Battery curve automatic compensation method and device, medium and computer equipment

Abstract

The invention provides a battery curve automatic compensation method and device, a medium and computer equipment, and belongs to the technical field of curve compensation, and the method comprises the steps: building a battery model based on a standard open-circuit voltage curve, a standard internal resistance characteristic curve and a standard self-discharge effect of a battery; capturing a process log of battery simulation modeling according to the battery model, and determining optimization information of simulation modeling in combination with a simulation script of simulation modeling and a simulation result to obtain a newest script; actual testing is carried out on the battery, actual index curves of the battery under different factor indexes are obtained, primary curve compensation is carried out on each actual index curve based on a linear interpolation mode, and secondary curve compensation is carried out on each actual index curve based on optimization information, so that the battery model is corrected; and re-operating the newest script to carry out simulation modeling on the corrected model, and determining the availability of the battery. The accuracy of obtaining the availability of the battery is improved, and the error of determining the SOC is effectively reduced.

Description

Technical Field

[0001] The invention relates to a battery curve automatic compensation method, device, medium and computer equipment, belonging to the technical field of curve compensation. Background Art

[0002] In a battery management system, the battery's remaining capacity estimation (State of Charge, SOC) is a very important indicator. Accurate SOC estimation can provide battery availability, help users arrange charging and discharging strategies reasonably, extend battery life, and ensure system reliability. However, battery SOC estimation faces many challenges, such as battery model determination, battery capacity calibration and temperature compensation, current efficiency, etc., which will lead to certain errors in battery SOC estimation.

[0003] The common battery SOC estimation method is the AH integration method, which estimates the battery SOC based on the initial SOC0. By calculating the integral of the charge and discharge current and the corresponding time within a certain period of time, the percentage of the changed power is calculated, and finally the difference between the initial SOC and the changed SOC, that is, the remaining power, is obtained. The disadvantage is that the SOC0 at the initial moment is not easy to determine and there is a certain error in the accuracy. Moreover, as time goes by, the error will also accumulate and increase, so the accuracy of the current measurement is very high. In addition, the error will increase under conditions where the current fluctuates violently.

[0004] Therefore, the present invention provides a battery curve automatic compensation method, device, medium and computer equipment. Summary of the invention

[0005] In view of the deficiencies of the above-mentioned prior art, the purpose of the present invention is to provide a battery curve automatic compensation method, device, medium and computer equipment, which are used to compensate the curves under different factor indicators twice from two aspects: simulation test of the battery model and actual test of the battery, so as to determine the accuracy of model correction, thereby improving the accuracy of battery availability acquisition and effectively reducing the error in determining SOC.

[0006] The present invention provides a battery curve automatic compensation method, comprising: Step 1: Establish a battery model based on the battery's standard open circuit voltage curve, standard internal resistance characteristic curve, and standard self-discharge effect; Step 2: Capture the process log of battery simulation modeling according to the battery model, and determine the optimization information of the simulation modeling in combination with the simulation script and simulation results of the simulation modeling to obtain the latest script; Step 3: performing actual testing on the battery, obtaining actual index curves of the battery under different factor indexes, and performing initial curve compensation on each actual index curve based on a linear interpolation method and performing secondary curve compensation on each actual index curve based on the optimization information to calibrate the battery model, wherein the factor index is related to the discharge efficiency, the charging efficiency and the battery temperature; Step 4: Re-run the latest script to simulate the corrected model and determine the availability of the battery.

[0007] Furthermore, each actual indicator curve is initially compensated based on a linear interpolation method, including: Placing the battery in different temperature scenarios, and randomly extracting charging instances and discharging instance pairs from a charge-discharge test comparison table to perform a charging test and a discharging test on the battery, and constructing a corresponding first remaining capacity curve and a second remaining capacity curve; According to the first remaining capacity curve and the second remaining capacity curve, and in combination with the charging and discharging characteristics of the charging instance and the discharging instance pair, determine a relevant data set of the battery in the corresponding temperature scene, wherein the relevant data set includes a battery characteristic value, a battery full capacity value, and a battery loss value; Draw the relevant required curve for each data in the determined relevant data set in turn according to the temperature size in the temperature scene, and perform linear interpolation on the required curve to obtain the final curve; Analyzing each final curve based on the compensation factor corresponding to the required curve, and combining the correlation between each factor index and the relevant data set to obtain an actual adjustment factor for capacity compensation of the battery; The ideal interpolation quantities matching the corresponding actual adjustment factors are obtained from the indicator-factor-compensation comparison table, and points are randomly inserted into the corresponding actual indicator curve according to the ideal interpolation quantities to achieve initial curve compensation.

