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

Abstract

The invention discloses an online reconstruction method and system for an OCV-SOC curve of a battery. The method comprises the steps of obtaining an OCV-SOC curve coincidence interval corresponding to a to-be-detected battery and an initial trusted OCV-SOC point set; obtaining an initial OCV value of the battery to be detected, and if the initial OCV value is in the OCV-SOC curve overlapping interval, obtaining a first SOC value; based on a preset discharge working condition and the first SOC value, obtaining a discharge end SOC value and a standing OCV value; obtaining discharge data in the discharge working condition, and performing parameter identification on the discharge data to obtain a parameter identification OCV-SOC curve; identifying an OCV-SOC curve based on the discharge end SOC value, the standing OCV value and the parameter, and obtaining a trusted OCV-SOC point; and on the basis of the trusted OCV-SOC points and the initial trusted OCV-SOC point set, an OCV-SOC curve of the battery to be tested is constructed, and the accuracy of OCV-SOC curve reconstruction is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and in particular to a method and system for online reconstruction of an OCV-SOC curve of a battery. Background Art

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

[0003] In the prior art, OCV and SOC curves are usually obtained through laboratory testing. In the process of obtaining the curves through laboratory testing, since the OCV and SOC curves change as the battery ages, the entire testing process requires high test costs and increases the algorithm development cycle to obtain the OCV and SOC curves for the entire life cycle.

[0004] Moreover, the laboratory testing technology limits the results of each test to only obtain the OCV state curve of a specific single cell under a specific test environment. And this offline calibration method is only for battery cells that have just left the factory, and cannot test aged battery cells on new energy vehicles. Furthermore, the offline testing method assumes that the OCV-SOC curves of batteries are consistent under the same SOH. In fact, due to differences in production processes and battery operating conditions, even if the SOH is the same, the OCV-SOC curves of different battery packs may be different. The test results obtained from laboratory testing cannot resolve the errors caused by this inconsistency. Summary of the invention

[0005] In order to solve the above technical problems, the present invention discloses a method and system for online reconstruction of an OCV-SOC curve of a battery, so as to solve the above technical problems and improve the accuracy of the OCV-SOC curve.

[0006] In order to achieve the above object, the present invention discloses a method for online reconstruction of an OCV-SOC curve of a battery, comprising:

[0007] Obtain the OCV-SOC curve overlap interval and initial trusted OCV-SOC point set corresponding to the battery to be tested;

[0008] Obtaining an initial OCV value of the battery to be tested, and if the initial OCV value is in the overlap interval of the OCV-SOC curve, obtaining a first SOC value;

[0009] Based on the preset discharge condition and the first SOC value, obtaining the discharge end SOC value and the static OCV value;

[0010] Acquiring discharge data in the discharge condition, and performing parameter identification on the discharge data to obtain a parameter-identified OCV-SOC curve;

[0011] Obtaining a trustworthy OCV-SOC point based on the end-of-discharge SOC value, the static OCV value and the parameter-identified OCV-SOC curve;

[0012] Based on the trusted OCV-SOC point and the initial trusted OCV-SOC point set, an OCV-SOC curve of the battery to be tested is constructed.

[0013] The present invention discloses a method for online reconstruction of an OCV-SOC curve of a battery. First, the overlap interval of the OCV-SOC curve and the initial trusted OCV-SOC point set corresponding to the battery to be tested are obtained, which provides a reference basis for data point screening and ensures the accuracy of the data points used for curve reconstruction. Then, the initial OCV value of the battery to be tested is obtained, and a first SOC value is obtained by matching the initial OCV value with the overlap interval of the OCV-SOC curve to ensure the accuracy of the first SOC value. Secondly, based on the preset discharge condition and the first SOC value, the discharge end SOC value and the static OCV value are obtained, and the discharge data in the discharge condition is collected at the same time, and the discharge data is parameter identified to obtain a parameter-identified OCV-SOC curve, so as to realize online reconstruction of the OCV-SOC curve according to the data corresponding to the operation process of the battery. Among them, based on the end-of-discharge SOC value, the static OCV value and the parameter-identified OCV-SOC curve, an effective and accurate trusted OCV-SOC point is obtained, and based on the trusted OCV-SOC point and the initial trusted OCV-SOC point set obtained as a reference, the OCV-SOC curve of the battery to be tested is constructed to ensure the accuracy of the OCV-SOC curve.

[0014] As a preferred example, the obtaining of the OCV-SOC curve overlap interval and the initial trusted OCV-SOC point set corresponding to the battery to be tested includes:

[0015] Obtaining an initial OCV-SOC curve of a test battery and an aged OCV-SOC curve of an aged battery corresponding to the test battery; wherein the test battery and the battery to be tested are of the same type;

[0016] Comparing the initial OCV-SOC curve with the aged OCV-SOC curve to obtain an OCV-SOC curve overlap interval; wherein the OCV-SOC curve overlap interval includes an OCV interval;

[0017] Based on the OCV interval, a plurality of initial trusted OCV-SOC points and an initial trusted SOC point corresponding to each of the initial trusted OCV-SOC points are obtained from a pre-constructed OCV-SOC point set to construct an initial trusted OCV-SOC point set.

[0018] The present invention first obtains a test battery of the same type as the battery currently undergoing curve reconstruction to ensure the accuracy of the constructed curve overlap interval, and then compares the OCV-SOC curve of the test battery with the aging battery corresponding to the test battery, 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.

[0019] As a preferred example, the obtaining of the initial OCV value of the battery to be tested, and if the initial OCV value is in the overlap interval of the OCV-SOC curve, obtaining the first SOC value, includes:

[0020] Obtaining a first OCV value of the battery to be tested at a first power-on moment, and performing OCV correction on the first OCV value to obtain an initial OCV value of the battery to be tested;

[0021] When it is determined that the initial OCV value is in the OCV range, a historical OCV-SOC curve of the battery to be tested is searched according to the initial OCV value to obtain a first SOC value.

[0022] The present invention ensures that an accurate first SOC value can still be obtained after the battery is aged based on the comparison between the initial OCV value and the OCV interval.

[0023] As a preferred example, the obtaining of the discharge end SOC value and the static OCV value based on the preset discharge condition and the first SOC value includes:

[0024] Collecting current data in the discharge condition;

[0025] Calculating, according to the current data, a discharge end SOC value of the battery under test corresponding to when the discharge condition ends;

[0026] Based on the preset dormancy condition and the discharge condition, a static OCV value of the battery to be tested at the second power-on moment is obtained.

