Lithium battery soh estimation method and device, electronic equipment and storage medium

Abstract

This application discloses a method, apparatus, electronic device, and storage medium for estimating the state of matter (SOH) of a lithium battery. The method includes: acquiring multiple SOH values ​​and corresponding multiple SOC-OCV sample curves of a lithium battery sample set, and constructing a local SOH estimation model; acquiring the real-time SOC-OCV curve of the lithium battery under test, and determining the SOH value of the lithium battery under test based on the local SOH estimation model. By analyzing and processing the SOH, SOC, and OCV data of the lithium battery, the data patterns existing in the local OCV are integrated, and then by constructing a local SOH estimation model, the data relationship between SOH and OCV is integrated. This achieves a one-to-one correspondence between the SOH and OCV of the lithium battery for a local OCV that meets the requirements, thereby greatly improving the efficiency of obtaining the estimated SOH value of the lithium battery.

Description

Technical Field

[0001] This invention relates to the field of lithium battery technology, and in particular to a lithium battery SOH estimation method, apparatus, electronic device, and storage medium. Background Technology

[0002] The new energy vehicle industry is booming, and new energy vehicles using lithium batteries as energy storage devices are becoming increasingly popular. State of Health (SOH) is a state parameter characterizing battery performance. SOH represents the battery's cycle life and directly affects the reliability and safety of electric vehicles. Accurate monitoring and estimation of SOH helps improve the Battery Management System (BMS), avoids overcharging and over-discharging, aids in fault diagnosis and safety warnings, and is of great significance to the overall safe and stable operation of electric vehicles.

[0003] Currently, SOH values ​​are mainly estimated using methods such as the definition method, internal resistance method, neural network method, Kalman filter method, and particle filter method. However, these methods require the collection of many parameters and data of lithium batteries, resulting in a slow speed in obtaining SOH estimates.

[0004] Therefore, existing technologies suffer from low efficiency in obtaining SOH estimates for lithium batteries. Summary of the Invention

[0005] In view of this, it is necessary to provide a lithium battery SOH estimation method, apparatus, electronic device and storage medium to solve the problem of low efficiency in obtaining SOH estimation values ​​in the prior art.

[0006] To address the above problems, this invention provides a lithium battery SOH estimation method, comprising:

[0007] Obtain multiple SOH values ​​and their corresponding multiple SOC-OCV sample curves from a lithium battery sample set;

[0008] Construct multiple OCV-SOC sample functions based on multiple SOC-OCV sample curves;

[0009] Differentiate the multiple OCV-SOC sample functions respectively to determine multiple U OCV Sample derivative;

[0010] Based on multiple SOH values ​​and their corresponding multiple U OCV Using the sample derivative function, a local SOH estimation model is constructed.

[0011] The real-time SOC-OCV curve of the lithium battery under test is obtained. Based on the real-time SOC-OCV curve and the local SOH estimation model, the SOH value of the lithium battery under test is determined.

[0012] Furthermore, based on multiple SOH values ​​and their corresponding multiple U... OCV The sample derivative function is used to construct a local SOH estimation model, including:

[0013] Get the preset local U OCV interval;

[0014] According to multiple U OCV Sample derivative function, obtain preset local U OCV Multiple U within the interval OCV Sample values ​​and their corresponding multiple SOH values, based on multiple U OCV The sample value and its corresponding multiple SOH values ​​determine multiple OCV-SOH sample data points;

[0015] Construct an OCV-SOH coordinate system and perform smooth spline fitting on multiple OCV-SOH sample data points to obtain the OCV-SOH curve;

[0016] Based on the OCV-SOH curve, a local SOH estimation model is constructed.

[0017] Furthermore, the calculation formula for the local SOH estimation model is as follows:

[0018] SOH=AU OCV 3 +BU OCV 2 +CU OCV +D

[0019] Where SOH is the SOH value of the lithium battery under test, U OCV Let OCV be the value of the lithium battery under test, and A, B, C and D are constants.

[0020] Furthermore, preset local U OCV The range is 3.20-3.25V.

[0021] Furthermore, based on multiple SOC-OCV sample curves, multiple OCV-SOC sample functions are constructed, including:

[0022] Data transformation is performed on multiple SOC-OCV sample curves to determine the corresponding multiple OCV-SOC sample curves;

[0023] Data fitting is performed on multiple OCV-SOC sample curves to determine the corresponding multiple OCV-SOC sample functions.

