Online reconstruction method and system for OCV-SOC curve of battery

Abstract

An online reconstruction method and system for an OCV-SOC curve of a battery. The method comprises: acquiring an OCV-SOC curve overlapping interval and an initial trusted OCV-SOC point set that correspond to a battery to be tested (step 101); acquiring an initial OCV value of said battery, and if the initial OCV value is in the OCV-SOC curve overlapping interval, acquiring a first SOC value (step 102); acquiring a discharge ending SOC value and a resting OCV value on the basis of a preset first discharge condition and the first SOC value (step 103); acquiring discharge data in the first discharge condition, and performing parameter identification on the discharge data to obtain a parameter identification OCV-SOC curve (step 104); acquiring a trusted OCV-SOC point on the basis of the discharge ending SOC value, the resting OCV value, and the parameter identification OCV-SOC curve (step 105); and constructing an OCV-SOC curve of said battery on the basis of the trusted OCV-SOC point and the initial trusted OCV-SOC point set (step 106). Thus, the accuracy of OCV-SOC curve reconstruction is improved.

Description

A method and system for online reconstruction of battery OCV-SOC curve

[0001] Cross-references to related applications

[0002] This application claims priority to Chinese Patent Application No. 202411529241.5, filed on October 30, 2024, entitled "A Method and System for Online Reconstruction of OCV-SOC Curve of a Battery", the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to the field of battery management technology, specifically to a method and system for online reconstruction of the OCV-SOC curve of a battery. Background Technology

[0004] The accuracy of estimating the State of Charge (SOC) of a battery is a core indicator for evaluating battery management systems. It relates to the battery's remaining capacity, charging and discharging power, and operational safety, ensuring the battery operates at its optimal state and extending its lifespan. Common SOC estimation algorithms typically combine the Open Circuit Voltage (OCV) method with the Ampere-Hour Integration method. The prerequisite for this algorithm is obtaining an effective OCV (terminal voltage in the open-circuit state), i.e., the curve of OCV versus SOC. This curve reflects the mapping relationship between SOC and OCV, providing an effective initial SOC value for the Ampere-Hour Integration method and correcting for its accumulated errors.

[0005] In existing technologies, OCV and SOC curves are typically obtained through laboratory testing. However, obtaining these curves through laboratory testing involves significant experimental costs and increases the algorithm development cycle to obtain OCV and SOC curves throughout the battery's lifespan, as the OCV and SOC curves change with battery aging.

[0006] Furthermore, the technical limitations of the laboratory testing methods mean that each test can only obtain the OCV state curve of a specific single battery cell under a specific test environment. This offline calibration method is only applicable to cells that have recently left the factory and cannot test aged cells used in new energy vehicles. Moreover, the offline testing method assumes that batteries under the same State of Health (SOH) will have consistent OCV-SOC curves. In reality, due to differences in manufacturing processes and battery usage conditions, even with the same SOH, the OCV-SOC curves of different battery packs may differ. Laboratory test results cannot resolve the errors caused by this inconsistency. Summary of the Invention

[0007] To address the aforementioned technical problems, this application discloses an online reconstruction method and system for the OCV-SOC curve of a battery, thereby solving the above-mentioned technical problems and improving the accuracy of the OCV-SOC curve.

[0008] To achieve the above objectives, this application discloses an online reconstruction method for the OCV-SOC curve of a battery, comprising:

[0009] Obtain the overlapping interval of the OCV-SOC curves of the battery under test and the initial set of trustworthy OCV-SOC points;

[0010] Obtain the initial OCV value of the battery under test. If the initial OCV value is within the overlap range of the OCV-SOC curve, then obtain the first SOC value.

[0011] Based on the preset first discharge condition and the first SOC value, obtain the discharge end SOC value and the static OCV value;

[0012] The discharge data in the first discharge condition is obtained, and the discharge data is parameter identified to obtain the parameter identified OCV-SOC curve;

[0013] Based on the discharge end SOC value, the static OCV value and the parameters, identify the OCV-SOC curve and obtain a reliable OCV-SOC point;

[0014] Based on the trusted OCV-SOC points and the initial set of trusted OCV-SOC points, the OCV-SOC curve of the battery under test is constructed.

[0015] This application discloses an online reconstruction method for the OCV-SOC curve of a battery. First, it obtains the overlapping interval of the OCV-SOC curve and the initial set of trustworthy OCV-SOC points corresponding to the battery under test, providing a reference for data point selection and ensuring the accuracy of the data points used for curve reconstruction. Next, it obtains the initial OCV value of the battery under test and obtains a first SOC value by matching the initial OCV value with the overlapping interval of the OCV-SOC curve, ensuring the accuracy of the first SOC value. Then, based on a preset first discharge condition and the first SOC value, it obtains the SOC value at the end of discharge and the OCV value at rest. Simultaneously, it collects discharge data in the first discharge condition and performs parameter identification on the discharge data to obtain a parameter-identified OCV-SOC curve, thereby achieving online reconstruction of the OCV-SOC curve based on the data corresponding to the battery's operating process. Specifically, based on the discharge end SOC value, the resting OCV value, and the parameter identification OCV-SOC curve, a valid and accurate reliable OCV-SOC point is obtained. Based on the reliable OCV-SOC point and the obtained initial reliable OCV-SOC point set as a reference, the OCV-SOC curve of the battery under test is constructed to ensure the accuracy of the OCV-SOC curve.

[0016] As a preferred example, obtaining the overlapping interval of the OCV-SOC curves and the initial set of trustworthy OCV-SOC points corresponding to the battery under test includes:

[0017] Obtain the initial OCV-SOC curve of the test battery and the aged OCV-SOC curve of the corresponding aged battery; wherein the test battery and the battery under test are of the same type;

[0018] By comparing the initial OCV-SOC curve and the aged OCV-SOC curve, the overlapping region of the OCV-SOC curves is obtained; wherein, the overlapping region of the OCV-SOC curves includes the OCV region;

[0019] Based on the OCV interval, multiple initial trusted OCV-SOC points and the initial trusted SOC point corresponding to each initial trusted OCV-SOC point are obtained from the pre-constructed OCV-SOC point set to construct the initial trusted OCV-SOC point set.

[0020] This application first obtains test batteries of the same type as the battery currently undergoing curve reconstruction to ensure the accuracy of the curve overlap interval acquisition. Then, it compares the OCV-SOC curves of the test battery with the corresponding aged battery to provide an accurate judgment basis for curve reconstruction using the obtained OCV-SOC curve overlap interval, thereby improving the accuracy of curve reconstruction.

[0021] As a preferred example, obtaining the initial OCV value of the battery under test, and if the initial OCV value is within the overlap range of the OCV-SOC curves, then obtaining the first SOC value, includes:

[0022] Obtain the first OCV value of the battery under test at the first power-on moment, and perform OCV correction on the first OCV value to obtain the initial OCV value of the battery under test;

[0023] When the initial OCV value is determined to be within the OCV range, the historical OCV-SOC curve of the battery under test is retrieved based on the initial OCV value to obtain the first SOC value.

[0024] This application ensures that an accurate first SOC value can still be obtained after the battery ages by comparing the initial OCV value with the OCV range.

[0025] As a preferred example, the step of obtaining the discharge end SOC value and the resting OCV value based on the preset first discharge condition and the first SOC value includes:

[0026] Collect current data under the first discharge condition;

[0027] Calculate the SOC value of the battery under test at the end of the first discharge condition based on the current data.

[0028] Based on preset dormancy conditions and the first discharge condition, the static OCV value of the battery under test at the second power-on time is obtained.