[0027] The present invention utilizes the current data in the discharge condition to ensure that a more accurate SOC value at the end of discharge is obtained, and then utilizes the discharge condition and the sleep condition to collect the stable static OCV value of the battery at the second power-on moment, thereby improving the accuracy of curve reconstruction.

[0028] As a preferred example, the performing parameter identification on the discharge data to obtain a parameter-identified OCV-SOC curve includes:

[0029] The discharge data is subjected to parameter identification based on a pre-established second-order FFRLS algorithm to obtain a parameter-identified OCV-SOC curve.

[0030] The present invention performs parameter identification based on the second-order FFRLS algorithm to ensure the accuracy of the parameter-identified OCV-SOC curve.

[0031] As a preferred example, the obtaining of a trustworthy OCV-SOC point based on the discharge end SOC value, the static OCV value and the parameter-identified OCV-SOC curve includes:

[0032] Searching the historical OCV-SOC curve according to the static OCV value to obtain a static SOC value corresponding to the static OCV value;

[0033] Obtaining a first deviation between the static SOC value and the end-of-discharge SOC value, and determining whether the first deviation satisfies a preset first deviation condition;

[0034] Searching the parameter identification OCV-SOC curve based on the end-of-discharge SOC value to obtain the end-of-discharge OCV value corresponding to the end-of-discharge SOC value in the parameter identification OCV-SOC curve;

[0035] A trusted OCV-SOC point is formed according to the end-of-discharge OCV value and the end-of-discharge SOC value.

[0036] The present invention obtains a stable static OCV value according to a preset sleep condition, so as to obtain a more accurate static SOC value from the historical OCV-SOC curve corresponding to the battery according to the stable static OCV value, and then compares the deviation between the static SOC value and the discharge end SOC value in the historical curve, so as to ensure the accuracy of the discharge end SOC value used for curve reconstruction according to the deviation and the preset deviation condition, and then use the accurate discharge end SOC value to obtain the corresponding discharge end OCV value, so as to construct the accurate trusted OCV-SOC point.

[0037] As a preferred example, forming a trustworthy OCV-SOC point according to the end-of-discharge OCV value and the end-of-discharge SOC value includes:

[0038] Obtaining a second deviation between the end-of-discharge OCV value and the static OCV value;

[0039] When the second deviation satisfies a preset second deviation condition, obtaining an intermediate OCV value between the end-of-discharge OCV value and the static OCV value and an intermediate SOC value between the static SOC value and the end-of-discharge SOC value;

[0040] A trusted OCV-SOC point is formed based on the intermediate OCV value and the intermediate SOC value.

[0041] The present invention uses SOC as a reference to calculate the deviation between the end-of-discharge OCV value and the static OCV value, further verifying the accuracy of the end-of-discharge OCV value and the end-of-discharge SOC value, thereby ensuring the accuracy of the trusted OCV-SOC point.

[0042] As a preferred example, the constructing the OCV-SOC curve of the battery to be tested based on the trusted OCV-SOC point and the initial trusted OCV-SOC point set includes:

[0043] Acquire, from the initial trusted OCV-SOC point set, an initial trusted OCV-SOC point that is closest to the trusted OCV-SOC point;

[0044] Calculating a third deviation between an initial trusted SOC value corresponding to the initial trusted OCV-SOC point and the end-of-discharge SOC value;

[0045] The initial trusted OCV-SOC point set is updated according to the third deviation and the trusted OCV-SOC point to obtain a trusted OCV-SOC point set.

[0046] After obtaining a trusted OCV-SOC point with a high degree of credibility, the present invention uses the trusted OCV-SOC point to update a pre-constructed standard initial trusted OCV-SOC point set, so that the updated trusted OCV-SOC point set fits the actual operating condition of the battery, thereby improving the accuracy of the OCV-SOC curve.

[0047] As a preferred example, the updating of 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 includes:

[0048] When the third deviation satisfies a preset third deviation condition, updating the initial trusted OCV-SOC point in the initial trusted OCV-SOC point set according to the trusted OCV-SOC point to obtain a trusted OCV-SOC point set;

[0049] When the third deviation does not meet the preset third deviation condition, the trusted OCV-SOC point is added to the initial trusted OCV-SOC point set to obtain a trusted OCV-SOC point set.

[0050] The present invention utilizes the deviation of the SOC to set different point set update modes, and 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.

[0051] As a preferred example, the constructing the OCV-SOC curve of the battery to be tested based on the trusted OCV-SOC point and the initial trusted OCV-SOC point set includes:

[0052] Obtaining the number of data points in the trusted OCV-SOC point set;

[0053] When the number reaches a preset quantity threshold, curve fitting is performed according to each data point in the trusted OCV-SOC point set to generate an OCV-SOC curve;

[0054] The historical OCV-SOC curve of the battery to be tested is replaced according to the OCV-SOC curve.

[0055] The present invention ensures the accuracy of curve reconstruction by setting the quantity threshold.

[0056] As a preferred example, the establishment process of the second-order FFRLS algorithm is:

[0057] Obtain the initial OCV-SOC curve corresponding to the test battery;

[0058] According to a preset first discharge condition, obtaining test discharge data generated when the test battery is discharged;

[0059] Performing parameter identification on the test discharge data by using a preset initial second-order FFRLS algorithm to generate a test OCV-SOC curve;

[0060] The curve deviation between the test OCV-SOC curve and the initial OCV-SOC curve is obtained to continuously adjust the forgetting factor of the initial second-order FFRLS algorithm according to the curve deviation until the curve deviation satisfies a preset curve deviation condition, thereby obtaining a second-order FFRLS algorithm.

[0061] The present invention performs a working condition test on a test battery 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.

[0062] On the other hand, the present invention discloses an online reconstruction system of an OCV-SOC curve of a battery, comprising a point set acquisition module, an SOC value search module, a data acquisition module, a parameter identification module, a data point screening module and a curve reconstruction module;

[0063] The point set acquisition module is used to obtain the OCV-SOC curve overlap interval and the initial trusted OCV-SOC point set corresponding to the battery to be tested;

[0064] The SOC value search module is used to obtain the initial OCV value of the battery to be tested, and if the initial OCV value is in the overlap interval of the OCV-SOC curve, a first SOC value is obtained;

[0065] The data acquisition module is used to acquire the discharge end SOC value and the static OCV value based on the preset discharge condition and the first SOC value;

[0066] The parameter identification module is used to obtain discharge data in the discharge condition, and perform parameter identification on the discharge data to obtain a parameter identification OCV-SOC curve;

[0067] The data point screening module is used to obtain a trustworthy OCV-SOC point based on the end-of-discharge SOC value, the static OCV value and the parameter-identified OCV-SOC curve;

[0068] The curve reconstruction module is used to construct the OCV-SOC curve of the battery to be tested based on the trusted OCV-SOC point and the initial trusted OCV-SOC point set.

