Battery capacity prediction method

Abstract

The invention relates to a battery capacity prediction method, which comprises the steps of charging a tested battery, and obtaining a voltage capacity curve and a charging linear fitting line in a charging process of the tested battery; secondly, according to a predicted voltage value and a first predicted capacity value corresponding to a voltage capacity curve and a charging linear fitting line; performing formation and capacity grading test on the tested battery by taking the predicted voltage value or the first predicted capacity value as the maximum value to obtain an actually measured capacity value, and fitting the dry cell weight of the tested battery, the capacity grading test charging capacity, the capacity grading test discharging capacity and the capacity grading test ending voltage to obtain a linear fitting data model; and when the capacity of the subsequent battery is predicted, importing the data obtained according to the dry cell weight, formation and capacity grading of the subsequent battery into the linear fitting data model to calculate a capacity predicted value. When the capacity of the battery is predicted, the battery does not need to be subjected to formation and capacity grading test in a full-charge state, the production efficiency of the battery is improved, and the charging and discharging energy consumption and the manufacturing cost are reduced.

Description

Technical Field

[0001] The present application relates to the field of battery manufacturing technology, and in particular to a battery capacity prediction method. Background Art

[0002] Secondary batteries are widely used in various industries due to their high energy density and long cycle life. Capacity is one of the important indicators to measure the performance of secondary batteries. The attenuation of the secondary battery capacity represents the life of the secondary battery. Therefore, the capacity of the secondary battery needs to be measured during the production process of the secondary battery.

[0003] In the related technology, secondary batteries on the actual production line will first be formed to activate the battery; then they need to be divided and multiple charge and discharge cycles are performed to calibrate the capacity of the secondary batteries and grade the products. Among them, measuring the capacity of the secondary battery using the full capacity method takes a long time and consumes a lot of electricity, which leads to the problems of reduced production efficiency of secondary batteries, high energy consumption and high cost. Summary of the invention

[0004] The embodiment of the present application provides a battery capacity prediction method to solve the problem that the capacity of a secondary battery is tested in a full-capacity manner in the related art, resulting in reduced production efficiency, high energy consumption and high cost of the secondary battery.

[0005] The present application provides a battery capacity prediction method, the method comprising:

[0006] Charge the battery under test according to the set charging rate, and obtain the real-time voltage value and real-time capacity value during the charging process, as well as the voltage-capacity curve during the charging process;

[0007] Perform linear fitting on the real-time voltage value and the real-time capacity value during the charging process to obtain a charging linear fitting line;

[0008] A voltage rise curve before full charge is extracted according to the voltage-capacity curve, and the intersection of the charging linear fitting line and the voltage rise curve is the predicted voltage value and the first predicted capacity value;

[0009] Perform formation and capacity division on at least two batteries to be tested, and record the formation test end voltage, capacity division test charge capacity, capacity division test discharge capacity and capacity division test end voltage;

[0010] In the formation test, the formation test end voltage is controlled to be less than the predicted voltage value, and the formation test end voltage is ≥ 2.5V; or; the formation test end capacity is controlled to be less than the first predicted capacity value and greater than 0;

[0011] The capacity division includes: charging the battery to be tested with a charging current of any multiple, and the charging cut-off voltage is the predicted voltage value; after the charging is completed, discharging the battery to be tested in a gradient discharge manner;

[0012] Performing full capacity tests on at least two batteries to be tested again to obtain measured capacity values, and fitting to obtain a linear fitting data model based on the measured capacity values, the dry cell weight of the batteries to be tested, the formation test end voltage, the divided capacity test charge capacity, the divided capacity test discharge capacity and the divided capacity test end voltage;

[0013] When predicting the capacity of the subsequent battery to be predicted, the dry cell weight, formation test end voltage, capacity test charge capacity, capacity test discharge capacity and capacity test end voltage of the battery to be predicted are imported into the linear fitting data model to predict the capacity prediction value of the battery to be predicted.

[0014] In some embodiments: the set charging rate includes 0.01C to 0.4C, and the charging method in charging the battery under test according to the set charging rate is constant current charging.

[0015] In some embodiments: in the voltage-capacity curve, as the battery capacity increases, multiple voltage rising curves are formed before being fully charged;

[0016] Select a voltage rise curve closest to full charge from among multiple voltage rise curves;

[0017] The intersection of the charging linear fitting line and the voltage rising curve closest to the fully charged section is the predicted voltage value and the first predicted capacity value.

