopes when the SOC of the eighth-order polynomial is 0%, 5%, 10%,..., 100% respectively, select 8 points with the largest slopes from them in turn without repetition as the test points, and record the corresponding SOC values;

[0027] An other temperature test module, which is used to test the OCV data when the SOC is 0%, 5%, 10%,..., 100% at other temperatures;

[0028] A fitting module, which is used to perform correlation analysis on the data measured in S3, generate an interpolation polynomial, the function image of the interpolation polynomial is used to reflect the fitted SOC and OCV curves, and the curve of the interpolation polynomial strictly passes through all test data points;

[0029] An other temperature curve drawing module, which is used to draw the relationship curves between SOC and OCV at other temperatures according to the interpolation polynomial.

[0030] The present invention also provides a medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, a method for calibrating the SOC-OCV curve of a lithium-ion battery is implemented.

[0031] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0032] By specifically testing the points with a relatively large change rate in the SOC-OCV relationship curve, that is, the test points, the present invention only needs to test 9 test points and two end points when SOC is 0 and 1, a total of 11 points, to complete the key points of the SOC-OCV relationship curve of the lithium battery. Compared with the complete test of 21 points, it reduces the waiting and static time of the lithium battery by nearly half, reduces nearly half of the data acquisition points, can save nearly 50% of the working time, greatly improves the efficiency of drawing the SOC-OCV relationship curve of the lithium battery at multiple temperatures, and predicts the function values, that is, OCV values, between each test data point through an interpolation polynomial, fits to generate the curve relationship of each test data point, and constructs the total interpolation polynomial by weighting the corresponding values with basis functions, so that all test data points are on the fitting curve, thereby improving the accuracy of the SOC-OCV curve of the lithium-ion battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0034] Figure 2 It is a schematic diagram of the overall system flow of the present invention;

[0035] Figure 3 It is a third-order RC equivalent circuit diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0036] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with specific embodiments.

[0037] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those with ordinary skills in the field to which the present invention pertains. The "first", "second" and similar terms used in the present invention do not denote any order, quantity or importance, but are only used to distinguish different components. Words such as "comprising" or "including" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. Words such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Upper", "lower", "left", "right", etc. are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0038] Embodiment:

[0039] Please refer to Figure 1 , the present invention provides a technical solution:

[0040] A method for calibrating the SOC-OCV curve of a lithium-ion battery. Among them, in the management and monitoring of lithium-ion batteries, SOC and OCV are two very important terms. SOC represents the state of the current battery charge, expressed as a percentage. It indicates the proportion of the current battery charge relative to its rated capacity. OCV refers to the voltage measured when the battery has no load (i.e., no charging or discharging). It reflects the chemical state inside the battery. The SOC-OCV curve is an important tool in the management and monitoring of lithium-ion batteries. It reflects the relationship between the state of charge (SOC) and the open-circuit voltage (OCV) of the battery. The SOC-OCV curve shows how the open-circuit voltage of the battery changes when the state of charge of the battery changes. Generally speaking, as the SOC increases, the OCV will also increase accordingly, but this relationship is not linear, but shows a certain curve shape. The present invention provides a calibration method for the SOC-OCV curve of a lithium-ion battery with high precision and capable of quickly calibrating at multiple temperatures. The specific steps include:

[0041] S1. Prepare a lithium-ion battery to be tested with 100% SOC, ensure that it stands still at 25 degrees Celsius for at least 1 hour to reach thermal equilibrium, use a voltmeter to measure the OCV values of the battery at different SOCs, record the data points, and draw the relationship curve between SOC and OCV at 25 degrees Celsius according to the data points;

[0042] Fully charge the battery to ensure that the battery is in a fully charged state at the beginning of the test. This is to obtain a complete SOC-OCV curve, covering the voltage range from full charge to full discharge. The internal temperature of the battery will change after charging or discharging. Letting it stand for 1 hour allows the battery to reach thermal equilibrium and avoid the influence of temperature on OCV measurement, thereby improving the accuracy of the test results.

[0043] The lithium battery was discharged at a discharge rate of 0.5C for 6 minutes to 95% SOC. 0.5C is used to describe the discharge rate of the battery. The current OCV value was measured after the battery was left to stand for 1 hour. The C value is a unit of battery capacity, which means that if it is discharged at a specific current, the battery will be fully discharged within one hour. Repeat this process to make the lithium battery at 90%, 85%, ..., 0% SOC, and measure its corresponding OCV value, recording a total of 21 sets of data points.

[0044] When determining the OCV value of a lithium battery, a third-order RC equivalent circuit model is used to determine the OCV value, in which the OCV value is output by measuring the terminal voltage and load current. Figure 3 According to Kirchhoff's law, the OCV value is output by combining the following formulas:

[0045]

[0046] Among them U oc is the value of OCV, U b is the terminal voltage, R1 and C1 are the ohmic polarization resistor and capacitor respectively, R2 and C2 are the electrochemical polarization resistor and capacitor respectively, R3 and C3 are the concentration polarization resistor and capacitor respectively, I is the load current, and R0 is the internal resistance.

