SOC estimation method based on Kalman filtering
By combining the open circuit voltage method and the ampere integration method, and using iterative correction of Kalman filtering, the current sampling error and resource occupation problems in the SOC estimation method are solved, and the accuracy and stability of SOC estimation are improved.
Patent Information
- Application Number
- CN202411358373.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-27
- Publication Date
- 2025-01-07
AI Technical Summary
In the existing SOC estimation methods, the ampere-time integral method is susceptible to current sampling errors, causing the SOC to deviate from the actual value. The Kalman filtering method requires the establishment of a complex battery model and the use of a large number of MCU resources.
The SOC estimation method based on Kalman filtering is adopted, combined with the open circuit voltage method and the ampere-time integral method, and iterative correction is used through Kalman filtering to reduce the impact of current sampling errors, avoiding the establishment of complex models and occupancy of too many resources.
It improves the accuracy and stability of SOC estimation, reduces the impact of current sampling error, avoids resource occupation, and achieves efficient SOC management.
Smart Images
Figure CN119270101A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of battery technology, and in particular to a SOC estimation method based on Kalman filtering. Background Art
[0002] The battery state of charge (SOC) is the ratio of the battery's available energy to its total energy. It is an important parameter of the battery that cannot be directly measured. The battery state of charge plays a core role in battery applications. Because the battery SOC cannot be directly measured by sensors like current value, voltage and temperature, it can only be estimated.
[0003] The commonly used SOC estimation method at present is the ampere-hour integration method, which estimates the SOC through the dynamic integration of the current. However, this method will cause the SOC to deviate from the actual value due to the accumulation of current sampling errors. The Kalman filter algorithm based on the establishment of an accurate battery equivalent circuit model and the state space equation of the battery can accurately estimate the SOC, but it requires the establishment of a huge battery model, equivalent model testing, and occupies more MCU resources.
[0004] Therefore, it is urgent to improve the SOC estimation method based on Kalman filtering so as to occupy less chip resources while ensuring accurate estimation. Summary of the invention
[0005] The main purpose of the embodiments of the present application is to solve at least one of the technical problems existing in the prior art, and to propose a SOC estimation method based on Kalman filtering, which occupies less chip resources while ensuring accurate estimation, thereby improving the performance of the battery management system.
[0006] To achieve the above objectives, in a first aspect, an embodiment of the present invention proposes a SOC estimation method based on Kalman filtering, comprising: Get the current SOC value of the battery; Get the current voltage and current of the battery; Performing Kalman filtering correction on the current SOC value: obtaining a first voltage according to the current voltage and the current, and estimating the first voltage by using an open circuit voltage method to obtain a SOC load value; obtaining a prediction error according to the current, and using the prediction error as a weight of the SOC load value; calculating the current and the current SOC value by using an ampere-hour integration method to obtain a SOC prediction value; obtaining a load estimation error according to the SOC load value, and using the load estimation error as a weight of the SOC prediction value; calculating a weighted average of the SOC load value and the SOC prediction value according to the weight to obtain a first correction value of the current SOC value; Perform Kalman filtering correction on the first correction value: obtain the SOC approximation value based on the current voltage; obtain a first correction error based on the prediction error and the load estimation error, and use the first correction error as the weight of the SOC approximation value; obtain a table lookup error based on the SOC approximation value, and use the table lookup error as the weight of the first correction value; calculate a weighted average of the SOC approximation value and the first correction value based on the weight to obtain a second correction value of the SOC current value. A SOC estimation method based on Kalman filtering provided in accordance with an embodiment of the present invention has at least the following beneficial effects: first, the static voltage of the battery is measured by the open circuit voltage method, and the current SOC value of the battery is estimated accordingly; then, the real-time current is integrated by the ampere-hour integration method, and the SOC prediction value is calculated in combination with the current SOC value obtained in the previous step. Since the ampere-hour integration method estimates the change of SOC based on the time accumulation of current, it is easily affected by the current sampling error, causing the SOC prediction value to gradually deviate from the true value. In order to correct this deviation, the Kalman filtering algorithm is introduced; the SOC prediction value is used as the expected value of the Kalman filtering, and at the same time, the SOC load value is calculated as the measured value based on the current voltage and current values; the Kalman filtering calculates their weighted value based on the difference between the two and their respective weights. The average value is used to obtain the first corrected value of SOC; the first corrected value is closer to the actual value of SOC, which reduces the influence of current sampling error. In order to make the estimated SOC more accurate, the first corrected value is used as the expected value of the next Kalman filter operation for iterative processing. Specifically, the first corrected value obtained by the first Kalman filter is used as the new expected value, and the SOC approximation value directly estimated according to the current voltage is used as the measured value. The weighted average value is calculated again through the second Kalman filter to obtain the second corrected value of SOC, so that the SOC estimated value is close to the true value. There is no need to establish a complex battery model or conduct a large number of equivalent model tests, and it also avoids occupying too many chip resources. By combining multiple estimation methods and iterative correction of Kalman filtering, the accuracy and stability of SOC estimation are effectively improved.
