Battery capacity prediction method and device, electronic equipment and storage medium

Abstract

The invention relates to the field of battery capacity detection, in particular to a battery capacity prediction method and device, electronic equipment and a storage medium. According to the method, the full-capacity-grading capacity is predicted by obtaining the target release capacity and the corresponding association relationship, so that the time consumption of the process can be effectively shortened, the situation that a large amount of time is consumed by conventional full-capacity-grading, full-capacity-grading and full-discharge-full-capacity-grading is avoided, and the overall production efficiency is improved. And secondly, the high-cost problems of high energy consumption, requirement on a large number of restraint trays, investment of a large number of capacity grading cabinets and the like in the conventional full capacity grading process are solved, and the investment cost can be reduced. And finally, in the process of predicting the full-capacity-grading capacity by using the incidence relation, the charge capacity of the battery in the charging step is at a relatively low level, and compared with a full-capacity-grading process, the safety is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of battery capacity detection, and in particular to a battery capacity prediction method, device, electronic equipment and storage medium. Background Art

[0002] The conventional lithium battery capacity division process follows the full charge and full discharge mode, which consumes a lot of energy. In large-scale production scenarios, the continuous high energy consumption leads to a surge in operating costs. At the same time, in order to adapt to the full charge and full discharge process, companies have to purchase a large number of high-specification equipment, which makes the equipment investment cost soar, greatly increasing the economic burden. In addition, the full charge process will cause the battery charge to climb to 100%, which may cause serious safety accidents such as thermal runaway if not handled with care.

[0003] In addition, the heat generated by the battery during the full charging and discharging process is extremely significant, and the capacity performance of the lithium battery is extremely sensitive to temperature. Excessive heat causes the internal chemical reaction of the battery to be unbalanced, ultimately resulting in an inflated measured battery capacity, which cannot accurately reflect the true performance of the battery. Summary of the invention

[0004] In view of this, the embodiments of the present invention provide a method, device, electronic device and storage medium for predicting battery capacity to solve the problem that the existing solution measures an inflated battery capacity and cannot accurately reflect the actual performance of the battery.

[0005] In a first aspect, an embodiment of the present invention provides a method for predicting battery capacity, the method comprising:

[0006] Obtaining a target release capacity of the battery under test when it is discharged to a target voltage under a target state of charge;

[0007] Obtaining a correlation between the released capacity and the full capacity, wherein the correlation is obtained by performing a correlation analysis based on the released capacity of the battery sample when it is discharged to the target voltage at the target state of charge and the capacity obtained by performing full capacity distribution on all battery samples;

[0008] Based on the target released capacity and the association relationship, a target full-capacity distribution capacity of the battery to be tested is predicted.

[0009] Furthermore, before obtaining the target release capacity of the battery to be tested when it is discharged to the target voltage under the target state of charge, the method further includes:

[0010] Acquire discharge data of the battery sample, and extract, based on the discharge data, a first released capacity obtained by discharging the battery sample to different voltages at different states of charge;

[0011] Obtaining a second released capacity obtained by performing full capacity division on the battery sample;

[0012] A correlation analysis is performed on the first released capacity and the second released capacity to obtain a state of charge and a voltage with the highest correlation with the second released capacity, and the state of charge and the voltage with the highest correlation are respectively used as the target state of charge and the target voltage.

[0013] Furthermore, the method further comprises:

[0014] Obtaining a third released capacity of the battery sample when discharged to the target voltage at the target state of charge;

[0015] Fitting is performed based on the third released capacity and the second released capacity to obtain a correlation relationship between the released capacity and the full divided capacity.

[0016] Further, the predicting of the target full capacity of the battery to be tested based on the target released capacity and the association relationship includes:

[0017] Determine a first coefficient, a second coefficient and a preset constant associated with the target release capacity based on the associated relationship;

[0018] calculating a first product between the first coefficient and the target release capacity;

[0019] calculating a square value corresponding to the target release capacity, and calculating a second product between the square value and the second coefficient;

[0020] A difference between the first product and the second product is calculated, and the target full capacity is calculated based on the difference and the constant.

[0021] Further, the calculating the target full capacity based on the difference and the constant includes:

[0022] Detecting the ambient temperature of the environment where the battery to be tested is located;

[0023] Obtaining a first influence factor of the ambient temperature on the battery to be tested when it is discharged to a target voltage under a target state of charge;

[0024] An initial full capacity division capacity is calculated based on the difference and the constant, and the target full capacity division capacity is calculated using the first influencing factor and the initial full capacity division capacity.

[0025] Further, the calculating the target full capacity based on the difference and the constant includes:

[0026] Obtaining the charge and discharge cycle number and internal resistance change data of the battery to be tested;

[0027] Determining the battery health status of the battery to be tested based on the number of charge and discharge cycles and the internal resistance change data;

[0028] Obtaining a second influence factor of the battery health state on the battery to be tested when it is discharged to a target voltage under a target state of charge;

[0029] An initial full capacity division capacity is calculated based on the difference and the constant, and the target full capacity division capacity is calculated using the second influencing factor and the initial full capacity division capacity.