[0029] This application utilizes the current data in the first discharge condition to ensure the acquisition of a relatively accurate SOC value at the end of discharge. Then, it uses the first discharge condition and the dormancy condition to collect the stable static OCV value of the battery at the second power-on moment, thereby improving the accuracy of curve reconstruction.

[0030] As a preferred example, the step of performing parameter identification on the discharge data to obtain the parameter-identified OCV-SOC curve includes:

[0031] The discharge data is parameter identified based on a pre-established second-order FFRLS algorithm to obtain the parameter-identified OCV-SOC curve.

[0032] This application uses the second-order FFRLS algorithm to identify parameters, ensuring the accuracy of the identified OCV-SOC curve.

[0033] As a preferred example, the step of identifying a reliable OCV-SOC point based on the discharge end SOC value, the resting OCV value, and the parameter identification OCV-SOC curve includes:

[0034] Based on the static OCV value, the historical OCV-SOC curve is found, and the static SOC value corresponding to the static OCV value is obtained;

[0035] Obtain the first deviation between the static SOC value and the discharge-end SOC value, and determine that the first deviation satisfies a preset first deviation condition;

[0036] Based on the discharge end SOC value, the parameter identification OCV-SOC curve is searched to obtain the discharge end OCV value corresponding to the discharge end SOC value in the parameter identification OCV-SOC curve;

[0037] A reliable OCV-SOC point is formed based on the discharge end OCV value and the discharge end SOC value.

[0038] This application obtains a stable static OCV value based on preset dormancy conditions, and then obtains a relatively accurate static SOC value from the historical OCV-SOC curve corresponding to the battery based on the stable static OCV value. Next, by comparing the deviation between the static SOC value and the discharge end SOC value in the historical curve, the accuracy of the discharge end SOC value used for curve reconstruction is ensured based on the deviation and preset deviation conditions. Then, the corresponding discharge end OCV value is obtained using the accurate discharge end SOC value to construct the accurate trustworthy OCV-SOC point.

[0039] As a preferred example, the step of forming a reliable OCV-SOC point based on the discharge end OCV value and the discharge end SOC value includes:

[0040] Obtain the second deviation between the discharge end OCV value and the static OCV value;

[0041] When the second deviation meets the preset second deviation condition, the intermediate OCV value between the discharge end OCV value and the static OCV value, and the intermediate SOC value between the static SOC value and the discharge end SOC value are obtained;

[0042] Based on the intermediate OCV value and the intermediate SOC value, a trustworthy OCV-SOC point is formed.

[0043] This application uses SOC as a benchmark to calculate the deviation between the discharge end OCV value and the static OCV value, further verifying the accuracy of the discharge end OCV value and the discharge end SOC value, thereby ensuring the accuracy of the reliable OCV-SOC point.

[0044] As a preferred example, the step of constructing the OCV-SOC curve of the battery under test based on the trusted OCV-SOC points and the initial set of trusted OCV-SOC points includes:

[0045] Obtain the initial trusted OCV-SOC point that is closest to the trusted OCV-SOC point from the initial trusted OCV-SOC point set;

[0046] Calculate the third deviation between the initial trusted SOC value corresponding to the initial trusted OCV-SOC point and the discharge end SOC value;

[0047] The initial set of trustworthy OCV-SOC points is updated based on the third deviation and the trustworthy OCV-SOC points to obtain a set of trustworthy OCV-SOC points.

[0048] After obtaining highly reliable OCV-SOC points, this application updates the pre-constructed standard initial reliable OCV-SOC point set using these reliable OCV-SOC points, so that the updated reliable OCV-SOC point set fits the actual operating condition of the battery, thereby improving the accuracy of the OCV-SOC curve.

[0049] As a preferred example, the step of updating the initial set of trustworthy OCV-SOC points based on the third deviation and the trustworthy OCV-SOC points to obtain a set of trustworthy OCV-SOC points includes:

[0050] When the third deviation meets the preset third deviation condition, the initial trusted OCV-SOC points in the initial trusted OCV-SOC point set are updated according to the trusted OCV-SOC points to obtain a trusted OCV-SOC point set.

[0051] When the third deviation does not meet the preset third deviation condition, the trustworthy OCV-SOC point is added to the initial trustworthy OCV-SOC point set to obtain a trustworthy OCV-SOC point set.

[0052] This application utilizes the deviation settings of the SOC to set different point set update methods, which can selectively reduce the amount of data used for curve reconstruction while ensuring the accuracy of curve reconstruction, thereby improving the efficiency of curve reconstruction.

[0053] As a preferred example, the step of constructing the OCV-SOC curve of the battery under test based on the trusted OCV-SOC points and the initial set of trusted OCV-SOC points includes:

[0054] Obtain the number of data points in the trusted OCV-SOC point set;

[0055] When the number reaches a preset threshold, curve fitting is performed on each data point in the set of trustworthy OCV-SOC points to generate an OCV-SOC curve.

[0056] Replace the historical OCV-SOC curve of the battery under test with the OCV-SOC curve.

[0057] This application ensures the accuracy of curve reconstruction by setting the aforementioned quantity threshold.

[0058] As a preferred example, the establishment process of the second-order FFRLS algorithm is as follows:

[0059] Obtain the initial OCV-SOC curve corresponding to the test battery;

[0060] According to the preset second discharge condition, the test discharge data generated when the test battery is discharged is obtained;

[0061] The test discharge data is parameter identified using a preset initial second-order FFRLS algorithm to generate a test OCV-SOC curve.

[0062] The curve deviation between the test OCV-SOC curve and the initial OCV-SOC curve is obtained, and the forgetting factor of the initial second-order FFRLS algorithm is continuously adjusted according to the curve deviation until the curve deviation meets the preset curve deviation condition, thus obtaining the second-order FFRLS algorithm.

[0063] This application uses experimental batteries to conduct operating condition tests and adjusts the forgetting factor of the second-order FFRLS algorithm to minimize the error generated by the second-order FFRLS algorithm, thereby improving the accuracy of the parameter identification OCV-SOC curve.

[0064] On the other hand, this application discloses an online OCV-SOC curve reconstruction system for batteries, including a point set acquisition module, a SOC value lookup module, a data acquisition module, a parameter identification module, a data point filtering module, and a curve reconstruction module;

[0065] The point set acquisition module is used to acquire the overlapping interval of the OCV-SOC curves of the battery under test and the initial trustworthy OCV-SOC point set.

[0066] The SOC value lookup module is used to obtain the initial OCV value of the battery under test. If the initial OCV value is in the overlapping range of the OCV-SOC curve, then the first SOC value is obtained.

[0067] The data acquisition module is used to acquire the discharge end SOC value and the static OCV value based on the preset first discharge condition and the first SOC value.

[0068] The parameter identification module is used to acquire discharge data in the first discharge condition and perform parameter identification on the discharge data to obtain the parameter identification OCV-SOC curve.

[0069] The data point filtering module is used to identify the OCV-SOC curve based on the discharge end SOC value, the static OCV value and the parameters, and to obtain trustworthy OCV-SOC points.

[0070] The curve reconstruction module is used to construct the OCV-SOC curve of the battery under test based on the trusted OCV-SOC points and the initial set of trusted OCV-SOC points.