[0008] Furthermore, the process of randomly inserting points into the corresponding actual indicator curve according to the ideal interpolation quantity also includes: The current position of each randomly inserted point in the corresponding actual indicator curve is locked respectively, and the values ​​of two points closest to the current position are locked from the corresponding actual indicator curve, and are regarded as the first value and the second value respectively; Locking a first curve segment before the point corresponding to the first value in the corresponding actual indicator curve, performing linear fitting on the first curve segment, and obtaining a first constant; Locking a second curve segment corresponding to the actual indicator curve after the point corresponding to the second value, performing linear fitting on the second curve, and obtaining a second constant; Wherein, when there is no first curve segment before the point corresponding to the first value, the first constant is regarded as the first value; When there is no second curve segment after the point corresponding to the second value, the second constant is regarded as the second value; According to the first slope between the first constant and the first value, the second slope between the first value and the second value, and the third slope between the second value and the second constant, and in combination with the first value and the second value, the value of the corresponding randomly inserted point is determined to achieve primary curve compensation.

[0009] Furthermore, the simulation modeling optimization information is determined by combining the simulation script and the simulation results of the simulation modeling, including: Extracting the same parameters at the same simulation time point from the log parameters of the process log, the setting parameters of the simulation script, and the result parameters of the simulation result to obtain simulation arrays of the same parameters at different simulation time points; Analyze the deviation information of all simulation arrays of the same parameter to obtain the initial adjustment value corresponding to the same parameter; The number of arrays of the same parameter is obtained, and the corresponding initial adjustment value is corrected according to the number of arrays to obtain optimization information for simulation modeling.

[0010] Further, each actual indicator curve is subjected to curve compensation again based on the optimization information, including: An optimization vector is constructed based on the optimization information, and a conversion vector is obtained according to a conversion relationship between the optimization vector and the factor index corresponding to each actual index curve; Inputting the conversion vector into a conversion model matching the factor index to obtain a conversion variable, and at the same time, obtaining a compensation coefficient of the battery's own loss function and a dependency relationship between the factor index and the battery's own loss function; The actual index curve is discretized to determine the discrete points and the deviation directions of the discrete points, and the corresponding discrete points are adjusted in size in combination with the conversion variables, compensation coefficients and dependencies to achieve curve compensation again.

[0011] Furthermore, obtaining the compensation coefficient of the battery's own loss function includes: Constructing a standard loss function of the battery according to the material system of the battery and the process flow of the battery; When the battery is caught in a virtual power state, the battery is charged and discharged at different times to obtain a result set based on each charge and discharge test. ,in, represents the charging time under the i-th charging test when the battery is in a virtual power state; Indicates that when the battery reaches the charging time under the i-th charging test, the battery is discharged, and the recorded discharge time of the battery to 99% of the remaining capacity; Build on all The first curve and all The second curve is aligned and plotted in the same coordinate system, wherein the abscissa of the coordinate system is related to the number of tests, and the ordinate is related to time; The turning points on each curve are locked and extended in a straight line, and the standard loss function is optimized to obtain the own loss function; A compensation coefficient matching the own loss function is obtained from the function-compensation comparison table.

[0012] Further, determining the availability of the battery includes: capturing again the current log of simulation modeling of the corrected model based on the latest script; Extracting index values ​​of predefined evaluation indexes related to the battery from the current log respectively; The availability of the battery is obtained according to the indicator value and the indicator weight of the predefined evaluation indicator.

[0013] The present invention provides a battery curve automatic compensation device, comprising: A model building module is used to build a battery model based on the battery's standard open circuit voltage curve, standard internal resistance characteristic curve, and standard self-discharge effect; The script optimization module is used to capture the process log of battery simulation modeling according to the battery model, and determine the optimization information of the simulation modeling in combination with the simulation script and simulation results of the simulation modeling to obtain the latest script; a curve compensation module, for performing actual testing on the battery, obtaining actual index curves of the battery under different factor indexes, and performing initial curve compensation on each actual index curve based on a linear interpolation method and performing secondary curve compensation on each actual index curve based on the optimization information, so as to calibrate the battery model, wherein the factor index is related to the discharge efficiency, the charging efficiency and the battery temperature; The availability determination module is used to re-run the latest script to simulate the corrected model and determine the availability of the battery.

[0014] The present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor is enabled to execute the steps of any one of the methods.

[0015] The present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of any one of the methods.

