A soc correction method for online identification of lithium battery parameters

Abstract

The application discloses a SOC correction method for online identification of lithium battery parameters, and comprises the following steps: firstly, an equivalent circuit model and a transfer function of a lithium battery are established and discretized; secondly, a comparison table of states of charge at different moments and open-circuit voltages is established based on a discharge experiment of an experimental lithium battery; thirdly, voltages and currents of a lithium battery to be measured are collected in real time, and an SOC of the lithium battery to be measured is estimated by using an ampere-hour integral method; fourthly, parameter online identification is carried out by using a recursive least square method, and an open-circuit voltage of the battery is directly identified and the SOC is estimated; and finally, the SOC estimated by using the ampere-hour integral method is corrected by using the SOC, so that the final SOC of the battery is obtained, and the calculation amount is reduced while the estimation accuracy is ensured.

Description

Technical Field

[0001] This invention belongs to the field of battery management system technology, and more specifically, relates to a SOC correction method for online identification of lithium battery parameters. Background Technology

[0002] State of Charge (SOC) is a key parameter of a power battery, measuring its remaining energy and serving as an important reference for preventing overcharging and over-discharging, as well as for overall vehicle energy control. As a crucial component of the Battery Management System (BMS), the fuel gauge chip estimates the battery's SOC using data such as current and voltage from the lithium-ion battery pack. With the continuous development of new energy technologies, the application of fuel gauge chips is increasing. Reducing the computational load of the algorithm while maintaining the accuracy of SOC estimation can decrease the hardware resources required for the fuel gauge chip. Therefore, researching lightweight SOC estimation algorithms is of great significance for improving the practicality of fuel gauge chips.

[0003] Currently, commonly used SOC estimation methods in the industry include: the ampere-hour integration method, but it has the disadvantage of large cumulative error; the open-circuit voltage method, which requires the battery to be stationary and the battery's operating conditions change complexly during application, so it is not suitable for real-time online estimation requirements; and model-based algorithms, which can be very accurate, but they rely on the model to accurately describe the system and are often used in conjunction with adaptive filters and state estimation algorithms, so the computational load is often very large. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a SOC correction method for online identification of lithium battery parameters, so as to improve the reliability of lithium battery SOC estimation and reduce the amount of computation.

[0005] To achieve the above-mentioned objective, the present invention provides a method for online identification of SOC correction of lithium battery parameters, characterized by comprising the following steps:

[0006] (1) Establish the equivalent circuit model of lithium battery;

[0007] (2) Establish and discretize the transfer function based on the equivalent circuit model of lithium battery;

[0008] (3) Conduct discharge experiments on the experimental lithium battery and record the state of charge (SOC(k)) and corresponding open-circuit voltage U at different times. oc,k k represents the sampling time; then linear interpolation is performed between two adjacent sets of data, and a lookup table is used to record their corresponding relationships;

[0009] (4) Real-time acquisition of the voltage U of the lithium battery under test k and current I k ;

[0010] (5) Estimate the SOC of the lithium battery under test at time k using the ampere-hour integration method. A (k);

[0011] SOC A (k)=SOC A (k-1)+I k ΔT / C N

[0012] Among them, C N The total capacity of the lithium battery under test is given by ΔT, which is the time interval between two adjacent sampling times.

[0013] (6) The parameters of the equivalent circuit model of the lithium battery are identified online using the recursive least squares method with a forgetting factor, and the open-circuit voltage is obtained.

[0014] (7) Based on open circuit voltage Search the lookup table for the corresponding SOC v (k);

[0015] (8) Based on the found SOC v (k) for SOC A (k) is corrected to obtain the corrected state of charge at the current time.

[0016] The objective of this invention is achieved as follows:

[0017] This invention discloses a method for correcting the State of Charge (SOC) of a lithium battery through online parameter identification. First, an equivalent circuit model and transfer function of the lithium battery are established and discretized. Then, based on discharge experiments of experimental lithium batteries, a table comparing the state of charge (SOC) with the open-circuit voltage at different times is established. Next, the voltage and current of the lithium battery under test are collected in real time, and the SOC of the battery is estimated using the ampere-hour integral method. Then, online parameter identification is performed using the recursive least squares method to directly identify the open-circuit voltage and estimate the SOC. Finally, the SOC estimated by the ampere-hour integral is corrected using this method to obtain the final SOC of the battery. This method reduces the computational load while ensuring the accuracy of the estimation.

