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

Abstract

The invention discloses a battery OCV-SOC curve online reconstruction method and device, equipment and a medium, is applied to the technical field of battery management, and is used for solving the problem that an accurate OCV-SOC curve cannot be obtained in the prior art. The method specifically comprises the following steps: inputting obtained current voltage and current discharge current into a parameter identification model to obtain a target OCV value; determining a target feature interval according to the target OCV value; determining a target SOC value by adopting a correction method corresponding to the target feature interval; the correction method is a static correction method corresponding to a correction interval or a dynamic correction method corresponding to a non-correction interval; and performing curve reconstruction according to the target OCV value and the target SOC value to obtain a target OCV-SOC curve. Therefore, online updating of the OCV value is realized by setting the parameter identification model, correction of SOC values in different intervals is realized by using a static correction method and a dynamic correction method, and an accurate OCV-SOC curve can be obtained through reconstruction.

Description

Technical Field

[0001] The present application relates to the field of battery management technology, and in particular to an online reconstruction method, device, equipment and medium for a battery OCV-SOC curve. Background Art

[0002] The estimation accuracy of the battery state of charge (SOC) is the core indicator for evaluating the battery management system, which is related to the remaining capacity, charge and discharge power, and safety of use of the battery. Accurate estimation of SOC can ensure that the battery works in the best state and improves its life. The commonly used SOC estimation algorithm is generally a combination of the open circuit voltage method and the ampere-hour integration method. The premise is to obtain an effective OCV-SOC curve, which reflects the mapping relationship between SOC and battery terminal voltage. It can provide an effective SOC initial value for the ampere-hour integration method and correct the accumulated error of the ampere-hour integration method.

[0003] At present, the traditional method of obtaining open circuit voltage (OCV, Output Current Voltage), namely the open circuit voltage experimental test, has many shortcomings. This method is cumbersome and time-consuming, and each test can only obtain the OCV state curve of a specific single cell under a specific environment, which cannot fully reflect the changes in the actual use of the battery. As an electrochemical product, the parameters of the battery, including OCV, will change with use and storage, so the offline calibration method is not applicable to aging cells. In addition, the offline test method assumes that the OCV curves of batteries with the same health state SOH are consistent, but in fact, due to differences in production processes and operating conditions, this assumption does not hold true. For some batteries, such as lithium iron phosphate (LFP) batteries, their OCV has a "platform interval" in the partial state of charge (SOC) interval, which makes SOC calibration difficult and the estimation deviation large. Long-term charging and discharging in the "platform interval" will further accumulate SOC estimation errors, which may cause system function abnormalities or even safety accidents. Therefore, obtaining accurate OCV-SOC curves over the entire life cycle of the battery to improve the accuracy of SOC estimation is a problem that needs to be solved urgently. Summary of the invention

[0004] The present application provides a method, device, equipment and medium for online reconstruction of a battery OCV-SOC curve, so as to solve the problem in the prior art that an accurate OCV-SOC curve cannot be obtained.

[0005] The technical solutions provided by this application are as follows:

[0006] On the one hand, the present application provides an online reconstruction method of a battery OCV-SOC curve, comprising:

[0007] Get the current voltage and current discharge current of the battery to be tested;

[0008] Input the current voltage and current discharge current into a preset parameter identification model to obtain a target OCV value;

[0009] According to the target OCV value, a parameter characteristic interval in which the target OCV value is located is determined from a preset parameter characteristic interval as a target characteristic interval; wherein the parameter characteristic interval includes a correction interval and a non-correction interval; the correction interval includes a high correction interval and a low correction interval, and the non-correction interval includes a high platform interval, a platform transition interval and a low platform interval;

[0010] The target SOC value is determined by adopting a correction method corresponding to the target characteristic 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;

[0011] The curve is reconstructed according to the target OCV value and the target SOC value to obtain the target OCV-SOC curve.

[0012] Optionally, if the target characteristic interval is a correction interval, the target SOC value is determined by using a correction method corresponding to the target characteristic interval, including:

[0013] Get the initial SOC value of the battery to be tested after it is powered on;

[0014] Based on the current discharge current and the initial SOC value, the target SOC value is determined by the discharge pure ampere-hour integration method.

[0015] Optionally, after obtaining the initial SOC value of the battery to be tested after it is powered on, the following further includes:

[0016] Determine whether the parameter characteristic interval in which the initial SOC value is located is a high correction interval in the correction interval;

[0017] If so, the SOC value at the voltage peak point is updated using the discharge pure ampere-hour integration method; wherein the voltage peak point is the data point with the largest rate of change of the OCV value in the platform transition interval of the initial OCV-SOC curve;

[0018] If not, the SOC value of the voltage inflection point is updated by the discharge pure ampere-hour integration method; wherein the voltage inflection point is the data point with the largest change rate of the OCV value in the low platform interval and the low correction interval in the initial OCV-SOC curve.

[0019] Optionally, if the target characteristic interval is a non-correction interval, a correction method corresponding to the target characteristic interval is used to determine the target SOC value, including:

[0020] Acquire multiple historical data points of the battery to be tested in the current detection cycle, wherein the historical data points represent the corresponding relationship between the historical OCV value and the historical SOC value of the battery to be tested in the current detection cycle;

[0021] Determine whether there is a data point that meets the characteristic point voltage change rule among the multiple historical data points; 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 the OCV value change rate in the low platform interval and the low correction interval;

[0022] If so, the data point that meets the voltage change rule of the characteristic point is taken as the target data point; the deviation value 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 rate of change of the OCV value in the platform transition interval of the initial OCV-SOC curve; the voltage inflection point is the data point with the largest rate of change of the OCV value in the low platform interval and the low correction interval of the initial OCV-SOC curve;

[0023] If not, the target SOC value is not determined, and the next set of voltage and discharge current is obtained.

[0024] Optionally, after determining the target SOC value based on the corrected historical SOC value or the deviation value, the method further includes:

[0025] The SOC value at the voltage peak point or the SOC value at the voltage inflection point is updated according to the SOC value at the target data point.