[0071] This application discloses an online OCV-SOC curve reconstruction system for a battery. First, it acquires the overlapping interval of the OCV-SOC curves of the battery under test and an initial set of trustworthy OCV-SOC points, providing a reference for data point selection and ensuring the accuracy of the data points used for curve reconstruction. Next, it acquires the initial OCV value of the battery under test and obtains a first SOC value by matching the initial OCV value with the overlapping interval of the OCV-SOC curves, ensuring the accuracy of the first SOC value. Then, based on a preset first discharge condition and the first SOC value, it acquires the SOC value at the end of discharge and the OCV value during rest. Simultaneously, it collects discharge data during the first discharge condition and performs parameter identification on the discharge data to obtain a parameter-identified OCV-SOC curve, thereby achieving online reconstruction of the OCV-SOC curve based on the data corresponding to the battery's operating process. Specifically, based on the discharge end SOC value, the resting OCV value, and the parameter identification OCV-SOC curve, a valid and accurate reliable OCV-SOC point is obtained. Based on the reliable OCV-SOC point and the obtained initial reliable OCV-SOC point set as a reference, the OCV-SOC curve of the battery under test is constructed to ensure the accuracy of the OCV-SOC curve. Attached Figure Description

[0072] Figure 1: A flowchart illustrating an online reconstruction method for the OCV-SOC curve of a battery disclosed in an embodiment of this application;

[0073] Figure 2: A schematic diagram of the structure of an online OCV-SOC curve reconfiguration system for a battery disclosed in an embodiment of this application;

[0074] Figure 3: A flowchart illustrating an online reconstruction method for the OCV-SOC curve of a battery according to another embodiment of this application;

[0075] Figure 4: A schematic diagram of the OCV-SOC curve and deviation curve of a battery before and after aging, as disclosed in another embodiment of this application;

[0076] Figure 5: A schematic diagram of an OCV-SOC point set disclosed in another embodiment of this application;

[0077] Figure 6: A schematic diagram of an initial trusted OCV-SOC point set disclosed in another embodiment of this application;

[0078] Figure 7: A schematic diagram of obtaining high-precision OCV-SOC curve points according to another embodiment of this application;

[0079] Figure 8: A schematic diagram of a parameter identification OCV-SOC curve and difference curve disclosed in another embodiment of this application;

[0080] Figure 9: A schematic diagram illustrating the acquisition of a trust point OCV_Trust_New according to another embodiment of this application;

[0081] Figure 10: A schematic diagram of the replacement process of initial trusted points in an initial trusted point set disclosed in another embodiment of this application;

[0082] Figure 11: A schematic diagram of the process of adding trusted points to an initial trusted point set according to another embodiment of this application;

[0083] Figure 12: A schematic diagram of a reconstructed OCV-SOC curve disclosed in another embodiment of this application. Detailed Implementation

[0084] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0085] Example 1

[0086] This embodiment discloses an online reconstruction method for the OCV-SOC curve of a battery. The specific implementation flow of the reconstruction method is shown in Figure 1, and mainly includes steps 101 to 106. The steps are as follows:

[0087] Step 101: Obtain the overlapping interval of the OCV-SOC curves of the battery under test and the initial set of trustworthy OCV-SOC points.

[0088] In this embodiment, the main steps are as follows: obtaining the initial OCV-SOC curve of the test battery and the aged OCV-SOC curve of the corresponding aged battery; wherein the test battery and the battery under test are of the same type; comparing the initial OCV-SOC curve and the aged OCV-SOC curve to obtain the OCV-SOC curve overlap interval; wherein the OCV-SOC curve overlap interval includes the OCV interval; based on the OCV interval, obtaining multiple initial trustworthy OCV-SOC points and the initial trustworthy SOC point corresponding to each initial trustworthy OCV-SOC point from the pre-constructed OCV-SOC point set to construct the initial trustworthy OCV-SOC point set.

[0089] In this embodiment, the first step is to obtain a test battery of the same type as the battery currently undergoing curve reconstruction, so as to ensure the accuracy of the curve overlap interval obtained. Then, the OCV-SOC curves of the test battery and the corresponding aged battery are compared, so as to use the obtained OCV-SOC curve overlap interval to provide an accurate judgment basis for curve reconstruction, thereby improving the accuracy of curve reconstruction.

[0090] Step 102: Obtain the initial OCV value of the battery under test. If the initial OCV value is within the overlap range of the OCV-SOC curve, then obtain the first SOC value.

[0091] In this embodiment, the step mainly includes: obtaining the first OCV value of the battery under test at the first power-on moment, and performing OCV correction on the first OCV value (that is, taking the terminal voltage of the battery under test after sufficient dormancy as the initial OCV value of the battery under test), and obtaining the initial OCV value of the battery under test; when it is determined that the initial OCV value is within the OCV range, searching the historical OCV-SOC curve of the battery under test according to the initial OCV value, and obtaining the first SOC value.

[0092] The first power-on time refers to the moment when the battery under test is first powered on after sufficient dormancy. In this embodiment, this step, based on the comparison between the initial OCV value and the OCV range, ensures that an accurate first SOC value can still be obtained after the battery has aged.

[0093] Step 103: Based on the preset first discharge condition and the first SOC value, obtain the discharge end SOC value and the static OCV value.

[0094] In this embodiment, the step mainly includes: collecting current data in the first discharge condition; calculating the discharge end SOC value of the battery under test at the end of the first discharge condition based on the current data; and obtaining the static OCV value of the battery under test at the second power-on time based on preset dormancy conditions and the first discharge condition.

[0095] The second power-on time refers to the moment when the battery under test is powered on after being discharged according to the preset first discharge condition and then undergoing a dormant period. The first power-on time comes first, and the second power-on time comes later.

[0096] In this embodiment, this step utilizes the current data in the first discharge condition to ensure the acquisition of a relatively accurate SOC value at the end of discharge. Then, using the first discharge condition and the dormancy condition, the stable static OCV value of the battery at the second power-on moment is collected, thereby improving the accuracy of curve reconstruction.

[0097] Step 104: Obtain the discharge data in the first discharge condition, and perform parameter identification on the discharge data to obtain the parameter identification OCV-SOC curve.

[0098] In this embodiment, the step mainly includes: performing parameter identification on the discharge data based on a pre-established second-order FFRLS algorithm to obtain the parameter-identified OCV-SOC curve.

[0099] Further, the establishment process of the second-order FFRLS algorithm is as follows: obtain the initial OCV-SOC curve corresponding to the test battery; according to the preset second discharge condition, obtain the test discharge data generated when the test battery is discharged; perform parameter identification on the test discharge data through the preset initial second-order FFRLS algorithm to generate the test OCV-SOC curve; obtain the curve deviation between the test OCV-SOC curve and the initial OCV-SOC curve, and continuously adjust the forgetting factor of the initial second-order FFRLS algorithm according to the curve deviation until the curve deviation meets the preset curve deviation condition to obtain the second-order FFRLS algorithm.

[0100] In this embodiment, this step is based on the second-order FFRLS algorithm for parameter identification, ensuring the accuracy of the identified OCV-SOC curve. Simultaneously, the establishment process of the second-order FFRLS algorithm involves testing the battery under operating conditions and adjusting the forgetting factor of the algorithm to minimize the error generated by the algorithm, thereby improving the accuracy of the identified OCV-SOC curve.

[0101] Step 105: Based on the discharge end SOC value, the static OCV value, and the parameters, identify the OCV-SOC curve and obtain a reliable OCV-SOC point.

[0102] In this embodiment, the main steps are as follows: First, find the historical OCV-SOC curve based on the stationary OCV value to obtain the stationary SOC value corresponding to the stationary OCV value; second, obtain the first deviation between the stationary SOC value and the discharge end SOC value, and determine that the first deviation satisfies a preset first deviation condition; third, find the parameter identification OCV-SOC curve based on the discharge end SOC value to obtain the discharge end OCV value corresponding to the discharge end SOC value in the parameter identification OCV-SOC curve; and fourth, form a reliable OCV-SOC point based on the discharge end OCV value and the discharge end SOC value.

[0103] Further, a second deviation between the discharge end OCV value and the stationary OCV value is obtained; when the second deviation meets a preset second deviation condition, an intermediate OCV value between the discharge end OCV value and the stationary OCV value, and an intermediate SOC value between the stationary SOC value and the discharge end SOC value are obtained; based on the intermediate OCV value and the intermediate SOC value, another reliable OCV-SOC point is formed.

