Online reconstruction method and apparatus for OCV-SOC curve of battery, device, and medium

Abstract

An online reconstruction method and apparatus for an OCV-SOC curve of a battery, a device, and a medium, which are applied to the technical field of battery management, and are used for solving the problem in the existing technology that an accurate OCV-SOC curve cannot be obtained. The method specifically comprises: acquiring a current voltage and a current discharge current of a battery under test (101); inputting the acquired current voltage and current discharge current into a parameter identification model to obtain a target OCV value (102); determining a target feature interval on the basis of the target OCV value (103); using a correction method corresponding to the target feature interval to determine a target SOC value, the correction method being a static correction method corresponding to a correction interval or a dynamic correction method corresponding to a non-correction interval (104); and performing curve reconstruction on the basis of the target OCV value and the target SOC value to obtain a target OCV-SOC curve (105). By means of the provision of the parameter identification model, the OCV value is updated online, SOC values in different intervals are corrected by using a static correction method and a dynamic correction method, and an accurate OCV-SOC curve can be reconstructed.

Description

Online reconstruction method, apparatus, equipment and medium for battery OCV-SOC curve

[0001] Cross-reference to related applications

[0002] This application claims priority to Chinese Patent Application No. 2024118348677, filed on December 12, 2024, entitled “Online Reconstruction Method, Apparatus, Device and Medium for Battery OCV-SOC Curve”, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to the field of battery management technology, and in particular to a method, apparatus, device and medium for online reconstruction of battery OCV-SOC curve. Background Technology

[0004] The accuracy of battery state of charge (SOC) estimation is a core indicator for evaluating battery management systems, affecting remaining battery capacity, charging and discharging power, and operational safety. Accurate SOC estimation ensures the battery operates at its optimal state and extends its lifespan. Commonly used SOC estimation algorithms typically combine the open-circuit voltage method with the ampere-hour integration method. This requires obtaining an effective OCV-SOC curve, which reflects the mapping relationship between SOC and battery terminal voltage. This curve provides an effective initial SOC value for the ampere-hour integration method and corrects for its accumulated errors.

[0005] Currently, traditional methods for obtaining open-circuit voltage (OCV), i.e., open-circuit voltage experimental testing, have many shortcomings. This method is cumbersome and time-consuming, and each test only obtains the OCV state curve of a specific single cell under a specific environment, failing to comprehensively reflect the changes in the battery during actual use. As an electrochemical product, the parameters of a battery, including OCV, change with use and storage; therefore, offline calibration methods are not applicable to aged cells. Furthermore, offline testing methods assume that batteries with the same state of health (SOH) have identical OCV curves, but this assumption is not valid due to differences in manufacturing processes and operating conditions. For some batteries, such as lithium iron phosphate (LFP) batteries, their OCV exhibits a "plateau range" in the partial state of charge (SOC) range, making SOC calibration difficult and resulting in large estimation errors. Prolonged charging and discharging within this "plateau range" further accumulates SOC estimation errors, potentially leading to system malfunctions or even safety accidents. Therefore, obtaining accurate OCV-SOC curves throughout the battery's entire lifespan to improve the accuracy of SOC estimation is a pressing issue that needs to be addressed. Summary of the Invention

[0006] This application provides an online reconstruction method, apparatus, device, and medium for battery OCV-SOC curves to solve the problem of existing technologies being unable to obtain accurate OCV-SOC curves.

[0007] The technical solution provided in this application is as follows:

[0008] On the one hand, this application provides an online reconstruction method for the battery OCV-SOC curve, including:

[0009] Obtain the current voltage and current discharge current of the battery under test;

[0010] The current voltage and current discharge current are input into a preset parameter identification model to obtain the target OCV value;

[0011] Based on the target OCV value, the parameter feature interval in which the target OCV value is located is determined from the preset parameter feature interval as the target feature interval; wherein, the parameter feature interval includes the correction interval and the non-correction interval; the correction interval includes the high correction interval and the low correction interval, and the non-correction interval includes the high plateau interval, the plateau transition interval and the low plateau interval;

[0012] The target SOC value is determined by using a correction method corresponding to the target feature interval; wherein the correction method is either a static correction method corresponding to the correction interval or a dynamic correction method corresponding to the non-correction interval.

[0013] The target OCV-SOC curve is obtained by reconstructing the curve based on the target OCV value and the target SOC value.

[0014] In one embodiment, if the target feature interval is a correction interval, the target SOC value is determined using the correction method corresponding to the target feature interval, including:

[0015] Obtain the initial SOC value of the battery under test after power-on;

[0016] Based on the current discharge current and the initial SOC value, the target SOC value is determined using the discharge pure ampere-hour integration method.

[0017] In one embodiment, after obtaining the initial SOC value of the battery under test after power-on, the method further includes:

[0018] Determine whether the parameter characteristic interval where the initial SOC value is located is the high correction interval in the correction interval;

[0019] If so, the SOC value of the voltage peak point is updated using the pure ampere-hour integration method of discharge; where the voltage peak point is the data point with the largest rate of change of OCV value in the plateau transition interval of the initial OCV-SOC curve;

[0020] If not, the SOC value of the voltage inflection point is updated using the pure ampere-hour integration method of discharge; where the voltage inflection point is the data point with the largest change in the rate of change of the OCV value in the low plateau interval and low correction interval of the initial OCV-SOC curve.

[0021] In one embodiment, if the target feature interval is a non-corrected interval, the target SOC value is determined using the correction method corresponding to the target feature interval, including:

[0022] Acquire multiple historical data points of the battery under test in the current testing cycle. The historical data points represent the correspondence between the historical OCV value and the historical SOC value of the battery under test in the current testing cycle.

[0023] Determine whether there are data points among multiple historical data points that conform to the characteristic point voltage change rule; wherein, the characteristic point voltage change rule is that the data point is the data point with the largest OCV value change rate in the platform transition interval, or the data point is the data point with the largest change amount of OCV value change rate in the low platform interval and the low correction interval.

[0024] If so, the data point that conforms to the characteristic point voltage change rule is taken as the target data point; the deviation between the SOC value of the target data point and the SOC value of the voltage peak point or the SOC value of the voltage inflection point is calculated, the historical SOC value of the historical data point is corrected according to the deviation value, and the target SOC value is determined based on the corrected historical SOC value or the deviation value; where the voltage peak point is the data point with the largest OCV value change rate in the plateau transition interval of the initial OCV-SOC curve; the voltage inflection point is the data point with the largest change in OCV value change rate in the low plateau interval and low correction interval of the initial OCV-SOC curve;

[0025] If not, the target SOC value will not be determined, and the next set of voltage and discharge current will be obtained.

[0026] In one embodiment, after determining the target SOC value based on the corrected historical SOC value or deviation value, the method further includes:

[0027] Update the SOC value of the voltage peak point or the SOC value of the voltage inflection point based on the SOC value of the target data point.

[0028] In one embodiment, before acquiring the current voltage and current discharge current of the battery under test, the method further includes:

[0029] Establish a second-order equivalent circuit model for the sample battery;

[0030] Based on the bilinear transformation rule, the state equation of the second-order equivalent circuit model is determined.

[0031] By using the recursive least squares method with a forgetting factor, the state equations of the second-order equivalent circuit model are processed to obtain the initial identification model.

[0032] Obtain sample battery test data, and train the initial identification model based on the sample battery test data to obtain the parameter identification model.