[0069] The present invention discloses an online reconstruction system for the OCV-SOC curve of a battery. First, the overlap interval of the OCV-SOC curve and the initial trusted OCV-SOC point set corresponding to the battery to be tested are obtained, which provides a reference basis for data point screening and ensures the accuracy of the data points used for curve reconstruction. Then, the initial OCV value of the battery to be tested is obtained, and a first SOC value is obtained by matching the initial OCV value with the overlap interval of the OCV-SOC curve to ensure the accuracy of the first SOC value. Secondly, based on the preset discharge condition and the first SOC value, the discharge end SOC value and the static OCV value are obtained, and the discharge data in the discharge condition is collected at the same time, and the discharge data is parameter identified to obtain a parameter-identified OCV-SOC curve, so as to realize the online reconstruction of the OCV-SOC curve according to the data corresponding to the operation process of the battery. Among them, based on the end-of-discharge SOC value, the static OCV value and the parameter-identified OCV-SOC curve, an effective and accurate trusted OCV-SOC point is obtained, and based on the trusted OCV-SOC point and the initial trusted OCV-SOC point set obtained as a reference, the OCV-SOC curve of the battery to be tested is constructed to ensure the accuracy of the OCV-SOC curve. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 : A schematic flow chart of an online reconstruction method of an OCV-SOC curve of a battery disclosed in an embodiment of the present invention;

[0071] Figure 2 : A structural schematic diagram of an online reconstruction system for an OCV-SOC curve of a battery disclosed in an embodiment of the present invention;

[0072] Figure 3 : A schematic flow chart of an online reconstruction method of an OCV-SOC curve of a battery disclosed in another embodiment of the present invention;

[0073] Figure 4 : A schematic diagram of an OCV-SOC curve and a deviation curve of a battery before and after aging disclosed in another embodiment of the present invention;

[0074] Figure 5 : A schematic diagram of an OCV-SOC point set disclosed in another embodiment of the present invention;

[0075] Figure 6 : A schematic diagram of an initial trusted OCV-SOC point set disclosed in another embodiment of the present invention;

[0076] Figure 7 : A schematic diagram of obtaining a high-precision OCV-SOC curve point disclosed in another embodiment of the present invention;

[0077] Figure 8 : A schematic diagram of a parameter identification OCV-SOC curve and a difference curve disclosed in another embodiment of the present invention;

[0078] Fig. 9 : A schematic diagram of obtaining a trusted point OCV_Trust_new disclosed in another embodiment of the present invention;

[0079] Fig.10 : A schematic diagram of a replacement process of an initial trusted point in an initial trusted point set disclosed in another embodiment of the present invention;

[0080] Fig.11 : A schematic diagram of a process of adding a trusted point to an initial trusted point set disclosed in another embodiment of the present invention;

[0081] Fig.12 : A schematic diagram of a reconstructed OCV-SOC curve disclosed in another embodiment of the present invention. DETAILED DESCRIPTION

[0082] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0083] Embodiment 1

[0084] This embodiment discloses a method for online reconstruction of the OCV-SOC curve of a battery. For the specific implementation process of the reconstruction method, please refer to Figure 1 , mainly including steps 101 to 106, the steps are:

[0085] Step 101: Obtain the OCV-SOC curve overlap interval and the initial trusted OCV-SOC point set corresponding to the battery to be tested.

[0086] In this embodiment, the step mainly includes: obtaining an initial OCV-SOC curve of a test battery and an aged OCV-SOC curve of an aged battery corresponding to the test battery; wherein the test battery and the battery to be tested are of the same type; comparing the initial OCV-SOC curve and the aged OCV-SOC curve to obtain an overlap interval of the OCV-SOC curve; wherein the overlap interval of the OCV-SOC curve includes an OCV interval; based on the OCV interval, obtaining a number of initial trusted OCV-SOC points and an initial trusted SOC point corresponding to each of the initial trusted OCV-SOC points from a pre-constructed OCV-SOC point set to construct an initial trusted OCV-SOC point set.

[0087] In this embodiment, this step first obtains a test battery of the same type as the battery currently undergoing curve reconstruction to ensure the accuracy of the constructed curve overlap interval, and then compares the OCV-SOC curve of the test battery with the aged battery corresponding to the test battery, 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.

[0088] Step 102: obtaining an initial OCV value of the battery to be tested, and if the initial OCV value is in the overlap interval of the OCV-SOC curve, obtaining a first SOC value.

[0089] In this embodiment, this step mainly includes: obtaining a first OCV value of the battery to be tested at a first power-on moment, and performing OCV correction on the first OCV value to obtain an initial OCV value of the battery to be tested; when it is determined that the initial OCV value is in the OCV range, searching for a historical OCV-SOC curve of the battery to be tested according to the initial OCV value to obtain a first SOC value.

[0090] In this embodiment, this step ensures that an accurate first SOC value can still be obtained after the battery is aged based on the comparison between the initial OCV value and the OCV interval.

[0091] Step 103: Based on the preset discharge condition and the first SOC value, obtain the discharge end SOC value and the static OCV value.

[0092] In this embodiment, this step mainly includes: collecting current data in the discharge condition; calculating the discharge end SOC value corresponding to the battery to be tested at the end of the discharge condition according to the current data; based on the preset sleep condition and the discharge condition, obtaining the static OCV value of the battery to be tested at the second power-on time.

[0093] In this embodiment, this step utilizes the current data in the discharge condition to ensure that a more accurate SOC value at the end of discharge is obtained, and then utilizes the discharge condition and the sleep condition to collect the stable static OCV value of the battery at the second power-on moment, thereby improving the accuracy of curve reconstruction.

[0094] Step 104: Obtain discharge data in the discharge condition, and perform parameter identification on the discharge data to obtain a parameter-identified OCV-SOC curve.

[0095] In this embodiment, this step mainly includes: performing parameter identification on the discharge data based on a pre-established second-order FFRLS algorithm to obtain a parameter-identified OCV-SOC curve.