[0018] In some embodiments: in the capacity division test, the charging method is constant current charging first and then constant voltage charging;

[0019] The method of first charging with constant current and then charging with constant voltage includes: charging at any rate to a predicted voltage value, and then continuing constant voltage charging at the predicted voltage value.

[0020] In some embodiments, the gradient discharge method is to discharge the battery to be tested with a constant current for multiple times.

[0021] In some embodiments: the discharge current of the multiple constant currents gradually decreases.

[0022] In some embodiments: in the capacity division test, the maximum charging temperature of the capacity division test and the maximum discharging temperature of the capacity division test are obtained;

[0023] The linear fitting data model is obtained by fitting according to the measured capacity value of the battery to be tested, the dry cell weight of the battery to be tested, the end voltage of the formation test, the charge capacity of the capacity test, the discharge capacity of the capacity test, the end voltage of the capacity test, the maximum charge temperature of the capacity test and the maximum discharge temperature of the capacity test;

[0024] When predicting the capacity of the subsequent battery to be predicted, the dry cell weight, formation test end voltage, capacity test charge capacity, capacity test discharge capacity, capacity test end voltage, capacity test maximum charge temperature and capacity test maximum discharge temperature of the battery to be predicted are imported into the linear fitting data model to predict the capacity prediction value of the battery to be predicted.

[0025] In some embodiments: the linear fitting data model is:

[0026] Y=A+B*X0+C*X1-D*X2+E*X3+F*X4-G*X5-H*X6;

[0027] Wherein: Y is the predicted capacity value of the battery to be predicted, A is a constant, B, C, D, E, F, G, H are the compensation values ​​of each factor, X0 is the dry cell weight of the battery to be predicted, X1 is the end voltage of the formation test of the battery to be predicted, X2 is the charge capacity of the capacity test of the battery to be predicted, X3 is the discharge capacity of the capacity test of the battery to be predicted, X4 is the maximum charge temperature of the capacity test of the battery to be predicted, X5 is the end voltage of the capacity test of the battery to be predicted, X6 is the maximum discharge temperature of the capacity test of the battery to be predicted, and the linear correlation coefficient R of the linear fitting is 2 ≥0.9.

[0028] In some embodiments: after the formation test and before the capacity separation test, the battery under test needs to be baked and then left to stand at room temperature.

[0029] In some embodiments: the discharge capacity of the capacity-differential test is the discharge capacity corresponding to the first constant current discharge in the gradient discharge.

[0030] The beneficial effects of the technical solution provided by this application include:

[0031] The battery capacity prediction method of the present application obtains the voltage capacity curve of the tested battery during the charging process and the real-time voltage value and real-time capacity value during the charging process when charging the tested battery, performs linear fitting on the real-time voltage value and real-time capacity value during the charging process, obtains the charging linear fitting line, extracts the voltage rise curve before full charge according to the voltage capacity curve, and the intersection of the charging linear fitting line and the voltage rise curve is the predicted voltage value and the first predicted capacity value. When the formation test and the fractional capacity test are subsequently performed on at least two batteries to be tested, the formation test and the fractional capacity test are limited to the batteries to be tested with the predicted voltage value or the first predicted capacity value.

[0032] At least two batteries to be tested are tested at full capacity again to obtain the measured capacity value, and a linear fitting data model is obtained by fitting the measured capacity value, the dry cell weight of the battery to be tested, the formation test end voltage, the divided capacity test charge-in capacity, the divided capacity test discharge capacity and the divided capacity test end voltage. When the capacity of the subsequent battery to be predicted is predicted, the dry cell weight of the battery to be predicted, the formation test end voltage, the divided capacity test charge-in capacity, the divided capacity test discharge capacity and the divided capacity test end voltage are imported into the linear fitting data model to calculate the capacity prediction value of the battery to be predicted. When the present application predicts the capacity of the battery, the formation and divided capacity charging do not need to be charged to a fully charged state, and the capacity prediction value of the battery to be predicted is calculated by a linear fitting data model, thereby improving the battery production efficiency, reducing the charging and discharging energy consumption and manufacturing cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0034] Figure 1 A flow chart of a method according to an embodiment of the present application;

[0035] Figure 2 A voltage capacity curve diagram of an embodiment of the present application;

[0036] Figure 3 It is a schematic diagram of the intersection of the charging linear fitting line and the voltage rising curve of the embodiment of the present application;

[0037] Figure 4 This is a DV / DQ curve diagram of an embodiment of the present application. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0039] The embodiment of the present application provides a battery capacity prediction method, which can solve the problem that the capacity of a secondary battery in the related art is tested in a full-capacity manner, resulting in reduced production efficiency, high energy consumption and high cost of the secondary battery.