[0016] Compared with the prior art, the present invention has the following beneficial effects: By performing simulation tests on the battery model and actual tests on the battery, the curves under different factor indicators are compensated twice to determine the accuracy of the model correction, thereby improving the accuracy of battery availability acquisition and effectively reducing the error in determining SOC. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flow chart of a battery curve automatic compensation method of the present invention; Figure 2 This is a structural diagram of a battery curve automatic compensation device of the present invention. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the technical solution in the present application, the following will be combined with the embodiments of the present application. The accompanying drawings clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only 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 creative work are within the scope of protection of the present application.

[0019] It should be noted that when an element is referred to as being "fixed on" or "set on" another component, it can be directly on the other component or indirectly set on the other component; when a component is referred to as being "connected to" another component, it can be directly connected to the other component or indirectly connected to the other component.

[0020] It should be understood that the orientation or position relationship indicated by terms such as "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside" and "outside" are based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or component referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0021] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, "multiple" and "several" mean two or more, unless otherwise clearly and specifically defined.

[0022] It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the conditions under which this application can be implemented. Therefore, they have no substantive technical significance. Any structural modification, change in proportional relationship or adjustment of size should still fall within the scope of the technical content disclosed in this application without affecting the effects and purposes that can be achieved by this application.

[0023] Embodiment 1: The present invention provides a battery curve automatic compensation method, such as Figure 1 As shown, including: Step 1: Establish a battery model based on the battery's standard open circuit voltage curve, standard internal resistance characteristic curve, and standard self-discharge effect; Step 2: Capture the process log of battery simulation modeling according to the battery model, and determine the optimization information of the simulation modeling in combination with the simulation script and simulation results of the simulation modeling to obtain the latest script; Step 3: performing actual testing on the battery, obtaining actual index curves of the battery under different factor indexes, and performing initial curve compensation on each actual index curve based on a linear interpolation method and performing secondary curve compensation on each actual index curve based on the optimization information to calibrate the battery model, wherein the factor index is related to the discharge efficiency, the charging efficiency and the battery temperature; Step 4: Re-run the latest script to simulate the corrected model and determine the availability of the battery.

[0024] In this embodiment, the standard open circuit voltage curve, standard internal resistance characteristic curve and standard self-discharge effect of the battery tested at the factory are determined according to the battery model. That is, different models of batteries will be tested before leaving the factory and the product will only be shipped if it is qualified. It should be noted that each model has its corresponding standard open circuit voltage curve, standard internal resistance characteristic curve and standard self-discharge effect.

[0025] In this embodiment, the standard open circuit voltage curve is the potential difference between the positive and negative electrodes of the battery when it is at rest. It is generally measured by charging or discharging to a specific SOC and then letting the battery rest for a period of time (usually two hours or longer) to obtain the battery voltage.

[0026] In this embodiment, the standard internal resistance characteristic curve is obtained by the ratio of the standard open circuit voltage to the current.

[0027] In this embodiment, the standard self-discharge effect refers to the power loss caused by the self-discharge phenomenon of the battery when it is idle.

[0028] In this embodiment, the basis for establishing the battery model may be an electrochemical model, an equivalent circuit model, etc.

[0029] In this embodiment, the simulation of the battery model is implemented based on the Simulink environment, and the execution code (script) needs to be pre-loaded before the simulation platform runs for different types of batteries. Therefore, the script can be directly run for simulation.

[0030] In this embodiment, when the platform is in simulation operation, the tools set therein will automatically capture the log of the simulation process, for example, whether there are simulation failures such as code anomalies during the operation process. If so, code adjustments are required.

[0031] For example, the initial SOC is set to 0.9, and the current is set to a pulse current of 1.35A. The model is simulated and verified by discharging for 10 seconds and resting for 40 seconds. Then, the simulated voltage curve can be obtained, and then compared with the standard voltage curve to obtain the error curve, and then the optimization information is obtained. The optimization information is used to improve the script and model to ensure the accuracy of the battery availability determination.

[0032] In this embodiment, for example, the simulation is performed using script 1. After the optimization information is determined, script 1 is adjusted to obtain script 2, that is, script 2 is made to be more in line with the battery factory standard, thereby avoiding errors in battery availability evaluation due to platform simulation errors.

[0033] In this embodiment, the actual test refers to charging the battery when the battery power is low to obtain the charging efficiency at each time point; When the battery is fully charged, a discharge test is performed on the battery to obtain the discharge efficiency at each time point; When the battery is in the process of discharging or charging test, the battery temperature is measured at each time point; Based on the relevant measurement parameters at different time points, the actual indicator curve under different factor indicators can be constructed, with the time point as the horizontal coordinate and the value measured at each time point as the vertical coordinate.

[0034] In this embodiment, the first curve compensation is implemented based on linear interpolation, and the second curve compensation is implemented based on optimization information.