[0007] In some embodiments, performing Kalman filtering correction on the first correction value further includes: A second corrected error is obtained according to the table lookup error and the first corrected error.
[0008] In some embodiments, the method of calculating the current and the SOC current value by using the ampere-hour integration method to obtain the SOC prediction value includes:
[0009] Wherein, SOC(predicted) is the predicted SOC value, SOC(current) is the current SOC value, Cn is the designed capacity of the battery, and i(t) is the current value at time t.
[0010] In some embodiments, obtaining the first voltage according to the current voltage and the current includes:
[0011] Among them, U1 is the first voltage, U is the current voltage, r is the battery internal resistance coefficient, R is the internal resistance of the battery, and I is the current.
[0012] In some embodiments, obtaining the first corrected error according to the prediction error and the load estimation error includes:
[0013] Among them, a is the prediction error, b is the load estimation error, and c is the first correction error.
[0014] In some embodiments, obtaining a second corrected error according to the table lookup error and the first corrected error includes:
[0015] Among them, c is the first corrected error, d is the table lookup error, and e is the second corrected error.
[0016] In some embodiments, obtaining the current SOC value of the battery includes: obtaining the static voltage of the battery after power-on, and estimating the static voltage by an open circuit voltage method to obtain the current SOC value of the battery.
[0017] In some embodiments, performing Kalman filtering correction on the first correction value further includes: replacing the second correction value of the current SOC value with the current SOC value.
[0018] In a second aspect, an embodiment of the present invention application proposes an operation control device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the SOC estimation method as described in any embodiment of the first aspect.
[0019] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the SOC estimation method as described in any one of the embodiments in the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS
[0020] The accompanying drawings are used to provide a further understanding of the technical solution of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the technical solution of the present invention and do not constitute a limitation on the technical solution of the present invention.
[0021] The present invention is further described below in conjunction with the accompanying drawings and embodiments; Figure 1 It is a logic control schematic diagram of a SOC estimation method based on Kalman filtering provided by the present invention; Figure 2 It is a schematic diagram comparing the effects of the SOC estimation method based on Kalman filtering and the SOC estimation method based on the ampere-hour integration algorithm provided by the present invention; Figure 3 It is a structural schematic diagram of an operation control device provided by the present invention. DETAILED DESCRIPTION
[0022] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.
[0023] It should be noted that the terms "first", "second", etc. in the specification, claims and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.
[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.
[0025] The battery state of charge (SOC) is the ratio of the battery's available energy to its total energy. It is an important parameter of the battery that cannot be directly measured. The battery state of charge plays a core role in battery applications. Because the battery SOC cannot be directly measured by sensors like current value, voltage and temperature, it can only be estimated.
[0026] The commonly used SOC estimation method at present is the ampere-hour integration method, which estimates the SOC through the dynamic integration of the current. However, this method will cause the SOC to deviate from the actual value due to the accumulation of current sampling errors. The Kalman filter algorithm based on the establishment of an accurate battery equivalent circuit model and the state space equation of the battery can accurately estimate the SOC, but it requires the establishment of a huge battery model, equivalent model testing, and occupies more MCU resources.
[0027] Based on this, an embodiment of the present invention provides a SOC estimation method based on Kalman filtering, which occupies less chip resources while ensuring accurate estimation, thereby improving the performance of the battery management system.
[0028] The embodiments of the present invention are further described below in conjunction with the accompanying drawings.
[0029] In the first aspect, the present invention proposes a SOC estimation method based on Kalman filtering, referring to Figure 1 , including steps S100 to S400: Step S100: Obtain the current SOC value of the battery.
[0030] In some embodiments, obtaining the current SOC value of the battery includes: obtaining the static voltage of the battery after power-on, and estimating the static voltage using an open circuit voltage method to obtain the current SOC value of the battery.