[0040] See also Figures 1 to 3 As shown, an embodiment of the present application provides a battery capacity prediction method, the method comprising:

[0041] S1, charging the battery under test according to the set charging rate, obtaining the real-time voltage value and real-time capacity value during the charging process, as well as the voltage-capacity curve during the charging process; the voltage-capacity curve is a curve diagram of the change of the voltage value of the battery under test as the battery capacity continues to increase during the charging process.

[0042] S2, performing linear fitting on the real-time voltage value and the real-time capacity value during the charging process to obtain a charging linear fitting line.

[0043] S3, extracting a voltage rise curve before full charge according to the voltage-capacity curve, and the intersection of the charging linear fitting line and the voltage rise curve is the predicted voltage value and the first predicted capacity value.

[0044] S4, performing formation and capacity division on at least two batteries to be tested, and recording the formation test end voltage, the capacity division test charge capacity, the capacity division test discharge capacity and the capacity division test end voltage;

[0045] In the formation test, the formation test end voltage is controlled to be less than the predicted voltage value, and the formation test end voltage is ≥ 2.5V; or; the formation test end capacity is controlled to be less than the first predicted capacity value and greater than 0;

[0046] The capacity division test includes: charging the battery to be tested with a charging current of any multiple, and the charging cut-off voltage is a predicted voltage value; after the charging is completed, the battery to be tested is discharged in a gradient discharge manner.

[0047] S5, performing full capacity test on at least two batteries to be tested again to obtain the measured capacity value, and performing fitting to obtain a linear fitting data model according to the measured capacity value, the dry cell weight of the battery to be tested, the formation test end voltage, the divided capacity test charge capacity, the divided capacity test discharge capacity and the divided capacity test end voltage.

[0048] S6, when predicting the capacity of the subsequent battery to be predicted, the dry cell weight, formation test end voltage, capacity test charge capacity, capacity test discharge capacity and capacity test end voltage of the battery to be predicted are imported into the linear fitting data model to predict the capacity prediction value of the battery to be predicted.

[0049] Among them, the tested battery, the battery to be tested and the battery to be predicted are batteries of the same type, and the raw materials, manufacturing processes and process parameters of the three batteries are exactly the same (that is, the tested battery, the battery to be tested and the battery to be predicted are batteries of the same model).

[0050] It should be noted that the test method for the measured capacity value of the full capacity test of the battery to be tested in this application is: charge with a 1C constant current to the charging cut-off voltage of the battery to be tested and let it stand for 1 minute; then use a 0.5C constant current to charge to the charging cut-off voltage of the battery to be tested and let it stand for 1 minute; finally, use a 0.25C constant current to charge to the charging cut-off voltage of the battery to be tested. This process requires the ambient temperature of the warehouse to be maintained at 25±5℃. In this way, the battery to be tested is fully charged, and then discharged with a 1C constant current to the safe lower limit voltage of 2.5V of the battery to be tested, and then discharged with a 0.5C constant current to 2.5V after standing for 1 minute, and finally discharged with a 0.25C constant current to 2.5V after standing for 1 minute. Finally, the capacities discharged by 1C, 0.5C, and 0.25C are added together as the full discharge capacity of the battery.

[0051] It should be noted that the maximum cut-off voltages of different types of batteries under test in this application are different, for example, the cut-off voltage of lithium iron phosphate battery is 3.65 V, and the cut-off voltage of ternary lithium battery is 4.2 V. In this application, "dry battery cell" refers to a bare battery cell formed by winding or stacking positive and negative electrode sheets and diaphragms, and is a bare battery cell that has not been infiltrated with electrolyte.

[0052] During the formation and capacity division process of the batteries to be tested, the more batteries to be tested, the better. The specific number of batteries to be tested can be hundreds or thousands of batteries as samples for formation and capacity division tests, so that hundreds or thousands of sets of test data can be obtained.

[0053] For example, one hundred batteries to be tested are used as samples for formation and capacity separation tests. In this way, test data such as the actual capacity values ​​of the one hundred groups of batteries to be tested, the weight of the dry cells of the batteries to be tested, the end voltage of the formation test, the charge capacity of the capacity separation test, the discharge capacity of the capacity separation test and the end voltage of the capacity separation test can be obtained.