[0047] The data point (SOC i ,OCV i ), the subscript i is used to index the 21 groups of data points, where (SOC1, OCV1) represents the OCV value of the lithium battery at 0% SOC, (SOC2, OCV2) represents the OCV value of the lithium battery at 5% SOC, (SOC3, OCV3) represents the OCV value of the lithium battery at 10% SOC, …, (SOC 21 ,OCV 21 ) represents the OCV value corresponding to the lithium battery when it is at 100% SOC, and a relationship curve between SOC and OCV at 25 degrees Celsius is drawn based on the data points.

[0048] S2. Identify data points through the lsqcurvefit fitting function of Matlab software and plot the relationship curve between SOC and OCV at 25 °C, where SOC is the abscissa and OCV is the ordinate. Use an eighth-order polynomial to fit the relationship curve between SOC and OCV at 25 °C through Matlab software, output the expression of the eighth-order polynomial, calculate the curve slopes when the SOC of the eighth-order polynomial is 0%, 5%, 10%, …, 100% respectively, sequentially select 8 points with the largest slopes without repetition as test points, and record the corresponding SOC values;

[0049] Use an eighth-order polynomial to fit the relationship curve between SOC and OCV at 25 °C through Matlab software, output the expression of the eighth-order polynomial, calculate the curve slopes when the SOC of the eighth-order polynomial is 0%, 5%, 10%, …, 100% respectively. By performing derivative processing on the output eighth-order polynomial, generate the derivatives when the SOC values are 0%, 5%, 10%, …, 100%. The derivatives are used to reflect the curve slopes. Sequentially select 8 points with the largest slopes without repetition as test points, and arrange them in ascending order according to the SOC values, record the corresponding SOC values. The test points are used to reflect the points with a higher change rate in the relationship curve between SOC and OCV at 25 °C.

[0050] Take SOC as the input independent variable and OCV as the output dependent variable to obtain the fitting polynomial coefficients. After the fitting is completed, record the output polynomial coefficients and substitute them into the polynomial expression. Sort the calculated slope values and select the SOC values corresponding to 8 largest slopes. Record the selected SOC values to form a set of test points. The test points are used to reflect the points with a higher change rate in the relationship curve between SOC and OCV at 25 °C, that is, at room temperature. These test points reflect the change points in the relationship curve between SOC and OCV of the lithium battery. By testing the data of these key points at other temperatures and based on this, analyze the relationship curve between SOC and OCV of the lithium battery at other temperatures.

[0051] S3. Test the OCV data when the SOC is 0%, 5%, 10%, …, 100% at other temperatures;

[0052] Prepare a lithium-ion battery to be tested with 100% SOC, ensure it stands still for at least 1 hour at other temperatures to achieve thermal equilibrium. Use a voltmeter to measure the OCV value of the lithium battery at the SOC corresponding to the test points, and measure the end point values, that is, the OCV values when the SOC is 0 and 1. Record the test data points P j , and P j =(x j ,y j ), the test data point P j(x j , y j ), x j is the SOC value corresponding to the lithium battery at the said test point, and y j is the OCV value of the lithium battery at the SOC corresponding to the said test point. The value of j is an integer between 0 and 9. Among them, the test data point P0(x0, y0) is (0, OCV0), and P9(x9, y9) is (1, OCV1). The abscissa of other test data points is the SOC value, and the ordinate is the corresponding OCV value. By performing targeted tests on the points with a relatively large change rate in the SOC-OCV relationship curve, that is, the test points, only 9 test points and two end points when SOC is 0 and 1, a total of 11 points, are required during the test process to complete the key points of the SOC-OCV relationship curve of the lithium battery. Compared with the complete test of 21 points, the waiting static time is reduced by nearly half, the data acquisition points are reduced by nearly half, and nearly 50% of the working time can be omitted, greatly improving the efficiency of drawing the SOC-OCV relationship curve of the lithium battery at multiple temperatures.

