BMS system life cycle accuracy correction method

Abstract

The present invention discloses a method for correcting the accuracy of a BMS system throughout its life cycle. Through an aging test, the accuracy change of the BMS system during its life cycle is tracked, and the accuracy change characteristic curve of the BMS system under different working conditions is simulated, which characterizes the different usage conditions of the BMS system by different users, the aging speed of the system under different working conditions, and other changes, making it possible to use mathematical methods to describe and estimate the aging condition of the BMS system itself during the design life, and providing reliable data support for the subsequent use of algorithms such as SOC estimation. The current life stage of the BMS system is determined by the current ambient temperature of the BMS system and the service time up to the present, and by calculating the aging characteristic proportional equation of the BMS system that has experienced the current service time and works at the current ambient temperature, a more accurate correction of the input current or input voltage of the BMS system is achieved through segmented correction.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and in particular to a method for correcting the full life accuracy of a BMS system. Background Art

[0002] At present, most of the battery health status detection and calibration are based on SOH (battery health status) and SOC (battery state of charge) algorithms to perform online, software-level algorithm accuracy correction. For example, SOC estimation methods based on neural networks and SOC estimation methods based on support vector machines. Such algorithms usually do not rely on the power battery model structure and battery parameters, but treat the power battery as a black box. For example, measured quantities (such as battery voltage, current, etc.) are used as inputs and SOC is used as output. A large amount of model training is performed to obtain accurate SOC estimation results. Such algorithms rely on input data, so they have high requirements for data quality. Inaccurate data will affect the results of neural network training.

[0003] The input data that the above existing algorithms rely on are usually collected through the BMS battery management system. The current and voltage reading accuracy of the BMS system directly affects the quality of the input data. Then, after the BMS system has been running for a long time under various working conditions such as high temperature, low temperature, and large current fluctuations, the detection accuracy of battery voltage, current and other data will change, which will greatly affect the accuracy of algorithms such as SOH and SOC that rely on these data collected by the BMS system. However, at present, there is no good calibration and correction method for the detection accuracy of the BMS system during its entire life cycle. Summary of the invention

[0004] The present invention aims to calibrate and correct the detection accuracy of a BMS system over its entire life cycle and reduce the accuracy deviation of reading battery voltage and current, and provides a method for correcting the accuracy of a BMS system over its entire life cycle.

[0005] To achieve this object, the present invention adopts the following technical solutions:

[0006] A method for correcting the accuracy of a BMS system throughout its life is provided, comprising the steps of:

[0007] S1, obtain the performance characteristic curves of the BMS system under different working conditions through aging tests;

[0008] S2, checking whether the correction accuracy of each interval segment in the performance characteristic curve meets the standard,

[0009] If yes, go to step S3;

[0010] If not, the accuracy of the interval segment that does not meet the standard is corrected;

[0011] S3, identifying the working condition of the BMS system to be evaluated, and then matching the performance characteristic curve corresponding to the identified working condition;

[0012] S4, calculate the service time as t1 ′ The currently measured ambient temperature T1 of the BMS system to be evaluated is a linear proportional equation of the aging characteristics in the performance characteristic curve matched in step S3;

[0013] S5, will be held at t1 ′ The service time is used as the independent variable to substitute the input current or input voltage of the BMS system to be evaluated working at the ambient temperature T1 into the aging characteristic linear proportional equation, and the obtained y value is used as the service time from the time of working at the ambient temperature T1 to t1. ′ The correction value of the input current or input voltage of the BMS system to be evaluated according to the service time.

[0014] Preferably, the characteristic data for fitting the performance characteristic curve include input current and / or voltage collected from the BMS system at a set frequency during an aging test on the BMS system under the operating conditions, the collection time point, the operating ambient temperature, and the real values ​​corresponding to the collected input current and / or input voltage.

[0015] Preferably, in step S1, the method for fitting the performance characteristic curve comprises the steps of:

[0016] S11, obtaining aging test characteristic data of the BMS system in each correction period under the expected correction frequency through time scaling, each correction period having a corresponding aging duration;

[0017] S12, evenly dividing the aging time to obtain a first aging time and a second aging time, and calculating a first average value for each input current or each input voltage supplied to the BMS system collected during the first aging time, and calculating a second average value for each input current or each input voltage supplied to the BMS system collected during the second aging time;

[0018] S13, searching for a first value closest to the first mean value among the input currents or input voltages collected during the first aging time, and searching for a second value closest to the second mean value among the input currents or input voltages collected during the second aging time;

[0019] S14, using the first value and the second value as independent variables and the first true value and the second true value corresponding to each other as dependent variables, solving the term coefficients and the constant term of the fitting function of the fitting piecewise curve;

[0020] S15, performing curve fitting on the corresponding interval segments using the fitting function, and the fitting curves of the respective interval segments are integrated to form the performance characteristic curve of the BMS system under the working condition.