[0024] Furthermore, the real-time SOC-OCV curve of the lithium battery under test is obtained, and the SOH value of the lithium battery under test is determined based on the local SOH estimation model, including:

[0025] Based on the real-time SOC-OCV curve, determine the U of the lithium battery under test. OCV derivative function;

[0026] According to U OCV The derivative function and the local SOH estimation model are used to determine the SOH value of the lithium battery under test.

[0027] Furthermore, multiple SOC-OCV sample curves of the lithium battery sample set were obtained, including:

[0028] At a preset temperature, the lithium battery sample set was charged to standard and left to stand for a preset time to obtain a fully charged lithium battery sample set.

[0029] The fully charged lithium battery sample set was discharged according to the preset SOC interval, and the discharge process data was recorded accordingly.

[0030] Based on the discharge process data, the SOC sample set and its corresponding OCV sample set of the lithium battery sample set are determined respectively;

[0031] Based on the SOC sample set and the corresponding OCV sample set, the corresponding data are fitted to determine multiple SOC-OCV sample curves of the lithium battery sample set.

[0032] Each SOC-OCV sample curve consists of a SOC sample and a corresponding OCV sample.

[0033] To address the above problems, the present invention also provides a lithium battery SOH estimation device, comprising:

[0034] The sample curve acquisition module is used to acquire multiple SOH values ​​and their corresponding multiple SOC-OCV sample curves of a lithium battery sample set.

[0035] The sample function acquisition module is used to construct multiple OCV-SOC sample functions based on multiple SOC-OCV sample curves;

[0036] The sample derivative function acquisition module is used to differentiate multiple OCV-SOC sample functions respectively to determine multiple U OCV Sample derivative;

[0037] The estimation model building module is used to estimate multiple SOH values ​​and their corresponding multiple U values. OCV Using the sample derivative function, a local SOH estimation model is constructed.

[0038] The SOH estimation module is used to acquire the real-time SOC-OCV curve of the lithium battery under test, and determine the SOH value of the lithium battery under test based on the real-time SOC-OCV curve and the local SOH estimation model.

[0039] To address the aforementioned problems, the present invention also provides an electronic device, including a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, it implements the lithium battery SOH estimation method as described above.

[0040] To address the aforementioned problems, the present invention also provides a storage medium storing computer program instructions, which, when executed by a computer, cause the computer to perform the lithium battery SOH estimation method described above.

[0041] The beneficial effects of adopting the above technical solution are as follows: This invention provides a lithium battery SOH estimation method, apparatus, electronic device, and storage medium. The method includes: acquiring multiple SOH values ​​and corresponding multiple SOC-OCV sample curves of a lithium battery sample set; constructing a local SOH estimation model based on the multiple SOC-OCV sample curves and multiple SOH values; acquiring the real-time SOC-OCV curve of the lithium battery under test; and determining the SOH value of the lithium battery under test based on the local SOH estimation model. By analyzing and processing the SOH, SOC, and OCV of the lithium battery, the data patterns existing in the local OCV are integrated, and then the data relationship between SOH and OCV is integrated by constructing a local SOH estimation model. This achieves a one-to-one correspondence between the SOH and OCV of the lithium battery for a local OCV that meets the requirements, thereby greatly improving the efficiency of obtaining the SOH estimation value of the lithium battery. Attached Figure Description

[0042] Figure 1 This is a flowchart illustrating an embodiment of the lithium battery SOH estimation method provided by the present invention.

[0043] Figure 2 This is a schematic flowchart of an embodiment of the present invention for obtaining multiple SOC-OCV sample curves of a lithium battery sample set;

[0044] Figure 3 A schematic diagram of the results of an embodiment of the SOC-OCV sample curve of the lithium battery provided by the present invention;

[0045] Figure 4 A schematic diagram of the results of an embodiment of the OCV-SOC sample curve of the lithium battery provided by the present invention;

[0046] Figure 5 The lithium battery U provided by the present invention OCV A schematic diagram showing the result of an embodiment of the sample derivative function;

[0047] Figure 6 This is a flowchart illustrating an embodiment of constructing a local SOH estimation model provided by the present invention.