[0053] S4. Perform correlation analysis on the data measured in step 3 to generate an interpolation polynomial. The function image of the interpolation polynomial is used to reflect the fitted SOC and OCV curves, and the curve of the interpolation polynomial strictly passes through all test data points;

[0054] Perform correlation analysis to generate an interpolation polynomial P(x), and the formula based on is:

[0055]

[0056] where L j (x) is the j-th basis function, defined as:

[0057]

[0058] where P(x) is the interpolation polynomial, and the subscripts k and j of x k and x j are both used to index the test data points. The value of k is an integer between 0 and 9, which is used to predict the function values between each test data point P j , that is, to fit and generate the curve relationship between each test data point P j . The basis function L j (x) is used to ensure that the basis function output is 1 at a specific test data point P j , that is, L j (x) = 1, while at other test data points x k , (k ≠ j), the basis function output is 0, that is, L j (x) = 0, which is used to weight the corresponding value y jConstruct the overall interpolation polynomial P(x) by using the basis function L j (x) to weight the corresponding value y j Construct the overall interpolation polynomial P(x), which can make all the test data points P j All lie on the curve of the interpolation polynomial P(x), thereby improving the accuracy of the SOC-OCV curve of the lithium-ion battery.

[0059] S5. Draw the relationship curve between SOC and OCV at other temperatures according to the interpolation polynomial P(x).

[0060] Refer to Figure 2 , the present invention provides a calibration device for the SOC-OCV curve of a lithium-ion battery, which is used to execute the calibration method of the SOC-OCV curve of a lithium-ion battery, including:

[0061] Normal temperature test module, the normal temperature test module is used to prepare the lithium-ion battery to be tested with 100% SOC, ensure that it stands still at 25 degrees Celsius for at least 1 hour to reach thermal equilibrium, measure the OCV value of the battery at different SOCs with a voltmeter, record the data points, and draw the relationship curve between SOC and OCV at 25 degrees Celsius according to the data points;

[0062] Test point selection module, the test point selection module is used to fit the relationship curve between SOC and OCV at 25 degrees Celsius by using an eighth-order polynomial through Matlab software, output the expression of the eighth-order polynomial, calculate the curve slopes when the SOC of the eighth-order polynomial is 0%, 5%, 10%,..., 100% respectively, select 8 of the maximum slope points in sequence without repetition as the test points, and record the corresponding SOC values;

[0063] Other temperature test module, the other temperature test module is used to test the OCV data when the SOC is 0%, 5%, 10%,..., 100% at other temperatures;

[0064] Fitting module, the fitting module is used to perform correlation analysis on the data measured in S3, generate an interpolation polynomial, the function image of the interpolation polynomial is used to reflect the fitted SOC and OCV curves, and the curve of the interpolation polynomial strictly passes through all the test data points;

[0065] Other temperature curve drawing module, the other temperature curve drawing module is used to draw the relationship curve between SOC and OCV at other temperatures according to the interpolation polynomial.

[0066] The present invention also provides a medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the calibration method of the SOC-OCV curve of a lithium-ion battery is implemented.

[0067] The above formulas are all dimensionless and only take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0068] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0069] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, and may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0070] As described above, the above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application.

Claims

1. A calibration method for the SOC-OCV curve of a lithium-ion battery, characterized in that, The specific steps include: S1. Prepare a lithium-ion battery to be tested with 100% SOC, ensure it stands still at 25 °C for at least 1 hour to achieve thermal equilibrium, measure the OCV values of the battery at different SOCs using a voltmeter, record the data points, and draw a relationship curve between SOC and OCV at 25 °C based on the data points; S2. Use an eighth-order polynomial to fit the relationship curve between SOC and OCV at 25 °C through Matlab software, output the expression of the eighth-order polynomial, calculate the curve slopes when the SOC of the eighth-order polynomial is 0%, 5%, 10%, …, 100% respectively, sequentially and without repetition select 8 points with the largest slopes as test points, and record the corresponding SOC values; S3. Test the OCV data when the SOC is 0%, 5%, 10%, …, 100% at other temperatures; S4. Conduct a correlation analysis on the data measured in S3 to generate an interpolation polynomial, and the function image of the interpolation polynomial is used to reflect the fitted SOC and OCV curves, and the curve of the interpolation polynomial strictly passes through all test data points; S5. Draw a relationship curve between SOC and OCV at other temperatures based on the interpolation polynomial.

2. The calibration method of the SOC-OCV curve of the lithium-ion battery according to claim 1, characterized in that: In S1, discharge the lithium battery at a discharge rate of 0.5C for 6 minutes to 95% SOC, measure its current OCV value after the battery stands still for 1 hour, repeat this process to make the lithium battery at 90%, 85%, …, 0% SOC respectively, and measure their corresponding OCV values, and record a total of 21 groups of data points.

3. The calibration method of the SOC-OCV curve of the lithium-ion battery according to claim 1, wherein: Use a third-order RC equivalent circuit model to measure the OCV value, where the OCV value is output by measuring the terminal voltage and the load current.