[0018] The SOC correction method for online identification of lithium battery parameters of the present invention also has the following beneficial effects:

[0019] This invention provides a lightweight online identification method for SOC correction of lithium batteries, which effectively estimates the battery SOC and corrects the cumulative error of traditional ampere-hour integral estimation. Compared with traditional adaptive filtering and other methods, it directly identifies the open-circuit voltage as a battery parameter, significantly reducing the amount of computation, and is very suitable for fuel gauge chips. Attached Figure Description

[0020] Figure 1 This is a flowchart of a method for online identification of lithium battery parameters and SOC correction according to the present invention;

[0021] Figure 2 It uses the Thevenin lithium battery equivalent circuit model;

[0022] Figure 3 This is a schematic diagram illustrating the principle of the linear interpolation method used.

[0023] Figure 4 This is the OCV-SOC curve fitted by the present invention. Detailed Implementation

[0024] The specific embodiments of the present invention will now be described with reference to the accompanying drawings to enable those skilled in the art to better understand the invention. It should be particularly noted that in the following description, detailed descriptions of known functions and designs that might obscure the main content of the invention will be omitted here.

[0025] Example

[0026] Figure 1 This is a flowchart of a method for online identification of lithium battery parameters and SOC correction according to the present invention.

[0027] In this embodiment, as Figure 1 As shown, the SOC correction method for online identification of lithium battery parameters according to the present invention includes the following steps:

[0028] S1. Establish the equivalent circuit model of the battery;

[0029] In this embodiment, the lithium battery is selected using the Thevenin equivalent circuit model, such as... Figure 2 As shown, according to Kirchhoff's laws, we can obtain:

[0030]

[0031] Where U represents the terminal voltage across the lithium battery, U oc U1 represents the open-circuit voltage of the lithium battery, U1 represents the polarization voltage across the RC network in the model, R0 represents the ohmic resistance of the lithium battery, and I represents the load current flowing through the battery.

[0032] S2. Establish and discretize the transfer function based on the equivalent circuit model of a lithium battery;

[0033] The transfer function of the equivalent circuit model based on Thevenin can be expressed as:

[0034]

[0035] Where R0 represents the ohmic internal resistance of the lithium battery, R1 represents the polarization internal resistance of the battery, C1 represents the polarization capacitance of the battery, and s represents the S-domain.

[0036] The transfer function above is discretized using the bilinear transform method, and s = 2(1-z) is transformed into... -1 ) / T(1+z -1 Substituting into the above equation, we obtain the discretized transfer function:

[0037]

[0038] Where z represents the Z-domain, and the intermediate variables c1, c2, and c3 satisfy:

[0039]

[0040]

[0041]

[0042] By rearranging and simplifying G(z), the corresponding difference equation is derived as follows:

[0043] U k =(1+c1)U oc,k -c1U k-1 -c2I k -c3I k-1

[0044] Wherein, the subscript k represents the current time, and the subscript k-1 represents the previous time;

[0045] S3. Create a lookup table;

[0046] In this embodiment, a discharge experiment was conducted on the experimental lithium battery under DST automotive discharge conditions, and the state of charge (SOC(k)) and corresponding open-circuit voltage (U) were recorded at different times. oc,k A total of 21 sets of data were recorded; then, linear interpolation was used to obtain the corresponding values ​​of all SOC and open-circuit voltage OCV, thereby fitting the data. Figure 4 The OCV-SOC curves are shown below; ultimately, we can establish a lookup table that maps SO to OCV one-to-one.

[0047] Linear interpolation connects two known quantities with a straight line and defines a function to find the value of the unknown quantity between the two known quantities.

[0048] Figure 3 This is a schematic diagram illustrating the principle of linear interpolation. Given the coordinates of (x0, y0) and (x1, y1) and the value of x, then...

[0049]

[0050] y = k(x - x0) + y0

[0051] The value of the unknown quantity y can then be obtained.

[0052] S4. Real-time acquisition of the voltage U of the lithium battery under test. k and current I k ;

[0053] S5. Estimate the SOC of the lithium battery under test at time k using the ampere-hour integration method. A (k), the estimation formula is:

[0054] SOC A (k)=SOC A (k-1)+I k ΔT / C N

[0055] Among them, C N The total capacity of the lithium battery under test is given by ΔT, which is the time interval between two adjacent sampling times.

[0056] In this embodiment, the selected battery nominal capacity C N It is 2Ah;

[0057] S6. Utilize the recursive least squares method with a forgetting factor to achieve online parameter identification of the equivalent circuit model of the lithium battery, and obtain the open-circuit voltage.

