CN119689267A

CN119689267A - Online reconstruction method, device, equipment and medium of battery OCV-SOC curve

Info

Publication number: CN119689267A
Application number: CN202411834867.7A
Authority: CN
Inventors: 刘雷; 李建杰; 潘斌彪; 林海军
Original assignee: Xinjie An Automotive Electronics Co ltd
Current assignee: Xinjie An Automotive Electronics Co ltd
Priority date: 2024-12-12
Filing date: 2024-12-12
Publication date: 2025-03-25

Abstract

The present application discloses an online reconstruction method, device, equipment and medium of a battery OCV-SOC curve, which is applied to the field of battery management technology to solve the problem that an accurate OCV-SOC curve cannot be obtained in the prior art. Specifically, the obtained current voltage and current discharge current are input into a parameter identification model to obtain a target OCV value; a target characteristic interval is determined according to the target OCV value; a target SOC value is determined using a correction method corresponding to the target characteristic interval; 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 is reconstructed according to the target OCV value and the target SOC value to obtain a target OCV-SOC curve. In this way, the online update of the OCV value is realized by setting a parameter identification model, and the correction of SOC values in different intervals is realized by using a static correction method and a dynamic correction method, so that an accurate OCV-SOC curve can be reconstructed.

Description

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

Technical Field

The present application relates to the field of battery management technologies, and in particular, to an online reconstruction method, apparatus, device, and medium for an OCV-SOC curve of a battery.

Background

The estimation accuracy of the State of Charge (SOC) is a core index for evaluating the battery management system, and is related to the remaining capacity, charge and discharge power, and use safety of the battery. Accurate estimation of SOC can ensure that the battery is operating at an optimal state and life is improved. The commonly used SOC estimation algorithm is generally a combination of an open-circuit voltage method and an ampere-hour integration method, and the premise is that an effective OCV-SOC curve is obtained, the curve reflects the mapping relation between the SOC and the battery terminal voltage, an effective SOC initial value can be provided for the ampere-hour integration method, and the accumulated error of the ampere-hour integration method is corrected.

Currently, there are a number of disadvantages in the conventional method of obtaining an open circuit voltage (OCV, output Current Voltage)), i.e., the open circuit voltage experimental test. The method is tedious and time-consuming, only the OCV state curve of the specific single battery under the specific environment can be obtained in each test, and the change condition of the battery in actual use can not be comprehensively reflected. As an electrochemical product, the parameters of the battery, including OCV, change with use and storage, so the off-line calibration method is not applicable to aged cells. Furthermore, the offline test method assumes that the OCV curves of the batteries of the same state of health SOH are consistent, but in reality this assumption is not true due to the differences in production process and usage conditions. For some batteries, such as lithium iron phosphate (LFP) batteries, the OCV has a "plateau" in a part state of charge (SOC) interval, resulting in difficult SOC calibration and large estimation bias. The SOC estimation error is further accumulated after long-time charging and discharging in the platform interval, and system function abnormality and even safety accidents may be caused. Therefore, obtaining an accurate OCV-SOC curve under the full life cycle of the battery to improve the accuracy of estimating the SOC is a current urgent problem to be solved.

Disclosure of Invention

The application provides an on-line reconstruction method, device, equipment and medium of an OCV-SOC curve of a battery, which are used for solving the problem that an accurate OCV-SOC curve cannot be obtained in the prior art.

The technical scheme provided by the application is as follows:

in one aspect, the application provides an on-line reconstruction method of an OCV-SOC curve of a battery, comprising the following steps:

acquiring the current voltage and the current discharge current of a 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, determining a parameter characteristic interval in which the target OCV value is positioned from a preset parameter characteristic interval as a target characteristic interval, wherein the parameter characteristic interval comprises a correction interval and a non-correction interval;

determining 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;

and performing curve reconstruction according to the target OCV value and the target SOC value to obtain a target OCV-SOC curve.

Optionally, if the target feature interval is a correction interval, determining the target SOC value by using a correction method corresponding to the target feature interval includes:

acquiring an initial SOC value of a battery to be tested after power-on;

and determining a target SOC value by adopting a discharging pure ampere-hour integration method based on the current discharging current and the initial SOC value.

Optionally, after obtaining the initial SOC value of the battery to be tested after power-up, the method further includes:

judging whether the parameter characteristic interval where the initial SOC value is located is a high correction interval in the correction intervals;

if so, updating the SOC value of a voltage peak point by adopting a discharge pure ampere-hour integration method, wherein the voltage peak point is a data point with the largest change rate of the OCV value in a platform transition section in an initial OCV-SOC curve;

If not, updating the SOC value of the voltage inflection point by adopting a discharging pure ampere-hour integration method, wherein the voltage inflection point is the data point with the largest change quantity of the OCV value change rate in the low platform interval and the low correction interval in the initial OCV-SOC curve.

Optionally, if the target feature interval is a non-correction interval, determining the target SOC value by using a correction method corresponding to the target feature interval includes:

Acquiring a plurality of historical data points of the battery to be tested in a current detection period, wherein the historical data points represent the corresponding relation between a historical OCV value and a historical SOC value of the battery to be tested in the current detection period;

Judging whether a plurality of historical data points exist data points conforming to a characteristic point voltage change rule, wherein the characteristic point voltage change rule is a data point with the largest change rate of an OCV value in a platform transition interval, or a data point with the largest change amount of the OCV value in a low platform interval and a low correction interval;

If so, taking the data point which accords with the characteristic point voltage change rule as a target data point, calculating a 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 a voltage inflection point, correcting the historical SOC value of the historical data point according to the deviation value, and determining a target SOC value based on the corrected historical SOC value or the deviation value, wherein the voltage peak point is the data point with the largest change rate of the OCV value in a platform transition section in an initial OCV-SOC curve;

If not, the target SOC value is not determined, and the next group of voltage and discharge current are continuously acquired.