[0048] Figure 7 Multiple U provided by the present invention OCV A schematic diagram showing the result of an embodiment of the sample derivative function;

[0049] Figure 8 This is a schematic diagram showing the results of an embodiment of the local SOH estimation model provided by the present invention;

[0050] Figure 9 U of the battery F provided by the present invention OCV A schematic diagram showing the result of an embodiment of the derivative function;

[0051] Figure 10 A schematic diagram showing the results of an embodiment of the local SOH estimation model for battery F provided by the present invention;

[0052] Figure 11 This is a schematic diagram of an embodiment of the lithium battery SOH estimation device provided by the present invention;

[0053] Figure 12 A structural block diagram of an embodiment of the electronic device provided by the present invention. Detailed Implementation

[0054] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0055] Before describing the embodiments, let's first explain SOH, SOC, and OCV:

[0056] State of Health (SOH), also known as battery health, represents the ratio of a battery's current capacity to its factory-set capacity, usually expressed as a percentage. Its value ranges from 0% to 100%. When SOH = 0%, the battery is completely unusable; when SOH = 100%, the battery is in excellent health.

[0057] SOC (State of Charge), also known as the remaining capacity of a battery, represents the ratio of the remaining dischargeable capacity of a battery after a period of use or long-term storage to its fully charged state of capacity, usually expressed as a percentage. Battery SOC cannot be directly measured; it can only be estimated using parameters such as battery terminal voltage, charging / discharging current, and internal resistance. These parameters are also affected by various uncertain factors, including battery aging, changes in ambient temperature, and vehicle driving conditions.

[0058] OCV (open circuit voltage), also known as open-circuit voltage, refers to the voltage between the positive and negative terminals of a lithium battery when no current flows. The OCV of a battery is an important characteristic physical quantity under static conditions, and it can well reflect the actual state of charge (SOC) of the battery.

[0059] Currently, the State of Harm (SOH) value is mainly estimated using methods such as the definition method, internal resistance method, neural network method, Kalman filter method, and particle filter method. In other words, obtaining the SOH value of a lithium battery currently requires extensive calculations and is time-consuming, resulting in a slow rate of SOH estimation.

[0060] Therefore, existing technologies suffer from low efficiency in obtaining SOH estimates for lithium batteries.

[0061] To address the aforementioned problems, this invention provides a method, apparatus, electronic device, and storage medium for estimating the state of harmonics (SOH) of lithium batteries, which will be described in detail below.

[0062] like Figure 1 As shown, Figure 1 A flowchart illustrating an embodiment of the lithium battery SOH estimation method provided by the present invention includes:

[0063] Step S101: Obtain multiple SOH values ​​and their corresponding multiple SOC-OCV sample curves from the lithium battery sample set;

[0064] Step S102: Construct multiple OCV-SOC sample functions based on multiple SOC-OCV sample curves;

[0065] Step S103: Differentiate the multiple OCV-SOC sample functions respectively to determine multiple U OCV Sample derivative;

[0066] Step S104: Based on multiple SOH values ​​and their corresponding multiple U values OCV Using the sample derivative function, a local SOH estimation model is constructed.

[0067] Step S105: Obtain the real-time SOC-OCV curve of the lithium battery under test, and determine the SOH value of the lithium battery under test based on the real-time SOC-OCV curve and the local SOH estimation model.

[0068] In this embodiment, firstly, multiple SOH values ​​and their corresponding multiple SOC-OCV sample curves of the lithium battery sample set are obtained. It should be noted that each SOH value corresponds to one SOC-OCV sample curve. Secondly, multiple OCV-SOC sample functions are constructed based on the multiple SOC-OCV sample curves. Next, the derivatives of the multiple OCV-SOC sample functions are calculated to determine multiple U values. OCV Sample derivative function; then, based on multiple SOH values ​​and their corresponding multiple U... OCV The sample derivative function is used to construct a local SOH estimation model. Finally, the real-time SOC-OCV curve of the lithium battery under test is obtained. Based on the real-time SOC-OCV curve and the local SOH estimation model, the SOH value of the lithium battery under test is determined.

[0069] In this embodiment, by analyzing and processing the SOH, SOC, and OCV of the lithium battery, the data patterns existing in the local OCV are integrated, and then a local SOH estimation model is constructed to integrate the data relationship between SOH and OCV. This achieves a one-to-one correspondence between the SOH and OCV of the lithium battery for the required local OCV, thereby greatly improving the efficiency of obtaining the estimated SOH value of the lithium battery.