[0035] In this embodiment, model correction is achieved by obtaining a correction factor from a model-curve-correction comparison table based on a difference curve between an actual indicator curve and the corresponding final compensation curve. The comparison table contains different battery models, difference curves based on different indicators under different battery models, and corresponding correction factors. Therefore, the model can be calibrated directly.

[0036] The beneficial effect of the above technical solution is: to compensate the curves under different factor indicators twice from two aspects: simulation test of battery model and actual test of battery, determine the accuracy of model correction, thereby improving the accuracy of battery availability acquisition and effectively reducing the error of determining SOC.

[0037] Embodiment 2: Based on the first embodiment, each actual index curve is initially compensated based on a linear interpolation method, including: Placing the battery in different temperature scenarios, and randomly extracting charging instances and discharging instance pairs from a charge-discharge test comparison table to perform a charging test and a discharging test on the battery, and constructing a corresponding first remaining capacity curve and a second remaining capacity curve; According to the first remaining capacity curve and the second remaining capacity curve, and in combination with the charging and discharging characteristics of the charging instance and the discharging instance pair, determine a relevant data set of the battery in the corresponding temperature scene, wherein the relevant data set includes a battery characteristic value, a battery full capacity value, and a battery loss value; Draw the relevant required curve for each data in the determined relevant data set in turn according to the temperature size in the temperature scene, and perform linear interpolation on the required curve to obtain the final curve; Analyzing each final curve based on the compensation factor corresponding to the required curve, and combining the correlation between each factor index and the relevant data set to obtain an actual adjustment factor for capacity compensation of the battery; The ideal interpolation quantities matching the corresponding actual adjustment factors are obtained from the indicator-factor-compensation comparison table, and points are randomly inserted into the corresponding actual indicator curve according to the ideal interpolation quantities to achieve initial curve compensation.

[0038] In this embodiment, the temperature scenario refers to placing the battery in different temperature environments.

[0039] In this embodiment, the charge-discharge test comparison table includes different charging instance and discharging instance pairs and the charge-discharge characteristics for the instance pairs, which are all preset, mainly to provide a data reference basis for the test.

[0040] In this embodiment, the charging test and the discharging test refer to the test being performed when the battery is not fully charged.

[0041] In this embodiment, the horizontal axis of the remaining capacity curve is time, and the vertical axis is the remaining capacity.

[0042] In this embodiment, the battery characteristic value=the average charging current of the first remaining capacity curve×the charging conversion coefficient determined by the charging and discharging characteristics+the average discharging current of the second remaining capacity curve×the discharging conversion coefficient determined by the charging and discharging characteristics.

[0043] In this embodiment, the full capacity value of the battery = the available capacity of the battery after it is fully charged.

[0044] In this embodiment, the battery loss value=(the average discharge current of the second remaining capacity curve−the set average discharge current)×the voltage difference.

[0045] In this embodiment, there is a relevant data set in each temperature scenario. Therefore, a relevant required curve can be drawn according to the temperature, and the horizontal axis is time and the vertical axis is the parameter value involved in the corresponding data set.

[0046] In this embodiment, linear interpolation refers to randomly inserting a certain number of points into the corresponding curve to ensure the reliability of the curve.

[0047] In this embodiment, the compensation factor is obtained based on the curve difference between the required curve and the final curve, and is then matched from a parameter type-difference-factor comparison table, and the comparison table contains compensation factors for different value parameters and curve differences, and the combination of the two, which is pre-planned.

[0048] In this embodiment, the correlation refers to the correlation coefficient between the factor index and certain parameters in the relevant data set. For example, the correlation coefficient between the compensation factor under index 1 and the relevant data set is 0.1. At this time, the actual adjustment factor is: 0.1×compensation factor.

[0049] In this embodiment, the index-factor-compensation comparison table includes different factor indexes, actual compensation factors, and the ideal interpolation number that matches the two, so that the number of random insertion points can be directly determined to achieve curve compensation.

[0050] The beneficial effects of the above technical solution are: by testing the battery in different temperature scenarios to obtain curves based on charging and discharging, and then to construct a relevant data set, and subsequently by drawing curves and performing linear interpolation to improve the reliability of the curves, providing a basis for the subsequent acquisition of compensation factors, and further combining with related relationships to facilitate the acquisition of ideal interpolation quantities, ensure the initial rationality of compensation, and briefly reduce the error in the subsequent determination of SOC.