[0096] Furthermore, the establishment process of the second-order FFRLS algorithm is as follows: obtaining an initial OCV-SOC curve corresponding to a test battery; obtaining test discharge data generated when the test battery is discharged according to a preset first discharge condition; performing parameter identification on the test discharge data through a preset initial second-order FFRLS algorithm to generate a test OCV-SOC curve; obtaining a curve deviation between the test OCV-SOC curve and the initial OCV-SOC curve, so as to 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, and a second-order FFRLS algorithm is obtained.

[0097] In this embodiment, this step performs parameter identification based on the second-order FFRLS algorithm to ensure the accuracy of the parameter identification OCV-SOC curve. At the same time, the establishment process of the second-order FFRLS algorithm is tested with a test battery to adjust 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.

[0098] Step 105: Obtain a trustworthy OCV-SOC point based on the end-of-discharge SOC value, the static OCV value and the parameter-identified OCV-SOC curve.

[0099] In this embodiment, the steps are mainly as follows: searching the historical OCV-SOC curve according to the resting OCV value to obtain the resting SOC value corresponding to the resting OCV value; obtaining a first deviation between the resting SOC value and the end-of-discharge SOC value, and determining that the first deviation satisfies a preset first deviation condition; searching the parameter identification OCV-SOC curve based on the end-of-discharge SOC value to obtain the end-of-discharge OCV value corresponding to the end-of-discharge SOC value in the parameter identification OCV-SOC curve; and forming a trusted OCV-SOC point based on the end-of-discharge OCV value and the end-of-discharge SOC value.

[0100] Furthermore, a second deviation between the end-of-discharge OCV value and the resting OCV value is obtained; when the second deviation satisfies a preset second deviation condition, an intermediate OCV value between the end-of-discharge OCV value and the resting OCV value and an intermediate SOC value between the resting SOC value and the end-of-discharge SOC value are obtained; and a trusted OCV-SOC point is formed based on the intermediate OCV value and the intermediate SOC value.

[0101] In this embodiment, this step obtains a stable static OCV value according to a preset sleep condition, so as to obtain a more accurate static SOC value from the historical OCV-SOC curve corresponding to the battery according to the stable static OCV value, and then compares the deviation between the static SOC value and the discharge end SOC value in the historical curve, so as to ensure the accuracy of the discharge end SOC value used for curve reconstruction according to the deviation and the preset deviation condition, and then uses the accurate discharge end SOC value to obtain the corresponding discharge end OCV value, so as to construct the accurate trusted OCV-SOC point. Furthermore, based on SOC, the deviation between the discharge end OCV value and the static OCV value is calculated, and the accuracy of the discharge end OCV value and the discharge end SOC value is further verified, so as to ensure the accuracy of the trusted OCV-SOC point.

[0102] Step 106: constructing an OCV-SOC curve of the battery to be tested based on the trusted OCV-SOC point and the initial trusted OCV-SOC point set.

[0103] In this embodiment, the step mainly includes: obtaining the initial trusted OCV-SOC point that is 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 end-of-discharge SOC value; updating the initial trusted OCV-SOC point set according to the third deviation and the trusted OCV-SOC point to obtain a trusted OCV-SOC point set.

[0104] Further, when the third deviation satisfies a preset third deviation condition, the initial trusted OCV-SOC point in the initial trusted OCV-SOC point set is updated according to the trusted OCV-SOC point to obtain a trusted OCV-SOC point set; when the third deviation does not satisfy the preset third deviation condition, the trusted OCV-SOC point is added to the initial trusted OCV-SOC point set to obtain a trusted OCV-SOC point set.

[0105] Finally, the number of data points in the trusted OCV-SOC point set is obtained; when the number reaches a preset quantity threshold, curve fitting is performed according to each data point in the trusted OCV-SOC point set to generate an OCV-SOC curve; and the historical OCV-SOC curve of the battery to be tested is replaced according to the OCV-SOC curve.

[0106] In this embodiment, after obtaining a trusted OCV-SOC point with a high degree of credibility, this step uses the trusted OCV-SOC point to update the pre-constructed standard initial trusted OCV-SOC point set, so that the updated trusted OCV-SOC point set fits the actual operating conditions of the battery, thereby improving the accuracy of the OCV-SOC curve. Furthermore, different point set update methods are set using the SOC deviation, and the amount of data used for curve reconstruction can be selectively reduced while ensuring the accuracy of curve reconstruction, thereby improving the efficiency of curve reconstruction. Finally, by setting the quantity threshold, the accuracy of curve reconstruction is guaranteed.

[0107] On the other hand, this embodiment also discloses a battery OCV-SOC curve online reconstruction system. For the specific structure of the system, please refer to Figure 2 , including a point set acquisition module 201, an SOC value search module 202, a data acquisition module 203, a parameter identification module 204, a data point screening module 205 and a curve reconstruction module 206.

[0108] The point set acquisition module 201 is used to obtain the OCV-SOC curve overlap interval and the initial trusted OCV-SOC point set corresponding to the battery to be tested.

[0109] The SOC value search module 202 is used to obtain the initial OCV value of the battery to be tested, and if the initial OCV value is in the overlap interval of the OCV-SOC curve, a first SOC value is obtained.

[0110] The data acquisition module 203 is used to acquire a discharge end SOC value and a static OCV value based on a preset discharge condition and the first SOC value.

[0111] The parameter identification module 204 is used to obtain the discharge data in the discharge condition, and perform parameter identification on the discharge data to obtain a parameter-identified OCV-SOC curve.

[0112] The data point screening module 205 is used to obtain a trustworthy OCV-SOC point based on the end-of-discharge SOC value, the static OCV value and the parameter-identified OCV-SOC curve.

[0113] The curve reconstruction module 206 is used to construct the OCV-SOC curve of the battery to be tested based on the trusted OCV-SOC point and the initial trusted OCV-SOC point set.

[0114] The present embodiment provides a method and system for online reconstruction of the OCV-SOC curve of a battery. First, the overlap interval of the OCV-SOC curve and the initial trusted OCV-SOC point set corresponding to the battery to be tested are obtained, which provides a reference basis for data point screening and ensures the accuracy of the data points used for curve reconstruction. Then, the initial OCV value of the battery to be tested is obtained, and the first SOC value is obtained by matching the initial OCV value with the overlap interval of the OCV-SOC curve to ensure the accuracy of the first SOC value. Secondly, based on the preset discharge condition and the first SOC value, the discharge end SOC value and the static OCV value are obtained, and the discharge data in the discharge condition is collected at the same time, and the discharge data is parameter identified to obtain the parameter identified OCV-SOC curve, so as to realize the online reconstruction of the OCV-SOC curve according to the data corresponding to the operation process of the battery. Among them, based on the end-of-discharge SOC value, the static OCV value and the parameter-identified OCV-SOC curve, an effective and accurate trusted OCV-SOC point is obtained, and based on the trusted OCV-SOC point and the initial trusted OCV-SOC point set obtained as a reference, the OCV-SOC curve of the battery to be tested is constructed to ensure the accuracy of the OCV-SOC curve.