[0054] However, the number of batteries to be tested should not be too large. Too large a number will increase the difficulty and workload of testing and data processing. These large amounts of test data are used to fit a linear fitting data model, so that the capacity prediction value obtained by the linear fitting data model is closer to the actual measured capacity value.

[0055] The battery capacity prediction method of the present application obtains the voltage capacity curve of the tested battery during the charging process and the real-time voltage value and real-time capacity value during the charging process when charging the tested battery, performs linear fitting on the real-time voltage value and real-time capacity value during the charging process, obtains the charging linear fitting line, extracts the voltage rise curve before full charge according to the voltage capacity curve, and the intersection of the charging linear fitting line and the voltage rise curve is the predicted voltage value and the first predicted capacity value. When the formation test and the capacity division test are subsequently performed on at least two batteries to be tested, the formation test and the capacity division test are limited to the predicted voltage value or the first predicted capacity value. Full capacity test is performed on at least two batteries to be tested again to obtain the measured capacity value, and a linear fitting data model is obtained by fitting according to the measured capacity value, the dry cell weight of the battery to be tested, the formation test end voltage, the capacity division test charge capacity, the capacity division test discharge capacity and the capacity division test end voltage.

[0056] When predicting the capacity of the subsequent battery to be predicted, the dry cell weight, formation test end voltage, capacity test charge-in capacity, capacity test discharge capacity and capacity test end voltage of the battery to be predicted are imported into the linear fitting data model to calculate the capacity prediction value of the battery to be predicted. When the present application predicts the capacity of the battery, the formation and capacity test do not need to be fully charged to calculate the capacity prediction value of the battery to be predicted through the linear fitting data model, which improves the battery production efficiency, reduces the charging and discharging energy consumption and manufacturing cost.

[0057] Specifically, the linear fitting data model is first confirmed through the battery to be tested. Then, for the battery to be predicted whose capacity needs to be predicted, it is only necessary to perform formation and capacity division tests like the battery to be tested to obtain the dry cell weight, formation test end voltage, capacity division test charge capacity, capacity division test discharge capacity and capacity division test end voltage of the battery to be predicted, and then bring them into the linear fitting data model to obtain the capacity prediction value of the battery to be predicted.

[0058] In some optional embodiments, the charging rate is set to include 0.01C to 0.4C, and the charging method in charging the battery under test according to the set charging rate is constant current charging. When charging, constant current charging with a rate of 0.01C to 0.4C is used to obtain a voltage-capacity curve during the charging process of the battery under test more accurately, and the accuracy of subsequent prediction is higher.

[0059] In some optional embodiments, when the tested battery is charged according to the set charging rate, the charging cut-off voltage is not set in this embodiment. Specifically, the charging cut-off voltage can be determined according to the model or specification of the tested battery. For example, if the tested battery is a lithium iron phosphate battery, the charging cut-off voltage of the lithium iron phosphate battery is 3.65V. For example, if the tested battery is a ternary lithium battery, the charging cut-off voltage of the ternary lithium battery is 4.2V.

[0060] In some optional embodiments, as the battery capacity increases, multiple voltage rising curves are formed before full charge in the voltage capacity curve, and a voltage rising curve closest to full charge is selected from the multiple voltage rising curves;

[0061] The intersection of the charging linear fitting line and the voltage rising curve closest to the fully charged section is the predicted voltage value and the first predicted capacity value.

[0062] Specifically; Figure 2-3 Take this as an example to illustrate: Figure 2 The voltage rise curve closest to full charge is as follows Figure 2 The rising curve in the middle box, Figure 3 The solid curve is Figure 2 The enlarged view of the rising curve in the middle frame, and Figure 3 The dotted linear line is the charging linear fitting line. The intersection of the charging linear fitting line and the voltage rising curve closest to full charge is the predicted voltage value and the first predicted capacity value, that is, the predicted voltage value is 3.35V, and the first predicted capacity value is 60% SOC.

[0063] See also Figure 2 and Figure 3 As shown, according to the voltage capacity curve, the voltage rise curve selected before full charge can be seen that the voltage tends to be stable for a long time before 3.33V and after 3.36V, while the voltage is in a rising state between 3.33V and 3.36V. Figure 2 and Figure 3 The horizontal axis represents the SOC of the tested battery during the charging process, and the vertical axis represents the voltage V corresponding to different SOCs.