[0051] Embodiment 3: Based on Example 2, the process of randomly inserting points into the corresponding actual indicator curve according to the ideal interpolation quantity also includes: The current position of each randomly inserted point in the corresponding actual indicator curve is locked respectively, and the values ​​of two points closest to the current position are locked from the corresponding actual indicator curve, and are regarded as the first value and the second value respectively; Locking a first curve segment before the point corresponding to the first value in the corresponding actual indicator curve, performing linear fitting on the first curve segment, and obtaining a first constant; Locking a second curve segment corresponding to the actual indicator curve after the point corresponding to the second value, performing linear fitting on the second curve, and obtaining a second constant; Wherein, when there is no first curve segment before the point corresponding to the first value, the first constant is regarded as the first value; When there is no second curve segment after the point corresponding to the second value, the second constant is regarded as the second value; According to the first slope between the first constant and the first value, the second slope between the first value and the second value, and the third slope between the second value and the second constant, and in combination with the first value and the second value, the value of the corresponding randomly inserted point is determined to achieve primary curve compensation.

[0052] In this embodiment, the values ​​of the two nearest points refer to two points in the actual indicator curve, that is, two points on the curve before point insertion is performed.

[0053] In this embodiment, the second value is located to the right of the first value.

[0054] In this embodiment, linear fitting belongs to the prior art, and after linear fitting, an average value based on the fitting line segment is obtained and regarded as a constant.

[0055] In this embodiment, the slope=(the value of the later point−the value of the earlier point) / the time difference between the two values, specifically: (the second value−the first value) / the time difference between the first value and the second value.

[0056] In this embodiment, the value of the random insertion point = Wherein, d1 represents the first value; d2 represents the second value; max and min represent the maximum value and minimum value symbols respectively; D0 represents the calculation function of the random insertion point; (the absolute value of the difference between the first constant and the first value + the absolute value of the difference between the first value and the second value + the absolute value of the difference between the second value and the second constant) / 3.

[0057] The beneficial effect of the above technical solution is: by locking the values ​​of the two points closest to each other in the curve, and then obtaining the constant by fitting the line segments on the left and right sides, and then calculating the value of the randomly inserted point by respectively performing the slope and the difference between the two adjacent values, the accuracy of the initial curve compensation is ensured, providing a basis for subsequent SOC analysis.

[0058] Embodiment 4: Based on Example 1, combined with the simulation script and simulation results of the simulation modeling, the optimization information of the simulation modeling is determined, including: Extracting the same parameters at the same simulation time point from the log parameters of the process log, the setting parameters of the simulation script, and the result parameters of the simulation result to obtain simulation arrays of the same parameters at different simulation time points; Analyze the deviation information of all simulation arrays of the same parameter to obtain the initial adjustment value corresponding to the same parameter; The number of arrays of the same parameter is obtained, and the corresponding initial adjustment value is corrected according to the number of arrays to obtain optimization information for simulation modeling.

[0059] In this embodiment, for example, the log parameters include: time t1: parameter 1--a1, parameter 2--a2, the setting parameters of the simulation script include: time t1: parameter 1--b1, parameter 2--b2, the result parameters include: time t1: parameter 1--c1, parameter 2--c2, at this time, the simulation array: {parameter 1 at time t1: a1, b1, c1}.

[0060] In this embodiment, the deviation information includes: the absolute value difference between a1 and b1, and the absolute value difference between b1 and c1.

[0061] In this embodiment, the initial adjustment value=the sum of the average of the sum of the absolute value differences in each simulation array under the same parameter multiplied by the weight of the corresponding time point.

[0062] In this embodiment, the corrected value=initial adjustment value×ln(2+number of arrays of the same parameter / number of arrays of all parameters).

[0063] In this embodiment, the optimization information is extracted based on the parameter-information comparison table, and then the optimization information for the parameter can be obtained, and the replacement script matching the optimization information can be obtained based on the information-script comparison table, thereby realizing the update optimization of the original simulation script, wherein the parameter-information comparison table includes different setting parameters and optimization information after the setting parameters are at different error corresponding correction values, and the information-script comparison table includes different optimization information and corresponding replacement scripts, and the optimization information refers to the information that certain parameters in the script need to be optimized, and when optimizing, it can be the replacement of a script involving a certain parameter, or it can be the replacement of the entire script.

[0064] The beneficial effect of the above technical solution is that by constructing a simulation array of the same parameter, it is easy to obtain the initial adjustment value, and combined with the number of arrays, it is easy to implement the correction of the value, thereby obtaining reasonable optimization information.