[0104] In this embodiment, this step obtains a stable static OCV value based on preset dormancy conditions. This stable static OCV value is then used to obtain a relatively accurate static SOC value from the historical OCV-SOC curve corresponding to the battery under test. Next, the deviation between the static SOC value and the discharge-end SOC value in the historical OCV-SOC curve is compared. Based on this deviation and preset deviation conditions, the accuracy of the discharge-end SOC value used for curve reconstruction is ensured. Furthermore, the accurate discharge-end SOC value is used to obtain the corresponding discharge-end OCV value, thus constructing the accurate trustworthy OCV-SOC point. Further, using SOC as a benchmark, the deviation between the discharge-end OCV value and the static OCV value is calculated to further verify the accuracy of the discharge-end OCV value and the discharge-end SOC value, thereby ensuring the accuracy of the trustworthy OCV-SOC point.

[0105] Step 106: Based on the trusted OCV-SOC points and the initial trusted OCV-SOC point set, construct the OCV-SOC curve of the battery under test.

[0106] In this embodiment, the main steps are as follows: obtaining the initial trusted OCV-SOC point closest to the trusted OCV-SOC point from the initial trusted OCV-SOC point set; calculating the third deviation between the initial trusted SOC value corresponding to the initial trusted OCV-SOC point and the discharge end SOC value; updating the initial trusted OCV-SOC point set according to the third deviation and the trusted OCV-SOC point to obtain the trusted OCV-SOC point set.

[0107] Furthermore, when the third deviation meets the preset third deviation condition, the initial trusted OCV-SOC points in the initial trusted OCV-SOC point set are updated according to the trusted OCV-SOC points to obtain a trusted OCV-SOC point set; when the third deviation does not meet the preset third deviation condition, the trusted OCV-SOC points are added to the initial trusted OCV-SOC point set to obtain a trusted OCV-SOC point set.

[0108] Finally, the number of data points in the trusted OCV-SOC point set is obtained; when the number reaches a preset threshold, curve fitting is performed on each data point in the trusted OCV-SOC point set to generate an OCV-SOC curve; the historical OCV-SOC curve of the battery under test is replaced by the OCV-SOC curve.

[0109] In this embodiment, after obtaining highly reliable OCV-SOC points, this step updates the pre-constructed standard initial reliable OCV-SOC point set using these reliable OCV-SOC points. This ensures that the updated reliable OCV-SOC point set closely reflects the actual operating condition of the battery, thereby improving the accuracy of the OCV-SOC curve. Furthermore, different point set update methods are set based on the SOC deviation. This allows for selective reduction of the amount of data used for curve reconstruction while maintaining accuracy, thus improving the efficiency of curve reconstruction. Finally, by setting the quantity threshold, the accuracy of curve reconstruction is guaranteed.

[0110] On the other hand, this embodiment also discloses an online OCV-SOC curve reconstruction system for a battery. The specific structure of the system is shown in Figure 2, including a point set acquisition module 201, a SOC value lookup module 202, a data acquisition module 203, a parameter identification module 204, a data point filtering module 205, and a curve reconstruction module 206.

[0111] The point set acquisition module 201 is used to acquire the overlapping range of the OCV-SOC curves of the battery under test and the initial trustworthy OCV-SOC point set.

[0112] The SOC value lookup module 202 is used to obtain the initial OCV value of the battery under test. If the initial OCV value is in the overlapping range of the OCV-SOC curve, then the first SOC value is obtained.

[0113] The data acquisition module 203 is used to acquire the discharge end SOC value and the static OCV value based on the preset first discharge condition and the first SOC value.

[0114] The parameter identification module 204 is used to acquire discharge data in the first discharge condition and perform parameter identification on the discharge data to obtain the parameter identification OCV-SOC curve.

[0115] The data point filtering module 205 is used to identify the OCV-SOC curve based on the discharge end SOC value, the static OCV value and the parameters, and obtain reliable OCV-SOC points.

[0116] The curve reconstruction module 206 is used to construct the OCV-SOC curve of the battery under test based on the trusted OCV-SOC points and the initial trusted OCV-SOC point set.

[0117] This embodiment provides a method and system for online reconstruction of the OCV-SOC curve of a battery. First, it obtains the overlapping interval of the OCV-SOC curve and the initial set of trustworthy OCV-SOC points corresponding to the battery under test, providing a reference for data point selection and ensuring the accuracy of the data points used for curve reconstruction. Next, it obtains the initial OCV value of the battery under test and obtains the first SOC value by matching the initial OCV value with the overlapping interval of the OCV-SOC curve, ensuring the accuracy of the first SOC value. Then, based on a preset first discharge condition and the first SOC value, it obtains the SOC value at the end of discharge and the OCV value at rest. Simultaneously, it collects discharge data in the first discharge condition and performs parameter identification on the discharge data to obtain a parameter-identified OCV-SOC curve, thereby achieving online reconstruction of the OCV-SOC curve based on the data corresponding to the battery's operating process. Specifically, based on the discharge end SOC value, the resting OCV value, and the parameter identification OCV-SOC curve, a valid and accurate reliable OCV-SOC point is obtained. Based on the reliable OCV-SOC point and the obtained initial reliable OCV-SOC point set as a reference, the OCV-SOC curve of the battery under test is constructed to ensure the accuracy of the OCV-SOC curve.

[0118] In the embodiments of this application, the point set acquisition module 201, the SOC value lookup module 202, the data acquisition module 203, the parameter identification module 204, the data point filtering module 205, and the curve reconstruction module 206 can each be one or more processors, controllers, or chips with communication interfaces capable of implementing communication protocols. If necessary, they may also include memory and related interfaces, system transmission buses, etc. The processor, controller, or chip executes program-related code to implement the corresponding functions. Alternatively, an alternative solution is that the point set acquisition module 201, the SOC value lookup module 202, the data acquisition module 203, the parameter identification module 204, the data point filtering module 205, and the curve reconstruction module 206 share an integrated chip or share a processor, controller, memory, or other devices. The shared processor, controller, or chip executes program-related code to implement the corresponding functions.

[0119] Example 2

[0120] This embodiment discloses an online reconstruction method for the OCV-SOC curve of a battery. Specifically, the implementation process of the online curve reconstruction method is shown in Figure 3, mainly including steps 301 to 306. The steps are as follows:

[0121] Step 301: Obtain the test battery corresponding to the battery to be reconstructed and the aged battery after aging treatment of the test battery, so as to perform an OCV-SOC curve calibration test on the test battery and the aged battery to obtain the OCV-SOC curve overlap interval corresponding to the battery to be reconstructed.

[0122] Specifically, in this step, a newly manufactured test battery is first obtained, wherein the test battery is of the same type as the battery to be reconstructed (i.e., the battery to be tested) and the state of health (SOH) of the test battery is 100%. Next, an OCV-SOC curve calibration test is performed on the test battery to obtain the corresponding OCV-SOC curve.

[0123] Next, to ensure the accuracy of the overlapping range of the OCV-SOC curves, the test battery was subjected to accelerated aging treatment under preset aging conditions to obtain the aged battery corresponding to the test battery. Then, the aged battery underwent the same OCV-SOC curve calibration test to obtain the corresponding OCV-SOC curve.

[0124] By comparing the OCV-SOC curve of the test battery and the OCV-SOC curve of the aged battery, the intervals in which the OCV-SOC curves of the battery to be reconstructed overlap during different aging processes can be obtained, that is, the OCV-SOC curve overlap intervals corresponding to the battery to be reconstructed can be obtained.