[0033] In one embodiment, the online reconstruction method for the battery OCV-SOC curve further includes:

[0034] An initial OCV-SOC curve is constructed based on the initial data points formed by the test SOC value and the test OCV value corresponding to the test SOC value in the sample battery test data.

[0035] Based on the variation pattern of each initial data point in the initial OCV-SOC curve, the parameter characteristic interval of the sample battery is divided to obtain the correction interval and the non-correction interval.

[0036] Based on the characteristic point voltage change rules, the initial data points in the platform transition interval are identified to obtain the voltage peak points;

[0037] Based on the characteristic point voltage change rules, the initial data points in the low plateau interval and low correction interval are identified to obtain the voltage inflection point.

[0038] In one embodiment, curve reconstruction is performed based on the target OCV value and the target SOC value to obtain the target OCV-SOC curve, including:

[0039] Based on the target OCV value and target SOC value in the previous detection period, construct the current OCV-SOC curve;

[0040] Based on the current OCV-SOC curve, update the OCV value of the corresponding data point in the earliest detection period among the stored multiple detection periods;

[0041] The OCV values ​​for all stored detection cycles are averaged.

[0042] Each mean data point is determined based on the mean-processed OCV value and the corresponding SOC value, and the target OCV-SOC curve is constructed based on each mean data point.

[0043] On the other hand, this application provides an online reconstruction device for the battery OCV-SOC curve, comprising:

[0044] The data acquisition unit is configured to acquire the current voltage and current discharge current of the battery under test;

[0045] The target OCV value determination unit is configured to input the current voltage and current discharge current into a preset parameter identification model to obtain the target OCV value;

[0046] The interval determination unit is configured to determine the parameter feature interval in which the target OCV value is located from the preset parameter feature interval based on the target OCV value; wherein, the parameter feature interval includes a correction interval and an uncorrected interval; the correction interval includes a high correction interval and a low correction interval, and the uncorrected interval includes a high plateau interval, a plateau transition interval, and a low plateau interval;

[0047] The target SOC value determination unit is configured to determine the target SOC value using a correction method corresponding to the target feature interval; wherein the correction method is either a static correction method corresponding to the correction interval or a dynamic correction method corresponding to the non-correction interval.

[0048] The curve reconstruction unit is configured to reconstruct the curve based on the target OCV value and the target SOC value to obtain the target OCV-SOC curve.

[0049] In one embodiment, the target SOC value determination unit is specifically configured as follows:

[0050] Obtain the initial SOC value of the battery under test after power-on;

[0051] Based on the current discharge current and the initial SOC value, the target SOC value is determined using the discharge pure ampere-hour integration method.

[0052] In one embodiment, the target SOC value determination unit is specifically configured as follows:

[0053] Determine whether the parameter characteristic interval where the initial SOC value is located is the high correction interval in the correction interval;

[0054] If so, the SOC value of the voltage peak point is updated using the pure ampere-hour integration method of discharge; where the voltage peak point is the data point with the largest rate of change of OCV value in the plateau transition interval of the initial OCV-SOC curve;

[0055] If not, the SOC value of the voltage inflection point is updated using the pure ampere-hour integration method of discharge; where the voltage inflection point is the data point with the largest change in the rate of change of the OCV value in the low plateau interval and low correction interval of the initial OCV-SOC curve.

[0056] In one embodiment, the target SOC value determination unit is specifically configured as follows:

[0057] Acquire multiple historical data points of the battery under test in the current testing cycle. The historical data points represent the correspondence between the historical OCV value and the historical SOC value of the battery under test in the current testing cycle.

[0058] Determine whether there are data points among multiple historical data points that conform to the characteristic point voltage change rule; wherein, the characteristic point voltage change rule is that the data point is the data point with the largest OCV value change rate in the platform transition interval, or the data point is the data point with the largest change amount of OCV value change rate in the low platform interval and the low correction interval.

[0059] If so, the data point that conforms to the characteristic point voltage change rule is taken as the target data point; the deviation between the SOC value of the target data point and the SOC value of the voltage peak point or the SOC value of the voltage inflection point is calculated, the historical SOC value of the historical data point is corrected according to the deviation value, and the target SOC value is determined based on the corrected historical SOC value or the deviation value; where the voltage peak point is the data point with the largest OCV value change rate in the plateau transition interval of the initial OCV-SOC curve; the voltage inflection point is the data point with the largest change in OCV value change rate in the low plateau interval and low correction interval of the initial OCV-SOC curve;

[0060] If not, the target SOC value will not be determined, and the next set of voltage and discharge current will be obtained.

[0061] In one embodiment, the target SOC value determination unit is further configured to:

[0062] Update the SOC value of the voltage peak point or the SOC value of the voltage inflection point based on the SOC value of the target data point.

[0063] In one embodiment, the online reconstruction device for the battery OCV-SOC curve further includes: a model building unit;

[0064] The model building unit is configured to establish a second-order equivalent circuit model of the sample battery; based on the bilinear change rule, the state equation of the second-order equivalent circuit model is determined; the state equation of the second-order equivalent circuit model is processed by the recursive least squares method with forgetting factor to obtain the initial identification model; the sample battery test data is acquired, and the initial identification model is trained based on the sample battery test data to obtain the parameter identification model.

[0065] In one embodiment, the online reconstruction device for the battery OCV-SOC curve further includes: an initialization unit;

[0066] The initialization unit is configured to generate initial data points based on the test SOC value and the corresponding test OCV value from the sample battery test data, and to construct an initial OCV-SOC curve. Based on the variation pattern of each initial data point in the initial OCV-SOC curve, the parameter characteristic interval of the sample battery is divided to obtain the correction interval and the non-correction interval. Based on the characteristic point voltage change rule, each initial data point in the platform transition interval is identified to obtain the voltage peak point. Based on the characteristic point voltage change rule, each initial data point in the low platform interval and the low correction interval is identified to obtain the voltage inflection point.

[0067] In one embodiment, the curve reconstruction unit is specifically configured as follows:

[0068] Based on the target OCV value and target SOC value within the current detection period, construct the current OCV-SOC curve;

[0069] Based on the current OCV-SOC curve, update the OCV value of the corresponding data point in the earliest detection period among the stored multiple detection periods;

[0070] The OCV values ​​for all stored detection cycles are averaged.

[0071] Each mean data point is determined based on the mean-processed OCV value and the corresponding SOC value, and the target OCV-SOC curve is constructed based on each mean data point.

[0072] On the other hand, this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the online reconstruction method for the battery OCV-SOC curve provided in this application.

[0073] On the other hand, this application also provides a computer-readable storage medium storing computer instructions, which, when executed by a processor, implement the online reconstruction method for the battery OCV-SOC curve provided in this application.

[0074] In this application, a parameter identification model is set up to identify the target OCV value based on the real-time collected current voltage and current discharge current, thereby realizing online updating of the OCV value. Based on the different voltage change characteristics of the correction interval and the non-correction interval, the SOC value is corrected in the correction interval through a static correction method and in the non-correction interval through a dynamic correction method, thereby realizing the correction of the SOC value. This yields accurate target OCV and target SOC values, and reconstructs an accurate target OCV-SOC curve, providing a good data foundation for improving the estimation accuracy of the SOC value throughout the entire life cycle. Attached Figure Description

[0075] Figure 1 is a schematic diagram of the overall framework of the online reconstruction method of the battery OCV-SOC curve in the embodiments of this application;

[0076] Figure 2 is a schematic diagram of the initial OCV-SOC curve in an embodiment of this application;

[0077] Figure 3 is a data storage table of OCV-SOC curves in an embodiment of this application;

[0078] Figure 4 is a schematic diagram of the overall framework of the parameter identification model training method in the embodiments of this application;

[0079] Figure 5 is a schematic diagram of the overall framework of the initialization method in the embodiments of this application;

[0080] Figure 6 is a table showing the dOCV-dSOC variation of voltage peak points in the embodiments of this application;

[0081] Figure 7 is a dOCV-dSOC curve of the voltage peak point in the embodiment of this application;

[0082] Figure 8 is a table showing the dOCV-dSOC variation at the voltage inflection point in the embodiments of this application;

[0083] Figure 9 is a graph of the dOCV-dSOC curves at the voltage inflection point in an embodiment of this application.