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

and updating the SOC value of the voltage peak point or the SOC value of the voltage inflection point according to the SOC value of the target data point.

Optionally, before obtaining the current voltage and the current discharge current of the battery to be tested, the method further includes:

Establishing a second-order equivalent circuit model of the sample battery;

Determining a state equation of a second-order equivalent circuit model based on a bilinear variation rule;

Processing a state equation of the second-order equivalent circuit model by a recursive least square method with forgetting factors to obtain an initial identification model;

and acquiring sample battery test data, and training the initial identification model based on the sample battery test data to obtain a parameter identification model.

Optionally, the method for online reconstructing the OCV-SOC curve of the battery further comprises:

constructing an initial OCV-SOC curve based on initial data points formed by the test SOC value in the sample battery test data and the test OCV value corresponding to the test SOC value;

Dividing parameter characteristic intervals 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;

Based on the characteristic point voltage change rule, identifying each initial data point in the platform transition section to obtain a voltage wave peak point;

And identifying 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.

Optionally, performing curve reconstruction according to the target OCV value and the target SOC value to obtain a target OCV-SOC curve, including:

Constructing a current OCV-SOC curve based on the target OCV value and the target SOC value in the previous detection period;

updating the stored OCV value of the corresponding data point in the detection period with earliest detection time in a plurality of detection periods according to the current OCV-SOC curve;

performing average value processing on the stored OCV values of all detection periods;

And determining each mean data point based on the OCV value after mean processing and the SOC value corresponding to the OCV value after mean processing, and constructing a target OCV-SOC curve according to each mean data point.

In another aspect, the present application provides an on-line reconstruction device for an OCV-SOC curve of a battery, comprising:

The data acquisition unit is used for acquiring the current voltage and the current discharge current of the battery to be tested;

The target OCV value determining unit is used for inputting the current voltage and the current discharge current into a preset parameter identification model to obtain a target OCV value;

The system comprises a zone determining unit, a parameter characteristic zone determining unit and a parameter characteristic zone determining unit, wherein the parameter characteristic zone is used for determining a parameter characteristic zone in which a target OCV value is located from preset parameter characteristic zones as a target characteristic zone according to the target OCV value, and comprises a correction zone and a non-correction zone;

the target SOC value determining unit is used for determining 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;

and the curve reconstruction unit is used for performing curve reconstruction according to the target OCV value and the target SOC value to obtain a target OCV-SOC curve.

In another aspect, the application provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method for online reconstruction of the OCV-SOC curve of the battery provided by the application when executing the computer program.

On the other hand, the application also provides a computer readable storage medium, wherein the computer readable storage medium stores computer instructions, and the computer instructions realize the online reconstruction method of the battery OCV-SOC curve provided by the application when being executed by a processor.

The beneficial effects of the application are as follows:

According to the application, the parameter identification model is set, the target OCV value is obtained by carrying out parameter identification according to the current voltage and the current discharge current acquired in real time, the on-line updating of the OCV value is realized, the correction of the SOC value is carried out on the correction interval by a static correction method based on the difference of voltage change characteristics of the correction interval and the non-correction interval, the correction of the SOC value is carried out on the non-correction interval by a dynamic correction method, the correction of the SOC value is realized, the accurate target OCV value and the accurate target SOC value are obtained, the accurate target OCV-SOC curve is obtained by reconstruction, and a good data basis is provided for improving the estimation precision of the SOC value under the full life cycle.

Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:

FIG. 1 is a schematic diagram of an overall framework of an on-line reconstruction method of a battery OCV-SOC curve in an embodiment of the application;

FIG. 2 is a schematic diagram of an initial OCV-SOC curve in an embodiment of the application;

FIG. 3 is a data storage table of an OCV-SOC curve according to an embodiment of the present application;

FIG. 4 is a schematic diagram of an overall framework of a parameter identification model training method according to an embodiment of the present application;

FIG. 5 is a schematic diagram of an initialization method according to an embodiment of the present application;

FIG. 6 is a plot of dOCV versus dSOC at the voltage peak point in an embodiment of the application;

FIG. 7 is a plot of dOCV versus dSOC at a voltage peak point in an embodiment of the application;

FIG. 8 is a plot of dOCV versus dSOC for voltage inflection points in an embodiment of the present application;

FIG. 9 is a plot of dOCV versus dSOC at a voltage inflection point in an embodiment of the present application;

FIG. 10 is a schematic functional diagram of an on-line reconstruction device of an OCV-SOC curve of a battery according to an embodiment of the present application;

fig. 11 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the application.

Detailed Description

In order to make the objects, technical solutions and advantageous effects of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

First, an application scenario and a design idea of the embodiment of the present application are briefly described.