[0026] Optionally, before obtaining the current voltage and current discharge current of the battery to be tested, the following steps are further included:

[0027] Establish a second-order equivalent circuit model of the sample battery;

[0028] Based on the bilinear change law, determine the state equation of the second-order equivalent circuit model;

[0029] The state equation of the second-order equivalent circuit model is processed by the recursive least square method with forgetting factor to obtain the initial identification model;

[0030] Sample battery test data is obtained, and an initial identification model is trained based on the sample battery test data to obtain a parameter identification model.

[0031] Optionally, the online reconstruction method of the battery OCV-SOC curve also includes:

[0032] Constructing an initial OCV-SOC curve based on initial data points consisting of a test SOC value in the sample battery test data and a test OCV value corresponding to the test SOC value;

[0033] 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 into a correction interval and a non-correction interval;

[0034] Based on the characteristic point voltage change rule, the initial data points in the platform transition interval are identified to obtain the voltage peak point;

[0035] Based on the characteristic point voltage variation rule, each initial data point in the low platform interval and the low correction interval is identified to obtain the voltage inflection point.

[0036] Optionally, a curve is reconstructed according to a target OCV value and a target SOC value to obtain a target OCV-SOC curve, including:

[0037] Based on the target OCV value and target SOC value in the previous detection cycle, the current OCV-SOC curve is constructed;

[0038] According to the current OCV-SOC curve, the OCV value of the corresponding data point in the earliest detection cycle among the stored multiple detection cycles is updated;

[0039] Perform average processing on the OCV values ​​of all stored detection cycles;

[0040] Each mean data point is determined based on the OCV value after mean processing and the SOC value corresponding to the OCV value after mean processing, and a target OCV-SOC curve is constructed according to each mean data point.

[0041] On the other hand, the present application provides an online reconstruction device for a battery OCV-SOC curve, comprising:

[0042] A data acquisition unit, used to obtain the current voltage and current discharge current of the battery to be tested;

[0043] A target OCV value determination unit, used for inputting the current voltage and the current discharge current into a preset parameter identification model to obtain a target OCV value;

[0044] An interval determination unit is used to determine, according to the target OCV value, a parameter characteristic interval in which the target OCV value is located from a preset parameter characteristic interval as a target characteristic interval; wherein the parameter characteristic interval includes a correction interval and a non-correction interval; the correction interval includes a high correction interval and a low correction interval, and the non-correction interval includes a high platform interval, a platform transition interval, and a low platform interval;

[0045] A target SOC value determination unit, configured to determine a target SOC value by adopting a correction method corresponding to a target characteristic interval; wherein the correction method is a static correction method corresponding to a correction interval or a dynamic correction method corresponding to a non-correction interval;

[0046] The curve reconstruction unit is used to reconstruct the curve according to the target OCV value and the target SOC value to obtain a target OCV-SOC curve.

[0047] On the other hand, the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the online reconstruction method of the battery OCV-SOC curve provided in the present application is implemented.

[0048] On the other hand, the present application also provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed by a processor, the online reconstruction method of the battery OCV-SOC curve provided in the present application is implemented.

[0049] The beneficial effects of this application are as follows:

[0050] In the present application, a parameter identification model is set up, and parameter identification is performed according to the current voltage and current discharge current collected in real time to obtain the target OCV value, thereby realizing online updating of the OCV value; based on the difference in voltage change characteristics between the correction interval and the non-correction interval, the SOC value of the correction interval is corrected by a static correction method, and the SOC value of the non-correction interval is corrected by a dynamic correction method, thereby realizing correction of the SOC value, obtaining accurate target OCV value and target SOC value, and reconstructing an accurate target OCV-SOC curve, thereby providing a good data basis for improving the estimation accuracy of the SOC value over the entire life cycle.

[0051] Other features and advantages of the present application will be described in the subsequent description, and in part, will become apparent from the description, or will be understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0053] Figure 1 Schematic diagram of the overall framework of the online reconstruction method of the battery OCV-SOC curve in the embodiment of the present application;

[0054] Figure 2 Schematic diagram of the initial OCV-SOC curve in the embodiment of the present application;

[0055] Figure 3 A data storage table of the OCV-SOC curve in the embodiment of the present application;

[0056] Figure 4 Schematic diagram of the overall framework of the parameter identification model training method in the embodiment of the present application;

[0057] Figure 5 Schematic diagram of the overall framework of the initialization method in the embodiment of the present application;

[0058] Figure 6 dOCV-dSOC variation table of voltage peak point in the embodiment of the present application;

[0059] Figure 7 dOCV-dSOC curve diagram of the voltage peak point in the embodiment of the present application;

[0060] Figure 8 dOCV-dSOC variation table of the voltage inflection point in the embodiment of the present application;

[0061] Fig. 9 dOCV-dSOC curve diagram of the voltage inflection point in the embodiment of the present application;

[0062] Fig.10 This is a functional structure diagram of an online reconstruction device for a battery OCV-SOC curve in an embodiment of the present application;

[0063] Fig.11 Schematic diagram of the hardware structure of the electronic device in the embodiment of the present application. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solution and beneficial effects of this application clearer, the technical solution in the embodiment of this application will be clearly and completely described below in conjunction with the drawings in the embodiment of this application. Obviously, the described embodiment is only a part of the embodiment of this application, not all the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0065] First, the application scenarios and design concepts of the embodiments of the present application are briefly introduced.

[0066] At present, the commonly used SOC estimation algorithm is generally a combination of the open circuit voltage method and the ampere-hour integration method. The premise is to obtain an effective open circuit voltage OCV and SOC curve. This curve reflects the mapping relationship between SOC and battery terminal voltage, can provide an effective SOC initial value for the ampere-hour integration method, and correct the cumulative error of the ampere-hour integration method. However, the OCV curve is usually obtained through laboratory testing, and as the battery ages, the OCV curve will also change. To obtain the OCV curve of the entire life cycle will greatly increase the algorithm development cycle and test cost. Moreover, the result of each test can only obtain the OCV state curve of a specific single cell under a specific test environment. And this offline calibration method is only for batteries that have just left the factory. It is not in line with the actual use of batteries in new energy vehicles to obtain the OCV curve using this offline calibration method. The offline test method assumes that the battery has the same OCV curve under the same SOH. In fact, due to different production processes and battery operating conditions, even if the SOH is the same, the OCV curves of different battery packs may be different. Offline data cannot solve the errors caused by this inconsistency.