[0125] In this embodiment, to further illustrate the content of the OCV-SOC curve overlap interval, an implementation method is provided. In this embodiment, the aging conditions are set to a high temperature of 45°C and a 10C rate current, and the aging battery is set to the battery corresponding to the healthy state (SOH) of 80% after the test battery has undergone aging treatment.

[0126] Specifically, after obtaining the test battery corresponding to the battery to be reconstructed, the OCV-SOC curve of the test battery in the Beginning of Life (BOL) state, i.e., SOH = 100%, is obtained through the HPPC (Hybrid Pulse Power Characteristic) operating condition. In a preferred embodiment, the type of the test battery is set to a lithium battery, the horizontal axis of the OCV-SOC curve is from 100% to 0% with an interval of 5%, and the vertical axis is the recorded OCV value. The OCV-SOC curve corresponding to the new lithium battery can be seen in Figure 4, which is the curve corresponding to the BOL state in Figure 4.

[0127] Furthermore, the test battery was subjected to full charge-discharge cycles at a high temperature of 45°C and a 10C current rate to accelerate battery aging until the state of oxygen (SOH) reached 80%, thus obtaining the aged battery corresponding to the test battery. The aged battery was then subjected to a curve calibration experiment under the same test conditions as the test battery, i.e., the HPPC conditions, to obtain the OCV-SOC curve of the aged battery. Preferably, referring to Figure 4, the OCV-SOC curve of the aged lithium battery is the curve corresponding to the Middle of Life (MOL) state in Figure 4, i.e., when the SOH is 80%.

[0128] By comparing the OCV-SOC curves of the test battery and the aged battery, the OCV interval corresponding to the curve overlap is obtained. This OCV interval is the OCV-SOC curve overlap interval. Preferably, referring to Figure 4, by comparing the BOL line and the MOL_SOH80% line in Figure 2, it can be identified that the aged battery has an approximately overlapping OCV-SOC curve with the unaged battery. Specifically, referring to Figure 4, when the battery type is a lithium battery, to ensure the accuracy of the curve overlap interval, the OCV deviation between the BOL and MOL_SOH80% curves is calculated based on SOC. The SOC interval where the deviation between the two curves is less than 15mV is taken. Therefore, the OCV-SOC curve overlap interval corresponding to the lithium battery includes Area_low (9.2%–18.0%), Area_middle (50.7%–67.3%), and Area_high (82.1%–100%).

[0129] Step 302: Construct an OCV-SOC point set based on the OCV-SOC curve corresponding to the test battery, and construct an initial trustworthy OCV-SOC point set based on the overlapping interval of the OCV-SOC curve and the OCV-SOC point set.

[0130] Specifically, in this step, in order to improve the accuracy of curve reconstruction, multiple OCV-SOC points are first selected from the OCV-SOC curves corresponding to the test batteries to construct an OCV-SOC point set. Then, multiple initial trustworthy OCV-SOC points are selected from the OCV-SOC point set using the overlapping intervals of the OCV-SOC curves to construct the initial trustworthy OCV-SOC point set.

[0131] Furthermore, to further illustrate the specific content of the OCV-SOC point set and the initial trustworthy OCV-SOC point set, in some embodiments of this example, referring to the BOL curve shown in FIG4, when the battery type is a lithium battery, taking a SOC percentage interval of 5% as an example, the OCV-SOC point set corresponding to the battery can be obtained from the BOL curve, as shown in FIG5. Specifically, it includes multiple OCV-SOC points such as (SOC=0%, OCV=3.133V), (SOC=5%, OCV=3.385V), and (SOC=10%, OCV=3.473V).

[0132] Referring to the OCV-SOC point set corresponding to the test battery shown in Figure 5, multiple OCV-SOC points are selected from the OCV-SOC point set using the overlapping intervals of the OCV-SOC curves to construct the initial trustworthy OCV-SOC point set. Preferably, when selecting OCV-SOC points using the Area_low interval, Area_middle interval, and Area_high interval provided in Figure 4, the constructed initial trustworthy OCV-SOC point set can be seen in Figure 6, mainly including multiple initial trustworthy OCV-SOC points such as (SOC = 10%, OCV = 3.473V), (SOC = 15%, OCV = 3.500V), and (SOC = 50%, OCV = 3.681V).

[0133] Step 303: Perform initial power-on and secondary power-on on the battery to be reconfigured to obtain the initial OCV value, initial SOC value, discharge end SOC value corresponding to the end of initial discharge, and static OCV value corresponding to the secondary power-on.

[0134] Specifically, in this embodiment, obtaining high-precision OCV-SOC points is a primary prerequisite for improving the accuracy of online OCV-SOC curve reconstruction. Since the battery to be reconstructed has aged during use, if the old OCV-SOC curve is used for OCV correction during the initial power-on of an aged battery, the obtained initial SOC value will contain a certain initial error. Therefore, before updating the OCV-SOC curve, ensuring that the corrected initial power-on OCV (i.e., the initial OCV value) falls within the high-precision OCV-SOC curve overlap range is a prerequisite for updating the OCV-SOC curve. This ensures a higher-precision initial SOC value and guarantees that the SOC value used for subsequent OCV-SOC curve updates has high accuracy. In one embodiment of this work, the process of obtaining high-precision OCV-SOC curve points can be seen in Figure 7.

[0135] Specifically, referring to Figure 7, in this embodiment, in order to ensure the accuracy of the initial OCV value, before initially powering on the battery to be reconfigured, it is ensured that the battery to be reconfigured has undergone sufficient dormancy. At this time, the terminal voltage of the battery to be reconfigured is stably equal to the open circuit voltage. That is, the battery to be reconfigured has undergone OCV correction based on the open circuit voltage after power-on, thereby obtaining the initial OCV value corresponding to the battery to be reconfigured, namely OCV_Init.

[0136] Determine whether the initial OCV value is within the OCV range to ensure the accuracy of the initial SOC value obtained after battery aging. If it is determined that it is within the OCV range, find the historical OCV-SOC curve of the battery to be reconstructed based on the initial OCV value to obtain the initial SOC value (SOC_Init). Construct the initial OCV-SOC point (SOC_Init, OCV_Init) based on the initial SOC value and the initial OCV value.

[0137] Next, after the battery to be reconfigured is initially powered on, data such as current I, voltage U, and temperature T of the battery to be reconfigured are collected to determine whether the battery to be reconfigured is in the first discharge condition. When it is determined that the battery to be reconfigured is in the first discharge condition, the discharge end SOC value, i.e., SOC_dsg, of the battery to be reconfigured at the end of the discharge condition is obtained using the aforementioned data such as current I, voltage U, and temperature T.

[0138] Specifically, in calculating the SOC value at the end of discharge, the collected current I is used for ampere-hour integration to obtain the discharge capacity Q_dsg, and the SOC_dsg at the end of the discharge condition is calculated using ampere-hour integration. Furthermore, to reduce the cumulative error caused by the ampere-hour integration method, the difference between the initial SOC value and the SOC value at the end of discharge can be set to be less than or equal to 40%. Also, the battery to be reconfigured was not charged by plug-in charging before it went into hibernation mode.

[0139] After the initial discharge ends, after the battery to be reconfigured has undergone sufficient dormancy, such as 1 hour (i.e., 1H), the battery to be reconfigured is powered on again to obtain the stable open-circuit voltage OCV_dsg corresponding to the battery to be reconfigured at the time of the second power-on. Then, a new OCV-SOC point (SOC_dsg, OCV_dsg) is formed based on the SOC value at the end of the discharge and the open-circuit voltage value OCV_dsg.

[0140] Step 304: Obtain the discharge data during the initial power-on, and perform parameter identification on the discharge data using a preset second-order FFRLS algorithm to generate the parameter identification OCV-SOC curve of the battery to be reconfigured during the initial power-on process.