At present, a commonly used SOC estimation algorithm is generally a combination of an open circuit voltage method and an ampere-hour integration method, and the precondition is that an effective open circuit voltage OCV and SOC curve is obtained, the curve reflects a mapping relationship between SOC and battery terminal voltage, an effective SOC initial value can be provided for the ampere-hour integration method, and an accumulated error of the ampere-hour integration method is corrected. However, OCV curves are typically obtained through laboratory testing and change as the battery ages, the development cycle and testing costs of the algorithm are greatly increased to obtain a full life cycle OCV curve. And the result of each test can only obtain the OCV state curve of the specific single battery under the specific test environment. Moreover, the off-line calibration method is only aimed at the battery cells which are soon shipped, and the OCV curve obtained by adopting the off-line calibration mode for the aged battery cells on the new energy automobile is not in accordance with the actual use condition. The OCV curves of the cells were considered consistent at the same SOH using the off-line test method. In fact, even if SOH is the same, there may be differences in OCV curves from pack to pack due to differences in manufacturing process and battery usage. Offline data cannot account for errors caused by such inconsistencies.

As an electrochemical product, the parameters of the cell, including OCV, vary with use and storage, i.e., the parameters vary from state to state of life. Moreover, for some batteries, such as lithium iron phosphate (LFP) batteries, conventional estimation cannot effectively calibrate the SOC due to the two-segment "plateau-segment" nature of their OCV in a partial SOC segment, resulting in excessive estimation bias. If the battery system is not fully charged for a long time and is charged and discharged in a platform interval, the accumulation of the SOC is larger and larger along with the time, so that the system has abnormal functions, and serious safety accidents such as overcharging or overdischarging can occur. Therefore, obtaining an accurate OCV-SOC curve under the full life cycle of the battery to improve the accuracy of estimating the SOC is a current urgent problem to be solved.

The method comprises the steps of obtaining current voltage and current discharging current of a battery to be tested, inputting the current voltage and the current discharging current into a preset parameter identification model to obtain a target OCV value output by the parameter identification model, determining a parameter characteristic interval where the target OCV value is located from a preset parameter characteristic interval according to the target OCV value as a target characteristic interval, determining 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 a non-correction interval, and finally performing curve reconstruction according to the target OCV value and the target SOC value to obtain a target OCV-SOC curve. The method comprises the steps of setting a parameter identification model, carrying out parameter identification according to current voltage and current discharge current acquired in real time to obtain a target OCV value, realizing online updating of the OCV value, carrying out SOC value correction on a correction interval by a static correction method based on different voltage change characteristics of the correction interval and a non-correction interval, carrying out SOC value correction on the non-correction interval by a dynamic correction method, realizing SOC value correction, obtaining an accurate target OCV value and an accurate target SOC value, and reconstructing to obtain an accurate target OCV-SOC curve, thereby providing a good data basis for improving the estimation precision of the SOC value under the whole life cycle.

After the application scenario and the design idea of the embodiment of the present application are introduced, the technical solution provided by the embodiment of the present application is described in detail below.

The embodiment of the application provides an online reconstruction method of an OCV-SOC curve of a battery, and referring to FIG. 1, the online reconstruction method of the OCV-SOC curve of the battery provided by the embodiment of the application has the following outline flow:

and 101, acquiring the current voltage and the current discharge current of the battery to be tested.

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 the current discharging current of the battery can be acquired in real time by a voltage sensor and a current sensor which are arranged in a circuit where the battery is positioned.

Step 102, inputting the current voltage and the current discharge current into a preset parameter identification model to obtain a target OCV value.

In practical application, the input of the parameter identification model is the current voltage and the current discharge current acquired in real time, and the output of the parameter identification model is the target OCV value, wherein the target OCV data refers to the corrected accurate OCV value. The parameter identification model is a parameter identification state observer model constructed based on a least square algorithm of forgetting factors.

And 103, determining a parameter characteristic interval in which the target OCV value is located from preset parameter characteristic intervals as a target characteristic interval according to the target OCV value, wherein the parameter characteristic interval comprises a correction interval and a non-correction interval, the correction interval comprises a high correction interval and a low correction interval, and the non-correction interval comprises a high platform interval, a platform transition interval and a low platform interval.

In practical applications, the parameter characteristic interval refers to an OCV value interval and an 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 the SOC, and the change rate of the battery OCV value acquired at adjacent time in the correction interval is far greater than a preset change rate threshold. The non-correction interval comprises a platform interval in which the battery OCV value is almost unchanged along with the change of the SOC and a platform transition interval between the platforms, and the change rate of the battery OCV value acquired at adjacent time in the platform interval in the non-correction interval is smaller than a preset change rate threshold. The parameter characteristic area is divided according to the change rule of the OCV value in the initial OCV-SOC curve. Two sections of platform sections with slower change of the OCV value exist in the initial OCV-SOC curve, wherein the platform section with higher SOC value in the two platform sections is a high platform section, and the platform section with lower SOC value in the two platform sections is a low platform section. The platform transition interval is between the high platform interval and the low platform interval. The interval of the SOC value higher than the high plateau interval is a high correction interval, and the interval of the SOC value lower than the low plateau interval is a low correction interval.

And 104, determining 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.

As can be seen from fig. 2, in the correction interval, both the SOC value and the OCV value change, and when the power-on initialization is in the correction interval, a static correction method may be adopted to correct the SOC value according to the real-time voltage, so as to obtain the target SOC value. However, the battery has two stage intervals, when the battery is in a high stage interval or a low stage interval, the SOC value changes, but the OCV value is almost unchanged, so that the power-on initialization cannot correct the SOC value according to the real-time voltage in the stage interval. However, there is a voltage peak point in the plateau transition zone, and a voltage inflection point between the low correction zone and the low plateau zone. And identifying the voltage peak point and the voltage inflection point by a dynamic correction method, and correcting the SOC value of the non-correction section 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 to obtain a target SOC value. The voltage peak point is the data point with the largest change rate of the OCV value in the platform transition section in the initial OCV-SOC curve, and the voltage inflection point is the data point with the largest change amount of the OCV value change rate in the low platform section and the low correction section in the initial OCV-SOC curve.