[0115] Embodiment 2

[0116] This embodiment further discloses a method for online reconstruction of an OCV-SOC curve of a battery. Specifically, the specific implementation process of the method for online reconstruction of the curve is shown in FIG. Figure 3 , mainly including steps 301 to 306, the steps are mainly:

[0117] Step 301: Obtain a test battery corresponding to the battery to be reconstructed and an aged battery after the test battery is aged, so as to perform an OCV-SOC curve calibration test on the test battery and the aged battery to obtain an overlap interval of the OCV-SOC curve corresponding to the battery to be reconstructed.

[0118] Specifically, in this step, firstly, a test battery just produced is obtained, wherein the test battery is of the same type as the battery to be reconstructed and the state of health (SOH) of the test battery is 100%. Then, an OCV-SOC curve calibration test is performed on the test battery to obtain the OCV-SOC curve corresponding to the test battery.

[0119] Next, in order to ensure the accuracy of the overlap interval of the OCV-SOC curve, the test battery is subjected to accelerated aging treatment under a preset aging condition to obtain an aged battery corresponding to the test battery. Then, the aged battery is subjected to the same OCV-SOC curve calibration test to obtain the OCV-SOC curve corresponding to the aged battery.

[0120] By comparing the OCV-SOC curve of the test battery and the OCV-SOC curve of the aged battery, the overlap interval of the OCV-SOC curves of the battery to be reconstructed during different aging degrees can be obtained, that is, the overlap interval of the OCV-SOC curve corresponding to the battery to be reconstructed is obtained.

[0121] In this embodiment, in order to further illustrate the content of the overlap interval of the OCV-SOC curve, an implementation method is provided. In this embodiment, the aging condition is set to a high temperature of 45°C and a rate current of 10C, and the aging battery is set to be a battery corresponding to a healthy life condition of 80% after the test battery is aged, that is, a SOH of 80%.

[0122] Specifically, after obtaining the test battery corresponding to the battery to be reconstructed, the OCV-SOC curve of the test battery in the BOL state, i.e., SOH=100%, is obtained through the HPPC working condition (Hybrid Pulse Power Characteristic). In a preferred embodiment, the type of the test battery is set to be a lithium battery, the horizontal axis of the OCV-SOC curve is 100% to 0%, the interval is 5%, and the vertical axis is the recorded OCV value, then the OCV-SOC curve corresponding to the new lithium battery can be referred to Figure 4 ,for Figure 4 The corresponding curve under the medium BOL state.

[0123] Furthermore, the test battery is fully charged and discharged at a high temperature of 45°C with a current rate of 10C to achieve the purpose of accelerating the aging of the battery, until SOH=80%, and an aged battery corresponding to the test battery is obtained. A curve calibration experiment is performed on the aged battery after the test according to 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 can be referred to Figure 4 ,for Figure 4 The curve corresponding to the medium MOL state, that is, when the SOH is 80%.

[0124] The OCV-SOC curve of the test battery and the OCV-SOC curve of the aged battery are compared to obtain the OCV interval corresponding to the overlap of the curves, wherein the OCV interval is the overlap interval of the OCV-SOC curves. Figure 4 ,contrast Figure 2 The BOL line and the MOL_SOH80% line can be used to identify the OCV-SOC curve of the aged battery and the non-aged battery. Figure 4When the battery type is a lithium battery, in order to ensure the accuracy of the curve overlap interval, the OCV deviation of the BOL and MOL_SOH80% curves is calculated based on SOC, and the SOC interval when the deviation of the two curves is less than 15mv is taken. 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%).

[0125] Step 302: construct an OCV-SOC point set according to the OCV-SOC curve corresponding to the test battery, so as to construct an initial trusted OCV-SOC point set according to the overlap interval of the OCV-SOC curve and the OCV-SOC point set.

[0126] Specifically, in this step, in order to improve the accuracy of curve reconstruction, a number of OCV-SOC points are first selected from the OCV-SOC curve corresponding to the test battery to construct an OCV-SOC point set, and then a number of initial trusted OCV-SOC points are selected from the OCV-SOC point set using the overlapping interval of the OCV-SOC curve to construct the initial trusted OCV-SOC point set.

[0127] Further, in order to further illustrate the specific contents of the OCV-SOC point set and the initial trusted OCV-SOC point set, in some implementations of this embodiment, reference is made to Figure 4 As shown in the BOL curve, when the battery type is a lithium battery, taking the SOC percentage interval of 5% as an example, the OCV-SOC point set corresponding to the battery can be obtained from the BOL curve. Figure 5 Specifically, there are several OCV-SOC points, including (SOC=0%, OCV=3.133V), (SOC=5%, OCV=3.385V), (SOC=10%, OCV=3.473V), etc.

[0128] Reference Figure 5 The OCV-SOC point set corresponding to the test battery shown in the figure is used to select a number of OCV-SOC points from the OCV-SOC point set using the overlap interval of the OCV-SOC curve to construct the initial trustworthy OCV-SOC point set. Figure 4 When the provided Area_low interval, Area_middle interval and Area_high interval are used to screen the OCV-SOC points, the initial trusted OCV-SOC point set constructed can refer to Figure 6, mainly including several initial trusted OCV-SOC points such as (SOC=10%, OCV=3.473V), (SOC=15%, OCV=3.500V), (SOC=50%, OCV=3.681V).

[0129] Step 303: Performing initial power-on and secondary power-on on the battery to be reconstructed to obtain an initial OCV value and an initial SOC value during the initial power-on process, a discharge end SOC value corresponding to the end of the initial discharge, and a static OCV value corresponding to the secondary power-on process.

[0130] Specifically, in this embodiment, in order to improve the accuracy of the online reconstructed OCV-SOC curve, obtaining high-precision OCV-SOC points is the primary prerequisite. Among them, since the battery to be reconstructed has aged during use, and if the aged battery is initially powered on and the OCV correction is performed using the old OCV-SOC curve, the initial SOC value obtained will have a certain initial error. Therefore, before updating the OCV-SOC curve, this step is based on the premise that the initial power-on OCV correction is in the overlap interval of the OCV-SOC curve, thereby obtaining a higher-precision initial SOC, ensuring that the subsequent SOC value used to update the OCV-SOC curve has a higher precision. In a certain implementation of this embodiment, the process of obtaining high-precision OCV-SOC curve points can refer to Figure 7 .