[0030] Furthermore, after predicting the target full capacity of the battery to be tested based on the target released capacity and the association relationship, the method further includes:

[0031] Comparing the target full capacity with a preset standard capacity;

[0032] If the target full-capacity capacity is lower than the preset standard capacity, the battery to be tested is tested according to a preset strategy to obtain a test result.

[0033] In a second aspect, an embodiment of the present invention provides a device for predicting battery capacity, the device comprising:

[0034] A first acquisition module is used to obtain a target release capacity of the battery under test when it is discharged to a target voltage under a target state of charge;

[0035] A second acquisition module is used to obtain the correlation between the released capacity and the full capacity, wherein the correlation is obtained by performing correlation analysis based on the released capacity of the battery sample discharged to the target voltage at the target state of charge and the capacity obtained by performing full capacity distribution on all battery samples;

[0036] A prediction module is used to predict the target full-capacity of the battery to be tested based on the target released capacity and the association relationship.

[0037] In a third aspect, an embodiment of the present invention provides an electronic device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, computer instructions being stored in the memory, and the processor executing the method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0038] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method of the first aspect or any corresponding embodiment thereof.

[0039] This application predicts the full-capacity capacity by obtaining the target release capacity and the corresponding correlation relationship, which can effectively shorten the process time, avoid the situation where the conventional full filling and discharging full-capacity takes a lot of time, and improve the overall production efficiency. Secondly, there is no need to face high energy consumption and high costs such as the need for a large number of restraint pallets and numerous capacity cabinets as in the previous full-capacity process, which can reduce investment costs. Finally, in the process of predicting the full-capacity capacity using the correlation relationship, the battery charge is at a lower level during the charging step, which significantly improves safety compared to the full-capacity process. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 is a schematic flow chart of a method for predicting battery capacity according to some embodiments of the present invention;

[0042] Figure 2 is a flow chart of another method for predicting battery capacity according to some embodiments of the present invention;

[0043] Figure 3 is a schematic diagram of correlation analysis results according to some embodiments of the present invention;

[0044] Figure 4 is a rendering of a fitting process according to some embodiments of the present invention;

[0045] Figure 5 is an effect diagram of the association relationship verification result according to some embodiments of the present invention;

[0046] Figure 6 is a schematic diagram of an association relationship verification result according to some embodiments of the present invention;

[0047] Figure 7 is a structural block diagram of a device for predicting battery capacity according to an embodiment of the present invention;

[0048] Figure 8 It is a schematic diagram of the hardware structure of the electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0050] According to an embodiment of the present invention, a method, device, electronic device and storage medium for predicting battery capacity are provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0051] In this embodiment, a method for predicting battery capacity is provided. Figure 1 is a flow chart of a method for predicting battery capacity according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0052] Step S101, obtaining a target release capacity of a battery to be tested when it is discharged to a target voltage under a target state of charge.

[0053] In the embodiment of the present application, first, the specific values ​​of the target state of charge and the target voltage are determined. This is usually based on a large number of previous experimental studies. For example, after analyzing the correlation between the release capacity and the full capacity when discharging at different SOCs to different voltages, the target state of charge is determined to be 40% SOC and the target voltage is 2.0V.

[0054] Select the battery to be tested, make sure it is in a normal testable state, and represents the type of battery that needs to be evaluated in actual application (such as blade lithium iron phosphate batteries of the same model and specification). Then accurately adjust the state of charge of the battery to be tested to the target state of charge (such as 40% SOC). This may require pre-charging and discharging the battery with a small current, and real-time monitoring of the power level to achieve precise control.

[0055] Use the prepared battery test equipment to discharge the battery under test in a predetermined discharge mode (commonly used are constant current discharge, constant power discharge, etc., which need to be selected according to the battery characteristics and test requirements), and continue the discharge process until the battery voltage drops to the target voltage (2.0V). During the discharge period, the battery test equipment will record detailed data such as the change of discharge current over time and the real-time value of battery voltage in real time. Finally, the target release capacity is calculated based on the data recorded by the battery test equipment.

[0056] Step S102, obtaining a correlation between the released capacity and the full capacity, wherein the correlation is obtained by performing a correlation analysis based on the released capacity of the battery sample when discharged to the target voltage at the target state of charge and the capacity obtained by performing full capacity distribution on all battery samples.

[0057] In the embodiment of the present application, the released capacity when discharged to the target voltage at the target state of charge, and the capacity obtained by fully dividing all these battery samples. These data are organized into a standardized data set format, for example, they can be organized into a two-dimensional table, each row corresponds to a battery sample, and the two columns of data are the released capacity of the sample when discharged to the target voltage at the target state of charge (set as a variable sample) and the capacity obtained by fully dividing it (set as a variable full dividing).

[0058] Then, using the selected correlation analysis method, the released capacity of the battery sample discharged to the target voltage at the target state of charge is used as one variable, and the corresponding full capacity is used as another variable to calculate and analyze the data of all samples. The correlation coefficient and other results obtained by analysis are used to determine the correlation between the released capacity and the full capacity.

[0059] If the correlation coefficient indicates that there is a strong linear relationship between the two, further fitting methods such as linear regression (such as least squares fitting) are used to obtain the expression of the specific correlation relationship. The expression of the correlation relationship is as follows: Among them, a is a constant, a=-310.3, b is the first coefficient, b=8.83, and c is the second coefficient, c=0.03224.