[0031] It should be noted that the open circuit voltage method measures the open circuit voltage value of the circuit when the battery stops working, that is, the static voltage, and estimates the SOC value at this time by looking up the OCV-SOC table.
[0032] Step S200: Obtain the current voltage and current of the battery.
[0033] Step S300: Perform Kalman filtering correction on the current SOC value: obtain a first voltage based on the current voltage and current, and estimate the first voltage using the open circuit voltage method to obtain the SOC load value; obtain a prediction error based on the current, and use the prediction error as the weight of the SOC load value; calculate the current and the current SOC value using the ampere-hour integration method to obtain the SOC prediction value; obtain the load estimation error based on the SOC load value, and use the load estimation error as the weight of the SOC prediction value; calculate the weighted average of the SOC load value and the SOC prediction value based on the weight to obtain the first correction value of the current SOC value.
[0034] It can be understood that the static voltage is first estimated by the open circuit voltage method to obtain the current SOC value of the battery, and then the current and the current SOC value are calculated by the ampere-hour integration method to obtain the SOC prediction value. The SOC prediction value obtained by these two methods will cause the SOC to deviate from the actual value due to the accumulation of current sampling errors. Therefore, it is necessary to introduce a Kalman filter algorithm to correct the current SOC value based on the calculated SOC prediction value. Specifically, the SOC prediction value is used as the expected value, and the SOC load value obtained according to the current voltage and current is used as the measured value, and the corresponding weight is calculated. The weighted average of the SOC load value and the SOC prediction value is calculated according to the weight to obtain the first correction value of the SOC current value. The corrected first correction value is closer to the actual SOC value, and the first correction value is used as the expected value data in the next Kalman operation, which further improves the estimation accuracy of the SOC.
[0035] In some embodiments, obtaining the first voltage according to the current voltage and current includes:
[0036] Among them, U1 is the first voltage, U is the current voltage, r is the battery internal resistance coefficient, R is the internal resistance of the battery, and I is the current.
[0037] It should be noted that the first voltage is a value close to the open circuit voltage that is calculated based on the current voltage, current, battery internal resistance coefficient and battery internal resistance when the battery is under load.
[0038] It can be understood that the voltage collected without load, that is, the open circuit voltage, is more accurate. Since the battery is in a power-consuming state when displaying the power level, in order to make the measured SOC load value more accurate, it is necessary to further process the current voltage to obtain the first voltage, and then obtain the SOC load value based on the first voltage, thereby improving the accuracy of the first correction value of the current SOC value.
[0039] It should be noted that after the battery is initially powered on, the prediction error is a current error.
[0040] In some embodiments, the current and the current SOC value are calculated using the ampere-hour integration method to obtain the SOC prediction value, including:
[0041] Among them, SOC(predicted) is the predicted SOC value, SOC(current) is the current SOC value, Cn is the design capacity of the battery, and i(t) is the current value at time t.
[0042] It should be noted that the design capacity of a battery refers to the amount of electricity that can be discharged when the battery is fully charged at the actual operating temperature and discharge rate.
[0043] In some embodiments, a load estimation error is obtained based on the SOC load value, wherein the load estimation error is obtained by comparing the SOC load value with pre-obtained first experimental test data, these pre-obtained first experimental data are discharged through multiple battery packs, and the corresponding SOC load value is calculated, and the error is calculated by comparing the corresponding SOC load value with the actual SOC value obtained by the actual high-precision discharge capacity collector.
[0044] In some embodiments, in step S300, the SOC current value is corrected by Kalman filtering, wherein a weighted average of the SOC load value and the SOC prediction value is calculated according to the weight, and the first correction value of the SOC current value is obtained, which includes:
[0045] Among them, SOC (correction 1) is the first correction value of the current SOC value, SOC (load) is the SOC load value, SOC (prediction) is the SOC prediction value, a is the prediction error; b is the load estimation error.
[0046] Step S400: Perform Kalman filtering correction on the first correction value: obtain the SOC approximation value based on the current voltage; obtain the first correction error based on the prediction error and the load estimation error, and use the first correction error as the weight of the SOC approximation value; obtain the table lookup error based on the SOC approximation value, and use the table lookup error as the weight of the first correction value; calculate the weighted average of the SOC approximation value and the first correction value based on the weight to obtain the second correction value of the current SOC value.