[0084] Figure 10 is a functional structure diagram of the online reconstructing device for the battery OCV-SOC curve in an embodiment of this application;

[0085] Figure 11 is a schematic diagram of the hardware structure of the electronic device in an embodiment of this application. Detailed Implementation

[0086] To make the objectives, technical solutions, and beneficial effects of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0087] First, the application scenarios and design concepts of the embodiments of this application will be briefly introduced.

[0088] Currently, commonly used SOC estimation algorithms generally combine the open-circuit voltage method and the ampere-hour integration method. The prerequisite for these algorithms is obtaining valid open-circuit voltage (OCV) and SOC curves. These curves reflect the mapping relationship between SOC and battery terminal voltage, providing an effective initial SOC value for the ampere-hour integration method and correcting its accumulated errors. However, OCV curves are typically obtained through laboratory testing, and they change as the battery ages. Obtaining OCV curves for the entire battery lifecycle significantly increases the algorithm's development cycle and testing costs. Furthermore, each test only yields the OCV state curve of a specific individual battery cell under specific test conditions. This offline calibration method is only suitable for recently manufactured cells; using it to obtain OCV curves for aged cells in new energy vehicles is not practical. Offline testing methods assume that batteries with the same SOH will have consistent OCV curves. In reality, due to differences in manufacturing processes and battery operating conditions, even with the same SOH, different battery packs may have different OCV curves. Offline data cannot resolve the errors caused by this inconsistency.

[0089] As an electrochemical product, the parameters of a battery, including its open-circuit valence (OCV), change with use and storage; these parameters differ across different lifespan states. Furthermore, for some batteries, such as lithium iron phosphate (LFP) batteries, the OCV exhibits two "plateau" characteristics within certain State of Charge (SOC) ranges, making conventional estimations ineffective for SOC calibration and leading to significant estimation errors. If the battery system is not fully charged for an extended period and is charged and discharged within these "plateau" ranges, the SOC will accumulate and increase over time, causing system malfunctions and potentially resulting in overcharge or over-discharge accidents. Therefore, obtaining accurate OCV-SOC curves throughout the battery's entire lifespan to improve SOC estimation accuracy is a pressing issue that needs to be addressed.

[0090] Therefore, in this embodiment, after obtaining the current voltage and current discharge current of the battery under test, the current voltage and current discharge current are input into a preset parameter identification model to obtain the target OCV value output by the parameter identification model; based on the target OCV value, the parameter feature interval in which the target OCV value is located is determined from the preset parameter feature interval as the target feature interval; the target SOC value is determined by the correction method corresponding to the target feature interval; wherein, the correction method is a static correction method corresponding to the correction interval or a dynamic correction method corresponding to the non-correction interval; finally, the curve is reconstructed based on the target OCV value and the target SOC value to obtain the target OCV-SOC curve. In this way, by setting a parameter identification model, the target OCV value is obtained by parameter identification based on the real-time collected current voltage and current discharge current, and the OCV value is updated online. Based on the different voltage change characteristics of the correction interval and the non-correction interval, the SOC value is corrected in the correction interval by static correction method and in the non-correction interval by dynamic correction method, thereby correcting the SOC value and obtaining accurate target OCV value and target SOC value. The accurate target OCV-SOC curve is then reconstructed, providing a good data foundation for improving the estimation accuracy of SOC value throughout the entire life cycle.

[0091] After introducing the application scenarios and design concepts of the embodiments of this application, the technical solutions provided by the embodiments of this application will be described in detail below.

[0092] This application provides an online reconstruction method for battery OCV-SOC curves. Referring to Figure 1, the general flow of the online reconstruction method for battery OCV-SOC curves provided in this application is as follows:

[0093] Step 101: Obtain the current voltage and current discharge current of the battery under test.

[0094] The current voltage refers to the real-time terminal voltage of the battery. The current discharge current refers to the real-time current flowing through the battery during the discharge process. The current voltage and current discharge current of the battery can be acquired in real time by voltage and current sensors installed in the battery's circuit.

[0095] Step 102: Input the current voltage and current discharge current into the preset parameter identification model to obtain the target OCV value.

[0096] In practical applications, the input to the parameter identification model is the real-time acquired current voltage and current discharge current, and the output is the target OCV value, where the target OCV data refers to the accurate OCV value after correction. Specifically, the parameter identification model is a parameter identification state observer model constructed based on a least squares algorithm with a forgetting factor.

[0097] Step 103: Based on the target OCV value, determine the parameter feature interval in which the target OCV value is located from the preset parameter feature interval as the target feature interval; wherein, the parameter feature interval includes the correction interval and the non-correction interval; the correction interval includes the high correction interval and the low correction interval, and the non-correction interval includes the high plateau interval, the plateau transition interval and the low plateau interval.

[0098] In practical applications, the parameter characteristic interval refers to the OCV value interval and the corresponding SOC value interval. The calibration interval is where the battery OCV value changes significantly with SOC; the rate of change of battery OCV values ​​collected at adjacent times within the calibration interval is much greater than a preset rate of change threshold. The uncalibrated interval includes plateau intervals where the battery OCV value remains almost unchanged with SOC changes, and plateau transition intervals between plateaus; the rate of change of battery OCV values ​​collected at adjacent times within the plateau intervals of the uncalibrated interval is less than a preset rate of change threshold. The parameter characteristic interval is divided based on the variation pattern of OCV values ​​in the initial OCV-SOC curve. The initial OCV-SOC curve contains two plateau intervals where OCV values ​​change relatively slowly. The plateau interval with the higher SOC value is the high plateau interval, and the plateau interval with the lower SOC value is the low plateau interval. The area between the high and low plateau intervals is the plateau transition interval. The interval with SOC values ​​higher than the high plateau interval is the high calibration interval, and the interval with SOC values ​​lower than the low plateau interval is the low calibration interval.

[0099] Step 104: Determine the target SOC value using the correction method corresponding to the target feature interval; wherein the correction method is either a static correction method corresponding to the correction interval or a dynamic correction method corresponding to the non-correction interval.

[0100] As shown in Figure 2, both the SOC and OCV values ​​change during the correction interval. During power-on initialization within the correction interval, a static correction method can be used to correct the SOC value based on the real-time voltage to obtain the target SOC value. However, the battery exhibits two plateau intervals. When in the high or low plateau interval, the SOC value changes, but the OCV value remains almost unchanged, making it impossible to correct the SOC value based on the real-time voltage during power-on initialization within the plateau interval. However, there are voltage peaks in the plateau transition interval, and voltage inflection points exist between the low correction interval and the low plateau interval. By using a dynamic correction method, the voltage peaks and inflection points are identified. Based on the deviations in the SOC value at the voltage peaks and inflection points, SOC value correction is applied to the non-correction interval to obtain the target SOC value. Specifically, the voltage peak is the data point with the largest rate of change of OCV value in the plateau transition interval of the initial OCV-SOC curve, and the voltage inflection point is the data point with the largest change in the rate of change of OCV value in the low plateau interval and the low correction interval of the initial OCV-SOC curve.