[0060] Step S103: predicting the target full capacity of the battery to be tested based on the target released capacity and the correlation relationship.

[0061] In the embodiment of the present application, based on the target release capacity and the correlation relationship, predicting the target full capacity of the battery to be tested includes the following steps A1-A4:

[0062] Step A1: determining a first coefficient, a second coefficient and a preset constant associated with the target release capacity based on the association relationship.

[0063] Specifically, the association expression is Among them, b (b=8.83) is the first coefficient corresponding to the target release capacity, which reflects the influence of the first-order term of the target release capacity in the entire correlation relationship; c (c=0.03224) is the second coefficient, which reflects the influence weight of the square term of the target release capacity on the overall correlation relationship; and a (a=-310.3) is a constant term, which is a fixed basic value in the entire calculation process.

[0064] Step A2, calculating a first product between the first coefficient and the target release capacity.

[0065] Specifically, the first coefficient (8.83) determined in the correlation is determined, and then it is multiplied by the target release capacity that has been obtained, that is, the formula 8.83×Q is used. 释放容量 The result of the calculation is the first product of the first coefficient and the target release capacity.

[0066] Step A3, calculating the square value corresponding to the target release capacity, and calculating the second product between the square value and the second coefficient.

[0067] Specifically, the obtained target release capacity is squared. Then, the second coefficient (here 0.03224) determined in the association relationship is used to multiply it by the square value of the target release capacity, that is, through the formula 0.03224×Q 释放容量 2 To calculate, the result is the second product between the square value and the second coefficient.

[0068] Step A4, calculating the difference between the first product and the second product, and calculating the target full capacity based on the difference and a constant.

[0069] Specifically, first calculate the difference between the first product and the second product, that is, use the first product (8.83×Q 释放容量 ) minus the second product (0.03224×Q 释放容量 2 ) to get the difference between the two. Then, based on this difference and the constant (-310.3), the target full capacity is calculated. The specific calculation method is to add the constant to this difference, that is, through the formula (8.83×Q 释放容量 -0.03224×Q 释放容量 2 )+(-310.3) is used to finally calculate the target full capacity. This calculation result can reflect the full capacity of the battery calculated based on the correlation relationship and the target release capacity, and is used for subsequent evaluation and analysis of battery performance and other aspects.

[0070] This application predicts the full-capacity capacity by obtaining the target release capacity and the corresponding correlation relationship, which can effectively shorten the process time, avoid the situation where the conventional full filling and discharging full-capacity takes a lot of time, and improve the overall production efficiency. Secondly, there is no need to face high energy consumption and high costs such as the need for a large number of restraint pallets and numerous capacity cabinets as in the previous full-capacity process, which can reduce investment costs. Finally, in the process of predicting the full-capacity capacity using the correlation relationship, the battery charge is at a lower level during the charging step, which significantly improves safety compared to the full-capacity process.

[0071] In an embodiment of the present application, the target full-capacity split capacity is calculated based on the difference and the constant, including the following steps: detecting the ambient temperature of the environment where the battery to be tested is located; obtaining a first influence factor of the ambient temperature on the battery to be tested when it is discharged to the target voltage under the target state of charge; calculating the initial full-capacity split capacity based on the difference and the constant, and calculating the target full-capacity split capacity using the first influence factor and the initial full-capacity split capacity.

[0072] Specifically, place the temperature measuring instrument in a suitable position around the battery to be tested. If testing a small battery in a laboratory environment, the instrument can be placed 10-15 cm away from the battery surface to avoid direct contact with the battery to prevent the battery from heating itself. At the same time, it should be kept away from other equipment that may generate heat interference (such as power supplies, heating test instruments, etc.). If it is a battery in an actual application scenario, such as an electric vehicle battery pack, it is necessary to reasonably arrange multiple temperature measurement points in the space where the battery pack is located to ensure that the temperature conditions of the entire battery pack environment can be fully reflected. Generally, the temperature sensors can be evenly distributed at certain intervals.

[0073] Select multiple groups of batteries to be tested with the same specifications, models and similar initial conditions as samples, and prepare thermostats or environmental simulation chambers that can accurately control the temperature, which can create different temperature environments, and the temperature control accuracy is within ±1°C to ensure that a variety of target temperature conditions can be accurately simulated. At the same time, prepare professional battery testing equipment that can accurately control the battery discharge process, set the target state of charge (SOC is 40%) and target voltage (2.0V), and accurately record various parameters during the discharge process, such as discharge current, discharge time, etc.

[0074] Set different temperature values ​​in the thermostat or environmental simulation chamber in sequence to cover the possible application temperature range, such as low temperature (-20℃, -10℃), normal temperature (25℃), high temperature (40℃, 50℃), etc. For each set temperature environment, first put the battery to be tested in it, and let the battery stand for a sufficient time (generally 1-2 hours) in this temperature environment to ensure that the internal temperature of the battery is balanced with the ambient temperature. Then, use the battery testing equipment to adjust the battery to the target state of charge, and then discharge the battery in a specific discharge mode (such as constant current discharge) until the battery voltage reaches the target voltage. During this process, the battery testing equipment records the detailed discharge data of each battery at the corresponding temperature, including discharge start time, end time, discharge current and other information.