[0065] Embodiment 5: Based on the fourth embodiment, each actual indicator curve is again compensated based on the optimization information, including: An optimization vector is constructed based on the optimization information, and a conversion vector is obtained according to a conversion relationship between the optimization vector and the factor index corresponding to each actual index curve; Inputting the conversion vector into a conversion model matching the factor index to obtain a conversion variable, and at the same time, obtaining a compensation coefficient of the battery's own loss function and a dependency relationship between the factor index and the battery's own loss function; The actual index curve is discretized to determine the discrete points and the deviation directions of the discrete points, and the corresponding discrete points are adjusted in size in combination with the conversion variables, compensation coefficients and dependencies to achieve curve compensation again.

[0066] In this embodiment, the optimization vector={the normalized result of the optimization information corresponding to each parameter}.

[0067] In this embodiment, the conversion relationship is obtained based on a vector-factor index comparison table and can be directly determined. The comparison table contains the optimization vector under different parameter combinations and the conversion relationship of the factor index matching the vector. The conversion relationship is to re-standardize the numerical values ​​of the elements in the optimization vector to obtain the conversion vector.

[0068] For example, the optimization vector = {u1 u2 u3}, the conversion relationship is that u1 needs to be converted according to h1, u2 needs to be converted according to h2, and u3 needs to be converted according to h3, and the resulting conversion vector = {u1h1 u2h2 u3h3}.

[0069] In this embodiment, the conversion model is obtained by training the neural network model using different conversion vectors and analysis results of the conversion vectors as samples. At this time, the conversion variables can be obtained.

[0070] In this embodiment, the dependency relationship is obtained based on matching of a function-indicator comparison table, which contains dependencies under different loss functions and factor indicators, and the value range of the dependency relationship is 0 to 1, and the larger the value, the greater the dependency.

[0071] In this embodiment, the discrete points are obtained based on fitting the curve, that is, points that are not on the fitting line.

[0072] In this embodiment, the deviation direction refers to whether the point is located above or below the fitting curve.

[0073] In this embodiment, when the discrete point is above the fitting straight line: When the discrete point is below the fitted line: in, , They represent the adjusted values ​​of the corresponding discrete points above and below the fitting curve, respectively; , They represent the values ​​of the corresponding discrete points above and below the fitting curve, respectively; Represents the value located at the same vertical coordinate as the corresponding discrete point and based on the fitted curve; It represents the sum of the absolute value differences between all discrete points and the values ​​whose corresponding vertical coordinates are located on the fitting curve; , They represent the closed area of ​​the fitting curve obtained by straight-line penetration of discrete points on the fitting curve and discrete points below the fitting curve; It represents the sum of all closed areas obtained by connecting the first and last points of the fitting curve with a straight line; Represents the variance of all values ​​on the fitted curve; Represents the total number of discrete points, and its value is greater than or equal to 1.

[0074] Among them, ys1 is adjusted downward according to ZS, and ys2 is adjusted upward according to ZX.

[0075] The beneficial effect of the above technical solution is: by constructing an optimization vector and combining the conversion relationship, it is easy to obtain a conversion vector, and then according to the combined analysis of several parameters, the deviation direction and deviation size of the discrete points are adjusted to achieve effective compensation of the curve.

[0076] Embodiment 6: Based on Example 5, the compensation coefficient of the battery's own loss function is obtained, including: Constructing a standard loss function of the battery according to the material system of the battery and the process flow of the battery; When the battery is caught in a virtual power state, the battery is charged and discharged at different times to obtain a result set based on each charge and discharge test. ,in, represents the charging time under the i-th charging test when the battery is in a virtual power state; Indicates that when the battery reaches the charging time under the i-th charging test, the battery is discharged, and the recorded discharge time of the battery to 99% of the remaining capacity; Build on all The first curve and all The second curve is aligned and plotted in the same coordinate system, wherein the abscissa of the coordinate system is related to the number of tests, and the ordinate is related to time; The turning points on each curve are locked and extended in a straight line, and the standard loss function is optimized to obtain the own loss function; A compensation coefficient matching the own loss function is obtained from the function-compensation comparison table.

[0077] In this embodiment, the material system and process flow are set before the battery is manufactured, and can be directly extracted from the system-process-function comparison table, and the comparison table contains the material systems, process flows and matching standard loss functions of different batteries, and the standard loss function is for the case of power loss.

[0078] In this embodiment, the virtual power state means that the battery power just reaches 100%, that is, the power display is very high, but the actual power is relatively low, which is not the real power of the battery. Then, the process is charged and discharged to test the turning point.

[0079] In this embodiment, the turning point is the point on the corresponding curve where the curve suddenly changes. Based on the locking of each point, the vertical straight line is extended to obtain the locking points of the two curves, and then the loss factor can be obtained, that is: Loss factor = the absolute value of the difference between the actual change and the set change of the two locking points corresponding to the vertical straight line × the weight of the corresponding charging and discharging time point.