[0101] In practical implementation, if the target feature interval is a correction interval, a static correction method can be used to determine the target SOC value. Specifically, the following methods can be used, but are not limited to:

[0102] First, obtain the initial SOC value of the battery under test after it is powered on.

[0103] Then, based on the current discharge current and the initial SOC value, the target SOC value is determined using the discharge pure ampere-hour integration method.

[0104] In practical applications, the battery pre-stores the SOC value at the last shutdown time. After the battery is powered on, the SOC value at the last shutdown time is generally used as the initial SOC value. However, after the current voltage is collected at the time of battery power-on, the initial SOC value needs to be updated according to the current voltage at the time of battery power-on. Specifically, the update method is to use the current voltage as the OCV value, find the SOC value corresponding to the OCV value in the stored data, and update the initial SOC value according to the SOC value obtained from the query. The initial SOC value obtained here after battery power-on is the initial SOC value updated according to the current voltage at the time of battery power-on. The pure ampere-hour integration method is adopted, that is, the target SOC value is estimated and determined by integrating the total charge flowing through the battery during the discharge process. Specifically, it can be calculated using the following formula (1).

[0105] Target SOC = (Initial SOC + ∫(I dt)) / Total battery capacity × 100% (1)

[0106] Where I is the current discharge current, t is time, and the total battery capacity is the battery capacity when fully charged.

[0107] In practice, while determining the target SOC value using the static correction method, the SOC value of the corresponding voltage peak point or voltage inflection point can be corrected based on the initial SOC value. The characteristic points include voltage peak points and voltage inflection points, and can be implemented in, but are not limited to, the following ways:

[0108] First, determine whether the parameter characteristic interval where the initial SOC value is located is the high correction interval in the correction interval.

[0109] Then, if the parameter characteristic range where the initial SOC value is located is the high correction range, the SOC value of the voltage peak point is updated using the discharge pure ampere-hour integration method; where the voltage peak point is the data point with the largest rate of change of OCV value in the plateau transition range of the initial OCV-SOC curve; if the parameter characteristic range where the initial SOC value is located is not the high correction range, the SOC value of the voltage inflection point is updated using the discharge pure ampere-hour integration method; where the voltage inflection point is the data point with the largest change in the rate of change of OCV value in the low plateau range and low correction range of the initial OCV-SOC curve.

[0110] In practical applications, the static correction method is based on the premise that the target OCV value corresponds to a high or low correction interval. The parameter characteristic interval corresponding to the initial SOC value is consistent with the parameter characteristic interval corresponding to the target OCV value; that is, the parameter characteristic interval corresponding to the initial SOC value is also either a high or low correction interval. If the parameter characteristic interval of the initial SOC value is in a high correction interval, the SOC value of the corrected voltage peak can be calculated using formula (1). If the parameter characteristic interval of the initial SOC value is in a low correction interval, the SOC value of the corrected voltage inflection point can be calculated using formula (1).

[0111] In practical implementation, if the target feature interval is a non-corrected interval, a dynamic correction method can be used to determine the target SOC value. Specifically, the following methods can be used, but are not limited to:

[0112] First, acquire multiple historical data points of the battery under test in the current testing cycle. The historical data points represent the correspondence between the historical OCV value and the historical SOC value of the battery under test in the current testing cycle.

[0113] Then, determine whether there are data points among multiple historical data points that conform to the characteristic point voltage change rule; wherein, the characteristic point voltage change rule is that the data point is the data point with the largest OCV value change rate in the platform transition interval, or the data point is the data point with the largest change amount of OCV value change rate in the low platform interval and the low correction interval.

[0114] Finally, if a target data point conforming to the characteristic point voltage change rule exists among multiple historical data points, then the data point conforming to the characteristic point voltage change rule is taken as the target data point; the deviation between the SOC value of the target data point and the SOC value of the voltage peak point or the SOC value of the voltage inflection point is calculated, the historical SOC value of the historical data point is corrected according to the deviation value, and the target SOC value is determined based on the corrected historical SOC value or the deviation value; where the voltage peak point is the data point with the largest OCV value change rate in the plateau transition interval of the initial OCV-SOC curve; the voltage inflection point is the data point with the largest change in OCV value change rate in the low plateau interval and low correction interval of the initial OCV-SOC curve; if no target data point conforming to the characteristic point voltage change rule exists among multiple historical data points, then the target SOC value is not determined, and the next set of voltage and discharge current is obtained.

[0115] In practical applications, multiple historical data points of the battery under test are acquired during the current testing cycle. Specifically, all historical data points in the current testing cycle can be acquired, or historical data points from the time interval from when the historical OCV value enters the plateau interval to the current moment can be acquired. The historical OCV value is the OCV value output by the parameter identification model after inputting the voltage and discharge current within a preset time period into a preset parameter identification model. The historical SOC value is calculated by inputting the current discharge current and the initial SOC value into the above formula (1).

[0116] In practical implementation, the rate of change of the OCV value can be obtained by the ratio of the difference between the OCV values ​​of two adjacent data points to the difference between the SOC values. The determination of whether a target data point conforming to the characteristic point voltage change rule exists among multiple historical data points specifically involves judging whether the rate of change of the OCV value satisfies the characteristic point voltage change rule. If the rate of change of the OCV value satisfies the characteristic point voltage change rule, then it is determined that a target data point conforming to the characteristic point voltage change rule exists among the historical data points; if the rate of change of the OCV value does not satisfy the characteristic point voltage change rule, then it is determined that no target data point conforming to the characteristic point voltage change rule exists among the historical data points. When a target data point conforming to the characteristic point voltage change rule exists among multiple historical data points, this data point is selected as the target data point. If the target data point is the one with the largest OCV value change rate in the platform transition interval, the difference between the SOC value of the target data point and the SOC value of the voltage peak is taken as the deviation value. In this case, the historical SOC values ​​of the historical data points in the platform transition interval and the high platform interval are summed with the deviation value to obtain the corrected historical SOC value. If the target data point is the one with the largest OCV value change rate in the low platform interval and the low correction interval, the difference between the SOC value of the target data point and the SOC value of the voltage inflection point is taken as the deviation value. In this case, the historical SOC values ​​of the historical data points in the low platform interval are summed with the deviation value to obtain the corrected historical SOC value. Historical data points adjacent to the current time are determined, and the target SOC value is determined using the discharge pure ampere-hour integration method based on the current discharge current and the historical SOC value. Alternatively, the SOC value can be determined using the pure ampere-hour integration method based on the current discharge current and the initial SOC value. The sum of this SOC value and the deviation value can then be used as the target SOC value. If no target data point matching the characteristic point voltage change rule is found among multiple historical data points, it is necessary to continue acquiring the next set of voltage and discharge current data to further determine the target OCV value for the next moment, until the target data point is found.

[0117] In practice, while using a dynamic correction method to determine the target SOC value, the SOC values ​​at voltage peaks and voltage inflection points can be corrected. Specifically, after determining the target SOC value using the corrected historical SOC values ​​or deviation values, the following steps are also included:

[0118] Update the SOC value of the voltage peak point or the SOC value of the voltage inflection point based on the SOC value of the target data point.