[0075] According to the recorded data, the capacity released by each battery when discharged from the target state of charge to the target voltage under different temperature environments is calculated by the power calculation formula. The first influencing factor is determined by mathematical analysis method based on the released capacity at room temperature (25°C). For example, the ratio of the released capacity at different temperatures to the released capacity at room temperature can be calculated, that is, the first influencing factor, or a complex fitting analysis method can be used (such as linear regression, polynomial regression, etc., with temperature as the independent variable and the released capacity as the dependent variable for fitting to obtain a fitting equation, and then the quantitative relationship of the influence of temperature on capacity is determined based on the equation coefficient as the first influencing factor), and finally the first influencing factor corresponding to different temperatures is obtained.

[0076] Finally, based on the first impact factor corresponding to the current ambient temperature obtained and the calculated initial full capacity, the initial full capacity is multiplied by the corresponding first impact factor to obtain the target full capacity.

[0077] The embodiments of the present application can fully consider the actual impact of the key factor of the battery working environment on the battery performance by detecting the ambient temperature. By obtaining the first influence factor corresponding to the ambient temperature, the role of temperature in the specific process of discharging the battery from the target state of charge to the target voltage can be quantified, making the impact of the temperature factor on the battery capacity intuitive and measurable. On this basis, the initial full capacity is calculated by combining the difference and constant, and then the target full capacity is calculated using the first influence factor. This calculation method comprehensively considers the ambient temperature factor, making the prediction of the battery capacity more accurate and better adapted to the needs of battery capacity evaluation under different temperature environments.

[0078] In an embodiment of the present application, a target full-capacity split capacity is calculated based on a difference and a constant, including: obtaining the number of charge and discharge cycles and internal resistance change data of the battery to be tested; determining the battery health status of the battery to be tested based on the number of charge and discharge cycles and the internal resistance change data; obtaining a second influencing factor of the battery health status on the battery to be tested when it is discharged to a target voltage at a target state of charge; calculating an initial full-capacity split capacity based on the difference and the constant, and calculating the target full-capacity split capacity using the second influencing factor and the initial full-capacity split capacity.

[0079] Specifically, first of all, for obtaining the number of charge and discharge cycles, if the battery to be tested is equipped with a built-in battery management system (BMS), the cycle number record information stored therein can be read directly through the communication interface between the BMS and the external device using special monitoring software. If there is no BMS, it is necessary to build an independent charge and discharge monitoring circuit, which includes a high-precision current and voltage sensor, a data acquisition card, and a control unit. When the battery goes through a complete process from full charge to empty and then full charge, the control unit records this cycle, thereby counting the number of charge and discharge cycles.

[0080] To obtain the internal resistance change data, an AC signal with a frequency ranging from a few Hz to several kilohertz can be applied to the battery under different charge and discharge conditions (such as a specific SOC value). By analyzing the battery's response to the AC signal and fitting the equivalent circuit model, the battery's internal resistance value can be obtained. The internal resistance at different times and conditions can be measured multiple times to determine the internal resistance change data.

[0081] After obtaining the charge and discharge cycle number and internal resistance change data, it is necessary to determine the battery health status. First, prepare multiple groups of battery samples of the same type through a large number of experiments. For these samples, full capacity tests are performed under different combinations of cycle numbers and internal resistance changes to obtain the actual remaining capacity, which is used as a measure of the battery health status (SOH) (SOH = current actual capacity / initial rated capacity × 100%). With the charge and discharge cycle number and the internal resistance change rate as independent variables and SOH as the dependent variable, data fitting is performed. For example, a multivariate linear regression method is used to obtain an evaluation model such as SOH = ab × Nc × ΔR (a, b, c are fitting coefficients, N is the number of cycles, and ΔR is the internal resistance change rate). After that, the number of cycles and internal resistance change data of the battery to be tested are substituted into this model to calculate its battery health status.

[0082] For different battery health states (achieved by adjusting the number of cycles and internal resistance of the sample battery), the battery is adjusted to the target state of charge (assuming 40% SOC). After stabilization in a constant temperature chamber, it is discharged in a specific manner (such as constant current discharge) to the target voltage (assuming 2.0V), and the discharge parameters are recorded to calculate the release capacity under different health states.

[0083] The second impact factor is calculated based on the released capacity in the new battery state (SOH=100%), or the impact factor is determined by fitting the relationship between the health state and the capacity. Finally, the calculated initial full capacity is multiplied by the corresponding second impact factor to obtain the target full capacity.

[0084] The embodiment of the present application first obtains the number of charge and discharge cycles and internal resistance change data of the battery to be tested, which can intuitively reflect the aging of the battery and the performance evolution from the perspective of the battery usage history and changes in internal electrical characteristics; determining the battery health status based on these data helps to accurately know the overall condition of the battery at the moment, and provide a key basis for the subsequent reasonable use and maintenance of the battery; then obtaining the second influencing factor of the battery health status on a specific discharge process can quantify the role of the health status in the process, and further deepen the understanding of the battery performance; finally, the initial full-capacity capacity is calculated by the difference and the constant, and the target full-capacity capacity is calculated using the second influencing factor and the initial full-capacity capacity, thereby achieving a comprehensive consideration of the influence of multiple factors including the battery health status, and more accurately calculating the target full-capacity capacity.