4. The calibration method of the SOC-OCV curve of the lithium-ion battery according to claim 2, characterized in that: The data points (SOC i , OCV i ), the subscript i is used to index 21 groups of data points, where (SOC1, OCV1) represents the OCV value corresponding to the lithium battery when it is at 0% SOC, (SOC2, OCV2) represents the OCV value corresponding to the lithium battery when it is at 5% SOC, (SOC3, OCV3) represents the OCV value corresponding to the lithium battery when it is at 10% SOC, …, (SOC 21 , OCV 21 ) represents the OCV value corresponding to the lithium battery when it is at 100% SOC, and a relationship curve of SOC and OCV at 25 degrees Celsius is plotted based on the data points.

5. The calibration method of the SOC-OCV curve of the lithium-ion battery according to claim 4, characterized in that: In S1, use the lsqcurvefit fitting function of Matlab software to identify data points and draw a relationship curve between SOC and OCV at 25 °C, where SOC is the abscissa and OCV is the ordinate.

6. The calibration method of the SOC-OCV curve of the lithium-ion battery according to claim 5, characterized in that: Use an eighth-order polynomial to fit the relationship curve between SOC and OCV at 25 °C through Matlab software, output the expression of the eighth-order polynomial, calculate the curve slopes when the SOC of the eighth-order polynomial is 0%, 5%, 10%, …, 100% respectively, generate derivatives when the SOC takes values of 0%, 5%, 10%, …, 100% by taking the derivative of the output eighth-order polynomial, the derivatives are used to reflect the curve slopes, sequentially and without repetition select 8 points with the largest slopes as test points, and arrange them in ascending order according to the SOC values, record the corresponding SOC values, and the test points are used to reflect the points with higher change rates in the relationship curve between SOC and OCV at 25 °C.

7. The calibration method of the SOC-OCV curve of the lithium-ion battery according to claim 6, characterized in that: In S3, a lithium-ion battery to be tested with 100% SOC is prepared, and it is ensured that it is left standing at other temperatures for at least 1 hour to achieve thermal equilibrium. A voltmeter is used to measure the OCV value of the lithium battery at the SOC corresponding to the test point, and the endpoint values, namely the OCV values when the SOC values are 0 and 1, are measured, and the test data points P are recorded. j , and P j = (x j , y j ), for the test data point P j (x j , y j ), in x j is the SOC value corresponding to the test point of the lithium battery, and y j is the OCV value of the lithium battery at the SOC corresponding to the test point. The value of j is an integer between 0 and 9. Among them, the test data point P0(x0, y0) is (0, OCV0), P9(x9, y9) is (1, OCV1), and the abscissa of other test data points is the SOC value, and the ordinate is the corresponding OCV value.

8. The calibration method for the SOC-OCV curve of a lithium-ion battery according to claim 7, characterized in that: Conduct a correlation analysis on the data measured in S3 to generate an interpolation polynomial P(x), and the formula relied on is: where L j (x) is the j-th basis function, defined as: Among them, P(x) is the interpolation polynomial, x k and x j The subscripts k and j are both used to index the test data points. The value of k is an integer between 0 and 9, which is used to predict the function values between each test data point P j The basis function L j (x) is used to ensure that the basis function output is 1 at a specific test data point P j , that is, L j (x) = 1, while at other test data points x k , (k≠j), the basis function output is 0, that is, L j (x) = 0, which is used to weight the corresponding value y j to construct the total interpolation polynomial P(x), and the relationship curves between SOC and OCV at other temperatures are plotted according to the interpolation polynomial.

9. A calibration device for the SOC-OCV curve of a lithium-ion battery, which is used to perform the calibration method for the SOC-OCV curve of the lithium-ion battery described in claim 1, and is characterized in that, Include: Normal temperature test module, which is used to prepare a lithium-ion battery to be tested with 100% SOC, ensure that it stands still at 25 degrees Celsius for at least 1 hour to achieve thermal equilibrium, measure the OCV value of the battery at different SOCs using a voltmeter, record data points, and draw a relationship curve between SOC and OCV at 25 degrees Celsius according to the data points; Test point selection module, which is used to fit the relationship curve between SOC and OCV at 25 degrees Celsius by using an eighth-order polynomial through Matlab software, output the expression of the eighth-order polynomial, calculate the curve slopes when the SOC of the eighth-order polynomial is 0%, 5%, 10%,..., 100% respectively, select 8 of the slope maximum points without repetition in sequence as test points, and record the corresponding SOC values; Other temperature test module, which is used to test the OCV data when the SOC is 0%, 5%, 10%,..., 100% at other temperatures; Fitting module, which is used to perform correlation analysis on the data measured in S3 to generate an interpolation polynomial. The function image of the interpolation polynomial is used to reflect the fitted SOC and OCV curves, and the curve of the interpolation polynomial strictly passes through all test data points; Other temperature curve drawing module, which is used to draw the relationship curve between SOC and OCV at other temperatures according to the interpolation polynomial.

10. A medium, characterized in that: A computer program instruction is stored on a storage medium, and when the computer program instruction is executed by a processor, the method described in claim 1 is implemented.

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