[0070] In a preferred embodiment, in step S101, in order to obtain multiple SOC-OCV sample curves of the lithium battery sample set, such as... Figure 2 As shown, Figure 2 A schematic flowchart of an embodiment of the present invention for obtaining multiple SOC-OCV sample curves of a lithium battery sample set includes:

[0071] Step S111: At a preset temperature, the lithium battery sample set is charged to standard and left to stand for a preset time to obtain a fully charged lithium battery sample set.

[0072] Step S112: Discharge the fully charged lithium battery sample set according to the preset SOC interval, and record the discharge process data accordingly;

[0073] Step S113: Based on the discharge process data, determine the SOC sample set and its corresponding OCV sample set of the lithium battery sample set respectively;

[0074] Step S114: Based on the SOC sample set and the corresponding OCV sample set, perform corresponding data fitting to determine multiple SOC-OCV sample curves of the lithium battery sample set.

[0075] Each SOC-OCV sample curve consists of a SOC sample and a corresponding OCV sample.

[0076] In this embodiment, by performing standard charging and discharging on the lithium battery sample set and sorting out the data during the process, the corresponding data during the process are obtained, thereby determining the SOC sample set and its corresponding OCV sample set of the lithium battery sample set. Finally, all the obtained samples are subjected to data fitting and image processing to determine multiple SOC-OCV sample curves of the lithium battery sample set.

[0077] In this embodiment, the relationship between the SOC and OCV of the lithium battery is extracted by integrating existing data, and then the relationship is expressed graphically by the SOC-OCV sample curve, so as to express the relationship between SOC and OCV more intuitively.

[0078] In one specific embodiment, in step S111, the preset temperature is preferably 25°C.

[0079] In other embodiments, the preset temperature can be adjusted to other values ​​as needed, and the temperature can be used as a variable for adaptive control through multiple measurements.

[0080] In one specific embodiment, in step S112, the preset SOC is preferably 5%, that is, each SOC is divided equally, and the battery is discharged at 5% SOC intervals. After each discharge, the lithium battery is left to stand for a period of time. Further, the standing time is at least 30 minutes. The discharge and standing actions are repeated until the discharge reaches the specified cutoff voltage.

[0081] In one specific embodiment, in step S114, the discharge process data is generally scattered data. In order to determine the SOC-OCV sample curve, it is necessary to perform data fitting on the obtained scattered data to obtain a curve with higher reliability.

[0082] In other embodiments, the SOC-OCV sample curve of the lithium battery can also be obtained in other ways, such as reviewing historical data and integrating the data, or directly searching for the SOC-OCV sample curve in historical literature.

[0083] In one specific embodiment, such as Figure 3 As shown, Figure 3 This is a schematic diagram illustrating the results of an embodiment of the SOC-OCV sample curve for a lithium battery provided by the present invention. A simple fitting method using the Smoothing Spline function in Matlab was employed to smooth and connect the 20 discrete data points obtained at 5% SOC intervals. It should be noted that, obviously, during the image fitting process, points with significant deviations were discarded, ensuring that most scattered points belong to the SOC-OCV sample curve, and the discarded points are evenly distributed on both sides of the SOC-OCV sample curve.

[0084] In a preferred embodiment, in step S102, in order to construct an OCV-SOC sample function based on the SOC-OCV sample curves, firstly, data transformation is performed on multiple SOC-OCV sample curves to determine the corresponding multiple OCV-SOC sample curves. That is, the horizontal and vertical coordinates of the original SOC-OCV sample curves are transformed accordingly to determine the corresponding OCV-SOC sample curves. Furthermore, the newly obtained OCV-SOC sample curves need to be adaptively integrated to smooth the image. Finally, data fitting is performed on multiple OCV-SOC sample curves to determine the corresponding multiple OCV-SOC sample functions. That is, the function expression of each OCV-SOC sample function is determined.

[0085] In one specific embodiment, such as Figure 4 As shown, Figure 4 This is a schematic diagram of the results of an embodiment of the OCV-SOC sample curve of the lithium battery provided by the present invention.

[0086] It should be noted that in this embodiment, the SOC value changed significantly when the OCV was in the range of 3.1-3.4.