[0085] In an embodiment of the present application, after predicting the target full capacity of the battery to be tested based on the target release capacity and the correlation relationship, the method also includes: comparing the target full capacity with the preset standard capacity; if the target full capacity is lower than the preset standard capacity, the capacity to be tested is tested according to the preset strategy to obtain a test result.

[0086] Specifically, the preset strategy is formulated based on the characteristics of the battery and past testing experience. The preset strategy includes the following aspects:

[0087] Appearance inspection: Check whether the battery shell is bulging, deformed, or damaged, because these appearance problems may indicate that the internal structure of the battery is abnormal, affecting the capacity. For example, a bulging battery shell may be due to excessive internal gas production, which causes changes in the internal space of the battery, thereby affecting the contact between the electrode material and the electrolyte and reducing the battery capacity.

[0088] Internal resistance detection: Use a professional internal resistance tester to measure the internal resistance of the battery again using the AC impedance method or the DC internal resistance measurement method (as described above). If the internal resistance is significantly increased compared to the normal range, it may mean that the electrode materials, electrolytes, etc. inside the battery have deteriorated, resulting in obstruction of ion transmission, thereby reducing the battery capacity. For example, under normal circumstances, the internal resistance of a certain model of battery is in the range of 1-2 milliohms. When the internal resistance is detected to exceed 3 milliohms, further investigation is required.

[0089] Retesting of charge and discharge performance: Perform multiple charge and discharge cycle tests on the battery to observe its charge and discharge curve, capacity retention rate and other indicators. For example, set different charge and discharge rates (such as 0.5C, 1C, 2C, etc., C is the current value corresponding to the rated capacity of the battery) for charge and discharge operations, record the battery voltage, current, time and other data during each charge and discharge process, analyze the shape of the charge and discharge curve, and calculate the capacity retention after each charge and discharge. If abnormal fluctuations in the charge and discharge curve are found (such as voltage drop, long charging time, etc.) or the capacity retention rate drops rapidly, it indicates that the battery may have performance problems that affect the capacity.

[0090] Perform the inspection operations in sequence according to the inspection contents determined in the preset strategy. During the appearance inspection, carefully observe each part of the battery shell with the naked eye or with the help of tools such as a magnifying glass, and record the appearance problems found in detail, such as the location, size, and degree of damage of the bulge.

[0091] When performing internal resistance testing, strictly follow the operating instructions of the internal resistance tester to ensure that the measurement environment is stable (such as temperature, humidity, etc. meet the requirements), take the average value of multiple measurements to improve data accuracy, and record the internal resistance value of each measurement as well as the corresponding measurement time, ambient temperature and other information.

[0092] During the retesting process of charge and discharge performance, professional battery charge and discharge test equipment is used to accurately set parameters such as charge and discharge rate and cut-off voltage, record various data during the charge and discharge process in real time, draw charge and discharge curves, and calculate the capacity retention rate after each charge and discharge. These data are organized into a detailed test report to clearly present the changes in battery charge and discharge performance.

[0093] Finally, the results of all test items are summarized and organized to form a complete test result report, which should cover the basic information of the battery (model, number, etc.), the comparison between the target full capacity and the preset standard capacity, the detailed findings of each test item and the comprehensive analysis conclusion, so that corresponding measures can be taken according to the test results, such as repairing the battery, scrapping it or feeding back to the production department to improve the production process.

[0094] In the embodiment of the present application, before obtaining the target release capacity of the battery under test when it is discharged to the target voltage under the target state of charge, as Figure 2 As shown, the method also includes the following steps:

[0095] Step S201 : obtaining discharge data of a battery sample, and extracting a first released capacity obtained by discharging the battery sample to different voltages at different states of charge based on the discharge data.

[0096] In the embodiment of the present application, a certain number of blade lithium iron phosphate batteries are selected as samples to ensure that these samples are representative and can reflect the general characteristics of this type of battery. At the same time, professional battery testing equipment is prepared, such as a high-precision battery tester, which can accurately control the discharge process and record relevant parameters such as current, voltage, time, etc.

[0097] For each battery sample, a different initial state of charge (SOC) value is set, and these SOC values ​​can be selected at certain intervals, such as 20%, 40%, 60%, 80%, etc. For each selected SOC initial value, a plurality of different discharge termination voltage values ​​are set respectively. For example, for lithium iron phosphate batteries, several different voltage values ​​between 2.0V and 3.5V can be set, such as 2.0V, 2.5V, 3.0V, etc.

[0098] Then, the battery test equipment is used to discharge the battery sample in a specific discharge mode (such as constant current discharge or constant power discharge, which needs to be determined according to the actual test requirements). During the discharge process, the battery test equipment records the curve of the discharge current changing with time and the corresponding voltage, time and other detailed data in real time.

[0099] According to the recorded data, the first release capacity Q1 is obtained by the corresponding power calculation method. If the discharge current is constant, the formula Q1 = I × T (where I is the discharge current and T is the time taken to discharge from the starting SOC to the set voltage) can be used for calculation. If the discharge current is not constant, the integration method is required to calculate the first release capacity Q1 value in each case of different SOC discharge to different voltages.

[0100] Step S202, obtaining a second released capacity obtained by performing full capacity division on the battery sample.