[0119] In practical applications, if the target data point is the data point with the largest rate of change of OCV value in the plateau transition interval, then the SOC value of the target data point is taken as the SOC value of the corrected voltage peak point; if the target data point is the data point with the largest change in the rate of change of OCV value in the low plateau interval and the low correction interval, then the SOC value of the target data point is taken as the SOC value of the corrected voltage inflection point.

[0120] Step 105: Reconstruct the curve based on the target OCV value and the target SOC value to obtain the target OCV-SOC curve.

[0121] In practical applications, curve fitting is performed based on the target OCV value and target SOC value determined in the current inspection cycle to obtain the current OCV-SOC curve. The stored OCV value is then updated according to the current OCV-SOC curve, and the target OCV-SOC curve is constructed based on the updated OCV and SOC values. Specifically, curve reconstruction based on the target OCV and target SOC values ​​to obtain the target OCV-SOC curve can be performed, but is not limited to, the following methods:

[0122] First, based on the target OCV value and target SOC value within the current detection period, the current OCV-SOC curve is constructed.

[0123] Then, based on the current OCV-SOC curve, update the OCV value of the corresponding data point in the earliest detection period among the stored multiple detection periods.

[0124] Next, the OCV values ​​for all stored detection cycles are averaged.

[0125] Finally, based on the mean-processed OCV value and the corresponding SOC value, each mean data point is determined, and the target OCV-SOC curve is constructed based on each mean data point.

[0126] In practical applications, the current detection period refers to the time from the moment the battery under test is powered on to the end of its discharge. The current OCV-SOC curve can be obtained by curve fitting based on the target OCV and target SOC values ​​from the previous detection period. The current OCV-SOC curve can be fitted based on a subset of the target OCV and target SOC values ​​from the current detection period, or it can be fitted based on all the target OCV and target SOC values ​​from the current detection period. Multiple sets of data are stored in the storage medium in a table format as shown in Figure 3, with each SOC value corresponding to multiple sets of OCV values. After obtaining the current OCV-SOC curve, the OCV values ​​corresponding to different SOC values ​​in the table are extracted from the current OCV-SOC curve and used as the new OCV values. The earliest detected OCV data in the table is then replaced with the new OCV value, and the OCV data in the table is updated online progressively. The target OCV-SOC curve is reconstructed using the average OCV value and SOC value in the table.

[0127] In one possible implementation, referring to Figure 4, before obtaining the current voltage and current discharge current of the battery, a parameter identification model needs to be pre-built. Specifically, it can be implemented in, but is not limited to, the following ways:

[0128] Step 201: Establish the second-order equivalent circuit model of the sample battery.

[0129] The mathematical model of the output voltage and input current of the power battery can be obtained from Kirchhoff's laws and Laplace's transform, as shown in formula (2). The transfer function of this mathematical model is shown in formula (3).

[0130] In the formula, U t For terminal voltage, U oc For open circuit voltage, i L For current, R i For ohmic internal resistance, R D1 and R D2 For polarization internal resistance, C D1 and C D2 It is a polarizing capacitor.

[0131] Let E L (s)=U t (s)-U oc (s), the second-order equivalent circuit model is as follows (4).

[0132] Step 202: Based on the bilinear transformation rule, determine the state equations of the second-order equivalent circuit model.

[0133] Based on the bilinear transformation rule, the s-plane can be mapped to the Z-plane for discretization:

[0134] Where b1, b2, b3, b4, and b5 are undetermined coefficients, and the difference form of the initial second-order equivalent circuit model state equation is shown in formula (9): U t,k =(1-b1-b2)U oc,k +b1U t,k-1 +b2U t,k-2 +b3i L,k +b4i L,k-1 +b5i L,k-2 (9)

[0135] In the formula, U t,k U is the current terminal voltage. oc,k U is the current open-circuit voltage. t,k-1 U is the terminal voltage at the previous moment. t,k-2 Let i be the terminal voltage at the previous two moments. L,k Let i be the current at the current moment. L,k-1 Let i be the current at the previous moment. L,k-2 The current at the previous two moments is denoted as .

[0136] Next, adjust U oc,k The coefficient (1-b1-b2) is 1, and the difference form of the state equation of the final second-order equivalent circuit model is shown in formula (10). In this way, the divergence of the identification parameter OCV curve can be transferred to other identification parameters, thereby making the identified OCV value less divergent and more stable.

[0137] Step 203: Process the state equations of the second-order equivalent circuit model using the recursive least squares method with a forgetting factor to obtain the initial identification model.

[0138] Define the system data matrix as follows:

[0139] The system parameter matrix is ​​defined as follows:

[0140] The transfer function corresponding to the state equation of the second-order equivalent circuit model can be simplified to:

[0141] y k =Φ 2,k θ 2,k (13)

[0142] The simplified model transfer function is processed using a least squares algorithm based on the forgetting factor, resulting in:

[0143] y k =φk θ k +e Ls,k (14)

[0144] In the formula, φ k Let θ be a data matrix. k Let e ​​be the parameter matrix. Ls,k For stationary zero-mean white noise, μ is the forgetting factor. When the forgetting factor is 1, the formula degenerates into the traditional recursive least squares method. K Ls,k P is the gain of the algorithm. Ls,k-1 Let P be the error covariance matrix of the state estimate at the previous time step. Ls,k Let be the error covariance matrix of the current state estimate. These are the parameter values ​​estimated at the previous time step. These are the parameter values ​​estimated in this study. This is the observed value at this moment, y k It is the actual observed value of the system, y k and Subtracting the two gives the system's prediction error. Then, the prediction error is multiplied by the gain matrix K. Ls,k Multiplying them together yields a correction value for the parameter estimate at this moment, compared to the estimate at the previous moment. The work was carried out and the final estimated value was obtained.

[0145] Step 204: Obtain sample battery test data, and train the initial identification model based on the sample battery test data to obtain the parameter identification model.

[0146] In practical applications, sample battery test data involves conducting multiple full-charge and full-discharge experiments on the battery to obtain current data, voltage data, test SOC values, and test OCV values ​​during the full discharge process. By using the current and voltage data as input to the initial identification model and the corresponding OCV values ​​as the target output, the initial identification model is trained using the battery test data. The corresponding squared gain and covariance are calculated, and the forgetting factor in the initial identification model is adjusted to obtain the parameter identification model.

[0147] In one possible implementation, referring to Figure 5, the online reconstruction method for the battery OCV-SOC curve further includes:

[0148] Step 301: Construct an initial OCV-SOC curve based on the initial data points formed by the test SOC value and the test OCV value corresponding to the test SOC value in the sample battery test data.

[0149] In practical applications, based on the test SOC values ​​and test OCV values ​​of multiple full discharge processes in the battery test data, the average value of multiple test OCV values ​​corresponding to each test SOC value is taken to obtain the average test OCV value. The test SOC value and the average test OCV value are used to form initial data points, and the initial OCV-SOC curve is obtained by fitting the initial data points.

[0150] Step 302: Based on the variation pattern of each initial data point in the initial OCV-SOC curve, the parameter characteristic interval of the sample battery is divided to obtain the correction interval and the non-correction interval.

[0151] In practical applications, based on the variation pattern of the OCV values ​​of each initial data point in the initial OCV-SOC curve, the intervals where the change in OCV value is less than the preset threshold are defined as two plateau intervals. The plateau interval with the higher SOC value is the high plateau interval, and the plateau interval with the lower SOC value is the low plateau interval. The area between the high and low plateau intervals is the plateau transition interval. The plateau interval with a SOC value higher than the high plateau interval is the high correction interval, and the plateau interval with a SOC value lower than the low plateau interval is the low correction interval.