[0067] As an electrochemical product, the various parameters of the battery, including OCV, will change with use and storage, that is, these parameters are different in different life states. In addition, for some batteries, such as lithium iron phosphate (LFP) batteries, because their OCV has two "platform interval" characteristics in some SOC intervals, conventional estimates cannot effectively calibrate the SOC, resulting in excessive estimation deviations. If the battery system is not fully charged for a long time and is charged and discharged in the "platform interval", the SOC will accumulate more and more over time, causing system malfunctions, and in severe cases, safety accidents such as overcharging or over-discharging may occur. Therefore, obtaining an accurate OCV-SOC curve over the entire life cycle of the battery to improve the accuracy of SOC estimation is an urgent problem to be solved.

[0068] To this end, in an embodiment of the present application, after obtaining the current voltage and the current discharge current of the battery to be tested, the current voltage and the current discharge current are input into a preset parameter identification model to obtain a target OCV value output by the parameter identification model; according to the target OCV value, the parameter characteristic interval in which the target OCV value is located is determined from the preset parameter characteristic interval as the target characteristic interval; the target SOC value is determined using a correction method corresponding to the target characteristic 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 according to the target OCV value and the target SOC value to obtain a target OCV-SOC curve. In this way, by setting the parameter identification model, the target OCV value is obtained by performing parameter identification according to the current voltage and current discharge current collected in real time, so as to realize the online update of the OCV value; based on the difference in voltage change characteristics between the correction interval and the non-correction interval, the SOC value of the correction interval is corrected by a static correction method, and the SOC value of the non-correction interval is corrected by a dynamic correction method, so as to realize the correction of the SOC value, obtain the accurate target OCV value and target SOC value, and reconstruct the accurate target OCV-SOC curve, which provides a good data basis for improving the estimation accuracy of the SOC value throughout the life cycle.

[0069] After introducing the application scenarios and design concepts of the embodiments of the present application, the technical solutions provided by the embodiments of the present application are described in detail below.

[0070] The present application embodiment provides an online reconstruction method of a battery OCV-SOC curve, see Figure 1 As shown, the overview process of the online reconstruction method of the battery OCV-SOC curve provided in the embodiment of the present application is as follows:

[0071] Step 101: Obtain the current voltage and current discharge current of the battery to be tested.

[0072] Among them, the current voltage refers to the real-time terminal voltage of the battery. The current discharge current refers to the current flowing through the battery in real time when the battery is in the discharge process. The current voltage and current discharge current of the battery can be collected in real time by the voltage sensor and current sensor set in the circuit where the battery is located.

[0073] Step 102: Input the current voltage and the current discharge current into a preset parameter identification model to obtain a target OCV value.

[0074] In practical applications, the input of the parameter identification model is the current voltage and current discharge current collected in real time, and the output of the parameter identification model is the target OCV value, where the target OCV data refers to the corrected and accurate OCV value. The parameter identification model is a parameter identification state observer model constructed based on the least squares algorithm of the forgetting factor.

[0075] Step 103: According to the target OCV value, determine the parameter characteristic interval in which the target OCV value is located from the preset parameter characteristic interval as the target characteristic interval; wherein the parameter characteristic interval includes a correction interval and a non-correction interval; the correction interval includes a high correction interval and a low correction interval, and the non-correction interval includes a high platform interval, a platform transition interval and a low platform interval.

[0076] In practical applications, the parameter characteristic interval refers to the OCV value interval and the SOC value interval corresponding to the OCV value interval. The correction interval is an interval in which the battery OCV value changes significantly with the change of SOC. The change rate of the battery OCV value collected at adjacent times in the correction interval is much greater than the preset change rate threshold. The non-correction interval includes the platform interval in which the battery OCV value is almost unchanged with the change of SOC and the platform transition interval between platforms. The change rate of the battery OCV value collected at adjacent times in the platform interval in the non-correction interval is less than the preset change rate threshold. The parameter characteristic area is divided according to the change law of the OCV value in the initial OCV-SOC curve. There are two platform intervals in the initial OCV-SOC curve where the OCV value changes relatively slowly. Among them, the platform interval with a higher SOC value in the two platform intervals is a high platform interval, and the platform interval with a lower SOC value in the two platform intervals is a low platform interval. The platform transition interval is between the high platform interval and the low platform interval. The interval with an SOC value higher than the high platform interval is a high correction interval, and the interval with an SOC value lower than the low platform interval is a low correction interval.

[0077] Step 104: Determine the target SOC value using a correction method corresponding to the target characteristic 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.

[0078] from Figure 2It can be seen that in the correction interval, both the SOC value and the OCV value change. When the power-on initialization is in the correction interval, the static correction method can be used to correct the SOC value according to the real-time voltage to obtain the target SOC value. However, the battery has two platform intervals. When it is in the high platform interval or the low platform interval, the SOC value changes, but the OCV value is almost unchanged, resulting in the inability to correct the SOC value according to the real-time voltage when the power-on initialization is in the platform area. However, there is a voltage peak point in the platform transition interval, and there is a voltage inflection point between the low correction interval and the low platform interval. Through the dynamic correction method, the voltage peak point and the voltage inflection point are identified and recognized. According to the deviation of the SOC value of the voltage peak point and the deviation of the SOC value of the voltage inflection point, the SOC value of the non-correction interval is corrected to obtain the target SOC value. Among them, the voltage peak point is the data point with the largest change rate of the OCV value in the platform transition interval in the initial OCV-SOC curve, and the voltage inflection point is the data point with the largest change rate of the OCV value in the low platform interval and the low correction interval in the initial OCV-SOC curve.

[0079] In specific implementation, if the target characteristic interval is a correction interval, correspondingly, a static correction method can be used to determine the target SOC value, which can be specifically adopted but not limited to the following methods:

[0080] First, obtain the initial SOC value of the battery to be tested after it is powered on.