[0131] Specifically, refer to Figure 7 In this embodiment, in order to ensure the accuracy of the initial OCV value, before the battery to be reconstructed is initially powered on, it is ensured that the battery to be reconstructed has been in sufficient dormancy. At this time, the terminal voltage of the battery to be reconstructed is stably equal to the open circuit voltage, that is, the battery to be reconstructed is subjected to an OCV correction based on the open circuit voltage after being powered on, thereby obtaining the initial OCV value corresponding to the battery to be reconstructed, namely, OCV_Init.

[0132] It is determined whether the initial OCV value is within the OCV range to ensure the accuracy of the initial SOC value obtained after the battery is aged. When it is determined to be within the OCV range, the historical OCV-SOC curve of the battery to be reconstructed is searched according to the initial OCV value to obtain the initial SOC value (SOC_Init), and the initial OCV-SOC point (SOC_Init, OCV_Init) is constructed according to the initial SOC value and the initial OCV value.

[0133] Next, after the battery to be reconstructed is initially powered on, data such as the current I, voltage U, and temperature T of the battery to be reconstructed are collected to determine whether the battery to be reconstructed is in a discharging condition. When it is determined that the battery to be reconstructed is in a discharging condition, the above data such as the current I, voltage U, and temperature T are used to obtain the discharge end SOC value of the battery to be reconstructed at the end of the discharge condition, that is, SOC_dsg.

[0134] Specifically, in the process of calculating the end-of-discharge SOC value, the collected current I is integrated by ampere-hour to calculate the discharge capacity Q_dsg, and the SOC_dsg at the end of the discharge condition is calculated by ampere-hour integration. Furthermore, in order to reduce the cumulative error caused by the ampere-hour integration method, the difference between the initial SOC value and the end-of-discharge SOC value can be set to be less than or equal to 40%. Before the battery to be reconstructed is powered off in sleep mode, the battery to be reconstructed is not charged with a plug.

[0135] After the initial discharge is completed, it is determined that the battery to be reconstructed has been dormant for a sufficient period of time, such as 1H of dormancy, and the battery to be reconstructed is powered on for the second time to obtain a stable open circuit voltage OCV_dsg corresponding to the battery to be reconstructed at the second power-on time, and then a new OCV-SOC point (SOC_dsg, OCV_dsg) is formed according to the end-of-discharge SOC value and the open circuit voltage value OCV_dsg.

[0136] Step 304: Obtain discharge data during the initial power-on process, and perform parameter identification on the discharge data using a preset second-order FFRLS algorithm to generate a parameter-identified OCV-SOC curve of the battery to be reconstructed during the initial power-on process.

[0137] Specifically, in this embodiment, the parameter identification method driven by data such as real-time measurement of current, voltage and temperature of the power battery can realize online update of model parameters and redundant design of prediction model. Therefore, in some implementations of this embodiment, the least squares algorithm FFRLS based on the forgetting factor is used to identify the OCV-SOC curve of the battery. Specifically, the least squares algorithm FFRLS based on the forgetting factor reduces the amount of information of the old data by inserting the forgetting factor into the measurement data, creating conditions for supplementing the information of the new data.

[0138] Furthermore, before performing parameter identification through the second-order FFRLS algorithm, a least squares algorithm FFRLS based on the forgetting factor is first constructed, and then the forgetting factor in the second-order FFRLS is adjusted so that the deviation between the OCV-SOC curve identified by the test battery data parameters and the offline calibrated OCV-SOC curve is minimized, thereby obtaining the best FFRLS algorithm for the battery characteristics.

[0139] Specifically, the establishment process of the second-order FFRLS algorithm is:

[0140] Step 1: Power battery model analysis:

[0141] Kirchhoff's law and Laplace transform can be used to obtain the mathematical relationship between the output voltage and input current of the power battery:

[0142]

[0143] The transfer function of this model is:

[0144]

[0145] Where U t is the terminal voltage, U oc is the open circuit voltage, i is the current, R i The internal resistance of the EU, R D is the polarization internal resistance, C D is a polarized capacitor.

[0146] Step 2: Derive the frequency domain discretization formula of the transfer function of the second-order FFRLS equivalent circuit model:

[0147] When n=2, the n-RC equivalent circuit model is transformed into the Thevenin model:

[0148]

[0149] Based on the bilinear change law, the s plane can be mapped to the Z plane for discretization:

[0150]

[0151] Definition b 1 、b 2 、b 3 、b 4 and b 5 Is the unknown coefficient, then according to:

[0152]

[0153] The differential form of the state equation of the second-order equivalent circuit model can be obtained:

[0154] U t,k =(1-b 1 -b 2 ) oc,k +b 1 U t,k-1 +b 2 U t,k-2 +b 3 i L,k +b4 i L,k-1 +b 5 i L,k-2 .

[0155] Step 3: Optimize the second-order equivalent circuit model to reduce the divergence of the OCV-SOC curve.

[0156] Adjust U oc,k The coefficient (1-b 1 -b 2 ) is 1, the divergence of the OCV-SOC curve of the identification parameter can be transferred to other identification parameters, thereby making the identified OCV value less divergent and more stable:

[0157]

[0158] Define the system data matrix as:

[0159] 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 ];

[0160] The parameter matrix of the system is defined as:

[0161]

[0162] The final Thevenin model transfer function (system output equation) can be simplified to:

[0163] y k =Φ 2,k θ 2,k ;

[0164] The input data variables are constructed based on the power battery current, voltage, temperature and other data obtained by real-time sampling, and then the corresponding square gain and covariance are calculated to carry out online identification and update of parameters.

[0165] Step 4: Import the measured working conditions and calibrate the forgetting factor of the FFRLS algorithm:

[0166] a. Perform CLTC test on the test battery, discharging the SOC from 80% to 30%.

[0167] b. Using the current and voltage data during discharge as the original data, the second-order FFRLS algorithm is used to identify the OCV parameters online.

[0168] c. Compare the online identified OCV-SOC curve with the OCV-SOC curve measured in the laboratory, and adjust the forgetting factor to minimize the error between the two.

[0169] d. For this battery characteristic, when the forgetting factor is 0.998, the identified OCV-SOC curve is the smoothest and has the smallest error.