In a specific implementation, if the target feature interval is a correction interval, a static correction method may be correspondingly used to determine the target SOC value, and specifically, but not limited to the following manner may be used:

firstly, an initial SOC value of a battery to be tested after power-on is obtained.

Then, a target SOC value is determined by a discharging pure ampere-hour integration method based on the current discharging current and the initial SOC value.

In practical application, the battery stores the SOC value of the last shutdown time in advance, and after the battery is powered on, the SOC value of the last shutdown time is generally defaulted to be the initial SOC value, but after the current voltage is collected at the battery power-on time, the initial SOC value needs to be updated according to the current voltage at the battery power-on time. The specific updating mode is that the current voltage is used as an OCV value, the SOC value corresponding to the OCV value is obtained by inquiring in the stored data, and the initial SOC value is updated according to the inquired SOC value. The initial SOC value obtained after the battery is electrified is the initial SOC value updated according to the current voltage at the time of the battery electrification. The target SOC value is estimated and determined by adopting a discharging pure ampere-hour integration method, namely by integrating the total charge quantity flowing in the discharging process of the battery, and the target SOC value can be calculated by adopting the following formula (1).

Target soc= (initial) SOC+ (I dt) 100% (1) of total capacity of battery)

Wherein I is the current discharge current, t is time, and the total capacity of the battery is the capacity value of the battery in the full-charge state.

In specific implementation, while the target SOC value is determined by adopting the static correction method, the SOC value of the corresponding voltage peak point or the SOC value of the voltage inflection point may be corrected according to the initial SOC value, where the characteristic point includes the voltage peak point and the voltage inflection point, specifically, but not limited to the following methods may be adopted:

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

And then, if the parameter characteristic interval where the initial SOC value is located is a high correction interval, updating the SOC value of a voltage peak point by adopting a discharge pure ampere-time integration method, wherein the voltage peak point is a data point with the largest change rate of the OCV value in a platform transition interval in the initial OCV-SOC curve, and if the parameter characteristic interval where the initial SOC value is located is not the high correction interval, updating the SOC value of a voltage inflection point by adopting the discharge pure ampere-time integration method, wherein the voltage inflection point is a data point with the largest change rate of the OCV value in a low platform interval and a low correction interval in the initial OCV-SOC curve.

In practical applications, since the static correction method is employed, the target OCV value corresponds to a high correction interval or a low correction interval. And the parameter characteristic interval corresponding to the initial SOC value is consistent with the parameter characteristic interval corresponding to the target OCV value, namely 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 in which the initial SOC value is located is a high correction interval, the SOC value of the corrected voltage peak point may be calculated by the formula (1). If the parameter characteristic interval where the initial SOC value is located is a low correction interval, the SOC value of the corrected voltage inflection point may be calculated by the formula (1).

In a specific implementation, if the target feature interval is a non-correction interval, a dynamic correction method may be used to determine the target SOC value, and specifically, but not limited to, the following methods may be used:

Firstly, acquiring a plurality of historical data points of a battery to be tested in a current detection period, wherein the historical data points represent the corresponding relation between a historical OCV value and a historical SOC value of the battery to be tested in the current detection period;

Judging whether a plurality of historical data points exist data points conforming to a characteristic point voltage change rule, wherein the characteristic point voltage change rule is that the data points are data points with the largest change rate of OCV values in a platform transition interval or data points with the largest change amounts of OCV values in a low platform interval and a low correction interval;

And finally, if a target data point which accords with the characteristic point voltage change rule exists in the plurality of historical data points, taking the data point which accords with the characteristic point voltage change rule as the target data point, calculating a 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, correcting the historical SOC value of the historical data point according to the deviation value, and determining the target SOC value based on the corrected historical SOC value or the deviation value, wherein the voltage peak point is the data point with the largest change rate of the OCV value in a platform transition section in an initial OCV-SOC curve, the voltage inflection point is the data point with the largest change rate of the OCV value in a low platform section and a low correction section in the initial OCV-SOC curve, and if the target data point which accords with the characteristic point voltage change rule does not exist in the plurality of the historical data points, determining the target SOC value is not carried out, and continuing to acquire the next set of voltage and discharge current.

In practical application, a plurality of historical data points of the battery to be tested in the current detection period are obtained, specifically, all the historical data points in the current detection period can be obtained, or the historical data points in the period from the time when the historical OCV value enters the platform interval to the current moment can be obtained, wherein the historical OCV value is an OCV value output by the parameter identification model by inputting the voltage and the discharge current in the preset time into the preset parameter identification model. The historical SOC value is calculated according to the current discharging current and the initial SOC value and is calculated by the formula (1).