[0064] See also Figure 4 As shown, the peak in the dV / dQ curve mainly reflects the phase change of the active material during the lithium insertion and delithiation process, that is, the consumption of active lithium. The consumption of active lithium can be included in the previous capacity climbing value by predicting at the peak point, avoiding the influence of the consumption difference on the prediction of subsequent capacity and increasing the accuracy of the prediction.

[0065] See also Figure 3 and Figure 4 As shown, a peak appears when the voltage is between 3.33V and 3.36V and the first predicted capacity value is between 55% and 63%, indicating that there is a rapid redox reaction in this process, and the height of the peak shows that the electrochemical reaction of the battery material is violent and the battery material has good electrochemical activity.

[0066] The voltage differential capacity curve (dV / dQ) is used to analyze the relationship between the voltage and charge of the battery under test during the charging and discharging process. It provides the differential relationship between the voltage and the charge during the battery charging and discharging process, so that the redox reaction process and reaction rate of the battery under test can be quickly analyzed.

[0067] In some alternative embodiments: See Figure 1 As shown, an embodiment of the present application provides a battery capacity prediction method. In step S4 of the method, in the formation test, the formation test end voltage is controlled to be less than the predicted voltage value and ≥2.5V, or the formation test end capacity is controlled to be less than the first predicted capacity value and greater than 0.

[0068] The formation test of the battery to be tested is a process of activating the internal positive and negative electrode materials through a certain charging and discharging method to improve the overall performance of the battery. The formation is a very complex process and also a very important process that affects the battery performance, because during the first charging of the battery, it is inevitable to form a passivation layer covering the surface of the carbon electrode at the interface between the carbon negative electrode and the electrolyte, which is called the solid electrolyte interface film or SEI film.

[0069] In order to form a good SEI film and high production efficiency, the general formation process is charged by a step-by-step charging current from small to large. In the battery capacity prediction method of the present application, the specific steps of the formation are not limited, and only the formation test end voltage is controlled to be less than the predicted voltage value and ≥ 2.5V, or the formation test end capacity is controlled to be less than the first predicted capacity value and greater than 0.

[0070] Specifically, taking the following Table 1 as an example, the formation in the battery capacity prediction method of the present application is described:

[0071] Table 1

[0072]

[0073] It can be seen from Table 1 that the formation is charged with a small to large step-type charging current (such as sequence number 2, sequence number 4 and sequence number 6), and then discharged (such as sequence number 8). It is only necessary to control the end voltage of the formation test to be less than the predicted voltage value and ≥2.5V, or to control the end capacity of the formation test to be less than the first predicted capacity value and greater than 0.

[0074] In some alternative embodiments: See Figure 1As shown, the embodiment of the present application provides a battery capacity prediction method. In step S4 of the method, the capacity division includes: charging the battery to be tested with a charging current of any multiple, the charging cut-off voltage is a predicted voltage value, and discharging the battery to be tested in a gradient discharge manner. When the battery to be tested is charged to the predicted voltage value, the battery to be tested is discharged in a gradient discharge manner.

[0075] In some embodiments: in the capacity division test, after discharging, charging can be performed again to charge to a certain factory capacity, and the specific charging conditions are not limited (this step can also be omitted); and after the charging, discharging is performed again, and the specific discharging conditions are not limited (this step can also be omitted).

[0076] In some optional embodiments: the battery under test is charged with constant current first and then constant voltage (i.e., constant current constant voltage charging) during the capacity division test. Specifically, constant current charging first and then constant voltage charging includes: charging to a predicted voltage value at an arbitrary rate, and then continuing constant voltage charging at the predicted voltage value, wherein the predicted voltage value continues to be charged at constant voltage until the capacity of the battery approaches the first predicted capacity value. The gradient discharge method is to discharge the battery under test with multiple constant currents, wherein the discharge current of the multiple constant currents gradually decreases.

[0077] Specifically, taking Table 2 as an example, the capacity division test of this application is described:

[0078] Table 2

[0079]

[0080]

[0081] As shown in Table 2, the battery to be tested is charged by first constant current charging and then constant voltage charging (sequence number 2), and then the battery to be tested is discharged with multiple constant currents (such as sequence numbers 4, 6 and 8); after the discharge, the battery can be charged (such as sequence number 10) and discharged (such as sequence number 12) again.