[0170] In this embodiment, after the second-order FFRLS algorithm based on the forgetting factor is constructed through the above steps 1 to 4, the second-order FFRLS algorithm is used to identify the OCV-SOC curve online. The voltage and current data collected in real time during the initial power-on process are used to identify the OCV-SOC value online using the second-order FFRLS algorithm to obtain the OCV-SOC curve of the battery to be reconstructed at each moment during the power-on period, wherein the OCV-SOC point corresponding to the parameter-identified OCV-SOC curve at the end of discharge is OCV_FFRLS. In some embodiments of this embodiment, refer to Figure 4 When the test battery shown in FIG. 1 is constructed using the second-order FFRLS algorithm, the difference calculated during the construction process is as follows: Figure 8 The difference line shown in Figure 2 is the difference line shown in Figure 2, and the parameter identification OCV-SOC curve generated by the established second-order FFRLS algorithm can be shown as Figure 8 The OCV_identify line is shown.

[0171] Step 305: Based on the static OCV value, the end-of-discharge SOC value and the parameter-identified OCV-SOC curve, a trusted OCV-SOC point is constructed to reconstruct the OCV-SOC curve of the battery to be reconstructed according to the trusted OCV-SOC point and the initial trusted OCV-SOC point set.

[0172] Specifically, in this embodiment, a new OCV-SOC point is obtained according to the discharge end SOC value corresponding to the end of the initial power-on and the static OCV value corresponding to the second power-on moment. In order to verify the reliability of the new OCV-SOC point, the historical OCV-SOC curve of the battery to be reconstructed can be searched based on the static OCV value to obtain the static SOC value corresponding to the static OCV value, that is, SOC_dsg_false.

[0173] Next, the error error between the SOC_dsg_false and the SOC_dsg is obtained, and when it is determined that the error is within a preset error range, i.e., error=|SOC_dsg-SOC_dsg_false|, error>3%, a new OCV-SOC point, i.e., OCV_Relax, composed of the end-of-discharge SOC value and the rest OCV value is deemed valid.

[0174] After the new OCV-SOC point is determined to be valid, the difference between the open circuit voltage in the OCV-SOC point OCV_FFRLS at the end of the initial power-on in the parameter identification OCV-SOC curve and the static OCV value, i.e., the SOC_dsg, is calculated, and it is determined whether the difference is within a preset tolerance range. In some implementations of this embodiment, the tolerance range is set to 30mv.

[0175] When the difference is within the preset tolerance range, the middle point between the OCV_FFRLS and the new OCV-SOC point is obtained as the trusted OCV-SOC point, namely, OCV_Trust_new. Specifically, the process of obtaining the OCV_Trust_new can refer to Fig. 9 ,like Fig. 9 As shown, the OCV_Relax obtained after standing for a sufficient time is Fig. 9 The square points shown in the figure, and the OCV_FFRLS identified by the second-order FFRLS parameters are as follows Fig. 9 For the triangle point shown in the figure, if the deviation between OCV_Relax and OCV_FFRLS is less than 30mv, the two methods can verify that the accuracy of the two-way verification is high enough at this point. Then, the middle point between OCV_Relax and OCV_FFRLS is taken as the new trusted OCV point OCV_Trust_New, as shown in the figure. Fig. 9 Large dot shown.

[0176] Specifically, in this embodiment, the initial trusted OCV-SOC point closest to the trusted OCV-SOC point is obtained from the initial trusted OCV-SOC point set, and the difference between the initial trusted OCV-SOC point and the trusted OCV-SOC point in SOC is obtained, so as to determine the update method of the initial trusted OCV-SOC point set according to the difference and a preset tolerance threshold. The tolerance threshold can be set to 5%.

[0177] When the difference is less than the tolerance threshold, the trusted OCV-SOC point replaces the initial trusted OCV-SOC point. Specifically, Fig.10 As shown, the point (SOC=50%, OCV=3.681V) is replaced by (SOC=48%, OCV=3.654V).

[0178] When the difference is greater than or equal to the tolerance threshold, the trusted OCV-SOC point is added to the initial trusted OCV-SOC point set. Specifically, Fig.11As shown, the point (SOC=35%, OCV=3.587V) is added between the point (SOC=15%, OCV=3.500V) and the point (SOC=50%, OCV=3.681V).

[0179] Further, refer to Figures 9 to 11 The method for updating the initial trusted OCV-SOC point set is shown, and the number of data points in the updated trusted OCV-SOC point set is obtained in real time. When the number reaches a preset number threshold, such as when the number is greater than or equal to 15, a curve fitting is performed according to each data point in the trusted OCV-SOC point set to generate an OCV-SOC curve. The reconstructed OCV-SOC curve can refer to Fig.12 , replacing the historical OCV-SOC curve of the battery to be reconstructed according to the OCV-SOC curve.

[0180] The present embodiment discloses an online reconstruction method for an OCV-SOC curve of a battery. An overlap interval is constructed to select an initial OCV-SOC curve point with high credibility. Then, a high-precision OCV-SOC point of the battery is obtained by repeated power-on and preset judgment conditions. The deviation between the OCV-SOC point and the OCV-SOC curve identified by a second-order FFRLS algorithm is calculated to ensure the accuracy of a new trusted point used for curve reconstruction. Finally, the new trusted point is used to update the finally constructed trusted point set, so as to reconstruct a more accurate OCV-SOC curve that conforms to the actual state of the current battery. The updated OCV-SOC curve will replace the old OCV-SOC curve, and during the power-on process of the battery, the new OCV-SOC curve will be used to perform a table lookup to obtain an initial SOC value.

[0181] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for online reconstruction of an OCV-SOC curve of a battery, characterized in that: include: Obtain the OCV-SOC curve overlap interval and initial trusted OCV-SOC point set corresponding to the battery to be tested; Obtaining an initial OCV value of the battery to be tested, and if the initial OCV value is in the overlap interval of the OCV-SOC curve, obtaining a first SOC value; Based on the preset discharge condition and the first SOC value, obtaining the discharge end SOC value and the static OCV value; Acquiring discharge data in the discharge condition, and performing parameter identification on the discharge data to obtain a parameter-identified OCV-SOC curve; Obtaining a trustworthy OCV-SOC point based on the end-of-discharge SOC value, the static OCV value and the parameter-identified OCV-SOC curve; Based on the trusted OCV-SOC point and the initial trusted OCV-SOC point set, an OCV-SOC curve of the battery to be tested is constructed.