[0101] In an embodiment of the present application, the battery sample is started from a fully charged state (generally SOC is 100%) and discharged according to specified discharge conditions (such as a specific constant current discharge rate, discharge cut-off voltage, etc., which are determined based on the battery specifications and relevant standards) until the battery voltage drops to a preset full-capacity cut-off voltage (for example, for a blade lithium iron phosphate battery, it may be around 2.5V).

[0102] During the full-capacity discharge process, the battery testing equipment continuously records relevant data, and finally obtains the capacity released by the full-capacity discharge of the battery sample, i.e., the second released capacity Q2, through a power calculation method similar to the above-mentioned first released capacity calculation method (based on information such as discharge current and time).

[0103] Step S203 , performing correlation analysis on the first released capacity and the second released capacity to obtain the state of charge and voltage with the highest correlation with the second released capacity, and taking the state of charge and voltage with the highest correlation as the target state of charge and target voltage respectively.

[0104] In the embodiments of the present application, a variety of statistical analysis methods can be used to perform correlation analysis, such as the Pearson correlation coefficient analysis method, which calculates the linear correlation between two variables (here Q1 and Q2), and its value range is between -1 and 1. The closer the absolute value is to 1, the stronger the correlation is. A positive correlation indicates that the two change in the same direction, and a negative correlation indicates that the two change in opposite directions.

[0105] Using the selected correlation analysis method, the correlation between the release capacity and the full capacity corresponding to each group of different SOC and different voltages is calculated to obtain a series of correlation coefficient values ​​(such as Figure 3 As shown). Then traverse these correlation coefficient values, find the SOC value and voltage value corresponding to the correlation coefficient with the largest absolute value. This SOC value is the state of charge (target state of charge) with the highest correlation with the second release capacity, and the corresponding voltage value is the target voltage. For example, after analysis, it is found that the correlation coefficient between Q1 and Q2 is the largest when the SOC is discharged to 2.0V at 40%, so 40% is the target state of charge and 2.0V is the target voltage.

[0106] In an embodiment of the present application, the method further includes: obtaining a third release capacity of the battery sample when discharged to a target voltage under a target state of charge; and performing fitting based on the third release capacity and the second release capacity to obtain a correlation between the release capacity and the full capacity.

[0107] Specifically, the third release capacity data corresponding to all battery samples obtained previously (i.e., the capacity released by each sample when discharged from 40% SOC to 2.0V) and the second release capacity data obtained previously through full capacity split operation (the full capacity released by each sample when discharged from a fully charged state to a specified cut-off voltage) are collected. These data are organized into a standardized data table format to facilitate subsequent fitting analysis. Each row of data corresponds to a battery sample, including the two key indicators of the third release capacity and the second release capacity of the sample.

[0108] The fitting methods include linear regression fitting, polynomial fitting, etc. Here, according to the existing fitting formula (Q2 = -310.3 + 8.83Q1-0.03224Q12), the polynomial fitting method is more appropriate. Polynomial fitting can find the best fitting curve through the principle of least squares method, so that the sum of square errors between the fitting curve and the actual data points is minimized, so as to better reflect the internal relationship between the variables.

[0109] The third release capacity is used as the independent variable and the second release capacity is used as the dependent variable. Professional data analysis software (such as MATLAB, Excel and other tools with data analysis and fitting functions) is used to perform polynomial fitting operations. Figure 4 As shown, during the fitting process, the coefficients in the fitting formula will be automatically calculated based on the input data, and finally Q2 = -310.3 + 8.83Q1 - 0.03224Q1 is obtained. 2 , which is the expression of the correlation between the released capacity and the full shared capacity.

[0110] The following is the verification process of the association relationship expression:

[0111] First, ensure that the host computer of the capacity division system is in a normal and operational state and has the function of receiving and running the corresponding formula. Then, accurately input the fitting formula obtained through experiments and data analysis into the corresponding setting interface or program module of the host computer. During the input process, it is necessary to carefully check the coefficients, variables and other contents in the formula to avoid input errors that affect the accuracy of subsequent capacity calculations.

[0112] Secondly, select about 2,500 batteries to be divided from multiple batches of batteries of the same model, and ensure that the initial charge of these batteries is around 50% SOC when selected. This can be accurately controlled through professional battery power detection equipment or combined with the battery's own power monitoring function to ensure the consistency of the initial state of the selected batteries and reduce the impact of initial power differences on subsequent experimental results.

[0113] Then use a suitable discharge device, set the discharge current to I1, and use constant current discharge to discharge the battery from 50% SOC to 10% SOC, that is, discharge to 40% SOC. After that, continue to maintain the discharge state until the battery voltage drops to 2.0V. During the entire discharge process, it is necessary to use high-precision current and voltage measuring instruments to monitor and record various parameters in the discharge process in real time, such as whether the discharge current is stable at I1, and the change of battery voltage over time. Through these recorded data, the capacity Q1 released by the battery in the process of discharging from 40% SOC to 2.0V can be accurately calculated based on the principle of power calculation.