[0080] In this embodiment, the own loss function = standard loss function + loss factor.

[0081] In this embodiment, the function-compensation comparison table includes the loss function itself and the compensation coefficient matching the function, which is pre-planned.

[0082] The beneficial effect of the above technical solution is that it is convenient to directly obtain the standard loss function based on the material system and the process flow, and then the battery in the virtual power state is charged and discharged to obtain the optimization factor to optimize the function and ensure the accuracy of the compensation coefficient.

[0083] Embodiment 7: Based on Example 1, determining the availability of the battery includes: capturing again the current log of simulation modeling of the corrected model based on the latest script; Extracting index values ​​of predefined evaluation indexes related to the battery from the current log respectively; The availability of the battery is obtained according to the indicator value and the indicator weight of the predefined evaluation indicator.

[0084] In this embodiment, the predefined evaluation indicators include: discharge efficiency, self-discharge effect, resistance-voltage characteristics, etc., and the indicator weights are all pre-set, and the sum of the indicator weights is 1.

[0085] The beneficial effect of the above technical solution is that by extracting the index value and performing corresponding calculation with the weight, the result obtained is regarded as availability.

[0086] Embodiment 8: The present invention provides a battery curve automatic compensation device, such as Figure 2 As shown, including: A model building module is used to build a battery model based on the battery's standard open circuit voltage curve, standard internal resistance characteristic curve, and standard self-discharge effect; The script optimization module is used to capture the process log of battery simulation modeling according to the battery model, and determine the optimization information of the simulation modeling in combination with the simulation script and simulation results of the simulation modeling to obtain the latest script; a curve compensation module, for performing actual testing on the battery, obtaining actual index curves of the battery under different factor indexes, and performing initial curve compensation on each actual index curve based on a linear interpolation method and performing secondary curve compensation on each actual index curve based on the optimization information, so as to calibrate the battery model, wherein the factor index is related to the discharge efficiency, the charging efficiency and the battery temperature; The availability determination module is used to re-run the latest script to simulate the corrected model and determine the availability of the battery.

[0087] The beneficial effect of the above technical solution is: to compensate the curves under different factor indicators twice from two aspects: simulation test of battery model and actual test of battery, determine the accuracy of model correction, thereby improving the accuracy of battery availability acquisition and effectively reducing the error of determining SOC.

[0088] Embodiment 9: The present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor is enabled to execute the steps of any one of the methods.

[0089] Embodiment 10: The present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of any one of the methods.

[0090] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A battery curve automatic compensation method, characterized in that: include: Step 1: Establish a battery model based on the battery's standard open circuit voltage curve, standard internal resistance characteristic curve, and standard self-discharge effect; Step 2: Capture the process log of battery simulation modeling according to the battery model, and determine the optimization information of the simulation modeling in combination with the simulation script and simulation results of the simulation modeling to obtain the latest script; Step 3: performing actual testing on the battery, obtaining actual index curves of the battery under different factor indexes, and performing initial curve compensation on each actual index curve based on a linear interpolation method and performing secondary curve compensation on each actual index curve based on the optimization information to calibrate the battery model, wherein the factor index is related to the discharge efficiency, the charging efficiency and the battery temperature; Step 4: Re-run the latest script to simulate the corrected model and determine the availability of the battery.

2. The battery curve automatic compensation method according to claim 1, characterized in that: Perform initial curve compensation for each actual indicator curve based on linear interpolation, including: Placing the battery in different temperature scenarios, and randomly extracting charging instances and discharging instance pairs from a charge-discharge test comparison table to perform a charging test and a discharging test on the battery, and constructing a corresponding first remaining capacity curve and a second remaining capacity curve; According to the first remaining capacity curve and the second remaining capacity curve, and in combination with the charging and discharging characteristics of the charging instance and the discharging instance pair, determine a relevant data set of the battery in the corresponding temperature scene, wherein the relevant data set includes a battery characteristic value, a battery full capacity value, and a battery loss value; Draw the relevant required curve for each data in the determined relevant data set in turn according to the temperature size in the temperature scene, and perform linear interpolation on the required curve to obtain the final curve; Analyzing each final curve based on the compensation factor corresponding to the required curve, and combining the correlation between each factor index and the relevant data set to obtain an actual adjustment factor for capacity compensation of the battery; The ideal interpolation quantities matching the corresponding actual adjustment factors are obtained from the indicator-factor-compensation comparison table, and points are randomly inserted into the corresponding actual indicator curve according to the ideal interpolation quantities to achieve initial curve compensation.