[0141] Specifically, in this embodiment, the parameter identification method driven by real-time measurement data such as current, voltage, and temperature of the power battery can realize online updating of model parameters and redundant design of the prediction model. Therefore, in some implementations of this embodiment, the least squares algorithm FFRLS based on the forgetting factor is used to identify the battery's OCV-SOC curve. Specifically, the least squares algorithm FFRLS based on the forgetting factor reduces the information content of old data by incorporating a forgetting factor into the measurement data, creating conditions for supplementing new data.

[0142] Furthermore, before parameter identification using the second-order FFRLS algorithm, a least squares algorithm based on the forgetting factor, FFRLS, is first constructed. Then, the forgetting factor in the second-order FFRLS is adjusted to minimize the deviation between the OCV-SOC curve identified by the experimental battery data parameters and the offline calibrated OCV-SOC curve, thus obtaining the optimal FFRLS algorithm for the battery characteristics.

[0143] Specifically, the process of establishing the second-order FFRLS algorithm is as follows:

[0144] Step 1: Establish the second-order FFRLS equivalent circuit model of the sample battery:

[0145] The mathematical model of the output voltage and input current of a power battery can be obtained from Kirchhoff's laws and Laplace's transform, as shown in the following formula:

[0146] The transfer function of this mathematical model is as follows:

[0147] In the formula, G(s) is a mathematical representation of the system's dynamic characteristics, describing the relationship between the power battery input (related to the load current) and output (the difference between the terminal voltage and the open-circuit voltage) in the Laplace domain (s-domain). t This refers to the terminal voltage of the power battery, that is, the actual voltage between the positive and negative terminals of the power battery. U t (s) refers to the terminal voltage U of the power battery. t The Laplace transform of U. oc (s) refers to the open-circuit voltage U oc The Laplace transform of U. oc This is the open-circuit voltage, which is the voltage of the power battery when it is under no-load (i.e., open-circuit) conditions. L (s) refers to the load current i L The Laplace transform of . i L This refers to the load current flowing through the power battery, i.e., the current supplied by the power battery to the external circuit (during discharge) or the current supplied by the external circuit to the power battery (during charging). R i R is the internal resistance in ohms. D1 and RD2 For different polarization resistances, C D1 and C D2 For different polarization capacitors.

[0148] Let E L (s)=U t (s)-U oc (s), the second-order FFRLS equivalent circuit model is as follows:

[0149] Step 2: Based on the bilinear transformation rule, determine the state equations of the second-order FFRLS equivalent circuit model.

[0150] Based on the bilinear transformation rule, the s-plane can be mapped to the Z-plane for discretization (i.e., discretization from the time domain to the frequency domain):

[0151] Where Δt is the time step, for example, the time interval from time k to time k+1, and b1, b2, b3, b4, and b5 are undetermined coefficients, the difference form of the state equation of the second-order FFRLS equivalent circuit model can be obtained: 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 .

[0152] Among them, U t,k U represents the terminal voltage of the power battery at time k; oc,k U represents the open-circuit voltage of the power battery at time k. t,k-1 and U t,k-2 These represent the terminal voltages of the power battery at time k-1 and k-2, respectively. L,k I L,k-1 and I L,k-2 These represent the load currents at times k, k-1, and k-2, respectively.

[0153] Step 3: Optimize the second-order FFRLS equivalent circuit model to reduce the divergence of the OCV-SOC curve.

[0154] Adjust U oc,k The coefficient (1-b1-b2) is 1, which can transfer the divergence of the identification parameter OCV-SOC curve to other identification parameters, thereby resulting in less divergence in the identified OCV value and better stability.

[0155] Define the system data matrix as: Φ 2,k =[1 Ut,k -U t，k-1 U t,k -U t，k-2 i L,k i L,k-1 i L，k-2 ];

[0156] The system parameter matrix is ​​defined as follows:

[0157] Finally, the Thevenin model transfer function (system output equation) can be simplified to: y k =Φ 2,k θ 2,k ;

[0158] Input data variables are constructed based on real-time sampled data such as current, voltage and temperature of the power battery. Then, the corresponding squared gain and covariance are calculated to carry out online identification and updating of parameters.

[0159] Step 4: Import actual test conditions and calibrate the forgetting factor of the second-order FFRLS algorithm:

[0160] a. The test battery was subjected to the China Light-Duty Vehicle Test Cycle (CLTC) test, with the SOC discharged from 80% to 30%.

[0161] b. Using the current and voltage data during discharge as raw data, the second-order FFRLS algorithm is used to identify the OCV online, and the OCV-SOC curve corresponding to the test battery is constructed based on the identified OCV and the SOC of the test battery during the discharge.

[0162] c. Compare the online identified OCV-SOC curve with the laboratory measured OCV-SOC curve, and adjust the forgetting factor to minimize the error between the two.

[0163] d. For this battery characteristic, when the forgetting factor is 0.998, the OCV-SOC curve is the smoothest and the error is the smallest.

[0164] In this embodiment, after constructing the second-order FFRLS algorithm based on the forgetting factor through steps one to four, the OCV-SOC curve is identified online using the second-order FFRLS algorithm. Specifically, using the voltage and current data collected in real-time during the initial power-on process, the OCV-SOC value is identified online using the second-order FFRLS algorithm to obtain the OCV-SOC curve of the battery to be reconstructed at each moment during power-on. The OCV-SOC point corresponding to the parameter-identified OCV-SOC curve at the end of discharge is called OCV_FFRLS. In some embodiments of this embodiment, when constructing the second-order FFRLS algorithm using the test battery shown in Figure 4, the difference calculated during the construction process is shown as the difference line in Figure 8, and the parameter-identified OCV-SOC curve generated according to the established second-order FFRLS algorithm can be represented by the OCV_identify line in Figure 8.

[0165] Step 305: Based on the static OCV value, the discharge end SOC value, and the parameter identification OCV-SOC curve, construct a trustworthy OCV-SOC point, and reconstruct the OCV-SOC curve of the battery to be reconstructed according to the trustworthy OCV-SOC point and the initial trustworthy OCV-SOC point set.

[0166] Specifically, in this embodiment, a new OCV-SOC point is obtained based on the discharge end SOC value corresponding to the initial power-on end and the resting OCV value corresponding to the second power-on moment. To verify the reliability of the new OCV-SOC point, the historical OCV-SOC curve of the battery to be reconstructed can be found based on the resting OCV value to obtain the resting SOC value corresponding to the resting OCV value, i.e., SOC_dsg_false.

[0167] Next, the error between SOC_dsg_false and SOC_dsg is obtained. When the error is determined to be within a preset error range, i.e., error = |SOC_dsg - SOC_dsg_false| and error < 3%, the new OCV-SOC point, i.e., OCV_Relax, composed of the discharge end SOC value and the resting OCV value, is deemed valid.

[0168] After confirming the validity of the new OCV-SOC point, the difference between the open-circuit voltage in the OCV_FFRLS point of the parameter identification OCV-SOC curve at the end of discharge and the resting OCV value, i.e., OCV_dsg, is calculated, and it is determined whether the difference is within a preset tolerance range. In some embodiments of this example, the tolerance range is set to 30mV.

[0169] When the difference is within a preset tolerance range, the midpoint between the OCV_FFRLS and the new OCV-SOC point is taken as the trustworthy OCV-SOC point, i.e., OCV_Trust_New. Specifically, the process of obtaining OCV_Trust_New can be referred to Figure 9. As shown in Figure 9, the OCV_Relax obtained after a sufficient resting time is the square point shown in Figure 9, while the OCV_FFRLS identified by the second-order FFRLS parameters is the triangular point shown in Figure 9. If the deviation between OCV_Relax and OCV_FFRLS is less than 30mv, then the bidirectional verification of the two methods is accurate enough at this point. Then, the midpoint between OCV_Relax and OCV_FFRLS is taken as the new trustworthy OCV point, OCV_Trust_New, as the large circle shown in Figure 9.