[0087] In a preferred embodiment, in step S103, after determining the OCV-SOC sample function, in order to more clearly represent the required data relationship, the derivatives of multiple OCV-SOC sample functions are calculated respectively, thereby determining multiple U values. OCV Sample derivative.

[0088] That is, for each OCV-SOC sample function obtained previously, the derivative of each corresponding function is calculated to determine multiple U. OCV Sample derivative.

[0089] It is understandable that the derivative function can more intuitively reflect the relationship between various data, thereby enabling a better discovery of patterns among the data.

[0090] In one specific embodiment, corresponding to Figure 4 To intuitively obtain the variation range of the SOC value, the derivative is calculated and represented graphically, as shown below. Figure 5 As shown, Figure 5 The lithium battery U provided by the present invention OCV A schematic diagram of the result of an embodiment of the sample derivative function.

[0091] It is understandable that in this embodiment, when the OCV is between 3.2 and 3.4, the derivative value fluctuates greatly and two peaks also appear.

[0092] In a preferred embodiment, in step S104, in order to construct a local SOH estimation model, such as... Figure 6 As shown, Figure 6 A flowchart illustrating an embodiment of constructing a local SOH estimation model provided by the present invention includes:

[0093] Step S141: Obtain the preset local U OCV interval;

[0094] Step S142: Based on multiple U OCV Sample derivative function, obtain preset local U OCV Multiple U within the interval OCV Sample values ​​and their corresponding multiple SOH values, based on multiple U OCV The sample value and its corresponding multiple SOH values ​​determine multiple OCV-SOH sample data points;

[0095] Step S143: Construct the OCV-SOH coordinate system, perform smooth spline fitting on multiple OCV-SOH sample data points, and obtain the OCV-SOH curve;

[0096] Step S144: Construct a local SOH estimation model based on the OCV-SOH curve.

[0097] In this embodiment, firstly, for multiple U OCV The sample derivative function determines the preset local U. OCV Interval; then, based on multiple U OCV Sample derivative function, obtain preset local U OCV Multiple U within the interval OCV Sample values ​​and their corresponding multiple SOH values, based on multiple U OCV The sample values ​​and their corresponding multiple SOH values ​​determine multiple OCV-SOH sample data points, that is, for the obtained U OCV Sample derivative function, based on preset local U OCV Interval, determining multiple U OCV Sample values, and corresponding to each U OCV Each sample value has a known SOH value corresponding to it, thus enabling the determination of multiple OCV-SOH sample data points. Next, an OCV-SOH coordinate system is constructed, and smooth splines are fitted to the multiple OCV-SOH sample data points to obtain the OCV-SOH curve. Finally, based on the OCV-SOH curve, a local SOH estimation model is constructed.

[0098] In this embodiment, by using multiple U OCV Comparative analysis of sample derivatives reveals that for local U... OCVThe relationship between OCV and SOH within a given interval can be fitted with data, thereby improving the construction of a local SOH estimation model and enabling the rapid determination of the SOH value corresponding to a lithium battery based on OCV.

[0099] Furthermore, since the OCV of this lithium battery is obtained in a relatively simple and reliable manner, it makes it easier to determine the SOH value of the lithium battery.

[0100] In a preferred embodiment, in step S141, a local U is preset. OCV The value range of the interval is 3.20-3.25V.

[0101] It should be noted that, corresponding to Figure 5 The preset local U in this application OCV The interval refers to U OCV The data is taken before the derivative of the sample reaches the first peak. In addition, for ease of calculation, the part with OCV value less than 3.2 is discarded.

[0102] In a preferred embodiment, in step S142, extensive data analysis and experiments were conducted to improve the reliability of data processing, such as... Figure 7 As shown, Figure 7 Multiple U provided by the present invention OCV A schematic diagram of the results of an embodiment of the sample derivative function. The degree of cell aging decreases sequentially from A to E. It is clear that the peak curve of this open-circuit voltage range shifts to the left as the degree of aging increases.

[0103] In this embodiment, by using the U of multiple lithium battery samples OCV The sample derivatives are compared in the same coordinate system, since for the preset local U OCV The OCV values ​​and the graphs of the derivative functions in the intervals are completely consistent, thus making it more intuitive to link the OCV of lithium batteries with SOH.