[0114] Next, after obtaining the Q1 value of each battery, the batteries are screened according to the pre-set Q1 standard. This Q1 standard is usually determined based on past experimental experience, battery specification requirements or industry standards, for example, the Q1 value should be set within a specific numerical range. For batteries that do not meet the Q1 standard, they are sent for secondary capacity division to further adjust the battery capacity state to meet the requirements as much as possible; for batteries that meet the Q1 standard, load adjustment operations are performed. The purpose of load adjustment is to further optimize the battery's state of charge and put it in a more suitable state, so as to facilitate subsequent accurate full capacity division and comparison and verification with the predicted capacity. The load adjustment operation also needs to be performed in accordance with strict process requirements and parameter settings, and the battery power adjustment is accurately controlled using charging and discharging equipment.

[0115] Then, after the host computer has received the accurate fitting formula and obtained the Q1 value corresponding to each battery, the host computer will automatically call the fitting formula and substitute the Q1 value into it for calculation. For example, if the fitting formula is Q2 = -310.3 + 8.83Q1-0.03224Q12, when the Q1 value is determined, the host computer will first calculate the square of Q1 according to the mathematical operation rules, then multiply it by the corresponding coefficients, and finally perform addition and subtraction operations to obtain the predicted capacity Q2 value corresponding to each battery. In this process, it is necessary to ensure that the calculation program of the host computer runs stably without freezing, freezing or calculation errors. At the same time, the Q2 value calculated by the host computer can be recorded and sorted for subsequent comparison and analysis with the actual capacity Q3.

[0116] For the batteries that have been screened and processed in the previous steps, the full capacity division process is used for operation. The full capacity division process generally refers to starting from the fully charged state of the battery, discharging the battery according to the established discharge rules (such as a specific constant current discharge rate, discharge cut-off voltage, etc., these parameters are determined according to the type of battery and relevant standards) until the battery voltage drops to the specified full capacity division cut-off voltage. During the entire discharge process, high-precision power measurement equipment is used to record the discharge current, voltage, time and other data in real time, and then the corresponding power calculation method (such as Q = I × t for constant current discharge, and integration and other appropriate methods for non-constant current discharge) is used to accurately calculate the total power released from the full charge state to the cut-off voltage, that is, the actual capacity of the battery Q3.

[0117] Finally, the data of the predicted capacity Q2 and the actual capacity Q3 corresponding to each battery are sorted together, and then the deviation ratio of the predicted capacity to the actual capacity of each battery is calculated one by one according to the calculation formula of the deviation ratio (deviation ratio = (|Q2-Q3| / Q3) × 100%). After that, the deviation ratio data of all batteries are statistically analyzed, for example, statistical indicators such as the average deviation ratio, the maximum deviation ratio, and the minimum deviation ratio can be calculated, and these indicators are used to comprehensively judge the reliability of the predicted capacity Q2. If the average deviation ratio is small and the deviation ratios of most batteries are within an acceptable range, it means that the reliability of the new capacity prediction process is high; otherwise, it is necessary to further analyze the reasons, which may be that the fitting formula is not accurate enough, there are errors in the discharge process of obtaining Q1, or problems in the execution process of the full capacity division process, etc., and then improve and perfect the corresponding links to improve the accuracy of capacity prediction and the reliability of the new process.

[0118] like Figure 5-Figure 6 As shown, the predicted capacity is highly consistent with the actual capacity distribution scatter diagram of the divided capacity, and the actual deviation ratio between the predicted capacity and the actual capacity is within 1%, of which 0.5% accounts for 97.55%, that is, the divided capacity prediction method of the present invention has a very high accuracy and stability.

[0119] In this embodiment, a battery capacity prediction device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0120] This embodiment provides a battery capacity prediction device, such as Figure 7 As shown, including:

[0121] The first acquisition module 701 is used to obtain the target release capacity of the battery under test when it is discharged to the target voltage under the target state of charge;

[0122] The second acquisition module 702 is used to obtain the correlation between the released capacity and the full capacity, wherein the correlation is obtained by performing correlation analysis on the released capacity of the battery sample when it is discharged to the target voltage at the target state of charge and the capacity obtained by performing full capacity distribution on all battery samples;

[0123] The prediction module 703 is used to predict the target full capacity of the battery to be tested based on the target released capacity and the association relationship.

[0124] In an embodiment of the present application, the device also includes: an analysis module, which is used to obtain discharge data of a battery sample, and extract a first release capacity obtained by discharging the battery sample to different voltages under different states of charge based on the discharge data; obtain a second release capacity obtained by fully dividing the battery sample; perform a correlation analysis on the first release capacity and the second release capacity to obtain a state of charge and voltage with the highest correlation with the second release capacity, and use the state of charge and voltage with the highest correlation as the target state of charge and target voltage, respectively.

[0125] In an embodiment of the present application, the device also includes: a fitting module for obtaining a third release capacity of the battery sample when discharged to a target voltage under a target state of charge; fitting is performed based on the third release capacity and the second release capacity to obtain a correlation between the release capacity and the full capacity.

[0126] In an embodiment of the present application, the prediction module 703 is used to determine the first coefficient, the second coefficient and the preset constant associated with the target release capacity based on the association relationship; calculate the first product between the first coefficient and the target release capacity; calculate the square value corresponding to the target release capacity, and calculate the second product between the square value and the second coefficient; calculate the difference between the first product and the second product, and calculate the target full capacity based on the difference and the constant.

