Construction method and estimation method of battery ocv-soc curve estimation model, device and medium

Abstract

The application discloses a battery OCV-SOC curve estimation model construction method, an estimation method, equipment and a medium. The method comprises the following steps: acquiring a charging curve of a battery under different charging rates; constructing a charging matrix under a corresponding rate according to the charging curve; calculating a charging coefficient of the charging matrix under different rates; and obtaining a battery OCV-SOC curve estimation model according to the charging curve and the charging coefficient. The method of the application can test the voltage curve under different rates, and obtain the voltage curve when the rate is zero through deduction, so that the time required for testing is greatly reduced, and the problem of unstable testing caused by a long testing process is avoided.

Description

Technical Field

[0001] This invention relates to the field of battery OCV-SOC estimation technology, and particularly to a method for constructing a battery OCV-SOC curve estimation model, an estimation method, equipment, and medium. Background Technology

[0002] Existing methods for testing the OCV-SOC curve of lithium-ion batteries are mainly divided into static and dynamic methods. The static method involves applying a pulse current to adjust the SOC after initial SOC testing and then allowing it to rest for a sufficient time before measuring the fully stable voltage as the open-circuit voltage of the current SOC. This process is repeated to obtain the OCV for multiple SOCs. The dynamic method uses a small current (typically 0.04C) for full charge or discharge testing, and the resulting voltage curve is approximately equivalent to the open-circuit voltage curve. The static method cannot obtain the OCV for all SOCs; the OCV for untested SOCs can only be obtained through interpolation, reducing the accuracy of the obtained OCV-SOC curve. Furthermore, the resting time is long, and a short resting time may not yet reach the thermodynamic conditions. The dynamic method has an excessively long testing time; obtaining a single charge or discharge OCV-SOC curve at 0.04C requires a theoretical time of 25 hours. Instability at any point during this process will lead to test failure, and 0.04C does not equal no current; in batteries with high impedance, significant polarization will still occur, causing the measured voltage curve to deviate from the OCV. Therefore, neither static nor dynamic methods can fully meet the current needs for OCV-SOC testing. Summary of the Invention

[0003] The embodiments of this application provide a method for constructing a battery OCV-SOC curve estimation model, an estimation method, an apparatus, and a medium to solve the technical problems of long testing time and low accuracy of test results in the prior art.

[0004] To address the aforementioned technical problems, embodiments of this application disclose the following technical solutions:

[0005] Firstly, a method for constructing a battery OCV-SOC curve estimation model is provided, the method comprising:

[0006] Obtain the charging curves of the battery at different charging rates;

[0007] Construct a charging matrix at the corresponding rate based on the charging curve;

[0008] Calculate the charging coefficient of the charging matrix at different rates;

[0009] The battery OCV-SOC curve estimation model is obtained based on the charging curve and the charging coefficient.

[0010] In conjunction with the first aspect, the method for constructing a charging matrix based on the charging data includes the following steps:

[0011] Obtain the voltage and current data from the charging data;

[0012] A voltage matrix, which is the charging matrix, is constructed based on the obtained voltage and current data.

[0013] The calculation formula is:

[0014]

[0015] Where U is the voltage matrix, U I1,SOC1 For specific voltage values, SOC1~SOC n This represents the n SOC charging data points acquired, I1~I m The current corresponding to the voltage in the SOC charging data, where n and m are both integers.

[0016] In conjunction with the first aspect, the method for calculating the charging coefficient of the charging matrix at different rates includes the following steps:

[0017] The obtained charging matrices at different multipliers are subjected to singular value decomposition respectively;

[0018] By retaining some valid data after singular value decomposition, the first decomposition matrix is ​​obtained;

[0019] The second decomposition matrix is ​​obtained by splitting the first decomposition matrix;

[0020] The charging coefficients are obtained by linear fitting of the elements in the second decomposition matrix.

[0021] In conjunction with the first aspect, the method for obtaining a battery OCV-SOC curve estimation model based on the charging curve and the charging coefficient includes the following steps:

[0022] The zero-rate charging coefficient is obtained by fitting the charging coefficients at different rates.

[0023] The zero-rate charging curve is obtained based on the zero-rate charging coefficient, which is the battery OCV-SOC curve estimation model.