In particular implementations, the rate of change of the OCV value may be derived from the ratio of the OCV difference to the SOC difference for two adjacent data points. Judging whether target data points conforming to the characteristic point voltage change rule exist in the plurality of historical data points, specifically judging whether the change rate of the OCV value meets the characteristic point voltage change rule, if so, determining that the target data points conforming to the characteristic point voltage change rule exist in the historical data points, and if not, determining that the target data points conforming to the characteristic point voltage change rule do not exist in the historical data points. When a target data point which accords with the characteristic point voltage change rule exists in a plurality of historical data points, the data point which accords with the characteristic point voltage change rule is taken as the target data point, when the target data point meets the condition that the data point is the data point with the largest OCV value change rate in a platform transition section, the difference value between the SOC value of the target data point and the SOC value of a voltage peak point is taken as a deviation value, at the moment, the historical SOC values of the historical data points of the platform transition section and a high platform section are summed with the deviation value to obtain a corrected historical SOC value, and when the target data point meets the condition that the data point is the data point with the largest change rate of the OCV value in a low platform section and a low correction section, the difference value between the SOC value of the target data point and the SOC value of a voltage inflection point is taken as the deviation value, and at the moment, the historical SOC value of the historical data point of the low platform section is summed with the deviation value to obtain the corrected historical SOC value. And determining a historical data point adjacent to the current moment, and determining a target SOC value by adopting a discharging pure ampere-hour integration method according to the current discharging current and the historical SOC value. Or after the SOC value is determined by adopting a discharging pure ampere-hour integration method according to the current discharging current and the initial SOC value, taking the sum of the SOC value and the deviation value as a target SOC value. When the target data points which accord with the characteristic point voltage change rule do not exist in the plurality of historical data points, the next group of voltage and discharge current acquisition is needed to be continued, and the target OCV value at the next moment is further determined until the target data points are found.

In the implementation, the dynamic correction method is adopted to determine the target SOC value, and meanwhile, the SOC values of the voltage peak point and the voltage inflection point can be corrected, specifically, after the corrected historical SOC value or deviation value determines the target SOC value, the method further includes:

In practical application, when the target data point is the data point with the largest change rate of the OCV value in the platform transition section, the SOC value of the target data point is used as the SOC value of the corrected voltage peak point, and when the target data point is the data point with the largest change amount of the OCV value change rate in the low platform section and the low correction section, the SOC value of the target data point is used as the SOC value of the corrected voltage inflection point.

And 105, performing curve reconstruction according to the target OCV value and the target SOC value to obtain a target OCV-SOC curve.

In practical application, curve fitting is carried out according to a target OCV value and a target SOC value determined in a current detection period to obtain a current OCV-SOC curve, a stored OCV value is correspondingly updated according to the current OCV-SOC curve, and a target OCV-SOC curve is constructed according to the updated OCV value and the updated SOC value. Specifically, the curve reconstruction is performed according to the target OCV value and the target SOC value to obtain the target OCV-SOC curve, which may be, but is not limited to, the following manner:

first, a current OCV-SOC curve is constructed based on a target OCV value and a target SOC value in a current detection period.

Then, according to the current OCV-SOC curve, the stored OCV value of the corresponding data point in the detection period with the earliest detection time in the plurality of detection periods is updated.

Next, the stored OCV values for all detection periods are subjected to an average process.

And finally, determining each mean value data point based on the OCV value after mean value processing and the SOC value corresponding to the OCV value after mean value processing, and constructing a target OCV-SOC curve according to each mean value data point.

In practical application, the current detection period refers to a period from the start of the current power-on time of the battery to be detected to the end of the battery discharge. And performing curve fitting based on the target OCV value and the target SOC value in the prior detection period to obtain a current OCV-SOC curve. The current OCV-SOC curve can be obtained by fitting a part of target OCV values and target SOC values in the current detection period, or can be obtained by fitting all target OCV values and target SOC values in the current detection period. The storage medium stores a plurality of sets of data in the form of a table as shown in fig. 3, and each SOC value corresponds to a plurality of sets of OCV values. After the current OCV-SOC curve is obtained, extracting OCV values corresponding to different SOC values in the table from the current OCV-SOC curve, taking the OCV values as new OCV values, correspondingly replacing the OCV data with the earliest detection time in the table with the new OCV values, and gradually updating the OCV data in the table on line. And reconstructing a target OCV-SOC curve by using the average value of the OCVs and the SOC value in the table.

In one possible embodiment, referring to fig. 4, before the current voltage and the current discharge current of the battery are obtained, a parameter identification model needs to be built in advance, and specifically, the following methods may be used but are not limited to:

and 201, establishing a second-order equivalent circuit model of the sample battery.

The mathematical model of the output voltage and the input current of the power battery can be obtained by kirchhoff's law and Laplace transformation as shown in the following formula (2). The transfer function of the mathematical model is as shown in equation (3).

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 are the polarized internal resistances, and C _D1 and C _D2 are the polarized capacitances.

Let E _L(s)＝U_t(s)-U_oc(s), the second-order equivalent circuit model is as follows equation (4).

And 202, determining a state equation of a second-order equivalent circuit model based on a bilinear variation rule.

Based on bilinear transformation rules, the s-plane can be mapped to the Z-plane for discretization:

Wherein b1, b2, b3, b4 and b5 are undetermined coefficients, and the differential 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)

Where U _t,k is the current terminal voltage, U _oc,k is the current open circuit voltage, U _t,k-1 is the last-time terminal voltage, U _t,k-1 is the last-time terminal voltage, i _k is the current at the current time, i _k-1 is the last-time current, and i _k-1 is the last-time current.

Next, the coefficient (1-b 1-b 2) of U _oc,k is adjusted to 1, and the differential form of the final second-order equivalent circuit model state equation is obtained as shown in formula (10). Therefore, the divergence condition of the OCV curve of the identification parameter can be transferred to other identification parameters, so that the divergence condition of the identified OCV value is promoted to be less, and the stability is better.

And 203, processing a state equation of the second-order equivalent circuit model by a recursive least square method with forgetting factors to obtain an initial identification model.