[0152] Step 303: Based on the characteristic point voltage change rules, identify each initial data point in the platform transition interval to obtain the voltage peak point.

[0153] In practical applications, the rate of change of OCV value at each initial data point in the platform transition interval is calculated. The rate of change of OCV value can be obtained by the ratio of the difference in OCV value between two adjacent data points to the difference in SOC value. The initial data point with the largest rate of change of OCV value in the platform transition interval is taken as the voltage peak point. Refer to the voltage peak point dOCV-dSOC change table in Figure 6 and the voltage peak point dOCV-SOC curve in Figure 7. Here, dSOC is taken as an integer value ±0.5%, for example, dSOC of 60% represents the interval 59.5%-60.5%, and dOCV is the decrease in OCV value during the discharge process within the dSOC interval. Based on this, 15 points of dOCV-dSOC are statistically analyzed between SOC of 66% and 52%. A clear peak appears in the voltage peak point dOCV-dSOC curve, indicating a peak in the rate of change of OCV value in the platform transition interval. Through multiple identifications, it can be determined that the SOC at this voltage peak point is 58%, with an accuracy within 1%.

[0154] Step 304: Based on the characteristic point voltage change rules, identify each initial data point in the low plateau interval and the low correction interval to obtain the voltage inflection point.

[0155] In practical applications, the rate of change of OCV values ​​at each initial data point in the low plateau and low correction intervals is calculated. The rate of change of OCV values ​​can be obtained by the ratio of the difference in OCV values ​​between two adjacent data points to the difference in SOC values. The data point with the largest change in the rate of change of OCV values ​​in the plateau transition interval is taken as the voltage inflection point. Referring to the voltage inflection point dOCV-dSOC change table in Figure 8 and the voltage inflection point dOCV-SOC curve in Figure 9, where dSOC is taken as an integer value ±0.5%, dOCV-dSOC values ​​are statistically analyzed from 23% to 37% of SOC, totaling 15 points. After the SOC decreases to 30%, the rate of change of OCV values ​​increases significantly, especially from 31% to 30%, where dOCV increases from 0.6mV to 1.1mV, an increase of 83%. Through multiple identifications, it can be determined that the SOC of this voltage inflection point is 30%.

[0156] Based on the above embodiments, this application provides an online reconstruction device for battery OCV-SOC curves. Referring to Figure 10, the online reconstruction device 400 for battery OCV-SOC curves provided in this application includes at least:

[0157] The data acquisition unit 401 is configured to acquire the current voltage and current discharge current of the battery under test;

[0158] The target OCV value determination unit 402 is configured to input the current voltage and the current discharge current into a preset parameter identification model to obtain the target OCV value;

[0159] The interval determination unit 403 is configured to determine the parameter feature interval in which the target OCV value is located from the preset parameter feature interval based on the target OCV value; wherein, the parameter feature interval includes a correction interval and an uncorrected interval; the correction interval includes a high correction interval and a low correction interval, and the uncorrected interval includes a high plateau interval, a plateau transition interval, and a low plateau interval;

[0160] The target SOC value determination unit 404 is configured to determine the target SOC value using a correction method corresponding to the target feature interval; wherein the correction method is either a static correction method corresponding to the correction interval or a dynamic correction method corresponding to the non-correction interval.

[0161] The curve reconstruction unit 405 is configured to reconstruct the curve based on the target OCV value and the target SOC value to obtain the target OCV-SOC curve.

[0162] In one embodiment, the target SOC value determination unit 404 is specifically configured as follows:

[0163] Obtain the initial SOC value of the battery under test after power-on;

[0164] Based on the current discharge current and the initial SOC value, the target SOC value is determined using the discharge pure ampere-hour integration method.

[0165] In one embodiment, the target SOC value determination unit 404 is further configured to:

[0166] Determine whether the parameter characteristic interval where the initial SOC value is located is the high correction interval in the correction interval;

[0167] If so, the SOC value of the voltage peak point is updated using the pure ampere-hour integration method of discharge; where the voltage peak point is the data point with the largest rate of change of OCV value in the plateau transition interval of the initial OCV-SOC curve;

[0168] If not, the SOC value of the voltage inflection point is updated using the pure ampere-hour integration method of discharge; where the voltage inflection point is the data point with the largest change in the rate of change of the OCV value in the low plateau interval and low correction interval of the initial OCV-SOC curve.

[0169] In one embodiment, the target SOC value determination unit 404 is specifically configured as follows:

[0170] Acquire multiple historical data points of the battery under test in the current testing cycle. The historical data points represent the correspondence between the historical OCV value and the historical SOC value of the battery under test in the current testing cycle.

[0171] Determine whether there are data points among multiple historical data points that conform to the characteristic point voltage change rule; wherein, the characteristic point voltage change rule is that the data point is the data point with the largest OCV value change rate in the platform transition interval, or the data point is the data point with the largest change amount of OCV value change rate in the low platform interval and the low correction interval.

[0172] If so, the data point that conforms to the characteristic point voltage change rule is taken as the target data point; the deviation between the SOC value of the target data point and the SOC value of the voltage peak point or the SOC value of the voltage inflection point is calculated, the historical SOC value of the historical data point is corrected according to the deviation value, and the target SOC value is determined based on the corrected historical SOC value or the deviation value; where the voltage peak point is the data point with the largest OCV value change rate in the plateau transition interval of the initial OCV-SOC curve; the voltage inflection point is the data point with the largest change in OCV value change rate in the low plateau interval and low correction interval of the initial OCV-SOC curve;

[0173] If not, the target SOC value will not be determined, and the next set of voltage and discharge current will be obtained.

[0174] In one embodiment, the target SOC value determination unit 404 is further configured to:

[0175] Update the SOC value of the voltage peak point or the SOC value of the voltage inflection point based on the SOC value of the target data point.

[0176] In one embodiment, the online reconstruction device for the battery OCV-SOC curve further includes: a model building unit 406;

[0177] Model building unit 406 is configured to establish a second-order equivalent circuit model of the sample battery; based on the bilinear change rule, the state equation of the second-order equivalent circuit model is determined; the state equation of the second-order equivalent circuit model is processed by the recursive least squares method with forgetting factor to obtain the initial identification model; sample battery test data is acquired, and the initial identification model is trained based on the sample battery test data to obtain the parameter identification model.

[0178] In one embodiment, the online reconstruction device for the battery OCV-SOC curve further includes: an initialization unit 407;

[0179] The initialization unit 407 is configured to construct an initial OCV-SOC curve based on the initial data points formed by the test SOC value and the test OCV value corresponding to the test SOC value in the sample battery test data. Based on the variation law of each initial data point in the initial OCV-SOC curve, the parameter characteristic interval of the sample battery is divided to obtain the correction interval and the non-correction interval. Based on the characteristic point voltage change rule, each initial data point in the platform transition interval is identified to obtain the voltage peak point. Based on the characteristic point voltage change rule, each initial data point in the low platform interval and the low correction interval is identified to obtain the voltage inflection point.

[0180] In one embodiment, the curve reconstruction unit 405 is specifically configured to: construct the current OCV-SOC curve based on the target OCV value and target SOC value in the previous detection period; update the OCV value of the corresponding data point in the earliest detection period among the stored multiple detection periods according to the current OCV-SOC curve; perform mean processing on the OCV values ​​of all stored detection periods; determine each mean data point based on the mean-processed OCV value and the SOC value corresponding to the mean-processed OCV value; and construct the target OCV-SOC curve according to each mean data point.