[0021] Preferably, in step S2, the method for performing precision correction on the interval segment in the performance characteristic curve comprises the steps of:

[0022] S21, obtaining a fitting function of the piecewise fitting curve fitted in the interval and each fitting data point, and substituting the measured value associated with each data point into the obtained fitting function as an independent variable, and calculating a y value corresponding to the measured value as a predicted value;

[0023] S22, solving the deviation between the predicted value and the true value corresponding to the measured value, and then judging whether the deviation corresponding to each of the data points is less than a preset deviation threshold,

[0024] If yes, it is determined that no accuracy correction is required for the interval segment;

[0025] If not, proceed to step S23;

[0026] S23, dividing the interval into a plurality of sub-intervals according to a preset step length, and then solving the fitting sub-function corresponding to the sub-interval according to the data points generated in the sub-interval;

[0027] S24, using the method described in steps S21-S22, determining whether the fitting sub-function needs to be corrected for accuracy,

[0028] If yes, then continue to divide the sub-interval segments according to the method recorded in step S23, and further solve the fitting sub-functions corresponding to the divided sub-interval segments, and then proceed to step S25;

[0029] If not, jump to step S26;

[0030] S25, return to step S24;

[0031] S26, determining whether all the sub-intervals belonging to the same interval and associated with the same parent node meet the correction accuracy requirement,

[0032] If yes, it is determined that the correction accuracy of the interval segment meets the standard;

[0033] If not, it is determined that the correction accuracy of the interval segment does not meet the standard.

[0034] Preferably, in step S24, the method for solving the fitting sub-function corresponding to the divided sub-interval segments comprises the steps of:

[0035] A1, evenly divide the aging time corresponding to the sub-interval segments to obtain a first aging time and a second aging time, and calculate a first mean value for each input current or each input voltage supplied to the BMS system collected during the first aging time, and calculate a second mean value for each input current or each input voltage supplied to the BMS system collected during the second aging time;

[0036] A2, searching for a first value closest to the first mean value among the input currents or input voltages collected during the first aging time, and searching for a second value closest to the second mean value among the input currents or input voltages collected during the second aging time;

[0037] A3, using the first value and the second value as independent variables and the corresponding first true value and the second true value as dependent variables, solve the term coefficients and the constant term of the fitting sub-function.

[0038] Preferably, the fitting function or the fitting sub-function is a binary linear equation expressed by the following formula (1):

[0039] Y=k×X+b(1)

[0040] In formula (1), k and b represent the coefficient of the term to be solved and the constant term respectively;

[0041] X represents the first value or the second value;

[0042] Y represents the first real value corresponding to the first value, or the second real value corresponding to the second value.

[0043] Preferably, the aging characteristic linear proportional equation calculated in step S4 is expressed as follows:

[0044]

[0045] In formula (2), X represents the experience t1 ′ The input current or input voltage value actually measured of the BMS system to be evaluated having a long service life and operating at the ambient temperature T1;

[0046] Y represents the correction value of X;

[0047] k T1 、b T1 They respectively represent the term coefficients and the constant term of the aging characteristic linear proportional equation expressed by formula (2);

[0048] k T1 It is calculated by the following formula (3):

[0049]

[0050] b T1 It is calculated by the following formula (4):

[0051]

[0052] In formulas (3)-(4), T r Indicates normal temperature value;

[0053] T h Indicates high temperature threshold;

[0054] k h1 、b h1 They are respectively working at the first temperature T r The test BMS system under t1 ′ The length of service is expressed as Y = k r1 ×X+b r1 The term coefficients and constant term of the first fitting function;

[0055] k r1 、b r1 They are respectively operated at the second temperature T h The test BMS system under t1 ′ The length of service is expressed as Y = k h1 ×X+b h1 The coefficients of the second fitting function and the constant term;

[0056] T r <T1≤T h .