[0024] In conjunction with the first aspect, the method for obtaining the charging curves of a battery at different charging rates includes the following steps:

[0025] The battery is discharged from a first voltage to a second voltage through a phased discharge process;

[0026] The battery is charged from a second voltage to a first voltage at a rate of A, and the voltage data at the rate of A is recorded.

[0027] The battery is charged from the second voltage to the first voltage at a rate of 0.5A, and the voltage data at the 0.5A rate is recorded.

[0028] The battery is charged from the second voltage to the first voltage at a rate of 0.05A, and the voltage data at the 0.05A rate is recorded.

[0029] The charging curves of the battery are obtained by plotting the voltage data at the A-rate, the 0.5A-rate, and the 0.05A-rate based on the charging data.

[0030] In conjunction with the first aspect, the charging curve is a voltage curve, which includes the relationship between the battery's voltage value and its charge value.

[0031] In conjunction with the first aspect, the method for discharging the battery from a first voltage to a second voltage through staged discharge includes the following steps:

[0032] The battery, which is at the first voltage, is discharged to the second voltage at the rate of 0.5A.

[0033] The battery, which has been discharged to the second voltage, is discharged again at a rate of 0.05A and then at a rate of 0.02A.

[0034] In conjunction with the first aspect, the A multiplier is 2C, the 0.5A multiplier is 1C, the 0.05A multiplier is 0.1C, and the 0.02A multiplier is 0.04C.

[0035] Secondly, a method for estimating battery SOC is provided, the method comprising the following steps:

[0036] Obtain the battery voltage;

[0037] The battery voltage is input into the estimation model obtained by the battery OCV-SOC curve estimation model construction method described in any one of the first aspects, and the SOC value of the battery is obtained through the calculation of the estimation model.

[0038] Thirdly, an electronic device is provided, including a memory and a processor; the memory is used to store a computer program; the processor is used to implement, when executing the computer program, the battery OCV-SOC curve estimation model construction method as described in any one of the first aspects, or the battery SOC estimation method as described in the second aspect.

[0039] Fourthly, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the battery OCV-SOC curve estimation model construction method as described in any of the first aspects, or implements the battery SOC estimation method as described in the second aspect.

[0040] One of the above technical solutions has the following advantages or beneficial effects:

[0041] This application provides a method for constructing a battery OCV-SOC curve estimation model. The method includes: obtaining the charging curves of the battery at different charging rates; constructing a charging matrix at the corresponding charging rate based on the charging curves; calculating the charging coefficients of the charging matrix at different charging rates; and obtaining the battery OCV-SOC curve estimation model based on the charging curves and charging coefficients. This method significantly reduces the testing time by testing the voltage curves at different charging rates and deriving the voltage curve at zero charging rate, thus avoiding the problem of unstable testing due to excessively long testing processes. Attached Figure Description

[0042] The technical solution and other beneficial effects of this application will become apparent from the following detailed description of specific embodiments in conjunction with the accompanying drawings.

[0043] Figure 1 This is a schematic diagram of the method flow structure provided in the embodiments of this application;

[0044] Figure 2 This is a schematic diagram of the method flow structure provided in the embodiments of this application;

[0045] Figure 3 This is a schematic diagram showing the results comparison structure provided in the embodiments of this application. Detailed Implementation

[0046] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. In the description of this application, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the accompanying drawings and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0047] The specific implementation methods of this application are further illustrated in the following examples:

[0048] Please see Figure 1 and Figure 2 This application provides a method for constructing a battery OCV-SOC curve estimation model, the method including:

[0049] S1: Obtain the charging curve of the battery at different charging rates;

[0050] The specific method includes the following steps:

[0051] S101: Discharge the battery from the first voltage to the second voltage through a phased discharge process;

[0052] S102: Discharge the battery at the first voltage to the second voltage at a rate of 0.5A;

[0053] S103: The battery that has been discharged to the second voltage will continue to be discharged at a rate of 0.05A and then at a rate of 0.02A.