[0047] It can be understood that the first correction value obtained by the first Kalman filter operation is used as the expected value in the second Kalman filter operation, the SOC approximation value obtained according to the current voltage is used as the measured value in the second Kalman filter operation, and the corresponding weight is calculated. The weighted average of the SOC approximation value and the first correction value is obtained according to the weight to obtain the second correction value of the current SOC value, which improves the estimation accuracy of the SOC, and the corrected second correction value is closer to the actual SOC value than the SOC estimated by the ampere-hour integration algorithm, such as Figure 2 shown. In some embodiments, the SOC approximation value is obtained according to the current voltage. Specifically, the SOC approximation value is obtained by querying a load voltage table by the current voltage, wherein the load voltage table contains different SOCs corresponding to different voltages under different load conditions.
[0048] In some embodiments, obtaining a first corrected error according to the prediction error and the load estimation error includes:
[0049] Wherein, c is the first correction error.
[0050] In some embodiments, a table lookup error is obtained based on the SOC approximation value, wherein the table lookup error is obtained from second experimental test data. These second experimental test data are subjected to load discharge tests on multiple battery packs, and the error is calculated by using the measured SOC approximation value and the actual SOC value obtained by the actual high-precision discharge capacity collector.
[0051] In some embodiments, in step S400, the first correction value is corrected by Kalman filtering, wherein a weighted average of the SOC approximation value and the first correction value is obtained according to a weight to obtain a second correction value of the current SOC value, including:
[0052] Wherein, d is the table lookup error, SOC (correction 2) is the second correction value of the current SOC value, and SOC (approximation) is the SOC approximation value.
[0053] In some embodiments, in step S400, performing Kalman filtering correction on the first correction value further includes: A second corrected error is obtained according to the table lookup error and the first corrected error.
[0054] In some embodiments, obtaining the second corrected error according to the table lookup error and the first corrected error includes:
[0055] Among them, c is the first correction error, d is the table lookup error, and e is the second correction error.
[0056] In some embodiments, in step S400, performing Kalman filtering correction on the first correction value also includes: replacing the second correction value of the SOC current value with the SOC current value.
[0057] It can be understood that, except for the initial power-on when the current SOC value is obtained based on the static voltage, the current SOC value is corrected twice by Kalman filtering at all other times to obtain a new current SOC value. For example, in the next round of SOC current value correction process, the current voltage and current of the battery are reacquired, the second correction value is replaced with the SOC current value display, and then the SOC current value is corrected for the first time: the current error is obtained according to the current, and the current error is added to the second correction error to obtain a new prediction error, the current and the current SOC value are calculated using the ampere-hour integration method to obtain the SOC prediction value, and a new first correction value is calculated based on the SOC prediction value, the SOC load value and the corresponding weights; further, the new first correction value is corrected by Kalman filtering to obtain a new second correction value and a new second correction error, and then the next round of correction of the SOC current value is carried out in the same correction process, and as the number of corrections increases, the displayed SOC current value gradually becomes the same as the SOC actual value.
[0058] According to an SOC estimation method based on Kalman filtering provided by an embodiment of the present invention, first, the static voltage of the battery is measured by the open circuit voltage method, and the current SOC value of the battery is estimated accordingly; then, the real-time current is integrated by the ampere-hour integration method, and the SOC prediction value is calculated in combination with the SOC current value obtained in the previous step. Since the ampere-hour integration method estimates the change of SOC based on the time accumulation of current, it is easily affected by the current sampling error, causing the SOC prediction value to gradually deviate from the true value. In order to correct this deviation, the Kalman filtering algorithm is introduced; the SOC prediction value is used as the expected value of the Kalman filtering, and at the same time, the SOC load value is calculated as the measured value based on the current voltage and current values; the Kalman filtering calculates their weighted average value based on the difference between the two and their respective weights, and obtains To the first corrected value of SOC; the first corrected value is closer to the actual value of SOC, reducing the influence of current sampling error. In order to make the estimated SOC more accurate, the first corrected value is used as the expected value of the next Kalman filter operation for iterative processing. Specifically, the first corrected value obtained by the first Kalman filter is used as the new expected value, and the SOC approximation value directly estimated according to the current voltage is used as the measured value. The weighted average value is calculated again through the second Kalman filter to obtain the second corrected value of SOC, so that the SOC estimated value is close to the true value. There is no need to establish a complex battery model or conduct a large number of equivalent model tests, while also avoiding occupying too many chip resources. By combining multiple estimation methods and iterative correction of Kalman filtering, the accuracy and stability of SOC estimation are effectively improved.