[0057] The present invention has the following beneficial effects:

[0058] 1. Through aging tests, the accuracy changes of the BMS system throughout its life cycle were tracked, and the accuracy change characteristic curve (i.e., performance characteristic curve) of the BMS system under different working conditions was simulated, characterizing the different usage of the BMS system by different users, the aging speed of the system under different working conditions, and other changes. This makes it possible to use mathematical methods to describe and predict the aging condition of the BMS system itself during its design life, providing reliable data support for the subsequent use of algorithms such as SOC estimation.

[0059] 2. The current life stage of the BMS system is determined by the current ambient temperature of the BMS system and the service time to date. The aging characteristic proportional equation of the BMS system that has experienced the current service time and works at the current ambient temperature is calculated, and the BMS system is corrected more accurately in input current or input voltage through segmented correction. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0061] Figure 1 It is a diagram of the implementation steps of the full-life accuracy correction method of the BMS system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0062] The technical solution of the present invention is further described below with reference to the accompanying drawings and through specific implementation methods.

[0063] Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on this patent; in order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product; for those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0064] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "inner", "outer", etc. appear, the orientation or position relationship indicated is based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as a limitation on this patent. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0065] In the description of the present invention, unless otherwise clearly specified and limited, if the term "connection" or the like appears to indicate the connection relationship between components, the term should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two components or the interaction relationship between two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0066] This embodiment provides a BMS system life-long accuracy correction method, the purpose of which is to solve the problem of voltage and current reading accuracy deviation caused by equipment aging and other reasons in the actual working conditions of the BMS system. In order to solve this technical problem, this embodiment adopts a technical means of comparing the data of the BMS system characteristics with a means of operating environment detection, so as to achieve a high-precision voltage and current reading correction of the BMS system life-long.

[0067] The technical means provided by this embodiment are described below from two aspects:

[0068] In a first aspect, this embodiment provides a method for obtaining an aging characteristic curve of a BMS system, including:

[0069] 1. Perform an aging test on the target BMS system. The aging test is mainly used to accelerate the aging of the equipment to simulate the impact of system aging under actual working conditions. In this embodiment, the changing parameters simulated by the aging test are temperature and time, and the aging test is used to obtain the performance characteristic curves of the BMS system under different working conditions. According to the accuracy requirements of the BMS system, 3-5 characteristic curves are taken. The characteristic curves are classified according to the working conditions of the BMS system, including: the first characteristic curve under high temperature conditions, the second characteristic curve under low temperature conditions, the third characteristic curve under normal temperature conditions, the fourth characteristic curve under high temperature to low temperature conditions, the fifth characteristic curve under low temperature to high temperature conditions, etc.

[0070] 2. According to the preset working condition classification, multiple BMS systems are subjected to aging tests at the same time. For example, under high temperature conditions, the BMS system runs at 85 degrees Celsius for a certain period of time, which is defined by the design time of the target BMS system. For example, if the target life of the BMS system is 7 years, and the design aging mode is to simulate half a year of aging time every 1 hour under high temperature operation, then the BMS system will be operated for 14 hours under high temperature conditions of 85 degrees Celsius. During the system aging operation, the target BMS system is supplied with a continuous large current of known current magnitude (such as 800A, determined by the designed carrying current of the BMS system, and the same applies to voltage), and is sampled at a set frequency such as once every 30s (the sampling data object is: the input current and voltage provided to the BMS system by the stable source), and the data is retained.

[0071] At the same time, for another BMS system with the same physical characteristics under the same environment (such as the same high temperature condition of 85 degrees Celsius), a known continuous small current (such as 5A, determined by the design of the target BMS system) is passed. Similarly, the data is sampled at a frequency of once every 30 seconds. During sampling, since the system is in a state of drastic performance changes during the aging test, in order to avoid deviations caused by system noise, in this embodiment, mean filtering or sliding filtering is used to eliminate system noise. The specific method is: for example, the BMS system outputs sampled data once every 30 seconds. In order to reduce system noise, the BMS system collects data every 200ms during these 30 seconds. After 150 times of data collection, the data collected for these 150 times, such as the current value, is averaged, and this average value is used as the sampled data output every 30 seconds.

[0072] According to the same aging test method for the BMS system under high temperature conditions, the characteristic data of the BMS system under low temperature conditions (such as -40 degrees Celsius), normal temperature conditions, high temperature to low temperature conditions, and low temperature to high temperature conditions are obtained and stored.

[0073] 3. Based on the characteristic data obtained through aging tests on the BMS system under different working conditions, the performance characteristic curves of the BMS system under different environmental conditions and different current working conditions are constructed.