[0104] It should be noted that, corresponding to Figure 7 Preset local U OCV The range refers to the portion of OCV values ​​between 3.20 and 3.23. It should also be noted that the portion of OCV values ​​greater than the OCV values ​​corresponding to the peaks is not analyzed in this application.

[0105] In a preferred embodiment, in step S144, the calculation formula for the local SOH estimation model is as follows:

[0106] SOH=AU OCV 3 +BU OCV 2 +CU OCV +D

[0107] Where SOH is the SOH value of the lithium battery under test, U OCV Let OCV be the value of the lithium battery under test, and A, B, C and D are constants.

[0108] In one specific embodiment, such as Figure 8 As shown, Figure 8 This is a schematic diagram illustrating the results of an embodiment of the local SOH estimation model provided by the present invention. Through the above image, it is possible to obtain the SOH value corresponding to any OCV value of a lithium battery, achieving rapid acquisition of the SOH value of the lithium battery.

[0109] In a preferred embodiment, in step S105, the real-time SOC-OCV curve includes a real-time global SOC-OCV curve and a real-time local SOC-OCV curve. In this embodiment, the real-time global SOC-OCV curve and the real-time local SOC-OCV curve are relative concepts, both referring to the SOC-OCV curve of the lithium battery.

[0110] Specifically, for any lithium battery under test, when its data is complete or the corresponding SOC-OCV curve is a known quantity, the real-time global SOC-OCV curve can be determined relatively quickly. However, in other cases, especially when the SOC-OCV curve needs to be calculated or the data completeness of the lithium battery under test is not high, in order to improve the efficiency of obtaining the SOH estimate of the lithium battery, it is only necessary to obtain the required part of the SOC-OCV curve, that is, the real-time local SOC-OCV curve.

[0111] It should be noted that the OCV interval in the real-time local SOC-OCV curve should at least meet the parameter requirements of the local SOH estimation model.

[0112] After obtaining the local SOH estimation model, in order to determine the SOH value of the lithium battery under test, it is first necessary to obtain the real-time SOC-OCV curve of the lithium battery under test, and then calculate the derivative to determine the U of the lithium battery under test. OCV Derivative function; then, according to U OCV The derivative function is used to determine the SOH value of the lithium battery under test based on the local SOH estimation model.

[0113] It should be noted that the range of OCV values ​​in the real-time local SOC-OCV curve is consistent with its corresponding local SOH estimation model.

[0114] In one specific embodiment, the OCV value range in the real-time local SOC-OCV curve includes at least 3.20-3.25V.

[0115] In other embodiments, the range of OCV values ​​in the real-time local SOC-OCV curve can be adjusted according to the actual situation. When the data is complete, a SOC-OCV curve with a smaller range of OCV values ​​can be obtained, or even only the local U value corresponding to a certain OCV value point can be obtained. OCV The derivative value.

[0116] In one specific embodiment, taking a size F battery (rated capacity of 100Ah) as an example, the method is verified as follows:

[0117] First, battery F was subjected to three charge-discharge cycles at 25°C using standard charge-discharge methods. The discharge capacity after these three cycles was taken as the remaining capacity C under this condition. F =81.4631, at this time That is, SOH F =81.46%;

[0118] As can be seen from the SOC-OCV curve of battery F, when the open-circuit voltage is in the range of 3.2-3.25V, the corresponding SOC range does not exceed 30%. Therefore, the range of the first peak of SOC-OCV can be quickly determined.

[0119] After obtaining the range of the first peak, data transformation is performed, followed by smooth spline fitting and derivative calculation to obtain the U of battery F. OCV Sample derivative, such as Figure 9 As shown, Figure 9 U of the battery F provided by the present invention OCV A schematic diagram illustrating the results of one embodiment of the derivative function. The diagram shows the variation of the derivative function for battery F when the open-circuit voltage is in the range of 3.2-3.25V.

[0120] Furthermore, based on Figure 9 It can also obtain such as Figure 2 The derivative value in the equation is used to obtain the corresponding U. OCVF It is 3.2219V. Finally, U OCVF Substituting into the local SOH estimation model, the estimated SOH value of battery F is 82.39%, as follows: Figure 10 As shown, Figure 10 This is a schematic diagram showing the results of an embodiment of the local SOH estimation model for battery F provided by the present invention.

[0121] Furthermore, the accuracy of the SOH value estimation for the lithium battery in this application can be determined by calculating the estimation error.