[0059] In the second aspect, the present invention application embodiment proposes an operation control device 500, referring to Figure 3, including a memory 520, a processor 510, and a computer program stored in the memory 520 and executable on the processor, the processor 510 executes the program to implement the SOC estimation method as any one of the embodiments in the first aspect.
[0060] In a third aspect, the present invention provides a computer-readable storage medium, the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the SOC estimation method of any one of the embodiments in the first aspect. It is to be understood that, although each step in each flow chart in the embodiment of the present application is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear description in the present embodiment, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the above-mentioned flow chart can include a plurality of steps or a plurality of stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of the steps or stages in other steps or other steps.
[0061] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate to describe the embodiments of the present application, for example, it can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.
[0062] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.
[0063] It should be understood that in the description of the embodiments of the present application, the meaning of multiple (or multiple items) is more than two, greater than, less than, exceed, etc. are understood to not include the number, and above, below, within, etc. are understood to include the number.
[0064] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.
[0065] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0066] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.
[0067] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store program codes.
[0068] It should also be understood that the various implementations provided in the embodiments of the present application can be combined arbitrarily to achieve different technical effects. The above is a specific description of the preferred implementation of the present application, but the present application is not limited to the above implementation mode. Technical personnel familiar with the field can also make various equivalent modifications or substitutions under the shared conditions without violating the spirit of the present application. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present application.
Claims
1. A SOC estimation method based on Kalman filtering, characterized in that: include: Get the current SOC value of the battery; Get the current voltage and current of the battery; Performing Kalman filtering correction on the current SOC value: obtaining a first voltage according to the current voltage and the current, and estimating the first voltage by using an open circuit voltage method to obtain an SOC load value; Obtain a prediction error according to the current, and use the prediction error as a weight of the SOC load value; calculate the current and the SOC current value using an ampere-hour integration method to obtain an SOC prediction value; Obtaining a load estimation error according to the SOC load value, and using the load estimation error as a weight of the SOC prediction value; Calculate a weighted average of the SOC load value and the SOC prediction value according to the weight to obtain a first correction value of the SOC current value; Performing Kalman filtering correction on the first correction value: obtaining the SOC approximation value according to the current voltage; Obtaining a first corrected error according to the prediction error and the load estimation error, and using the first corrected error as a weight of the SOC approximation value; Obtaining a table lookup error according to the SOC approximation value, and using the table lookup error as a weight of the first correction value; A weighted average of the SOC approximation value and the first correction value is calculated according to the weight to obtain a second correction value of the SOC current value.
2. The SOC estimation method according to claim 1, characterized in that: The performing Kalman filtering correction on the first correction value further includes: A second corrected error is obtained according to the table lookup error and the first corrected error.
3. The SOC estimation method according to claim 1, characterized in that: The method of calculating the current and the SOC current value by using the ampere-hour integration method to obtain the SOC prediction value includes: Wherein, SOC(predicted) is the predicted SOC value, SOC(current) is the current SOC value, Cn is the designed capacity of the battery, and i(t) is the current value at time t.
4. The SOC estimation method according to claim 3, characterized in that: The obtaining a first voltage according to the current voltage and the current includes: Among them, U1 is the first voltage, U is the current voltage, r is the battery internal resistance coefficient, R is the internal resistance of the battery, and I is the current.
5. The SOC estimation method according to claim 1, characterized in that: The obtaining the first corrected error according to the prediction error and the load estimation error includes: Among them, a is the prediction error, b is the load estimation error, and c is the first correction error.
6. The SOC estimation method according to claim 2, characterized in that: The obtaining a second corrected error according to the table lookup error and the first corrected error comprises: Among them, c is the first corrected error, d is the table lookup error, and e is the second corrected error.
7. The SOC estimation method according to claim 1, characterized in that: The obtaining of the current SOC value of the battery includes: After power-on, the static voltage of the battery is obtained, and the static voltage is estimated using an open circuit voltage method to obtain a current SOC value of the battery.
8. The SOC estimation method according to claim 1, characterized in that: The performing Kalman filtering correction on the first correction value further includes: The second correction value of the SOC current value is replaced with the SOC current value.
9. An operation control device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the SOC estimation method according to any one of claims 1 to 8.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the SOC estimation method according to any one of claims 1 to 8.