[0081] Then, based on the current discharge current and the initial SOC value, the target SOC value is determined by the discharge pure ampere-hour integration method.

[0082] In actual 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 defaulted as the initial SOC value. However, after the current voltage is collected when the battery is powered on, the initial SOC value needs to be updated according to the current voltage at the time of battery power-on. The specific update method is to use the current voltage as the OCV value, obtain the SOC value corresponding to the OCV value by querying the stored data, and update the initial SOC value according to the queried SOC value. The initial SOC value after the battery is powered on obtained here is the initial SOC value updated according to the current voltage at the time of battery power-on. The pure ampere-hour integration method of discharge is adopted, that is, the target SOC value is estimated and determined by integrating the total charge flowing through the battery during discharge. Specifically, it can be calculated using the following formula (1).

[0083] Target SOC = (initial SOC + ∫ (I dt)) / total battery capacity × 100% (1)

[0084] Among them, I is the current discharge current, t is the time, and the total battery capacity is the capacity value of the battery when it is fully charged.

[0085] In specific implementation, while the static correction method is used to determine the target SOC value, the SOC value of the corresponding voltage peak point or the SOC value of the voltage inflection point can be corrected according to the initial SOC value, wherein the characteristic points include the voltage peak point and the voltage inflection point, and the specific methods can be but not limited to the following:

[0086] First, it is determined whether the parameter characteristic interval in which the initial SOC value is located is a high correction interval in the correction interval.

[0087] Then, if the parameter characteristic interval in which the initial SOC value is located is the high correction interval, the discharge pure ampere-hour integration method is used to update the SOC value of the voltage peak point; wherein the voltage peak point is the data point with the largest rate of change of the OCV value in the platform transition interval in the initial OCV-SOC curve; if the parameter characteristic interval in which the initial SOC value is located is not the high correction interval, the discharge pure ampere-hour integration method is used to update the SOC value of the voltage inflection point, wherein the voltage inflection point is the data point with the largest rate of change of the OCV value in the low platform interval and the low correction interval in the initial OCV-SOC curve.

[0088] In practical applications, the premise of using the static correction method is that the target OCV value corresponds to a high correction interval or a 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 a high correction interval or a low correction interval. If the parameter characteristic interval of the initial SOC value is a high correction interval, the SOC value of the corrected voltage peak point can be calculated by formula (1). If the parameter characteristic interval of the initial SOC value is a low correction interval, the SOC value of the corrected voltage inflection point can be calculated by formula (1).

[0089] In specific implementation, if the target characteristic interval is a non-correction interval, correspondingly, a dynamic correction method can be used to determine the target SOC value, which can be specifically used but not limited to the following methods:

[0090] First, a plurality of historical data points of the battery to be tested in the current detection cycle are obtained, wherein the historical data points represent the corresponding relationship between the historical OCV value and the historical SOC value of the battery to be tested in the current detection cycle;

[0091] Then, it is determined whether there is a data point that meets the characteristic point voltage change rule among the multiple historical data points; 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 the OCV value change rate in the low platform interval and the low correction interval;

[0092] Finally, if there is a target data point that meets the characteristic point voltage change rule among multiple historical data points, the data point that meets the characteristic point voltage change rule is used as the target data point; the deviation value 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 rate of change of the OCV value in the platform transition interval of the initial OCV-SOC curve; the voltage inflection point is the data point with the largest change rate of the OCV value in the low platform interval and the low correction interval of the initial OCV-SOC curve; if there is no target data point that meets the characteristic point voltage change rule among multiple historical data points, the target SOC value is not determined, and the next set of voltage and discharge current is obtained.

[0093] In practical applications, multiple historical data points of the battery to be tested in the current detection cycle are obtained. Specifically, all historical data points in the current detection cycle can be obtained, or historical data points in the time period from the historical OCV value entering the platform interval to the current moment. The historical OCV value is the OCV value output by the parameter identification model after the voltage and discharge current within the preset time are input into the preset parameter identification model. The historical SOC value is calculated based on the current discharge current and the initial SOC value, which are input into the above formula (1).

[0094] In specific implementation, the OCV value change rate can be obtained by the ratio of the OCV difference and the SOC difference of two adjacent data points. The judgment of whether there is a target data point that meets the characteristic point voltage change rule among multiple historical data points is specifically to judge whether the OCV value change rate meets the characteristic point voltage change rule. If it does, it is determined that there is a target data point that meets the characteristic point voltage change rule among the historical data points; if not, it is determined that there is no target data point that meets the characteristic point voltage change rule among the historical data points. When there is a target data point that meets the characteristic point voltage change rule among multiple historical data points, the data point that meets the characteristic point voltage change rule is used as the target data point. When the target data point satisfies that the data point is the data point 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 point is used as the deviation value. At this time, the historical SOC value of the historical data points in the platform transition interval and the high platform interval is summed with the deviation value to obtain the corrected historical SOC value; when the target data point satisfies that the data point is the data point with the largest change rate of the OCV value 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 used as the deviation value. At this time, the historical SOC value of the historical data points in the low platform interval is summed with the deviation value to obtain the corrected historical SOC value. Determine the historical data points adjacent to the current moment, and determine the target SOC value using the discharge pure ampere-hour integration method based on the current discharge current and the historical SOC value. Alternatively, after the SOC value is determined by the pure ampere-hour integration method according to the current discharge current and the initial SOC value, the sum of the SOC value and the deviation value is used as the target SOC value. When there is no target data point that meets the characteristic point voltage change rule among multiple historical data points, it is necessary to continue to obtain the next set of voltage and discharge current to further determine the target OCV value at the next moment until the target data point is found.

[0095] In specific implementation, while the target SOC value is determined by the dynamic correction method, the SOC values ​​of the voltage peak point and the voltage inflection point can be corrected. Specifically, after the target SOC value is determined by the corrected historical SOC value or the deviation value, the following is also included:

[0096] The SOC value at the voltage peak point or the SOC value at the voltage inflection point is updated according to the SOC value at the target data point.