[0122] For battery F in this embodiment, the estimation error is:

[0123]

[0124] The estimation error of the substituted values ​​is 1.14%.

[0125] By analyzing and processing the SOH, SOC, and OCV of lithium batteries, the data patterns existing in the local OCV are integrated, and a local SOH estimation model is constructed to integrate the data relationship between SOH and OCV. This results in a one-to-one correspondence between the SOH and OCV of lithium batteries for local OCVs that meet the requirements, thereby greatly improving the efficiency of obtaining the estimated SOH value of lithium batteries.

[0126] To address the aforementioned problems, the present invention also provides a lithium battery SOH estimation device, such as... Figure 11 As shown, Figure 11 This is a schematic diagram of an embodiment of the lithium battery SOH estimation device provided by the present invention. The lithium battery SOH estimation device 1100 includes:

[0127] The sample curve acquisition module 1101 is used to acquire multiple SOH values ​​and their corresponding multiple SOC-OCV sample curves of a lithium battery sample set.

[0128] The sample function acquisition module 1102 is used to construct multiple OCV-SOC sample functions based on multiple SOC-OCV sample curves;

[0129] The sample derivative function acquisition module 1103 is used to differentiate multiple OCV-SOC sample functions respectively to determine multiple U OCV Sample derivative;

[0130] The estimation model building module 1104 is used to estimate multiple SOH values ​​and their corresponding multiple U values. OCV Using the sample derivative function, a local SOH estimation model is constructed.

[0131] The SOH estimation module 1105 is used to acquire the real-time SOC-OCV curve of the lithium battery under test and determine the SOH value of the lithium battery under test based on the local SOH estimation model.

[0132] The present invention also provides an electronic device, such as... Figure 12 As shown, Figure 12 This is a structural block diagram of an embodiment of the electronic device provided by the present invention. The electronic device 1200 can be a computing device such as a mobile terminal, desktop computer, laptop, handheld computer, and server. The electronic device 1200 includes a processor 1201, a memory 1202, and a display 1203, wherein the memory 1202 stores a lithium battery SOH estimation program.

[0133] In some embodiments, memory 1202 may be an internal storage unit of a computer device, such as a hard disk or RAM. In other embodiments, memory 1202 may be an external storage device of the computer device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc. Furthermore, memory 1202 may include both internal and external storage units of the computer device. Memory 1202 is used to store application software and various types of data installed on the computer device, such as program code for installing the computer device. Memory 1202 can also be used to temporarily store data that has been output or will be output. In one embodiment, the lithium battery SOH estimation program can be executed by processor 1201 to implement the lithium battery SOH estimation method of various embodiments of the present invention.

[0134] In some embodiments, processor 1201 may be a central processing unit (CPU), microprocessor or other data processing chip, used to run program code stored in memory 1202 or process data, such as executing a lithium battery SOH estimation program.

[0135] In some embodiments, display 1203 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 1203 is used to display information from electronic device 1200 and to display a visual user interface. Components 1201-1203 of electronic device 1200 communicate with each other via a system bus.

[0136] This embodiment also provides a computer-readable storage medium storing a lithium battery SOH estimation program thereon. When the program is executed by a processor, it implements the lithium battery SOH estimation method as described in any of the above technical solutions.