Defining a system data matrix as:

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)

Defining a parameter matrix of the system as follows:

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

y_k＝Φ_2,kθ_2,k (13)

the simplified model transfer function is processed by a least square algorithm based on forgetting factors, and the model transfer function is obtained:

y_k＝φ_kθ_k+e_Ls,k (14)

Wherein phi _k is a data matrix, theta _k is a parameter matrix, e _Ls,k is stable zero-mean white noise, mu is a forgetting factor, and when the value of the forgetting factor is 1, the formula is degenerated into a traditional recursive least square method. K _Ls,k is the gain of the algorithm, P _Ls,k-1 is the error covariance matrix of the state estimate at the previous time, P _Ls,k is the error covariance matrix of the state estimate at the current time, Is the parameter value estimated at the last time,Is the parameter value of the current estimation,Is the observation at this time, y _k is the actual observation of the system, y _k andSubtracting to obtain the prediction error of the system, multiplying the prediction error by the gain matrix K _Ls,k to obtain the correction value of the parameter estimated value at the time and the estimated value at the last timeAnd finally obtaining the estimation value of the time

And 204, acquiring sample battery test data, and training the initial identification model based on the sample battery test data to obtain a parameter identification model.

In practical application, the sample battery test data is to perform multiple groups of full-charge and full-discharge experiments on the battery, and obtain multiple groups of current data, voltage data, test SOC values and test OCV values in the full-discharge process. The current data and the voltage data are used as input of an initial identification model, the corresponding OCV value is used as target output, the initial identification model is trained by battery test data, the corresponding square gain and covariance are calculated, and forgetting factors in the initial identification model are adjusted to obtain a parameter identification model.

In one possible embodiment, referring to fig. 5, the method for online reconstruction of the OCV-SOC curve of the battery further includes:

Step 301, constructing an initial OCV-SOC curve based on 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.

In practical application, according to the test SOC values and the test OCV values of a plurality of groups of full discharge processes in the battery test data, average values are obtained for a plurality of groups of test OCV values corresponding to each test SOC value, initial data points are formed according to the test SOC values and the average test OCV values, and an initial OCV-SOC curve is obtained according to initial data point fitting.

And 302, dividing 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.

In practical application, according to the change rule of the OCV value of each initial data point in the initial OCV-SOC curve, the interval where the change amount of the OCV value is smaller than the OCV value of the preset threshold value is formed into two platform intervals, the platform interval with higher SOC value in the two platform intervals is a high platform interval, and the platform interval with 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 plateau section with the SOC value higher than the high plateau section is a high correction section, and the plateau section with the SOC value lower than the low plateau section is a low correction section.

And 303, identifying each initial data point in the platform transition section based on the characteristic point voltage change rule to obtain a voltage wave peak point.

In practical application, the change rate of the OCV value of each initial data point in the platform transition interval is calculated, wherein the change rate of the OCV value can be obtained by the ratio of the OCV difference value to the SOC difference value of two adjacent data points. And taking the initial data point with the largest change rate of the OCV value in the platform transition interval as a voltage wave peak point. Referring to the voltage peak point dccv-dcoc change table in fig. 6 and the voltage peak point dccv-SOC graph in fig. 7, dcoc is a value of ±0.5% of an integer value, for example, 60% of dcoc represents a range of 59.5% -60.5% and dcocv is a decrease amount of OCV value during discharge in the dcoc range. Thus, the statistical SOC is from 66% to 52% dOCV-dSOC, 15 points are taken as the total, and the voltage peak point dOCV-dSOC curve shows obvious peaks, namely, the peak point of the change rate of the OCV value appears in the platform transition section. The voltage peak point is determined by a plurality of times of identification, the SOC is 58%, and the precision is within 1%.

And 304, identifying 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.

In practical application, the change rate of the OCV value of each initial data point in the low-plateau interval and the low-correction interval is calculated, wherein the change rate of the OCV value can be obtained by the ratio of the OCV difference value and the SOC difference value of two adjacent data points. And taking the data point with the largest change amount of the OCV value change rate in the platform transition interval as a voltage inflection point. Referring to the voltage inflection point dOCV-dSOC variation table in FIG. 8 and the voltage inflection point dOCV-SOC graph in FIG. 9, dSOC takes a value of + -0.5% of an integer value, so that dOCV-dSOC between 23% and 37% is counted, 15 points are added, after the SOC is reduced to 30%, the change rate of the OCV value is greatly improved, particularly, the dOCV is increased from 31% to 30%, from 0.6mV to 1.1mV and 83%. The SOC of the voltage inflection point was determined to be 30% by a plurality of times of identification.

Based on the above embodiments, the embodiment of the present application provides an online reconstruction device of an OCV-SOC curve of a battery, and referring to fig. 10, the online reconstruction device 400 of an OCV-SOC curve of a battery provided in the embodiment of the present application at least includes:

The data acquisition unit 401 is used for acquiring the current voltage and the current discharge current of the battery to be tested;

a target OCV value determining unit 402, configured to input a current voltage and a current discharge current to a preset parameter identification model, to obtain a target OCV value;

the interval determining unit 403 is configured to determine, according to the target OCV value, a parameter characteristic interval in which the target OCV value is located from preset parameter characteristic intervals as a target characteristic interval, where the parameter characteristic interval includes a correction interval and a non-correction interval;

A target SOC value determining unit 404, configured to determine a target SOC value by using a correction method corresponding to the target feature interval, where the correction method is a static correction method corresponding to the correction interval or a dynamic correction method corresponding to the non-correction interval;

and a curve reconstruction unit 405, configured to perform curve reconstruction according to the target OCV value and the target SOC value, so as to obtain a target OCV-SOC curve.