[0097] In practical applications, when the target data point is the data point with the largest OCV value change rate in the platform transition interval, the SOC value of the target data point is used as the SOC value of the corrected voltage peak point; when the target data point is the data point with the largest change rate of the OCV value in the low platform interval and the low correction interval, the SOC value of the target data point is used as the SOC value of the corrected voltage inflection point.

[0098] Step 105: Reconstruct a curve according to the target OCV value and the target SOC value to obtain a target OCV-SOC curve.

[0099] In practical applications, a curve is fitted according to the target OCV value and the target SOC value determined in the current inspection cycle to obtain the current OCV-SOC curve, and the stored OCV value is updated according to the current OCV-SOC curve, and the target OCV-SOC curve is constructed according to the updated OCV value and SOC value. Specifically, the curve is reconstructed according to the target OCV value and the target SOC value to obtain the target OCV-SOC curve, which can be adopted but not limited to the following methods:

[0100] First, based on the target OCV value and target SOC value in the current detection cycle, the current OCV-SOC curve is constructed.

[0101] Then, according to the current OCV-SOC curve, the OCV value of the corresponding data point in the detection cycle with the earliest detection time among the stored multiple detection cycles is updated.

[0102] Next, the stored OCV values ​​of all detection cycles are averaged.

[0103] Finally, each mean data point is determined based on the mean-processed OCV value and the SOC value corresponding to the mean-processed OCV value, and a target OCV-SOC curve is constructed according to each mean data point.

[0104] In practical applications, the current test cycle refers to the time period from the moment the battery to be tested is powered on to the end of battery discharge. The current OCV-SOC curve can be obtained by curve fitting based on the target OCV value and target SOC value in the previous test cycle. The current OCV-SOC curve can be obtained by fitting a portion of the target OCV value and target SOC value in the current test cycle, or by fitting all the target OCV value and target SOC value in the current test cycle. Figure 3 The table shown stores multiple sets of data, and each SOC value corresponds to multiple sets of OCV values. After obtaining the current OCV-SOC curve, extract the OCV values ​​corresponding to different SOC values ​​in the table from the current OCV-SOC curve, and use them as new OCV values. Correspondingly, replace the OCV data with the earliest detection time in the table with the new OCV value, and gradually update the OCV data in the table online. Reconstruct the target OCV-SOC curve with the average value of OCV and SOC value in the table.

[0105] In one possible implementation, see Figure 4As shown, before obtaining the current voltage and current discharge current of the battery, a parameter identification model needs to be pre-built, which can be specifically adopted but not limited to the following methods:

[0106] Step 201: Establish a second-order equivalent circuit model of a sample battery.

[0107] Kirchhoff's law and Laplace transform can be used to obtain the mathematical model of the output voltage and input current of the power battery as shown in formula (2). The transfer function of the mathematical model is shown in formula (3).

[0108]

[0109] Where U t is the terminal voltage, U oc is the open circuit voltage, i L is the current, R i is the ohmic internal resistance, R D1 and R D2 is the polarization internal resistance, C D1 and C D2 is a polarized capacitor.

[0110] Let E L (s)=U t (s)-U oc (s), the second-order equivalent circuit model is as follows formula (4).

[0111]

[0112] Step 202: Determine the state equation of the second-order equivalent circuit model based on the bilinear change law.

[0113] Based on the bilinear change law, the s plane can be mapped to the Z plane for discretization:

[0114]

[0115] Among them, b1, b2, b3, b4 and b5 are unknown coefficients, and the differential form of the initial second-order equivalent circuit model state equation is shown in formula (9):

[0116] 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)

[0117] Where U t,k is the current terminal voltage, U oc，k is the current open circuit voltage, Ut,k-1 is the terminal voltage at the previous moment, U t,k-1 is the terminal voltage at the last two moments, i k is the current at the current moment, i k-1 is the current at the previous moment, i k-1 is the current at the previous two moments.

[0118] Next, adjust U oc,k The coefficient (1-b1-b2) is 1, and the final differential form of the state equation of the second-order equivalent circuit model is shown in formula (10). In this way, the divergence of the OCV curve of the identification parameter can be transferred to other identification parameters, thereby making the identified OCV value less divergent and more stable.

[0119]

[0120] Step 203: Process the state equation of the second-order equivalent circuit model by using the recursive least square method with a forgetting factor to obtain an initial identification model.

[0121] Define the system data matrix as:

[0122] Φ 2,k =[1 U t,k -U t,k-1 U t,k -U t,k-2 i L,k i L,k-1 i L,k-2 ] (11)

[0123] The parameter matrix of the system is defined as:

[0124]

[0125] The model transfer function corresponding to the state equation of the second-order equivalent circuit model can be simplified as:

[0126] y k =Φ 2，k θ 2，k (13)

[0127] The least squares algorithm based on the forgetting factor processes the simplified model transfer function and obtains:

[0128] y k =φ k θ k +e Ls,k (14)

[0129]

[0130] In the formula, φ k is the data matrix, θk is the parameter matrix, e Ls,k is a stationary zero-mean white noise, μ is the forgetting factor, and when the forgetting factor is 1, the formula degenerates into the traditional recursive least squares method. Ls,k is the gain of the algorithm, P Ls,k-1 is the error covariance matrix of the state estimate at the previous moment, P Ls,k is the error covariance matrix of the current state estimate, is the parameter value estimated at the previous moment, is the estimated parameter value, is the observed value at this moment, y k is the actual observed value of the system, y k and Subtract the prediction error of the system and add the prediction error to the gain matrix K Ls,k The multiplication results in a correction value of the parameter estimate at this time, which is equal to the estimate at the previous moment. Finally, we get the estimated value

[0131] Step 204: Obtain sample battery test data, and train an initial identification model based on the sample battery test data to obtain a parameter identification model.

[0132] In practical applications, the sample battery test data is to conduct multiple groups of full charge and discharge experiments on the battery to obtain multiple groups of current data, voltage data, test SOC value and test OCV value of the full discharge process. By taking the current data and voltage data as the input of the initial identification model and the corresponding OCV value as the target output, the initial identification model is trained on the battery test data, the corresponding square gain and covariance are calculated, and the forgetting factor in the initial identification model is adjusted to obtain the parameter identification model.