[0054] In this embodiment, the first voltage is the upper limit voltage of the battery when it is fully charged, which is 4.4V, and the second voltage is the lower limit voltage of the battery after it is fully discharged, which is 2.9V. The positive electrode of the battery is made of NCM material, the negative electrode is made of graphite material, and the battery capacity is 157.9Ah.

[0055] The specific discharge process of the battery is as follows: First, charge the battery to full capacity, that is, charge it to the upper limit voltage of 4.4V. Then, discharge the 4.4V battery to 2.9V at a rate of 1C, then discharge it to 2.9V at a rate of 0.1C, and finally discharge it to 2.9V at a rate of 0.04C. After the discharge is completed, let the battery stand for 0.5 to 2 hours.

[0056] In the embodiments of this application, the multiplier A is 2C, the multiplier 0.5A is 1C, the multiplier 0.05A is 0.1C, and the multiplier 0.02A is 0.04C.

[0057] S104: Charge the battery from the second voltage to the first voltage at a rate of A, and record the voltage data at the rate of A;

[0058] S105: Charge the battery from the second voltage to the first voltage at a rate of 0.5A, and record the voltage data at the 0.5A rate;

[0059] S106: Charge the battery from the second voltage to the first voltage at a rate of 0.05A, and record the voltage data at the 0.05A rate;

[0060] S107: Based on the charging data, the voltage data at A rate, 0.5A rate and 0.05A rate are plotted to obtain the battery charging curve.

[0061] The specific charging steps are as follows: After the battery has been fully discharged and allowed to rest, charge it from 2.9V to 4.4V at a charging rate of 2C to obtain a charging curve with a charging rate of 2C; repeat the phased discharge steps of S101 to S103 to fully discharge the battery and allow it to rest; charge the fully discharged and allowed-to-rest battery from 2.9V to 4.4V at a charging rate of 1C to obtain a charging curve with a charging rate of 1C; repeat the phased discharge steps of S101 to S103 to fully discharge the battery and allow it to rest; charge the fully discharged and allowed-to-rest battery from 2.9V to 4.4V at a charging rate of 0.1C to obtain a charging curve with a charging rate of 0.1C.

[0062] The charging curves in this application are voltage curves, including the relationship between the battery voltage value and the battery capacity value. Once charging is complete, the 2C charging curve, 1C charging curve, and 0.1C charging curve are obtained.

[0063] It is understood that in this embodiment, the staged discharge is to fully release the battery's charge, preventing residual charge from affecting subsequent data monitoring and reading. Since high-rate discharge may result in incomplete charge release, the battery is first discharged at a high rate to the lower voltage limit, then at a medium rate to the lower voltage limit, and finally at a low rate to fully release the charge. In this application, the high rate is 1C, the medium rate is 0.1C, and the low rate is 0.04C. It should be noted that the discharge rate used in this application is not fixed. In some other embodiments, the high rate can be 1C–3C, the medium rate can be 0.1C–0.8C, and the low rate can be 0.01C–0.09C; therefore, the battery discharge rate can be selected arbitrarily according to different purposes.

[0064] S2: Construct a charging matrix at the corresponding rate based on the charging curve;

[0065] The specific method includes the following steps:

[0066] S201: Obtain voltage and current data from the charging data;

[0067] S202: Construct a voltage matrix, which is the charging matrix, based on the obtained voltage and current data;

[0068] The calculation formula is:

[0069]

[0070] Where U is the voltage matrix, U I1,SOC1 For specific voltage values, SOC1~SOC n This represents the n SOC charging data points acquired, I1~I m The current corresponding to the voltage in the SOC charging data, where n and m are both integers.

[0071] Obtain the 2C, 1C, and 0.1C charging curves obtained in steps S101 to S107. Construct a voltage matrix based on the voltage values ​​on the charging curves and the corresponding current values. It should be noted that a 2C voltage matrix is ​​obtained when the charging rate is 2C, a 1C voltage matrix is ​​obtained when the charging rate is 1C, and a 0.1C voltage matrix is ​​obtained when the charging rate is 0.1C.