[0074] Specifically, curve fitting is performed on the characteristic data obtained under each current working condition in each environmental condition. For example, in the aging test, curve fitting is performed on the characteristic data obtained from the BMS system working with a large current under high temperature conditions. The characteristic data for curve fitting includes the input current and / or input voltage (as the detection value) collected by the BMS system at a set frequency during the aging test of the BMS system under the aging test conditions, the collection time point, the ambient temperature of the working condition (such as 85 degrees Celsius under high temperature conditions), and the real values ​​corresponding to the collected input current and / or input voltage.

[0075] There are various methods for curve fitting, such as the least squares method, LM iterative algorithm, etc. However, it should be noted that the curve fitting algorithm must match the full-life precision correction algorithm of the subsequent BMS system. In order to ensure the accuracy of the full-life precision correction of the BMS system, in this embodiment, the performance characteristic curve is fitted by segmented fitting. For example, for a BMS system with a design life of 7 years, it is expected that the BMS system will be precision corrected every month. Assuming that in the aging test, the accuracy change of the BMS system for half a year is simulated every 1 hour, then through time scaling, it is inferred that in the aging test, the characteristic data collected every 10 minutes is used to characterize the accuracy change of the BMS system for one month. Perform a linear fit on the characteristic data collected within these 10 minutes, take the first 5 minutes of data to calculate the first mean (such as taking the mean of each input current of the BMS system collected within the first 5 minutes, defined as the first mean), and take the last 5 minutes of data to calculate the second mean (the calculation method is the same as the first mean, no further description), then find the data closest to the first mean and the second mean in the characteristic data collected within these 10 minutes, take the collection time point as the horizontal coordinate, and the input current or input voltage collected by the BMS system as the vertical coordinate, and solve the term coefficients of the linear equation of this section through a set of two-variable linear equations. Repeat the above curve segmentation fitting process, and finally obtain the performance characteristic curve of the BMS system supplied with large current or small current in the aging test under the corresponding environmental conditions.

[0076] In this embodiment, the data collected under the same working condition are subjected to segmented curve fitting by means of a multi-segment function expressed as follows:

[0077] Define the system running time as t i , the actual value of the data (such as the input current or input voltage supplied to the BMS system) is Y, and the detected value is X. When the aging time point of the system is t i1 <t<t i2 When the aging time of the system is t i2 <t<t i3 When , the corrected current is Y=k2×X+b2, k2 and b2 are also the coefficients and constant terms to be solved respectively, and so on, the piecewise fitting curves in the segmented intervals within each aging time are obtained, and the piecewise fitting curves are finally integrated to form a performance characteristic curve of the BMS system under the corresponding working conditions (such as high temperature conditions with large current).

[0078] 4. After obtaining the performance characteristic curve of the BMS system under each working condition of the aging test, this embodiment also performs fitting verification on the data of each section to ensure that the fitting function used to describe the segmented fitting curve meets the accuracy correction requirements for the BMS system during the corresponding life period. For example, according to the design requirements, the maximum deviation of the system accuracy of the BMS system during the entire life does not exceed one percent. For each interval segment of the fitting, the deviation of all data points in the interval segment and the results obtained by the fitting function corresponding to the interval segment can be set to not exceed five percent. If there are data points that cannot be completely fitted by the fitting function, the interval segment is further divided into at least two sub-interval segments, and then the data points in each sub-interval segment are segmented and curve fitted in the same way as the interval segment is curve fitted, and then it is verified whether the deviation between the detection value and the true value of all data points in each sub-interval segment in the corresponding fitting function is within the preset five percent deviation range. If not, the sub-interval segment is further segmented until the correction accuracy requirement of the deviation is less than five percent is met.

[0079] In a second aspect, this embodiment provides a method for estimating and processing the aging degree of a BMS system under actual working conditions, including:

[0080] 1. Estimate the aging degree of the BMS system. The estimation requires two sets of parameters, one is the operating temperature of the BMS system; the other is the input current or input voltage value supplied to the BMS system during operation. In the first aspect above, this embodiment has constructed a sampling deviation performance characteristic curve of the BMS system under different ambient temperatures and different currents over the entire life cycle. Using the two parameters of the ambient temperature and supply current of the BMS system under actual working conditions, the current aging degree of the BMS system can be estimated.

[0081] First, the BMS system obtains the NTC temperature on the target PCB board. The NTC temperature is the temperature value when the BMS system is powered on as the ambient temperature of the BMS system.