[0181] It should be noted that the principle of the online reconstruction device 400 for battery OCV-SOC curves provided in this application embodiment to solve the technical problem is similar to the online reconstruction method for battery OCV-SOC curves provided in this application embodiment. Therefore, the implementation of the online reconstruction device 400 for battery OCV-SOC curves provided in this application embodiment can refer to the implementation of the online reconstruction method for battery OCV-SOC curves provided in this application embodiment, and the repeated parts will not be described again.

[0182] After introducing the online reconstruction method and apparatus for battery OCV-SOC curves provided in the embodiments of this application, the electronic device provided in the embodiments of this application will be briefly introduced next.

[0183] Referring to Figure 11, the electronic device 500 provided in this application embodiment includes at least: a processor 501, a memory 502, and a computer program stored in the memory 502 and executable on the processor 501. When the processor 501 executes the computer program, it implements the online reconstruction method of the battery OCV-SOC curve provided in this application embodiment.

[0184] It should be noted that the electronic device 500 shown in Figure 11 is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0185] The electronic device 500 provided in this application embodiment may further include a bus 503 connecting different components (including processor 501 and memory 502). The bus 503 represents one or more types of bus structures, including memory bus, peripheral bus, local area bus, etc.

[0186] The memory 502 may include a readable medium in the form of volatile memory, such as random access memory (RAM) 5021 and / or cache memory 5022, and may further include read-only memory (ROM) 5023.

[0187] The memory 502 may also include a program tool 5025 having a set (at least one) of program modules 5024, including but not limited to: an operating subsystem, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0188] Electronic device 500 can also communicate with one or more external devices 504 (e.g., keyboard, remote control, etc.), and with one or more devices that enable users to interact with electronic device 500 (e.g., mobile phone, computer, etc.), and / or with any device that enables electronic device 500 to communicate with one or more other electronic devices 500 (e.g., router, modem, etc.). This communication can be performed through input / output (I / O) interface 505. Furthermore, electronic device 500 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) through network adapter 506. As shown in Figure 11, network adapter 506 communicates with other modules of electronic device 500 through bus 503. It should be understood that, although not shown in Figure 11, other hardware and / or software modules may be used in conjunction with the electronic device 500, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, Redundant Arrays of Independent Disks (RAID) subsystems, tape drives, and data backup storage subsystems.

[0189] The computer-readable storage medium provided in the embodiments of this application is described below. The computer-readable storage medium provided in the embodiments of this application stores computer instructions, which, when executed by a processor, implement the online reconstruction method for the battery OCV-SOC curve provided in the embodiments of this application. Specifically, the computer instructions can be built into or installed in the electronic device 500, so that the electronic device 500 can implement the online reconstruction method for the battery OCV-SOC curve provided in the embodiments of this application by executing the built-in or installed computer instructions.

[0190] Furthermore, the online reconstruction method for the battery OCV-SOC curve provided in this application embodiment can also be implemented as a program product. The program product includes program code. When the program product can run on the electronic device 500, the program code is configured to cause the electronic device 500 to execute the online reconstruction method for the battery OCV-SOC curve provided in this application embodiment.

[0191] The program product provided in this application embodiment can be any combination of one or more readable media, wherein the readable media can be a readable signal medium or a readable storage medium, and the readable storage medium can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination thereof. Specifically, more specific examples of readable storage media (a non-exhaustive list) include: electrical connections with one or more wires, portable disks, hard disks, RAM, ROM, erasable programmable read-only memory (EPROM), optical fibers, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0192] The program product provided in this application embodiment can be a CD-ROM and include program code, and can also run on a computing device. However, the program product provided in this application embodiment is not limited thereto. In this application embodiment, the readable storage medium can be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0193] It should be noted that although several units or sub-units of the device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units.

[0194] Furthermore, although the operations of the method of this application are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0195] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0196] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations. Industrial applicability

[0197] By applying the technical solution of this application, the SOC value is corrected, and the accurate target OCV value and target SOC value are obtained. The accurate target OCV-SOC curve is also reconstructed, providing a good data foundation for improving the estimation accuracy of SOC value throughout the entire life cycle.

Claims

1. An online reconstruction method for battery OCV-SOC curves, wherein, include: Obtain the current voltage and current discharge current of the battery under test; The current voltage and the current discharge current are input into a preset parameter identification model to obtain the target OCV value; Based on the target OCV value, the parameter feature interval in which the target OCV value is located is determined from the preset parameter feature interval as the target feature interval; wherein, the parameter feature interval includes a corrected interval and an uncorrected interval; the corrected interval includes a high-corrected interval and a low-corrected interval, and the uncorrected interval includes a high plateau interval, a plateau transition interval, and a low plateau interval; The target SOC value is determined using a correction method corresponding to the target feature interval; wherein the correction method is either a static correction method corresponding to the correction interval or a dynamic correction method corresponding to the non-correction interval. The target OCV-SOC curve is obtained by reconstructing the curve based on the target OCV value and the target SOC value.

2. The online reconstruction method for the battery OCV-SOC curve as described in claim 1, wherein, If the target feature interval is a correction interval, then the target SOC value is determined using the correction method corresponding to the target feature interval, including: Obtain the initial SOC value of the battery under test after power-on; Based on the current discharge current and the initial SOC value, the target SOC value is determined using the discharge pure ampere-hour integration method.

3. The online reconstruction method for the battery OCV-SOC curve as described in claim 2, wherein, After obtaining the initial SOC value of the battery under test after power-on, the method further includes: Determine whether the parameter feature interval in which the initial SOC value is located is the high correction interval in the correction interval; If so, the SOC value of the voltage peak point is updated using the pure ampere-hour integration method of discharge; wherein, the voltage peak point is the data point with the largest rate of change of OCV value in the plateau transition interval of the initial OCV-SOC curve; If not, the SOC value of the voltage inflection point is updated using the pure ampere-hour integration method of discharge; wherein, the voltage inflection point is the data point with the largest change in the rate of change of OCV value in the low plateau interval and low correction interval of the initial OCV-SOC curve.

4. The online reconstruction method for the battery OCV-SOC curve as described in claim 1, wherein, If the target feature interval is a non-corrected interval, then the target SOC value is determined using the correction method corresponding to the target feature interval, including: Multiple historical data points of the battery under test in the current testing cycle are obtained, wherein the historical data points represent the correspondence between the historical OCV value and the historical SOC value of the battery under test in the current testing cycle; Determine whether there are any data points among the multiple historical data points that conform to the characteristic point voltage change rule; wherein, the characteristic point voltage change rule is that the data point is the data point with the largest OCV value change rate in the platform transition interval, or the data point is the data point with the largest change amount of OCV value change rate in the low platform interval and the low correction interval. If so, the data point that conforms to the characteristic point voltage change rule is taken as the target data point; the deviation between the SOC value of the target data point and the SOC value of the voltage peak point or the SOC value of the voltage inflection point is calculated, the historical SOC value of the historical data point is corrected according to the deviation value, and the target SOC value is determined based on the corrected historical SOC value or the deviation value; wherein, the voltage peak point is the data point with the largest OCV value change rate in the plateau transition interval of the initial OCV-SOC curve; the voltage inflection point is the data point with the largest change in OCV value change rate in the low plateau interval and low correction interval of the initial OCV-SOC curve; If not, the target SOC value will not be determined, and the next set of voltages and discharge currents will be obtained.