3. The battery curve automatic compensation method according to claim 2, characterized in that: The process of randomly inserting points into the corresponding actual indicator curve according to the ideal interpolation quantity also includes: The current position of each randomly inserted point in the corresponding actual indicator curve is locked respectively, and the values ​​of two points closest to the current position are locked from the corresponding actual indicator curve, and are regarded as the first value and the second value respectively; Locking a first curve segment before the point corresponding to the first value in the corresponding actual indicator curve, performing linear fitting on the first curve segment, and obtaining a first constant; Locking a second curve segment corresponding to the actual indicator curve after the point corresponding to the second value, performing linear fitting on the second curve, and obtaining a second constant; Wherein, when there is no first curve segment before the point corresponding to the first value, the first constant is regarded as the first value; When there is no second curve segment after the point corresponding to the second value, the second constant is regarded as the second value; According to the first slope between the first constant and the first value, the second slope between the first value and the second value, and the third slope between the second value and the second constant, and in combination with the first value and the second value, the value of the corresponding randomly inserted point is determined to achieve primary curve compensation.

4. The battery curve automatic compensation method according to claim 1, characterized in that: Combined with the simulation script and simulation results of the simulation modeling, the optimization information of the simulation modeling is determined, including: Extracting the same parameters at the same simulation time point from the log parameters of the process log, the setting parameters of the simulation script, and the result parameters of the simulation result to obtain simulation arrays of the same parameters at different simulation time points; Analyze the deviation information of all simulation arrays of the same parameter to obtain the initial adjustment value corresponding to the same parameter; The number of arrays of the same parameter is obtained, and the corresponding initial adjustment value is corrected according to the number of arrays to obtain optimization information for simulation modeling.

5. The battery curve automatic compensation method according to claim 4, characterized in that: Performing curve compensation again on each actual indicator curve based on the optimization information includes: An optimization vector is constructed based on the optimization information, and a conversion vector is obtained according to a conversion relationship between the optimization vector and the factor index corresponding to each actual index curve; Inputting the conversion vector into a conversion model matching the factor index to obtain a conversion variable, and at the same time, obtaining a compensation coefficient of the battery's own loss function and a dependency relationship between the factor index and the battery's own loss function; The actual index curve is discretized to determine the discrete points and the deviation directions of the discrete points, and the corresponding discrete points are adjusted in size in combination with the conversion variables, compensation coefficients and dependencies to achieve curve compensation again.

6. The battery curve automatic compensation method according to claim 5, characterized in that: Obtaining the compensation coefficient of the battery's own loss function includes: Constructing a standard loss function of the battery according to the material system of the battery and the process flow of the battery; When the battery is caught in a virtual power state, the battery is charged and discharged at different times to obtain a result set based on each charge and discharge test. ,in, represents the charging time under the i-th charging test when the battery is in a virtual power state; Indicates that when the battery reaches the charging time under the i-th charging test, the battery is discharged, and the recorded discharge time of the battery to 99% of the remaining capacity; Build on all The first curve and all The second curve is aligned and plotted in the same coordinate system, wherein the abscissa of the coordinate system is related to the number of tests, and the ordinate is related to time; The turning points on each curve are locked and extended in a straight line, and the standard loss function is optimized to obtain the own loss function; A compensation coefficient matching the own loss function is obtained from the function-compensation comparison table.

7. The battery curve automatic compensation method according to claim 1, characterized in that: Determining the availability of the battery, including: capturing again the current log of simulation modeling of the corrected model based on the latest script; Extracting index values ​​of predefined evaluation indexes related to the battery from the current log respectively; The availability of the battery is obtained according to the indicator value and the indicator weight of the predefined evaluation indicator.

8. A battery curve automatic compensation device, characterized in that: include: A model building module is used to build a battery model based on the battery's standard open circuit voltage curve, standard internal resistance characteristic curve, and standard self-discharge effect; The script optimization module is used to capture the process log of battery simulation modeling according to the battery model, and determine the optimization information of the simulation modeling in combination with the simulation script and simulation results of the simulation modeling to obtain the latest script; a curve compensation module, for performing actual testing on the battery, obtaining actual index curves of the battery under different factor indexes, and performing initial curve compensation on each actual index curve based on a linear interpolation method and performing secondary curve compensation on each actual index curve based on the optimization information, so as to calibrate the battery model, wherein the factor index is related to the discharge efficiency, the charging efficiency and the battery temperature; The availability determination module is used to re-run the latest script to simulate the corrected model and determine the availability of the battery.

9. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 7.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method according to any one of claims 1 to 7.

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