[0082] 2. After obtaining the ambient temperature values collected continuously twice for the BMS system to be evaluated under real working conditions, calculate the difference between the ambient temperature values collected continuously twice. For example, assume that the ambient temperature collected for the first time for the same BMS system to be evaluated is 85 degrees Celsius. After an interval of 3 hours, assume that the ambient temperature collected for the second time for the BMS system to be evaluated is 40 degrees Celsius. The temperature difference between the two times is 45 degrees Celsius. Assume that the set temperature difference threshold is 30 degrees Celsius. Since the temperature difference of 45 degrees Celsius is greater than the temperature difference threshold of 30 degrees Celsius, it is determined that the working condition of the BMS system to be evaluated is a high-temperature to low-temperature working condition. When the temperature difference between the front and back is less than the temperature difference threshold, it is determined that the BMS system to be evaluated is always in a high-temperature or low-temperature working condition. In addition, obtain the input current value for the BMS system to be evaluated. For example, when a large current of 800 A is supplied, match the ambient working condition of "high-temperature to low-temperature" and the working condition of "800 A large current" for the BMS system to be evaluated to the performance characteristic curve corresponding to this comprehensive working condition.

[0083] 3. Conduct aging prediction for the BMS system to be evaluated.

[0084] Assume that the currently measured ambient temperature of the BMS system to be evaluated is T1, and the length of time since the BMS system left the factory (was in service) is t1 ′ . First, calculate the aging characteristic linear ratio of T1 in the matched performance characteristic curve. The specific operation is as follows:

[0085] Take the example of the BMS system to be evaluated working in a high-temperature ambient working condition to illustrate how to calculate the aging characteristic linear ratio of T1 in the performance characteristic curve corresponding to the high-temperature working condition.

[0086] Define the normal temperature value as T r , the high-temperature threshold as T h , T r <T1 ≤ T h . In the aging test, assume that the first fitting function of the test BMS system working at the normal temperature T r for the service duration of t1 ′ is expressed as: Y = k r1 ×X + b r1 ; assume that the second fitting function of the test BMS system working at the high temperature T h for the service duration of t1 ′ is expressed as: Y = k h1 ×X + b h1 ; t1 < t1 ′ <t2, where t1 and t2 are the starting point and ending point of the time corresponding interval segments used for curve fitting of the first fitting function or the second fitting function respectively. Then, at the T1 ambient temperature, after experiencing the service duration of t1 ′The linear proportional equation of the aging characteristics of the BMS system to be evaluated is expressed by the following formula (2):

[0087]

[0088] In formula (2), X represents the experience t1 ′ The input current or input voltage value actually measured of the BMS system to be evaluated having a long service life and operating at the ambient temperature T1;

[0089] Y represents the correction value of X;

[0090] k T1 , b T1 They respectively represent the term coefficients and the constant term of the aging characteristic linear proportional equation expressed by formula (2);

[0091] k T1 It is calculated by the following formula (3):

[0092]

[0093] b T1 It is calculated by the following formula (4):

[0094]

[0095] In formulas (3)-(4), T r Indicates normal temperature value;

[0096] T h Indicates high temperature threshold;

[0097] k h1 , b h1 Working at room temperature T r The test BMS system under t1 ′ The length of service is expressed as Y = k r1 ×X+b r1 The term coefficients and constant term of the first fitting function;

[0098] k r1 , b r1 Working at high temperature T h The test BMS system under t1 ′ The length of service is expressed as Y = k h1 ×X+b h1 The coefficients of the second fitting function and the constant term.

[0099] In summary, this application has the following beneficial effects:

[0100] 1. Through aging tests, the accuracy changes of the BMS system throughout its life cycle were tracked, and the accuracy change characteristic curve (i.e., performance characteristic curve) of the BMS system under different working conditions was simulated, characterizing the different usage of the BMS system by different users, the aging speed of the system under different working conditions, and other changes. This makes it possible to use mathematical methods to describe and predict the aging condition of the BMS system itself during its design life, providing reliable data support for the subsequent use of algorithms such as SOC estimation.

[0101] 2. The current life stage of the BMS system is determined by the current ambient temperature of the BMS system and the service time up to now. The aging characteristic proportional equation of the BMS system after the current service time and working at the current ambient temperature is calculated, and the BMS system is corrected more accurately in a segmented manner to achieve input current or input voltage correction.