[0127] In an embodiment of the present application, the prediction module 703 is used to detect the ambient temperature of the environment where the battery to be tested is located; obtain the first influence factor of the ambient temperature on the battery to be tested when it is discharged to the target voltage under the target state of charge; calculate the initial full-capacity based on the difference and the constant, and calculate the target full-capacity using the first influence factor and the initial full-capacity.

[0128] In an embodiment of the present application, the prediction module 703 is used to obtain the number of charge and discharge cycles and the internal resistance change data of the battery to be tested; based on the number of charge and discharge cycles and the internal resistance change data, determine the battery health status of the battery to be tested; obtain the second influencing factor of the battery health status on the battery to be tested when it is discharged to the target voltage at the target state of charge; calculate the initial full-capacity capacity based on the difference and the constant, and calculate the target full-capacity capacity using the second influencing factor and the initial full-capacity capacity.

[0129] In an embodiment of the present application, the device also includes: a comparison module, which is used to compare the target full capacity with the preset standard capacity; if the target full capacity is lower than the preset standard capacity, the capacity to be tested is tested according to a preset strategy to obtain a test result.

[0130] See also Figure 8 , Figure 8 is a schematic diagram of the structure of an electronic device provided by an optional embodiment of the present invention, such as Figure 8 As shown, the electronic device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the electronic device, including instructions stored in or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple electronic devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system).

[0131] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0132] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0133] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created by the use of an electronic device based on the presentation of a small program landing page, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0134] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0135] The electronic device further comprises a communication interface 30 for the electronic device to communicate with other devices or a communication network.

[0136] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0137] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for predicting battery capacity, characterized in that: The method comprises: Obtaining a target release capacity of the battery under test when it is discharged to a target voltage under a target state of charge; Obtaining a correlation between the released capacity and the full capacity, wherein the correlation is obtained by performing a correlation analysis based on the released capacity of the battery sample when it is discharged to the target voltage at the target state of charge and the capacity obtained by performing full capacity distribution on all battery samples; Based on the target released capacity and the association relationship, a target full-capacity distribution capacity of the battery to be tested is predicted.

2. The method according to claim 1, characterized in that Before obtaining the target release capacity of the battery to be tested when it is discharged to the target voltage under the target state of charge, the method further includes: Acquire discharge data of the battery sample, and extract, based on the discharge data, a first released capacity obtained by discharging the battery sample to different voltages at different states of charge; Obtaining a second released capacity obtained by performing full capacity division on the battery sample; A correlation analysis is performed on the first released capacity and the second released capacity to obtain a state of charge and a voltage with the highest correlation with the second released capacity, and the state of charge and the voltage with the highest correlation are respectively used as the target state of charge and the target voltage.

3. The method according to claim 2, characterized in that The method further comprises: Obtaining a third released capacity of the battery sample when discharged to the target voltage at the target state of charge; Fitting is performed based on the third released capacity and the second released capacity to obtain a correlation relationship between the released capacity and the full divided capacity.

4. The method according to claim 1, characterized in that The predicting the target full capacity of the battery to be tested based on the target released capacity and the association relationship includes: Determine a first coefficient, a second coefficient and a preset constant associated with the target release capacity based on the associated relationship; calculating a first product between the first coefficient and the target release capacity; calculating a square value corresponding to the target release capacity, and calculating a second product between the square value and the second coefficient; A difference between the first product and the second product is calculated, and the target full capacity is calculated based on the difference and the constant.

5. The method according to claim 4, characterized in that The calculating the target full capacity based on the difference and the constant includes: Detecting the ambient temperature of the environment where the battery to be tested is located; Obtaining a first influence factor of the ambient temperature on the battery to be tested when it is discharged to a target voltage under a target state of charge; An initial full capacity division capacity is calculated based on the difference and the constant, and the target full capacity division capacity is calculated using the first influencing factor and the initial full capacity division capacity.

6. The method according to claim 5, characterized in that The calculating the target full capacity based on the difference and the constant includes: Obtaining the charge and discharge cycle number and internal resistance change data of the battery to be tested; Determining the battery health status of the battery to be tested based on the number of charge and discharge cycles and the internal resistance change data; Obtaining a second influence factor of the battery health state on the battery to be tested when it is discharged to a target voltage under a target state of charge; An initial full capacity division capacity is calculated based on the difference and the constant, and the target full capacity division capacity is calculated using the second influencing factor and the initial full capacity division capacity.

7. The method according to claim 1, characterized in that After predicting the target full capacity of the battery to be tested based on the target released capacity and the association relationship, the method further includes: Comparing the target full capacity with a preset standard capacity; If the target full-capacity capacity is lower than the preset standard capacity, the battery to be tested is tested according to a preset strategy to obtain a test result.

8. A battery capacity prediction device, characterized in that: The device comprises: A first acquisition module is used to obtain a target release capacity of the battery under test when it is discharged to a target voltage under a target state of charge; A second acquisition module is used to obtain the correlation between the released capacity and the full capacity, wherein the correlation is obtained by performing correlation analysis based on the released capacity of the battery sample discharged to the target voltage at the target state of charge and the capacity obtained by performing full capacity distribution on all battery samples; A prediction module is used to predict the target full-capacity of the battery to be tested based on the target released capacity and the association relationship.

9. An electronic device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 7.

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