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

acquiring an initial SOC value of a battery to be tested after power-on;

Optionally, the target SOC value determination unit 404 is further configured to:

Optionally, the on-line reconstruction device of the battery OCV-SOC curve further comprises a model construction unit 406;

The model construction unit 406 is configured to establish a second-order equivalent circuit model of the sample battery, determine a state equation of the second-order equivalent circuit model based on a bilinear variation rule, process the state equation of the second-order equivalent circuit model by a recursive least square method with forgetting factors 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.

Optionally, the on-line reconstruction device of the battery OCV-SOC curve further comprises an initialization unit 407;

The initialization unit 407 is configured to construct an initial OCV-SOC curve based on initial data points formed by a test SOC value in test data of the sample battery and a test OCV value corresponding to the test SOC value, divide a parameter characteristic interval of the sample battery based on a 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 a platform transition interval based on a characteristic point voltage change rule to obtain a voltage peak point, and identify each initial data point in a low platform interval and a low correction interval based on the characteristic point voltage change rule to obtain a voltage inflection point.

Optionally, the curve reconstruction unit 405 is specifically configured to construct a current OCV-SOC curve based on the target OCV value and the target SOC value in the previous detection period, update the OCV values of corresponding data points in the detection period with the earliest detection time in the stored multiple detection periods according to the current OCV-SOC curve, perform an average process on the stored OCV values of all detection periods, determine each average data point based on the OCV value after the average process and the SOC value corresponding to the OCV value after the average process, and construct the target OCV-SOC curve according to each average data point.

It should be noted that, the principle of solving the technical problem of the online reconstruction device 400 for the OCV-SOC curve of the battery provided by the embodiment of the present application is similar to that of the online reconstruction method for the OCV-SOC curve of the battery provided by the embodiment of the present application, so that the implementation of the online reconstruction device 400 for the OCV-SOC curve of the battery provided by the embodiment of the present application can refer to the implementation of the online reconstruction method for the OCV-SOC curve of the battery provided by the embodiment of the present application, and the repetition is not repeated.

After the on-line reconstruction method and device of the battery OCV-SOC curve provided by the embodiment of the application are introduced, the electronic equipment provided by the embodiment of the application is briefly introduced.

Referring to fig. 11, an electronic device 500 according to an embodiment of the present application at least includes a processor 501, a memory 502, and a computer program stored in the memory 502 and capable of running on the processor 501, where the processor 501 implements the method for online reconstruction of the OCV-SOC curve of the battery according to the embodiment of the present application when executing the computer program.

It should be noted that the electronic device 500 shown in fig. 11 is only an example, and should not be construed as limiting the function and the application scope of the embodiment of the present application.

The electronic device 500 provided by embodiments of the present application may also include a bus 503 that connects the different components, including the processor 501 and the memory 502. Where bus 503 represents one or more of several types of bus structures, including a memory bus, a peripheral bus, a local bus, and so forth.

The Memory 502 may include readable media in the form of volatile Memory, such as random access Memory (Random Access Memory, RAM) 5021 and/or cache Memory 5022, and may further include Read Only Memory (ROM) 5023.

The memory 502 may also include a program tool 5025 having a set (at least one) of program modules 5024, the 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 which may include an implementation of a network environment.

The electronic device 500 may also communicate with one or more external devices 504 (e.g., keyboard, remote control, etc.), with one or more devices that enable a user to interact with the electronic device 500 (e.g., cell phone, computer, etc.), and/or with any device that enables the electronic device 500 to communicate with one or more other electronic devices 500 (e.g., router, modem, etc.). Such communication may be through an Input/Output (I/O) interface 505. Also, electronic device 500 may communicate with one or more networks such as a local area network (Local Area Network, LAN), a wide area network (Wide Area Network, WAN), and/or a public network such as the internet via network adapter 506. As shown in fig. 11, the network adapter 506 communicates with other modules of the electronic device 500 via the bus 503. It should be appreciated that although not shown in FIG. 11, other hardware and/or software modules may be used in connection with electronic device 500, including, but not limited to, microcode, device drivers, redundant processors, external disk drive arrays, disk array (Redundant Arrays of INDEPENDENT DISKS, RAID) subsystems, tape drives, and data backup storage subsystems, among others.

The following describes a computer-readable storage medium provided by an embodiment of the present application. The computer readable storage medium provided by the embodiment of the 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 by the embodiment of the application is realized. Specifically, the computer instruction may be built into or installed in the electronic device 500, so that the electronic device 500 may implement the method for online reconstruction of the OCV-SOC curve of the battery according to the embodiment of the present application by executing the built-in or installed computer instruction.

In addition, the method for online reconstruction of the OCV-SOC curve of the battery provided by the embodiment of the present application may be implemented as a program product, which includes program code for causing the electronic device 500 to execute the method for online reconstruction of the OCV-SOC curve of the battery provided by the embodiment of the present application when the program product is executable on the electronic device 500.