[0133] In one possible implementation, see Figure 5 As shown, the online reconstruction method of the battery OCV-SOC curve also includes:

[0134] Step 301: construct an initial OCV-SOC curve based on initial data points consisting of a test SOC value in sample battery test data and a test OCV value corresponding to the test SOC value.

[0135] In practical applications, based on multiple groups of test SOC values ​​and test OCV values ​​of the full discharge process in the battery test data, the average of multiple groups of test OCV values ​​corresponding to each test SOC value is taken to obtain an average test OCV value, and initial data points are formed based on the test SOC values ​​and the average test OCV values. The initial OCV-SOC curve is obtained by fitting the initial data points.

[0136] 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 a correction interval and a non-correction interval.

[0137] In practical applications, according to the variation law of the OCV values ​​of each initial data point in the initial OCV-SOC curve, the interval where the OCV value variation is less than the preset threshold is composed of two platform intervals. The platform interval with a higher SOC value in the two platform intervals is the high platform interval, and the platform interval with a lower SOC value in the two platform intervals is the low platform interval. The platform transition interval is between the high platform interval and the low platform interval. The platform interval with an SOC value higher than the high platform interval is the high correction interval, and the platform interval with an SOC value lower than the low platform interval is the low correction interval.

[0138] Step 303: Based on the characteristic point voltage variation rule, each initial data point in the platform transition interval is identified to obtain the voltage peak point.

[0139] In practical applications, the OCV value change rate of each initial data point in the platform transition interval is calculated, where the OCV value change rate can be obtained by the ratio of the OCV difference to the SOC difference between two adjacent data points. The initial data point with the largest OCV value change rate in the platform transition interval is taken as the voltage peak point. Figure 6 The voltage peak point dOCV-dSOC change table and Figure 7 The voltage peak point dOCV-SOC curve in the figure, where dSOC is taken as ±0.5% of the integer value, for example, dSOC of 60% represents the interval of 59.5%-60.5%, and dOCV is the decrease in the OCV value during the discharge process in the dSOC interval. In this way, the dOCV-dSOC between SOC 66% and 52% is counted, a total of 15 points, and the voltage peak point dOCV-dSOC curve has an obvious peak, that is, in the platform transition interval, there is a peak point of the OCV value change rate. Through multiple identifications, it can be determined that the SOC at this voltage peak point is 58%, with an accuracy within 1%.

[0140] Step 304: Based on the characteristic point voltage variation rule, each initial data point in the low platform interval and the low correction interval is identified to obtain the voltage inflection point.

[0141] In practical applications, the OCV value change rate of each initial data point in the low platform interval and the low correction interval is calculated, where the OCV value change rate can be obtained by the ratio of the OCV difference to the SOC difference of two adjacent data points. The data point with the largest change in the OCV value change rate in the platform transition interval is taken as the voltage inflection point. Figure 8 The voltage inflection point dOCV-dSOC change table and Fig. 9The voltage inflection point dOCV-SOC curve in the figure shows that dSOC is taken as ±0.5% of the integer value, and dOCV-dSOC between 23% and 37% is counted, with a total of 15 points. After SOC drops to 30%, the rate of change of OCV value is greatly improved, especially from 31% to 30%, 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%.

[0142] Based on the above embodiments, the present application provides an online reconstruction device for a battery OCV-SOC curve. Fig.10 As shown, the online reconstruction device 400 of the battery OCV-SOC curve provided in the embodiment of the present application at least includes:

[0143] The data acquisition unit 401 is used to obtain the current voltage and current discharge current of the battery to be tested;

[0144] A target OCV value determination unit 402 is used to input the current voltage and the current discharge current into a preset parameter identification model to obtain a target OCV value;

[0145] The interval determination unit 403 is used to determine the parameter characteristic interval in which the target OCV value is located from the preset parameter characteristic interval as the target characteristic interval according to the target OCV value; wherein the parameter characteristic interval includes a correction interval and a non-correction interval; the correction interval includes a high correction interval and a low correction interval, and the non-correction interval includes a high platform interval, a platform transition interval and a low platform interval;

[0146] A target SOC value determination unit 404 is used to determine the target SOC value by adopting a correction method corresponding to the target characteristic 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;

[0147] The curve reconstruction unit 405 is used to reconstruct the curve according to the target OCV value and the target SOC value to obtain a target OCV-SOC curve.

[0148] Optionally, the target SOC value determining unit 404 is specifically configured to:

[0149] Get the initial SOC value of the battery to be tested after it is powered on;

[0150] Based on the current discharge current and the initial SOC value, the target SOC value is determined by the discharge pure ampere-hour integration method.

[0151] Optionally, the target SOC value determining unit 404 is further configured to:

[0152] Determine whether the parameter characteristic interval in which the initial SOC value is located is a high correction interval in the correction interval;

[0153] If so, the SOC value at the voltage peak point is updated using the discharge pure ampere-hour integration method; wherein the voltage peak point is the data point with the largest rate of change of the OCV value in the platform transition interval of the initial OCV-SOC curve;

[0154] If not, the SOC value of the voltage inflection point is updated by the discharge pure ampere-hour integration method; wherein the voltage inflection point is the data point with the largest change rate of the OCV value in the low platform interval and the low correction interval in the initial OCV-SOC curve.

[0155] Optionally, the target SOC value determining unit 404 is specifically configured to:

[0156] Acquire multiple historical data points of the battery to be tested in the current detection cycle, wherein the historical data points represent the corresponding relationship between the historical OCV value and the historical SOC value of the battery to be tested in the current detection cycle;

[0157] Determine whether there is a data point that meets the characteristic point voltage change rule among the multiple historical data points; 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 the OCV value change rate in the low platform interval and the low correction interval;

[0158] If so, the data point that meets the voltage change rule of the characteristic point is taken as the target data point; the deviation value 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 rate of change of the OCV value in the platform transition interval of the initial OCV-SOC curve; the voltage inflection point is the data point with the largest rate of change of the OCV value in the low platform interval and the low correction interval of the initial OCV-SOC curve;

[0159] If not, the target SOC value is not determined, and the next set of voltage and discharge current is obtained.