[0170] Specifically, in this embodiment, the nearest initially trusted OCV-SOC point to the trusted OCV-SOC point is obtained from the initial trusted OCV-SOC point set, and the difference in SOC between the initial trusted OCV-SOC point and the trusted OCV-SOC point is obtained. The update method of the initial trusted OCV-SOC point set is then determined based on the difference and a preset tolerance threshold. The tolerance threshold can be set to 5%.

[0171] When the difference is less than the tolerance threshold, the trusted OCV-SOC point replaces the initial trusted OCV-SOC point. Specifically, as shown in Figure 10, the point (SOC = 50%, OCV = 3.681V) is replaced with (SOC = 48%, OCV = 3.654V).

[0172] When the difference is greater than or equal to the tolerance threshold, the trustworthy OCV-SOC point is added to the initial trustworthy OCV-SOC point set. Specifically, as shown in Figure 11, the point (SOC = 35%, OCV = 3.587V) is added between the points (SOC = 15%, OCV = 3.500V) and (SOC = 50%, OCV = 3.681V).

[0173] Furthermore, referring to the method of updating the initial trusted OCV-SOC point set as shown in Figures 9 to 11, the number of data points in the updated trusted OCV-SOC point set is obtained in real time. When the number reaches a preset threshold, such as when the number is greater than or equal to 15, curve fitting is performed on each data point in the trusted OCV-SOC point set to generate an OCV-SOC curve. The reconstructed OCV-SOC curve can be referred to Figure 12, and the historical OCV-SOC curve of the battery to be reconstructed is replaced by the OCV-SOC curve.

[0174] This embodiment discloses an online reconstruction method for the OCV-SOC curve of a battery. It constructs overlapping intervals to select initial OCV-SOC curve points with high reliability. Then, it uses repeated power-on cycles and preset judgment conditions to obtain higher-precision OCV-SOC points for the battery. By calculating the deviation between these OCV-SOC points and the OCV-SOC curve identified by the second-order FFRLS algorithm, the accuracy of the new trustworthy points used for curve reconstruction is ensured. Finally, the newly constructed trustworthy points are used to update the final set of trustworthy points, thereby reconstructing a more accurate OCV-SOC curve that conforms to the current actual state of the battery. The updated OCV-SOC curve replaces the old one. During the battery's power-on process, the initial SOC value is obtained by looking up a table using the new OCV-SOC curve.

[0175] Specific application scenarios

[0176] Scenario 1: When the battery system of an electric vehicle is in a static state (e.g., when the electric vehicle is first powered on after parking, or after charging has ended and the battery has been idle for a period of time), the Battery Management System (BMS) initiates an initial state calibration procedure. This initial state calibration procedure specifically includes: measuring the voltage across the battery terminals; after confirming that the load is disconnected and the voltage is stable, identifying this voltage value as the current open-circuit voltage value. Subsequently, the BMS accesses and calls the OCV-SOC curve database (which includes updated OCV-SOC curves) generated online through this application and reflects the current aging state of the battery. The Battery Management System performs real-time comparison and lookup mapping between the measured OCV value and the updated OCV-SOC curve, outputting an accurate initial SOC value. The initial SOC value is a real-time estimated SOC value when the electric vehicle's battery system is in a static state.

[0177] The embodiments of this application can effectively eliminate or reset the accumulated SOC error caused by factors such as current sensor cumulative error, coulomb count drift, and battery self-discharge, providing an accurate starting point for all subsequent real-time estimations based on ampere-hour integration (such as real-time SOC estimates).

[0178] Scenario 2: When the battery enters a quasi-steady state (e.g., during constant speed driving of an electric vehicle, brief parking, or the constant voltage phase of charging), the load current is small and stable. The BMS collects the terminal voltage at this time and, combined with known load current and battery internal resistance information, estimates an instantaneous OCV value using the EKF algorithm. The battery management system then matches the estimated OCV value with the updated OCV-SOC curve to generate an independent, voltage-based SOC reference value. This SOC reference value is the real-time estimated SOC value of the battery system when it is in a quasi-steady state.

[0179] For aged batteries, the updated OCV-SOC curve provides an accurate, non-linear OCV-SOC mapping relationship, making the benchmark for this feedback correction circuit accurate.

[0180] The embodiments of this application can realize real-time correction of integral error: dynamically correct the cumulative deviation of SOC generated during the dynamic operation of electric vehicles.

[0181] Suppressing estimation drift: Ensures that the SOC estimate does not diverge significantly with time and mileage throughout the driving process, improving the driver's confidence in the remaining driving range prediction throughout the driving process.

[0182] Improve accuracy throughout the entire battery lifecycle: Enable the BMS to adapt to battery aging and maintain high accuracy in SOC estimation throughout the battery life, dynamically correcting the cumulative SOC deviation generated during the dynamic operation of electric vehicles.

[0183] Specifically, the electric vehicle uses the real-time estimated SOC value as the current remaining battery power and outputs the current remaining battery power to the vehicle screen. The method provided in this application embodiment can improve the accuracy of estimating the current remaining battery power. For example, the electric vehicle outputs the current remaining battery power to the vehicle screen. When the current remaining battery power is less than a preset level, the electric vehicle issues a warning message. For instance, the electric vehicle can output a voice warning message such as "low battery" through the audio system, or display a warning message such as "low battery" on the vehicle screen, so that the driver can know the accurate current remaining battery power and promptly detect whether the current remaining battery power is too low, so as to charge it in time.

[0184] In this embodiment, the term "multiple" refers to two or more.

[0185] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above descriptions are merely specific embodiments of this application and are not intended to limit the scope of protection of this application. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application for those skilled in the art.

Claims

1. A method for online reconstruction of the OCV-SOC curve of a battery, wherein, include: Obtain the overlapping interval of the OCV-SOC curves of the battery under test and the initial set of trustworthy OCV-SOC points; Obtain the initial OCV value of the battery under test. If the initial OCV value is within the overlap range of the OCV-SOC curve, then obtain the first SOC value. Based on the preset first discharge condition and the first SOC value, obtain the discharge end SOC value and the static OCV value; The discharge data in the first discharge condition is obtained, and the discharge data is parameter identified to obtain the parameter identified OCV-SOC curve; Based on the discharge end SOC value, the static OCV value and the parameters, identify the OCV-SOC curve and obtain a reliable OCV-SOC point; Based on the trusted OCV-SOC points and the initial set of trusted OCV-SOC points, the OCV-SOC curve of the battery under test is constructed.

2. The online reconstruction method for the OCV-SOC curve of a battery according to claim 1, wherein, The acquisition of the overlapping interval of the OCV-SOC curves of the battery under test and the initial set of trustworthy OCV-SOC points includes: Obtain the initial OCV-SOC curve of the test battery and the aged OCV-SOC curve of the corresponding aged battery; wherein the test battery and the battery under test are of the same type; By comparing the initial OCV-SOC curve and the aged OCV-SOC curve, the overlapping region of the OCV-SOC curves is obtained; wherein, the overlapping region of the OCV-SOC curves includes the OCV region; Based on the OCV interval, multiple initial trusted OCV-SOC points and the initial trusted SOC point corresponding to each initial trusted OCV-SOC point are obtained from the pre-constructed OCV-SOC point set to construct the initial trusted OCV-SOC point set.