The program product provided by embodiments of the present application may take the form of any combination of one or more readable media, which may be a readable signal medium or a readable storage medium, and which may be, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof, and more specific examples (a non-exhaustive list) of a readable storage medium include an electrical connection having one or more wires, a portable disk, a hard disk, RAM, ROM, erasable programmable read-Only Memory (Erasable Programmable Read Only Memory, EPROM), optical fiber, portable compact disk read-Only Memory (Compact Disc Read-Only Memory, CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

The program product provided by embodiments of the present application may be implemented as a CD-ROM and include program code that may also be run on a computing device. However, the program product provided by the embodiments of the present application is not limited thereto, and in the embodiments of the present application, the readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

It should be noted that although several units or sub-units of the apparatus are mentioned in the above detailed description, such a division is merely exemplary and not mandatory. Indeed, the features and functions of two or more of the elements described above may be embodied in one element in accordance with embodiments of the present application. Conversely, the features and functions of one unit described above may be further divided into a plurality of units to be embodied.

Furthermore, although the operations of the methods of the present application are depicted in the drawings in a particular order, this is not required or suggested that these operations must be performed in this particular order or that all of the illustrated operations must be performed in order to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform.

While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.

It will be apparent to those skilled in the art that various modifications and variations can be made to the embodiments of the present application without departing from the spirit or scope of the embodiments of the application. Thus, if such modifications and variations of the embodiments of the present application fall within the scope of the claims and the equivalents thereof, the present application is also intended to include such modifications and variations.

Claims

1. An on-line reconstruction method of an OCV-SOC curve of a battery, comprising:

According to the target OCV value, determining 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 comprises a correction interval and a non-correction interval, the correction interval comprises a high correction interval and a low correction interval, and the non-correction interval comprises a high platform interval, a platform transition interval and a low platform interval;

Determining 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;

2. The on-line reconstruction method of a battery OCV-SOC curve of claim 1, wherein if the target feature interval is a correction interval, determining a target SOC value using a correction method corresponding to the target feature interval comprises:

acquiring an initial SOC value of the battery to be tested after power-on;

3. The method for online reconstruction of an OCV-SOC curve of a battery according to claim 2, further comprising, after obtaining the initial SOC value of the battery to be measured after power-up:

Judging whether a parameter characteristic interval in which the initial SOC value is positioned is a high correction interval in the correction interval or not;

if not, updating the SOC value of a voltage inflection point by adopting a discharge pure ampere-hour integration method, wherein the voltage inflection point is a data point with the largest change amount of the OCV value change rate in a low platform interval and a low correction interval in an initial OCV-SOC curve.

4. The on-line reconstruction method of a battery OCV-SOC curve of claim 1, wherein if the target feature interval is a non-corrected interval, determining a target SOC value using a correction method corresponding to the target feature interval comprises:

judging whether a plurality of historical data points exist data points which accord with a characteristic point voltage change rule, wherein the characteristic point voltage change rule is that the data point is the data point with the largest change rate of the OCV value in a platform transition interval or the data point is that the data point with the largest change amount of the OCV value in a low platform interval and a low correction interval;

If so, taking the data point which accords with the characteristic point voltage change rule as a target data point, calculating a deviation value between the SOC value of the target data point and the SOC value of a voltage peak point or the SOC value of a voltage inflection point, correcting the historical SOC value of the historical data point according to the deviation value, and determining a target SOC value based on the corrected historical SOC value or the deviation value, wherein the voltage peak point is the data point with the largest change rate of the OCV value in a platform transition section in an initial OCV-SOC curve, and the voltage inflection point is the data point with the largest change rate of the OCV value in a low platform section and a low correction section in the initial OCV-SOC curve;

5. The on-line reconstruction method of a battery OCV-SOC curve according to claim 4, further comprising, after determining the target SOC value based on the corrected historical SOC value or deviation value:

6. The method for online reconstruction of an OCV-SOC curve of a battery as defined in any of claims 1-5, further comprising, prior to obtaining the present voltage and the present discharge current of the battery under test:

Establishing a second-order equivalent circuit model of the sample battery;

determining a state equation of the second-order equivalent circuit model based on a bilinear variation rule;

processing a state equation of the second-order equivalent circuit model through a recursive least square method with forgetting factors to obtain an initial identification model;

and acquiring the sample battery test data, and training the initial identification model based on the sample battery test data to obtain a parameter identification model.

7. The on-line reconstruction method of a battery OCV-SOC curve of claim 6, further comprising:

Dividing parameter characteristic intervals of the sample battery based on the change rule of each initial data point in the initial OCV-SOC curve to obtain the correction interval and the non-correction interval;

Based on a characteristic point voltage change rule, identifying each initial data point in the platform transition section to obtain a voltage wave peak point;

And identifying each initial data point in the low-plateau interval and the low-correction interval based on the characteristic point voltage change rule to obtain a voltage inflection point.

8. The on-line reconstruction method of a battery OCV-SOC curve according to claim 1, wherein performing curve reconstruction according to the target OCV value and the target SOC value to obtain a target OCV-SOC curve comprises:

Constructing a current OCV-SOC curve based on a target OCV value and the target SOC value in a current detection period;

9. An on-line reconstruction device for an OCV-SOC curve of a battery, comprising:

The system comprises a target OCV value determining unit, a section determining unit and a control unit, wherein the target OCV value is used for determining a parameter characteristic section where the target OCV value is located from preset parameter characteristic sections as a target characteristic section, the parameter characteristic sections comprise a correction section and a non-correction section, the correction section comprises a high-correction section and a low-correction section, and the non-correction section comprises a high-platform section, a platform transition section and a low-platform section;

The target SOC value determining unit is used for determining 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;

10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method for online reconstruction of the OCV-SOC curve of a battery as claimed in any of claims 1-7 when the computer program is executed by the processor.

11. A computer readable storage medium storing computer instructions which, when executed by a processor, implement the method of online reconstruction of a battery OCV-SOC curve as claimed in any of claims 1 to 7.