[0072] S3: Calculate the charging coefficient of the charging matrix under different rates;

[0073] The specific calculation method includes the following steps:

[0074] S301: Perform singular value decomposition on the charging matrices obtained at different rates;

[0075] Obtain the 2C, 1C, and 0.1C voltage matrices in S202, and perform singular value decomposition on the 2C, 1C, and 0.1C voltage matrices respectively. The decomposition formula is as follows:

[0076] U=VΣY T ;

[0077] Wherein, V, Σ and Y T These are the three matrices resulting from the singular value decomposition, and T is the transpose symbol.

[0078] S302: Retain some valid data after singular value decomposition to obtain the first decomposition matrix;

[0079] Choose a parameter p as the Karhunen–Loeve rank, where the Karhunen–Loeve rank is the KL rank, p∈[1,n]. Based on parameter p, filter the elements of the three matrices in the 2C voltage matrix after singular value decomposition, retaining the elements in p rows and p columns of each matrix, to obtain the first decomposition matrix. The decomposition formula is:

[0080] U p =V p Σ p Y p T ;

[0081] The three matrices in the 1C voltage matrix and the 0.1C voltage matrix are filtered using the same method to obtain their respective first decomposition matrices.

[0082] S303: Obtain the second decomposition matrix by splitting the first decomposition matrix;

[0083] The second decomposition matrix is ​​obtained by splitting the matrices in the filtered 2C first decomposition matrix, 1C first decomposition matrix, and 0.1C first decomposition matrix, i.e., by using U p =V p Σ p Y p T Obtain matrix V p Σ p .

[0084] S304: Obtain the charging coefficients by linear fitting of the elements in the second decomposition matrix;

[0085] Linear fitting is performed on the elements of the second decomposition matrix, i.e., through V p Σ p Calculate V p Σ p The product of these two elements yields matrix A:

[0086]

[0087] Where α is V p Σ p The elements of matrix A after multiplication are k∈[1,n], j∈[1,p];

[0088] Relate matrix A to current I k Linear fitting yielded the following:

[0089]

[0090] Where D is the number of selected current values, D∈[1, p]; through the known α k,j I k The fitting yielded (a) j +b j I k The relationship between ) is used to obtain the coefficient a. j and coefficient b j The value of is the charging coefficient.

[0091] S4: Obtain the battery OCV-SOC curve estimation model based on the charging curve and charging coefficient.

[0092] The solution method includes the following steps:

[0093] S401: Fit the charging coefficients at different rates to obtain the zero-rate charging coefficient;

[0094] Based on the obtained charging coefficient a j and charging coefficient b j Derive the current I k The charging coefficient when it is 0, i.e.:

[0095] α 0,j =a j .

[0096] The obtained α 0,j This is the zero-rate charging factor.

[0097] S402: Obtain the zero-rate charging curve based on the zero-rate charging coefficient, that is, obtain the battery OCV-SOC curve estimation model;

[0098] Based on the obtained zero-rate charging coefficient α 0,j The voltage matrix at zero rate is constructed;

[0099] U0=[α 0,1 α 0,2 …α 0,j ]Y j T ;

[0100] The zero-rate charging curve is obtained by constructing the voltage matrix at zero rate, and this zero-rate charging curve is the OCV-SOC curve estimation model.

[0101] The prediction tools proposed in steps S1-S4 are constructed using programming languages, including but not limited to C, C++, Matlab, Python, Julia, and Fortran.

[0102] like Figure 3 The figure shows a comparison between the voltage curves at zero rate and 0.04 rate obtained by the method of this application. It can be understood that the zero-rate charging curve is the voltage curve of the battery when it is not charged. This curve provides the relationship between voltage and capacity, thus allowing the estimation model of the battery's OCV-SOC curve to be derived. The method of this application obtains the voltage curve at 0 rate by extrapolating from the voltage curve at a medium rate, significantly reducing testing time and avoiding instability issues that may arise during prolonged testing.

[0103] This application provides a battery SOC estimation method, which includes the following steps:

[0104] S1101: Obtain battery voltage;

[0105] S1102: Input the battery voltage into the estimation model obtained by the battery OCV-SOC curve estimation model construction method in any of steps S1-S4, and obtain the battery's SOC value through the calculation of the estimation model. After obtaining the estimation tool, simply input the obtained battery voltage into the estimation tool to obtain the battery's current capacity. This method greatly simplifies the battery testing process and facilitates the estimation of battery capacity.