[0137] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, database, or other storage media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0138] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for estimating the state of harm (SOH) of a lithium battery, characterized in that, include: Multiple SOH values ​​of a lithium battery sample set are obtained, and multiple SOC-OCV sample curves corresponding to the lithium battery sample set are obtained through the following steps: Data analysis and processing of the SOH, SOC, and OCV of the lithium batteries are performed to integrate the data patterns existing in the local OCV. Then, a local SOH estimation model is constructed to integrate the data relationship between SOH and OCV, achieving a one-to-one correspondence between the SOH and OCV of the lithium battery for a qualified local OCV. At a preset temperature, the lithium battery sample set is standard-charged and left to stand for a preset time to obtain a fully charged lithium battery sample set. The fully charged lithium battery sample set is discharged according to a preset SOC interval, and the discharge process data is recorded accordingly. Based on the discharge process data, the SOC sample set and its corresponding OCV sample set of the lithium battery sample set are determined. Based on the SOC sample set and the corresponding OCV sample set, corresponding data fitting is performed to determine multiple SOC-OCV sample curves of the lithium battery sample set. Each SOC-OCV sample curve corresponds to one SOC sample and one corresponding OCV sample. Data transformation is performed on the multiple SOC-OCV sample curves to determine the corresponding multiple OCV-SOC sample curves; data fitting is performed on the multiple OCV-SOC sample curves to determine the corresponding multiple OCV-SOC sample functions; Differentiate the plurality of OCV-SOC sample functions respectively to determine the plurality of U OCV Sample derivative; Get the preset local U OCV Interval; the preset local U OCV The range is 3.20-3.25V; according to the aforementioned multiple U OCV Sample derivative function, to obtain the preset local U OCV Multiple U within the interval OCV Sample values ​​and their corresponding multiple SOH values, based on the multiple U OCV The sample values ​​and their corresponding multiple SOH values ​​determine multiple OCV-SOH sample data points; an OCV-SOH coordinate system is constructed, and smooth spline fitting is performed on the multiple OCV-SOH sample data points to obtain the OCV-SOH curve; based on the OCV-SOH curve, a local SOH estimation model is constructed; the calculation formula of the local SOH estimation model is: SOH=AU OCV 3 +BU OCV 2 +CU OCV +D Where SOH is the SOH value of the lithium battery under test, U OCV Let A, B, C, and D be the OCV value of the lithium battery under test, where A, B, C, and D are all constants. The U value of the lithium battery under test is determined based on the real-time SOC-OCV curve. OCV Derivative function; according to the U OCV The derivative function and the local SOH estimation model are used to determine the SOH value of the lithium battery under test.

2. A lithium battery SOH estimation device, characterized in that, include: The sample curve acquisition module is used to acquire multiple SOH values ​​of a lithium battery sample set, and to acquire multiple SOC-OCV sample curves corresponding to the lithium battery sample set through the following steps: At a preset temperature, the lithium battery sample set is standard-charged and left to stand for a preset time to obtain a fully charged lithium battery sample set; the fully charged lithium battery sample set is discharged according to a preset SOC interval, and the discharge process data is recorded accordingly; based on the discharge process data, the SOC sample set and its corresponding OCV sample set of the lithium battery sample set are determined; based on the SOC sample set and the corresponding OCV sample set, corresponding data fitting is performed to determine multiple SOC-OCV sample curves of the lithium battery sample set; wherein each SOC-OCV sample curve corresponds to one SOC sample and one corresponding OCV sample. The sample function acquisition module is used to perform data transformation on the multiple SOC-OCV sample curves to determine the corresponding multiple OCV-SOC sample curves; and to perform data fitting on the multiple OCV-SOC sample curves to determine the corresponding multiple OCV-SOC sample functions. The sample derivative function acquisition module is used to differentiate the plurality of OCV-SOC sample functions respectively to determine the plurality of U OCV Sample derivative; The estimation model building module is used to obtain the preset local U. OCV Interval; the preset local U OCV The range is 3.20-3.25V; according to the aforementioned multiple U OCV Sample derivative function, to obtain the preset local U OCV Multiple U within the interval OCV Sample values ​​and their corresponding multiple SOH values, based on the multiple U OCV The sample values ​​and their corresponding multiple SOH values ​​determine multiple OCV-SOH sample data points; an OCV-SOH coordinate system is constructed, and smooth spline fitting is performed on the multiple OCV-SOH sample data points to obtain the OCV-SOH curve; based on the OCV-SOH curve, a local SOH estimation model is constructed; the calculation formula of the local SOH estimation model is: SOH=AU OCV 3 +BU OCV 2 +CU OCV +D Where SOH is the SOH value of the lithium battery under test, U OCV Let A, B, C, and D be the OCV value of the lithium battery under test, where A, B, C, and D are all constants. The SOH estimation module is used to determine the U of the lithium battery under test based on the real-time SOC-OCV curve. OCV Derivative function; according to the U OCV The derivative function and the local SOH estimation model are used to determine the SOH value of the lithium battery under test.

3. An electronic device, characterized in that, It includes a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, it implements the lithium battery SOH estimation method as described in claim 1.

4. A storage medium, characterized in that, The storage medium stores computer program instructions, which, when executed by a computer, cause the computer to perform the lithium battery SOH estimation method according to claim 1.

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