3. The online reconstruction method for the OCV-SOC curve of a battery according to claim 2, wherein, The process of obtaining the initial OCV value of the battery under test, and if the initial OCV value is within the overlap range of the OCV-SOC curve, then obtaining the first SOC value, includes: Obtain the first OCV value of the battery under test at the first power-on moment, and perform OCV correction on the first OCV value to obtain the initial OCV value of the battery under test; When the initial OCV value is determined to be within the OCV range, the historical OCV-SOC curve of the battery under test is retrieved based on the initial OCV value to obtain the first SOC value.

4. The online reconstruction method for the OCV-SOC curve of a battery according to claim 2, wherein, The step of obtaining the discharge end SOC value and the resting OCV value based on the preset first discharge condition and the first SOC value includes: Collect current data under the first discharge condition; Calculate the SOC value of the battery under test at the end of the first discharge condition based on the current data. Based on preset dormancy conditions and the first discharge condition, the static OCV value of the battery under test at the second power-on time is obtained.

5. The online reconstruction method for the OCV-SOC curve of a battery according to claim 4, wherein, The step of parameter identification of the discharge data to obtain the parameter-identified OCV-SOC curve includes: The discharge data is parameter identified based on a pre-established second-order FFRLS algorithm to obtain the parameter-identified OCV-SOC curve.

6. The online reconstruction method for the OCV-SOC curve of a battery according to claim 3, wherein, The step of identifying the OCV-SOC curve based on the discharge end SOC value, the resting OCV value, and the parameters to obtain a reliable OCV-SOC point includes: Based on the static OCV value, the historical OCV-SOC curve is found, and the static SOC value corresponding to the static OCV value is obtained; Obtain the first deviation between the static SOC value and the discharge-end SOC value, and determine that the first deviation satisfies a preset first deviation condition; Based on the discharge end SOC value, the parameter identification OCV-SOC curve is searched to obtain the discharge end OCV value corresponding to the discharge end SOC value in the parameter identification OCV-SOC curve; A reliable OCV-SOC point is formed based on the discharge end OCV value and the discharge end SOC value.

7. The online reconstruction method for the OCV-SOC curve of a battery according to claim 6, wherein, The step of forming a reliable OCV-SOC point based on the discharge end OCV value and the discharge end SOC value includes: Obtain the second deviation between the discharge end OCV value and the static OCV value; When the second deviation meets the preset second deviation condition, the intermediate OCV value between the discharge end OCV value and the static OCV value, and the intermediate SOC value between the static SOC value and the discharge end SOC value are obtained; Based on the intermediate OCV value and the intermediate SOC value, a trustworthy OCV-SOC point is formed.

8. The online reconstruction method for the OCV-SOC curve of a battery according to claim 6, wherein, The step of constructing the OCV-SOC curve of the battery under test based on the trusted OCV-SOC points and the initial set of trusted OCV-SOC points includes: Obtain the initial trusted OCV-SOC point that is closest to the trusted OCV-SOC point from the initial trusted OCV-SOC point set; Calculate the third deviation between the initial trusted SOC value corresponding to the initial trusted OCV-SOC point and the discharge end SOC value; The initial set of trustworthy OCV-SOC points is updated based on the third deviation and the trustworthy OCV-SOC points to obtain a set of trustworthy OCV-SOC points.

9. The online reconstruction method for the OCV-SOC curve of a battery according to claim 8, wherein, The step of updating the initial set of trustworthy OCV-SOC points based on the third deviation and the trustworthy OCV-SOC points to obtain a set of trustworthy OCV-SOC points includes: When the third deviation meets the preset third deviation condition, the initial trusted OCV-SOC points in the initial trusted OCV-SOC point set are updated according to the trusted OCV-SOC points to obtain a trusted OCV-SOC point set. When the third deviation does not meet the preset third deviation condition, the trustworthy OCV-SOC point is added to the initial trustworthy OCV-SOC point set to obtain a trustworthy OCV-SOC point set.

10. A method for online reconstruction of the OCV-SOC curve of a battery according to any one of claims 1-9, wherein, The step of constructing the OCV-SOC curve of the battery under test based on the trusted OCV-SOC points and the initial set of trusted OCV-SOC points includes: Obtain the number of data points in the trusted OCV-SOC point set; When the number reaches a preset threshold, curve fitting is performed on each data point in the set of trustworthy OCV-SOC points to generate an OCV-SOC curve. Replace the historical OCV-SOC curve of the battery under test with the OCV-SOC curve.

11. The online reconstruction method for the OCV-SOC curve of a battery according to claim 5, wherein, The process of establishing the second-order FFRLS algorithm is as follows: Obtain the initial OCV-SOC curve corresponding to the test battery; According to the preset second discharge condition, the test discharge data generated when the test battery is discharged is obtained; The test discharge data is parameter identified using a preset initial second-order FFRLS algorithm to generate a test OCV-SOC curve. The curve deviation between the test OCV-SOC curve and the initial OCV-SOC curve is obtained, and the forgetting factor of the initial second-order FFRLS algorithm is continuously adjusted according to the curve deviation until the curve deviation meets the preset curve deviation condition, thus obtaining the second-order FFRLS algorithm.

12. An online reconfiguration system for the OCV-SOC curve of a battery, wherein, It includes a point set acquisition module, a SOC value lookup module, a data acquisition module, a parameter identification module, a data point filtering module, and a curve reconstruction module; The point set acquisition module is used to acquire the overlapping interval of the OCV-SOC curves of the battery under test and the initial trustworthy OCV-SOC point set. The SOC value lookup module is used to obtain the initial OCV value of the battery under test. If the initial OCV value is in the overlapping range of the OCV-SOC curve, then the first SOC value is obtained. The data acquisition module is used to acquire the discharge end SOC value and the static OCV value based on the preset first discharge condition and the first SOC value. The parameter identification module is used to acquire discharge data in the first discharge condition and perform parameter identification on the discharge data to obtain the parameter identification OCV-SOC curve. The data point filtering module is used to identify the OCV-SOC curve based on the discharge end SOC value, the static OCV value and the parameters, and to obtain trustworthy OCV-SOC points. The curve reconstruction module is used to construct the OCV-SOC curve of the battery under test based on the trusted OCV-SOC points and the initial set of trusted OCV-SOC points.

13. The online OCV-SOC curve reconstruction system for a battery according to claim 12, wherein, The point set acquisition module is specifically configured as follows: Obtain the initial OCV-SOC curve of the test battery and the aged OCV-SOC curve of the corresponding aged battery; wherein the test battery and the battery under test are of the same type; By comparing the initial OCV-SOC curve and the aged OCV-SOC curve, the overlapping region of the OCV-SOC curves is obtained; wherein, the overlapping region of the OCV-SOC curves includes the OCV region; Based on the OCV interval, multiple initial trusted OCV-SOC points and the initial trusted SOC point corresponding to each initial trusted OCV-SOC point are obtained from the pre-constructed OCV-SOC point set to construct the initial trusted OCV-SOC point set.

14. The online OCV-SOC curve reconstruction system for a battery according to claim 13, wherein, The SOC value lookup module is specifically configured as follows: Obtain the first OCV value of the battery under test at the first power-on moment, and perform OCV correction on the first OCV value to obtain the initial OCV value of the battery under test; When the initial OCV value is determined to be within the OCV range, the historical OCV-SOC curve of the battery under test is retrieved based on the initial OCV value to obtain the first SOC value.

15. The online OCV-SOC curve reconstruction system for a battery according to claim 13, wherein, The data acquisition module is specifically configured as follows: Collect current data under the first discharge condition; Calculate the SOC value of the battery under test at the end of the first discharge condition based on the current data. Based on preset dormancy conditions and the first discharge condition, the static OCV value of the battery under test at the second power-on time is obtained.

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