5. The online reconstruction method for the battery OCV-SOC curve as described in claim 4, wherein, After determining the target SOC value based on the corrected historical SOC value or deviation value, the following is also included: Update the SOC value of the voltage peak point or the SOC value of the voltage inflection point based on the SOC value of the target data point.

6. The online reconstruction method for the battery OCV-SOC curve as described in any one of claims 1-5, wherein, Before acquiring the current voltage and current discharge current of the battery under test, the method further includes: Establish a second-order equivalent circuit model for the sample battery; Based on the bilinear transformation rule, the state equation of the second-order equivalent circuit model is determined; The state equations of the second-order equivalent circuit model are processed by the recursive least squares method with a forgetting factor to obtain the initial identification model. The sample battery test data is obtained, and the initial identification model is trained based on the sample battery test data to obtain the parameter identification model.

7. The online reconstruction method for the battery OCV-SOC curve as described in claim 6, wherein, Also includes: An initial OCV-SOC curve is constructed based on the initial data points formed by the test SOC value and the test OCV value corresponding to the test SOC value in the sample battery test data. Based on the variation pattern of each initial data point in the initial OCV-SOC curve, the parameter characteristic interval of the sample battery is divided to obtain the correction interval and the non-correction interval. Based on the characteristic point voltage change rule, each initial data point in the platform transition interval is identified to obtain the voltage peak point; Based on the characteristic point voltage change rules, each initial data point in the low plateau interval and the low correction interval is identified to obtain the voltage inflection point.

8. The online reconstruction method for the battery OCV-SOC curve as described in claim 1, wherein, Based on the target OCV value and the target SOC value, curve reconstruction is performed to obtain the target OCV-SOC curve, including: Based on the target OCV value and the target SOC value within the current detection period, construct the current OCV-SOC curve; Based on the current OCV-SOC curve, update the OCV value of the corresponding data point in the detection period with the earliest detection time among the stored multiple detection periods; The OCV values ​​for all stored detection cycles are averaged. Each mean data point is determined based on the mean-processed OCV value and the corresponding SOC value, and the target OCV-SOC curve is constructed based on each mean data point.

9. An online reconfiguration device for the OCV-SOC curve of a battery, wherein, include: The data acquisition unit is configured to acquire the current voltage and current discharge current of the battery under test; The target OCV value determination unit is configured to input the current voltage and the current discharge current into a preset parameter identification model to obtain the target OCV value; The interval determination unit is configured to determine the parameter feature interval in which the target OCV value is located from a preset parameter feature interval as the target feature interval, based on the target OCV value; wherein, the parameter feature interval includes a correction interval and an uncorrected interval; the correction interval includes a high correction interval and a low correction interval, and the uncorrected interval includes a high plateau interval, a plateau transition interval, and a low plateau interval; The target SOC value determination unit is configured to determine the target SOC value using a correction method corresponding to the target feature interval; wherein the correction method is either a static correction method corresponding to the correction interval or a dynamic correction method corresponding to the non-correction interval. The curve reconstruction unit is configured to reconstruct the curve based on the target OCV value and the target SOC value to obtain the target OCV-SOC curve.

10. The online reconstruction device for the battery OCV-SOC curve as described in claim 9, wherein, The target SOC value determination unit is specifically configured as follows: Obtain the initial SOC value of the battery under test after power-on; Based on the current discharge current and the initial SOC value, the target SOC value is determined using the discharge pure ampere-hour integration method.

11. The online reconstruction device for the battery OCV-SOC curve as described in claim 10, wherein, The target SOC value determination unit is specifically configured as follows: Determine whether the parameter feature interval in which the initial SOC value is located is the high correction interval in the correction interval; If so, the SOC value of the voltage peak point is updated using the pure ampere-hour integration method of discharge; wherein, the voltage peak point is the data point with the largest rate of change of OCV value in the plateau transition interval of the initial OCV-SOC curve; If not, the SOC value of the voltage inflection point is updated using the pure ampere-hour integration method of discharge; wherein, the voltage inflection point is the data point with the largest change in the rate of change of OCV value in the low plateau interval and low correction interval of the initial OCV-SOC curve.

12. The online reconstruction device for the battery OCV-SOC curve as described in claim 9, wherein, The target SOC value determination unit is specifically configured as follows: Multiple historical data points of the battery under test in the current testing cycle are obtained, wherein the historical data points represent the correspondence between the historical OCV value and the historical SOC value of the battery under test in the current testing cycle; Determine whether there are any data points among the multiple historical data points that conform to the characteristic point voltage change rule; wherein, the characteristic point voltage change rule is that the data point is the data point with the largest OCV value change rate in the platform transition interval, or the data point is the data point with the largest change amount of OCV value change rate in the low platform interval and the low correction interval. If so, the data point that conforms to the characteristic point voltage change rule is taken as the target data point; the deviation between the SOC value of the target data point and the SOC value of the voltage peak point or the SOC value of the voltage inflection point is calculated, the historical SOC value of the historical data point is corrected according to the deviation value, and the target SOC value is determined based on the corrected historical SOC value or the deviation value; wherein, the voltage peak point is the data point with the largest OCV value change rate in the plateau transition interval of the initial OCV-SOC curve; the voltage inflection point is the data point with the largest change in OCV value change rate in the low plateau interval and low correction interval of the initial OCV-SOC curve; If not, the target SOC value will not be determined, and the next set of voltages and discharge currents will be obtained.

13. The online reconstruction device for the battery OCV-SOC curve as described in claim 12, wherein, The target SOC value determination unit is further configured to: Update the SOC value of the voltage peak point or the SOC value of the voltage inflection point based on the SOC value of the target data point.

14. The online reconstruction apparatus for the battery OCV-SOC curve as described in any one of claims 9-13, wherein, It also includes: model building units; The model building unit is configured to establish a second-order equivalent circuit model of the sample battery; based on the bilinear transformation rule, the state equation of the second-order equivalent circuit model is determined; the state equation of the second-order equivalent circuit model is processed by the recursive least squares method with a forgetting factor to obtain an initial identification model; the test data of the sample battery is acquired, and the initial identification model is trained based on the test data of the sample battery to obtain a parameter identification model.

15. The online reconstruction device for the battery OCV-SOC curve as described in claim 14, wherein, Also includes: Initialization unit; The initialization unit configures initial data points based on the test SOC value and the corresponding test OCV value in the sample battery test data to construct an initial OCV-SOC curve; based on the variation pattern of each initial data point in the initial OCV-SOC curve, the parameter characteristic interval of the sample battery is divided to obtain the correction interval and the non-correction interval; based on the characteristic point voltage change rule, each initial data point in the platform transition interval is identified to obtain the voltage peak point; based on the characteristic point voltage change rule, each initial data point in the low platform interval and the low correction interval is identified to obtain the voltage inflection point.

16. The online reconstruction device for the battery OCV-SOC curve as described in claim 8, wherein, The curve reconstruction unit is specifically configured as follows: Based on the target OCV value and the target SOC value within the current detection period, construct the current OCV-SOC curve; Based on the current OCV-SOC curve, update the OCV value of the corresponding data point in the detection period with the earliest detection time among the stored multiple detection periods; The OCV values ​​for all stored detection cycles are averaged. Each mean data point is determined based on the mean-processed OCV value and the corresponding SOC value, and the target OCV-SOC curve is constructed based on each mean data point.

17. An electronic device, wherein, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the online reconfiguration method for the battery OCV-SOC curve as described in any one of claims 1-8.

18. A computer-readable storage medium, wherein, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the online reconstruction method for the battery OCV-SOC curve as described in any one of claims 1-8.

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