[0160] Optionally, the target SOC value determining unit 404 is further configured to:

[0161] The SOC value at the voltage peak point or the SOC value at the voltage inflection point is updated according to the SOC value at the target data point.

[0162] Optionally, the online reconstruction device of the battery OCV-SOC curve further includes: a model building unit 406;

[0163] The model building unit 406 is used to establish a second-order equivalent circuit model of the sample battery; determine the state equation of the second-order equivalent circuit model based on the bilinear change law; process the state equation of the second-order equivalent circuit model by the recursive least squares method with a forgetting factor to obtain an initial identification model; obtain sample battery test data, and train the initial identification model based on the sample battery test data to obtain a parameter identification model.

[0164] Optionally, the online reconstruction device of the battery OCV-SOC curve further includes: an initialization unit 407;

[0165] Initialization unit 407 is used to construct an initial OCV-SOC curve based on initial data points consisting of a test SOC value in the sample battery test data and a test OCV value corresponding to the test SOC value; divide the parameter characteristic interval of the sample battery based on the change rule of each initial data point in the initial OCV-SOC curve to obtain a correction interval and a non-correction interval; identify each initial data point in the platform transition interval based on the characteristic point voltage change rule to obtain a voltage peak point; identify each initial data point in the low platform interval and the low correction interval based on the characteristic point voltage change rule to obtain a voltage inflection point.

[0166] Optionally, the curve reconstruction unit 405 is specifically used to: construct a current OCV-SOC curve based on the target OCV value and target SOC value in the previous detection cycle; update the OCV value of the corresponding data point in the detection cycle with the earliest detection time among the multiple detection cycles stored according to the current OCV-SOC curve; perform mean processing on the OCV values ​​of all the stored detection cycles; determine each mean data point based on the OCV value after the mean processing and the SOC value corresponding to the OCV value after the mean processing, and construct the target OCV-SOC curve according to each mean data point.

[0167] It should be noted that the principle of solving the technical problem by the online reconstruction device 400 of the battery OCV-SOC curve provided in the embodiment of the present application is similar to the online reconstruction method of the battery OCV-SOC curve provided in the embodiment of the present application. Therefore, the implementation of the online reconstruction device 400 of the battery OCV-SOC curve provided in the embodiment of the present application can refer to the implementation of the online reconstruction method of the battery OCV-SOC curve provided in the embodiment of the present application, and the repeated parts will not be repeated.

[0168] After introducing the online reconstruction method and device of the battery OCV-SOC curve provided in the embodiment of the present application, the electronic device provided in the embodiment of the present application is briefly introduced next.

[0169] See also Fig.11As shown, the electronic device 500 provided in the embodiment of the present application 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, the online reconstruction method of the battery OCV-SOC curve provided in the embodiment of the present application is implemented.

[0170] It should be noted that Fig.11 The electronic device 500 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0171] The electronic device 500 provided in the embodiment of the present application may further include a bus 503 connecting different components (including the processor 501 and the memory 502). The bus 503 represents one or more of several types of bus structures, including a memory bus, a peripheral bus, a local bus, and the like.

[0172] The memory 502 may include a readable medium in the form of a volatile memory, such as a random access memory (RAM) 5021 and / or a cache memory 5022 , and may further include a read-only memory (ROM) 5023 .

[0173] The memory 502 may also include a program tool 5025 having a set (at least one) of program modules 5024, wherein the program modules 5024 include but are not limited to: an operating subsystem, one or more application programs, other program modules, and program data, each of which or some combination may include the implementation of a network environment.

[0174] The electronic device 500 may also communicate with one or more external devices 504 (e.g., keyboards, remote controls, etc.), and may also communicate with one or more devices that enable a user to interact with the electronic device 500 (e.g., mobile phones, computers, etc.), and / or communicate with any device that enables the electronic device 500 to communicate with one or more other electronic devices 500 (e.g., routers, modems, etc.). Such communication may be performed through an input / output (I / O) interface 505. Furthermore, the electronic device 500 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 506. Fig.11 As shown, the network adapter 506 communicates with other modules of the electronic device 500 via the bus 503. It should be understood that although Fig.11Not shown, 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, disk arrays (Redundant Arrays of Independent Disks, RAID) subsystems, tape drives, and data backup storage subsystems.

[0175] The computer-readable storage medium provided in the embodiment of the present application is introduced below. The computer-readable storage medium provided in the embodiment of the present application stores computer instructions, and when the computer instructions are executed by the processor, the online reconstruction method of the battery OCV-SOC curve provided in the embodiment of the present application is implemented. Specifically, the computer instructions can be built-in or installed in the electronic device 500, so that the electronic device 500 can implement the online reconstruction method of the battery OCV-SOC curve provided in the embodiment of the present application by executing the built-in or installed computer instructions.

[0176] In addition, the online reconstruction method of the battery OCV-SOC curve provided in the embodiment of the present application can also be implemented as a program product, which includes a program code. When the program product can be run on the electronic device 500, the program code is used to enable the electronic device 500 to execute the online reconstruction method of the battery OCV-SOC curve provided in the embodiment of the present application.

[0177] The program product provided in the embodiments of the present application may adopt any combination of one or more readable media, wherein the readable medium may be a readable signal medium or a readable storage medium, and the readable storage medium may be but is not limited to an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any combination of the above. Specifically, more specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, RAM, ROM, Erasable Programmable Read Only Memory (EPROM), optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0178] The program product provided in the embodiment of the present application may adopt a CD-ROM and include program code, and may also be run on a computing device. However, the program product provided in the embodiment of the present application is not limited thereto. In the embodiment of the present application, the readable storage medium may be any tangible medium containing or storing a program, and the program may be used by or in combination with an instruction execution system, apparatus, or device.

[0179] It should be noted that, although several units or subunits of the device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present 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 into multiple units to be embodied.