[0106] This application provides an electronic device, including a memory and a processor; the memory is used to store a computer program; the processor is used to implement, when executing the computer program, the battery OCV-SOC curve estimation model construction method as in steps S1-S4, or the battery SOC estimation method as in steps S1101-S1102.

[0107] This application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the battery OCV-SOC curve estimation model construction method as in steps S1-S4, or the battery SOC estimation method as in steps S1101-S1102.

[0108] The foregoing has provided a detailed description of the construction method, estimation method, equipment, and medium for a battery OCV-SOC curve estimation model provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the technical solutions and core ideas of this application. Those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for constructing a battery OCV-SOC curve estimation model, characterized in that, The method includes: Obtain the charging curves of the battery at different charging rates; Constructing a charging matrix at the corresponding rate based on the charging curve includes: acquiring voltage and current data from the charging data; constructing a voltage matrix based on the acquired voltage and current data, which is the charging matrix; the calculation formula is: ； in, U For voltage matrix, For specific voltage values, SOC 1~ SOC n Indicates what was obtained n SOC charging data, I 1~ I m The current corresponding to the voltage in the SOC charging data. n and m All are integers; Calculating the charging coefficients of the charging matrix at different rates includes: performing singular value decomposition on the obtained charging matrices at different rates; retaining some valid data after singular value decomposition to obtain a first decomposition matrix; splitting the first decomposition matrix to obtain a second decomposition matrix; and performing linear fitting on the elements in the second decomposition matrix to obtain the charging coefficients. The method for obtaining the battery OCV-SOC curve estimation model based on the charging curve and the charging coefficient includes: fitting the charging coefficient at different rates to obtain the zero-rate charging coefficient; and obtaining the zero-rate charging curve based on the zero-rate charging coefficient, i.e., obtaining the battery OCV-SOC curve estimation model.

2. The method for constructing the battery OCV-SOC curve estimation model as described in claim 1, characterized in that, The method for obtaining the charging curves of a battery at different charging rates includes the following steps: The battery is discharged from a first voltage to a second voltage through a phased discharge process; The battery is charged from a second voltage to a first voltage at a rate of A, and the voltage data at the rate of A is recorded. The battery is charged from the second voltage to the first voltage at a rate of 0.5A, and the voltage data at the 0.5A rate is recorded. The battery is charged from the second voltage to the first voltage at a rate of 0.05A, and the voltage data at the 0.05A rate is recorded. The charging curve of the battery is plotted based on the voltage data at the A-rate, the 0.5A-rate, and the 0.05A-rate.

3. The method for constructing the battery OCV-SOC curve estimation model as described in claim 1, characterized in that, The charging curve is a voltage curve, which includes the relationship between the battery's voltage and charge value.

4. The method for constructing the battery OCV-SOC curve estimation model as described in claim 2, characterized in that, The method for discharging the battery from a first voltage to a second voltage through staged discharge includes the following steps: The battery, which is at the first voltage, is discharged to the second voltage at the rate of 0.5A. The battery, which has been discharged to the second voltage, is discharged again at a rate of 0.05A and then at a rate of 0.02A.

5. The method for constructing the battery OCV-SOC curve estimation model as described in claim 4, characterized in that, The A multiplier is 2C, the 0.5A multiplier is 1C, the 0.05A multiplier is 0.1C, and the 0.02A multiplier is 0.04C.

6. A method for estimating battery SOC, characterized in that, The method includes the following steps: Obtain the battery voltage; The battery voltage is input into the estimation model obtained by the battery OCV-SOC curve estimation model construction method according to any one of claims 1 to 5, and the SOC value of the battery is obtained through the calculation of the estimation model.

7. An electronic device, characterized in that: It includes a memory and a processor; the memory is used to store a computer program; the processor is used to implement, when executing the computer program, the battery OCV-SOC curve estimation model construction method as described in any one of claims 1 to 5, or the battery SOC estimation method as described in claim 6.

8. A computer-readable storage medium, characterized in that: The storage medium stores a computer program that, when executed by a processor, implements the battery OCV-SOC curve estimation model construction method as described in any one of claims 1 to 5, or implements the battery SOC estimation method as described in claim 6.

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