[0082] In some alternative embodiments: See Figure 1 As shown, an embodiment of the present application provides a battery capacity prediction method. In step S4 of the method, in the capacity division test, the maximum charging temperature and the maximum discharging temperature of the capacity division test are obtained.

[0083] In step S5, the linear fitting data model is obtained by fitting according to the measured capacity value of the battery to be tested, the dry cell weight of the battery to be tested, the end voltage of the formation test, the charge capacity of the capacity test, the discharge capacity of the capacity test, the end voltage of the capacity test, the maximum charge temperature of the capacity test and the maximum discharge temperature of the capacity test.

[0084] When predicting the capacity of the subsequent battery to be predicted, the dry cell weight, formation test end voltage, capacity test charge capacity, capacity test discharge capacity, capacity test end voltage, capacity test maximum charge temperature and capacity test maximum discharge temperature of the battery to be predicted are imported into the linear fitting data model to predict the capacity prediction value of the battery to be predicted.

[0085] The prediction method of the present application adopts temperature compensation, which involves the maximum charging temperature of the capacity test and the maximum discharging temperature of the capacity test, thereby improving the prediction accuracy and avoiding capacity anomalies caused by the difference in charging and discharging capacity due to fluctuations in environmental factors.

[0086] In some alternative embodiments: See Figure 1 As shown, the embodiment of the present application provides a battery capacity prediction method. In step S5 of the method, the linear fitting data model is: Y=A+B*X0+C*X1-D*X2+E*X3+F*X4-G*X5-H*X6;

[0087] Wherein: Y is the predicted capacity value of the battery to be predicted, A is a constant, B, C, D, E, F, G, H are the compensation values ​​of each factor, X0 is the dry cell weight of the battery to be predicted, X1 is the end voltage of the formation test of the battery to be predicted, X2 is the charge capacity of the capacity test of the battery to be predicted, X3 is the discharge capacity of the capacity test of the battery to be predicted, X4 is the maximum charge temperature of the capacity test of the battery to be predicted, X5 is the end voltage of the capacity test of the battery to be predicted, X6 is the maximum discharge temperature of the capacity test of the battery to be predicted, and the linear correlation coefficient R of the linear fitting is 2 ≥0.9. Among them, A, B, C, D, E, F, G, and H can be obtained by fitting software.

[0088] In some optional embodiments, the battery under test needs to be baked and then left to stand at room temperature after the formation test and before the capacity separation test.

[0089] Furthermore, the baking time is 24-68 hours, and the standing time at room temperature is 24-68 hours.

[0090] Furthermore, the baking time is 48 hours and the standing time at room temperature is 48 hours.

[0091] In some optional embodiments, the discharge capacity of the capacity division test is the discharge capacity corresponding to the first constant current discharge in the gradient discharge. Taking the capacity division process in Table 2 as an example, the discharge capacity of the capacity division test is the discharge capacity corresponding to sequence number 4.

[0092] In some embodiments, the present application is verified by testing 100 groups of tested batteries, and the following linear fitting data model is obtained: Y = 260.7 + 0.07502 * X0 + 42.7 * X1-1.2599 * X2 + 0.6634 * X3 + 0.2075 * X4-123.27 * X5-0.1165 * X6, where R 2 It is 95.98%.

[0093] Specifically, the battery capacity prediction results obtained by bringing the following nine groups of batteries to be predicted into the above fitting data model are as follows in Table 3:

[0094] Table 3

[0095]

[0096]

[0097] It can be seen from Table 3 above that, by using the prediction method of the present application, the capacity prediction value obtained is almost equal to the actual full capacity. Therefore, it can be proved that the prediction result of the prediction method of the present application is relatively accurate.

[0098] In the description of the present application, it should be noted that the terms "upper", "lower", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application. Unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances.

[0099] It should be noted that, in this application, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0100] The above description is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest range consistent with the principles and novel features applied for herein.