2. The method for online reconstruction of an OCV-SOC curve of a battery according to claim 1, characterized in that: The obtaining of the OCV-SOC curve overlap interval and the initial trusted OCV-SOC point set corresponding to the battery to be tested includes: Obtaining an initial OCV-SOC curve of a test battery and an aged OCV-SOC curve of an aged battery corresponding to the test battery; wherein the test battery and the battery to be tested are of the same type; Comparing the initial OCV-SOC curve with the aged OCV-SOC curve to obtain an overlap interval of the OCV-SOC curve; wherein the overlap interval of the OCV-SOC curve includes an OCV interval; Based on the OCV interval, a plurality of initial trusted OCV-SOC points and an initial trusted SOC point corresponding to each of the initial trusted OCV-SOC points are obtained from a pre-constructed OCV-SOC point set to construct an initial trusted OCV-SOC point set.

3. The method for online reconstruction of an OCV-SOC curve of a battery according to claim 2, characterized in that: The obtaining of the initial OCV value of the battery to be tested, and obtaining a first SOC value if the initial OCV value is in the overlap interval of the OCV-SOC curve, includes: Obtaining a first OCV value of the battery to be tested at a first power-on moment, and performing OCV correction on the first OCV value to obtain an initial OCV value of the battery to be tested; When it is determined that the initial OCV value is in the OCV range, a historical OCV-SOC curve of the battery to be tested is searched according to the initial OCV value to obtain a first SOC value.

4. The method for online reconstruction of an OCV-SOC curve of a battery according to claim 2, characterized in that: The obtaining of the discharge end SOC value and the static OCV value based on the preset discharge condition and the first SOC value includes: Collecting current data in the discharge condition; Calculating, according to the current data, a discharge end SOC value of the battery under test corresponding to when the discharge condition ends; Based on the preset dormancy condition and the discharge condition, a static OCV value of the battery to be tested at the second power-on moment is obtained.

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

6. The method for online reconstruction of an OCV-SOC curve of a battery according to claim 3, characterized in that: The obtaining of a trustworthy OCV-SOC point based on the end-of-discharge SOC value, the static OCV value and the parameter-identified OCV-SOC curve includes: Searching the historical OCV-SOC curve according to the static OCV value to obtain a static SOC value corresponding to the static OCV value; Obtaining a first deviation between the static SOC value and the end-of-discharge SOC value, and determining whether the first deviation satisfies a preset first deviation condition; Searching the parameter identification OCV-SOC curve based on the end-of-discharge SOC value to obtain the end-of-discharge OCV value corresponding to the end-of-discharge SOC value in the parameter identification OCV-SOC curve; A trusted OCV-SOC point is formed according to the end-of-discharge OCV value and the end-of-discharge SOC value.

7. The method for online reconstruction of an OCV-SOC curve of a battery according to claim 6, characterized in that: The forming a trustworthy OCV-SOC point according to the end-of-discharge OCV value and the end-of-discharge SOC value includes: Obtaining a second deviation between the end-of-discharge OCV value and the static OCV value; When the second deviation satisfies a preset second deviation condition, obtaining an intermediate OCV value between the end-of-discharge OCV value and the static OCV value and an intermediate SOC value between the static SOC value and the end-of-discharge SOC value; A trusted OCV-SOC point is formed based on the intermediate OCV value and the intermediate SOC value.

8. The method for online reconstruction of an OCV-SOC curve of a battery according to claim 6, characterized in that: The constructing the OCV-SOC curve of the battery to be tested based on the trusted OCV-SOC point and the initial trusted OCV-SOC point set includes: Acquire, from the initial trusted OCV-SOC point set, an initial trusted OCV-SOC point that is closest to the trusted OCV-SOC point; Calculating a third deviation between an initial trusted SOC value corresponding to the initial trusted OCV-SOC point and the end-of-discharge SOC value; The initial trusted OCV-SOC point set is updated according to the third deviation and the trusted OCV-SOC point to obtain a trusted OCV-SOC point set.

9. The method for online reconstruction of an OCV-SOC curve of a battery according to claim 8, characterized in that: The updating of the initial trusted OCV-SOC point set according to the third deviation and the trusted OCV-SOC point to obtain a trusted OCV-SOC point set includes: When the third deviation satisfies a preset third deviation condition, updating the initial trusted OCV-SOC point in the initial trusted OCV-SOC point set according to the trusted OCV-SOC point to obtain a trusted OCV-SOC point set; When the third deviation does not meet the preset third deviation condition, the trusted OCV-SOC point is added to the initial trusted OCV-SOC point set to obtain a trusted OCV-SOC point set.

10. The method for online reconstruction of an OCV-SOC curve of a battery according to any one of claims 1 to 9, characterized in that: The constructing the OCV-SOC curve of the battery to be tested based on the trusted OCV-SOC point and the initial trusted OCV-SOC point set includes: Obtaining the number of data points in the trusted OCV-SOC point set; When the number reaches a preset quantity threshold, curve fitting is performed according to each data point in the trusted OCV-SOC point set to generate an OCV-SOC curve; The historical OCV-SOC curve of the battery to be tested is replaced according to the OCV-SOC curve.

11. The method for online reconstruction of an OCV-SOC curve of a battery according to claim 5, characterized in that: The establishment process of the second-order FFRLS algorithm is: Obtain the initial OCV-SOC curve corresponding to the test battery; According to a preset first discharge condition, obtaining test discharge data generated when the test battery is discharged; Performing parameter identification on the test discharge data by 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 to continuously adjust the forgetting factor of the initial second-order FFRLS algorithm according to the curve deviation until the curve deviation satisfies a preset curve deviation condition, thereby obtaining a second-order FFRLS algorithm.

12. A battery OCV-SOC curve online reconstruction system, characterized in that: It includes a point set acquisition module, an SOC value search module, a data acquisition module, a parameter identification module, a data point screening module and a curve reconstruction module; The point set acquisition module is used to obtain the OCV-SOC curve overlap interval and the initial trusted OCV-SOC point set corresponding to the battery to be tested; The SOC value search module is used to obtain the initial OCV value of the battery to be tested, and if the initial OCV value is in the overlap interval of the OCV-SOC curve, a 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 discharge condition and the first SOC value; The parameter identification module is used to obtain discharge data in the discharge condition, and perform parameter identification on the discharge data to obtain a parameter identification OCV-SOC curve; The data point screening module is used to obtain a trustworthy OCV-SOC point based on the end-of-discharge SOC value, the static OCV value and the parameter-identified OCV-SOC curve; The curve reconstruction module is used to construct the OCV-SOC curve of the battery to be tested based on the trusted OCV-SOC point and the initial trusted OCV-SOC point set.

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