[0058] S6.1 Write the difference equation of the model transfer function into a mathematical expression that can be applied to the recursive least squares method;

[0059] Let variable y k =U k Then the difference equation can be expressed as:

[0060] y k =θ k φ k

[0061] Where, θ k Let φ be the parameter matrix. k The data matrices are represented as follows:

[0062] θ k =[(1+c1)U oc,k -c1 -c2 -c3]

[0063] φ k =[1 U k-1 I k I k-1 ] T

[0064] S6.2, The real-time collected voltage U k and current I k Substitute into the above formula and calculate using the following recursive least squares formula:

[0065]

[0066] The superscript T indicates transpose. K represents the estimated value of the target vector. k P represents the gain coefficient matrix; k Let represent the covariance matrix; λ is the forgetting factor, representing the weight of new information.

[0067] The open-circuit voltage of the lithium battery at the current moment can be obtained:

[0068]

[0069] Where θ[0] represents θ k The first element, θ[1] represents θ k The second element.

[0070] S7. Obtain the current open-circuit voltage based on the established lookup table. Corresponding SOC v (k);

[0071] S8. Based on the searched SOC v (k) for SOC A (k) is corrected to obtain the corrected state of charge at the current time.

[0072] The correction method is as follows:

[0073]

[0074] Among them, SOC V The SOC value is estimated using the open-circuit voltage. A The value represents the SOC value before and after correction, estimated by the ampere-hour integral. μ is the correction coefficient for SOC. In this embodiment, μ is 0.2.

[0075] When the next moment arrives, the above process can be repeated to continuously update the model parameters and output the corrected SOC.

[0076] Although the illustrative specific embodiments of the present invention have been described above to enable those skilled in the art to understand the invention, it should be understood that the invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the invention as defined and determined by the appended claims, and all inventions utilizing the concept of the present invention are protected.

Claims

1. A method for SOC correction based on online identification of lithium battery parameters, characterized in that, Includes the following steps: (1) Establish an equivalent circuit model of a lithium battery; (2) Establish and discretize the transfer function based on the equivalent circuit model of lithium battery; (3) Conduct discharge experiments on the experimental lithium battery and record the state of charge at different times. and the corresponding open circuit voltage , The sampling time is indicated; then linear interpolation is performed between two adjacent sets of data, and a lookup table is used to record their corresponding relationships. (4) Real-time acquisition of the voltage of the lithium battery under test and current ; (5) Estimate the current ampere-hour integration method of the lithium battery under test. Moment ; ； in, The total capacity of the lithium battery under test. The interval between two adjacent sampling times; (6) The parameters of the equivalent circuit model of the lithium battery are identified online using the recursive least squares method with a forgetting factor, and the open-circuit voltage is obtained. ; (7) Based on the open circuit voltage Search the lookup table for the corresponding... ; (8) Based on the search right Make corrections to obtain the corrected state of charge at the current time. ; The equivalent circuit model of the lithium battery is as follows: ； in, This indicates the terminal voltage across the lithium battery. This indicates the open-circuit voltage of the lithium battery. This represents the polarization voltage across the RC network in the model. This indicates the ohmic resistance of a lithium battery. This indicates the load current flowing through the battery for lithium batteries; The transfer function based on the lithium battery equivalent circuit model is expressed as follows: ； in, Indicates the ohmic internal resistance of a lithium battery, Indicates the polarization internal resistance of the battery, Indicates the polarization capacitance of the battery. Represents the S-domain; The transfer function is discretized using the bilinear transform method, and then... Substituting into the above equation, we obtain the discretized transfer function: ； in, Represents the Z-domain, intermediate variable , , satisfy: ； right By rearranging and simplifying, the corresponding difference equation is derived as follows: ； Among them, subscript Indicates the current time, subscript Indicates the previous moment; The process of online parameter identification of the equivalent circuit model of a lithium battery using the recursive least squares method with a forgetting factor is as follows: 1) Write the difference equation of the model transfer function in a mathematical form that can be applied to the recursive least squares method; Let the variable Then the difference equation can be expressed as: ； in, For parameter matrices, The data matrices are represented as follows: ； 2) Real-time collected voltage and current Substitute into the above formula and calculate using the following recursive least squares formula: ； Among them, superscript Indicates transpose. This represents the estimated value of the target vector; Represents the gain coefficient matrix; Represent the covariance matrix; The forgetting factor represents the weight of new information. The open-circuit voltage of the lithium battery at the current moment can be obtained: ； in, express The first element, express The second element.

2. The SOC correction method for online identification of lithium battery parameters according to claim 1, characterized in that, The The correction method is as follows: ； in, This is the correction factor for SOC.

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