Claims

1. A battery capacity prediction method, characterized in that: The method comprises: Charge the battery under test according to the set charging rate, and obtain the real-time voltage value and real-time capacity value during the charging process, as well as the voltage-capacity curve during the charging process; Perform linear fitting on the real-time voltage value and the real-time capacity value during the charging process to obtain a charging linear fitting line; A voltage rise curve before full charge is extracted according to the voltage-capacity curve, and the intersection of the charging linear fitting line and the voltage rise curve is the predicted voltage value and the first predicted capacity value; Perform formation and capacity division on at least two batteries to be tested, and record the formation test end voltage, capacity division test charge capacity, capacity division test discharge capacity and capacity division test end voltage; In the formation test, the formation test end voltage is controlled to be less than the predicted voltage value, and the formation test end voltage is ≥ 2.5V; or; the formation test end capacity is controlled to be less than the first predicted capacity value and greater than 0; The capacity division includes: charging the battery to be tested with a charging current of any multiple, and the charging cut-off voltage is the predicted voltage value; after the charging is completed, discharging the battery to be tested in a gradient discharge manner; Performing full capacity tests on at least two batteries to be tested again to obtain measured capacity values, and fitting to obtain a linear fitting data model based on the measured capacity values, the dry cell weight of the batteries to be tested, the formation test end voltage, the divided capacity test charge capacity, the divided capacity test discharge capacity and the divided capacity test end voltage; When predicting the capacity of the subsequent battery to be predicted, the dry cell weight, formation test end voltage, capacity test charge capacity, capacity test discharge capacity and capacity test end voltage of the battery to be predicted are imported into the linear fitting data model to predict the capacity prediction value of the battery to be predicted.

2. A battery capacity prediction method as claimed in claim 1, characterized in that: The set charging rate includes 0.01C to 0.4C, and the charging method of charging the battery under test according to the set charging rate is constant current charging.

3. A battery capacity prediction method as claimed in claim 1, characterized in that: In the voltage-capacity curve, as the battery capacity increases, multiple voltage rising curves are formed before being fully charged; Select a voltage rise curve closest to full charge from among multiple voltage rise curves; The intersection of the charging linear fitting line and the voltage rising curve closest to the fully charged section is the predicted voltage value and the first predicted capacity value.

4. A battery capacity prediction method as claimed in claim 1, characterized in that: In the capacity division test, the charging method is constant current charging first and then constant voltage charging; The method of first charging with constant current and then charging with constant voltage includes: charging at any rate to a predicted voltage value, and then continuing constant voltage charging at the predicted voltage value.

5. A battery capacity prediction method as claimed in claim 1, characterized in that: The gradient discharge method is to discharge the battery to be tested with a constant current for multiple times.

6. A battery capacity prediction method as claimed in claim 5, characterized in that: The discharge current of the multiple constant currents gradually decreases.

7. A battery capacity prediction method as claimed in claim 1, characterized in that: In the capacity division test, obtaining the maximum charging temperature and the maximum discharging temperature of the capacity division test; The linear fitting data model is obtained by fitting according to the measured capacity value of the battery to be tested, the dry cell weight of the battery to be tested, the end voltage of the formation test, the charge capacity of the capacity test, the discharge capacity of the capacity test, the end voltage of the capacity test, the maximum charge temperature of the capacity test and the maximum discharge temperature of the capacity test; When predicting the capacity of the subsequent battery to be predicted, the dry cell weight, formation test end voltage, capacity test charge capacity, capacity test discharge capacity, capacity test end voltage, capacity test maximum charge temperature and capacity test maximum discharge temperature of the battery to be predicted are imported into the linear fitting data model to predict the capacity prediction value of the battery to be predicted.

8. A battery capacity prediction method as claimed in claim 7, characterized in that: The linear fitting data model is: Y=A+B*X0+C*X1-D*X2+E*X3+F*X4-G*X5-H*X6; in: Y is the predicted capacity value of the battery to be predicted, A is a constant, B, C, D, E, F, G, H are the compensation values ​​of each factor, X0 is the dry cell weight of the battery to be predicted, X1 is the end voltage of the formation test of the battery to be predicted, X2 is the charge capacity of the capacity test of the battery to be predicted, X3 is the discharge capacity of the capacity test of the battery to be predicted, X4 is the maximum charge temperature of the capacity test of the battery to be predicted, X5 is the end voltage of the capacity test of the battery to be predicted, X6 is the maximum discharge temperature of the capacity test of the battery to be predicted, and the linear correlation coefficient R of the linear fitting 2 ≥0.

9.

9. A battery capacity prediction method as claimed in claim 1, characterized in that: After the formation test and before the capacity separation test, the battery under test needs to be baked and then left to stand at room temperature.

10. A battery capacity prediction method as claimed in claim 5, characterized in that: The discharge capacity of the capacity-gradient test is the discharge capacity corresponding to the first constant current discharge in the gradient discharge.

Images