[0180] In addition, although the operations of the method of the present application are described in a specific order in the drawings, this does not require or imply that the operations must be performed in this specific order, or that all the operations shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0181] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0182] Obviously, those skilled in the art can make various changes and modifications to the embodiments of the present application without departing from the spirit and scope of the embodiments of the present application. Thus, if these modifications and variations of the embodiments of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. An online reconstruction method of a battery OCV-SOC curve, characterized in that: include: Get the current voltage and current discharge current of the battery to be tested; Inputting the current voltage and the current discharge current into a preset parameter identification model to obtain a target OCV value; According to the target OCV value, a parameter characteristic interval in which the target OCV value is located is determined from a preset parameter characteristic interval as a target characteristic interval; wherein the parameter characteristic interval includes a correction interval and a non-correction interval; the correction interval includes a high correction interval and a low correction interval, and the non-correction interval includes a high platform interval, a platform transition interval, and a low platform interval; Determine the target SOC value by using the correction method corresponding to the target characteristic 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; A curve is reconstructed according to the target OCV value and the target SOC value to obtain a target OCV-SOC curve.

2. The online reconstruction method of the battery OCV-SOC curve according to claim 1, characterized in that: If the target characteristic interval is a correction interval, the target SOC value is determined using a correction method corresponding to the target characteristic interval, including: Obtaining an initial SOC value of the battery to be tested after powering on; Based on the current discharge current and the initial SOC value, a target SOC value is determined by using a discharge pure ampere-hour integration method.

3. The online reconstruction method of the battery OCV-SOC curve according to claim 2, characterized in that: After obtaining the initial SOC value of the battery to be tested after being powered on, the method further includes: Determining whether the parameter characteristic interval in which the initial SOC value is located is a high correction interval in the correction interval; If yes, the SOC value of the voltage peak point is updated by using the discharge pure ampere-hour integration method; wherein the voltage peak point is the data point with the largest rate of change of the OCV value in the platform transition interval of the initial OCV-SOC curve; If not, the SOC value of the voltage inflection point is updated by the discharge pure ampere-hour integration method; wherein the voltage inflection point is the data point where the OCV value change rate in the low platform interval and the low correction interval in the initial OCV-SOC curve has the largest change.

4. The online reconstruction method of the battery OCV-SOC curve according to claim 1, characterized in that: If the target characteristic interval is a non-correction interval, a correction method corresponding to the target characteristic interval is used to determine a target SOC value, including: Acquire a plurality of historical data points of the battery to be tested in a current detection cycle, wherein the historical data points represent a correspondence between a historical OCV value and a historical SOC value of the battery to be tested in the current detection cycle; Determine whether there is a data point that meets the characteristic point voltage change rule among the multiple historical data points; 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 the OCV value change rate in the low platform interval and the low correction interval; If so, the data point that meets the characteristic point voltage change rule is taken as the target data point; the deviation value 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 rate of change of the OCV value in the platform transition interval of the initial OCV-SOC curve; the voltage inflection point is the data point with the largest amount of change of the OCV value in the low platform interval and the low correction interval of the initial OCV-SOC curve; If not, the target SOC value is not determined, and the next set of voltage and discharge current is obtained.

5. The method for online reconstruction of the battery OCV-SOC curve according to claim 4, characterized in that: After the target SOC value is determined based on the corrected historical SOC value or the deviation value, the method further includes: The SOC value of the voltage peak point or the SOC value of the voltage inflection point is updated according to the SOC value of the target data point.

6. The method for online reconstruction of the battery OCV-SOC curve according to any one of claims 1 to 5, characterized in that: Before obtaining the current voltage and current discharge current of the battery to be tested, the method further includes: Establish a second-order equivalent circuit model of the sample battery; Based on the bilinear change law, determining the state equation of the second-order equivalent circuit model; The state equation of the second-order equivalent circuit model is processed by a recursive least square method with a forgetting factor to obtain an 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 a parameter identification model.

7. The method for online reconstruction of the battery OCV-SOC curve according to claim 6, characterized in that: Also includes: Constructing an initial OCV-SOC curve based on initial data points consisting of a test SOC value in the sample battery test data and a test OCV value corresponding to the test SOC value; Based on the variation rule 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 variation rule, each initial data point in the platform transition interval is identified to obtain a voltage peak point; Based on the characteristic point voltage variation rule, each initial data point in the low platform interval and the low correction interval is identified to obtain a voltage inflection point.

8. The online reconstruction method of the battery OCV-SOC curve according to claim 1, characterized in that: Reconstructing a curve according to the target OCV value and the target SOC value to obtain a target OCV-SOC curve includes: Constructing a current OCV-SOC curve based on the target OCV value and the target SOC value in the current detection cycle; According to the current OCV-SOC curve, the OCV value of the corresponding data point in the earliest detection period among the stored multiple detection periods is updated; Perform average processing on the OCV values ​​of all stored detection cycles; Each mean data point is determined based on the OCV value after mean processing and the SOC value corresponding to the OCV value after mean processing, and a target OCV-SOC curve is constructed according to each mean data point.

9. An online reconstruction device for a battery OCV-SOC curve, characterized in that: include: A data acquisition unit, used to obtain the current voltage and current discharge current of the battery to be tested; a target OCV value determination unit, configured to input the current voltage and the current discharge current into a preset parameter identification model to obtain a target OCV value; An interval determination unit, configured to determine, according to the target OCV value, from a preset parameter characteristic interval, a parameter characteristic interval in which the target OCV value is located as a target characteristic interval; wherein the parameter characteristic interval includes a correction interval and a non-correction interval; the correction interval includes a high correction interval and a low correction interval, and the non-correction interval includes a high platform interval, a platform transition interval, and a low platform interval; a target SOC value determination unit, configured to determine a target SOC value by adopting a correction method corresponding to the target characteristic 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; The curve reconstruction unit is used to reconstruct the curve according to the target OCV value and the target SOC value to obtain a target OCV-SOC curve.

10. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the online reconstruction method of the battery OCV-SOC curve according to any one of claims 1 to 7 is implemented.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the online reconstruction method of the battery OCV-SOC curve according to any one of claims 1 to 7 is implemented.

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