[0102] It should be noted that the above specific implementations are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art should understand that various modifications, equivalent substitutions, changes, etc. can be made to the present invention. However, as long as these changes do not deviate from the spirit of the present invention, they should be within the scope of protection of the present invention. In addition, some terms used in the specification and claims of this application are not restrictive, but are only for the convenience of description.

Claims

1. A method for correcting the accuracy of a BMS system throughout its life, characterized in that: Includes steps: S1, obtain the performance characteristic curves of the BMS system under different working conditions through aging tests; S2, checking whether the correction accuracy of each interval segment in the performance characteristic curve meets the standard, If yes, go to step S3; If not, the accuracy of the interval segment that does not meet the standard is corrected; S3, identifying the working condition of the BMS system to be evaluated, and then matching the performance characteristic curve corresponding to the identified working condition; S4, calculate the service time as t1 ′ The currently measured ambient temperature T1 of the BMS system to be evaluated is a linear proportional equation of the aging characteristics in the performance characteristic curve matched in step S3; S5, will be held at t1 ′ The service time is used as the independent variable to substitute the input current or input voltage of the BMS system to be evaluated working at the ambient temperature T1 into the aging characteristic linear proportional equation, and the obtained y value is used as the service time from the time of working at the ambient temperature T1 to t1. 1 The correction value of the input current or input voltage of the BMS system to be evaluated according to the service time.

2. The BMS system lifecycle accuracy correction method according to claim 1 is characterized in that: The characteristic data for fitting the performance characteristic curve include input current and / or voltage collected from the BMS system at a set frequency during an aging test on the BMS system under the operating conditions, a collection time point, an operating ambient temperature, and true values ​​corresponding to the collected input current and / or input voltage.

3. The BMS system lifecycle accuracy correction method according to claim 1, characterized in that: In step S1, the method for fitting the performance characteristic curve comprises the steps of: S11, obtaining aging test characteristic data of the BMS system in each correction period under the expected correction frequency through time scaling, each correction period having a corresponding aging duration; S12, evenly dividing the aging time to obtain a first aging time and a second aging time, and calculating a first average value for each input current or each input voltage supplied to the BMS system collected during the first aging time, and calculating a second average value for each input current or each input voltage supplied to the BMS system collected during the second aging time; S13, searching for a first value closest to the first mean value among the input currents or input voltages collected during the first aging time, and searching for a second value closest to the second mean value among the input currents or input voltages collected during the second aging time; S14, using the first value and the second value as independent variables and the first true value and the second true value corresponding to each other as dependent variables, solving the term coefficients and the constant term of the fitting function of the fitting piecewise curve; S15, performing curve fitting on the corresponding interval segments using the fitting function, and the fitting curves of the respective interval segments are integrated to form the performance characteristic curve of the BMS system under the working condition.

4. The BMS system lifecycle accuracy correction method according to claim 1 is characterized in that: In step S2, the method for performing precision correction on the interval segment in the performance characteristic curve comprises the steps of: S21, obtaining a fitting function of the piecewise fitting curve fitted in the interval and each fitting data point, and substituting the measured value associated with each data point into the obtained fitting function as an independent variable, and calculating a y value corresponding to the measured value as a predicted value; S22, solving the deviation between the predicted value and the true value corresponding to the measured value, and then judging whether the deviation corresponding to each of the data points is less than a preset deviation threshold, If yes, it is determined that no accuracy correction is required for the interval segment; If not, proceed to step S23; S23, dividing the interval into a plurality of sub-intervals according to a preset step length, and then solving the fitting sub-function corresponding to the sub-interval according to the data points generated in the sub-interval; S24, using the method described in steps S21-S22, determining whether the fitting sub-function needs to be corrected for accuracy, If yes, then continue to divide the sub-interval segments according to the method recorded in step S23, and further solve the fitting sub-functions corresponding to the divided sub-interval segments, and then proceed to step S25; If not, jump to step S26; S25, return to step S24; S26, determining whether all the sub-intervals belonging to the same interval and associated with the same parent node meet the correction accuracy requirement, If yes, it is determined that the correction accuracy of the interval segment meets the standard; If not, it is determined that the correction accuracy of the interval segment does not meet the standard.

5. The BMS system lifecycle accuracy correction method according to claim 4 is characterized in that: In step S24, the method for solving the fitting sub-function corresponding to the divided sub-interval segments comprises the steps of: A1, evenly divide the aging time corresponding to the sub-interval segments to obtain a first aging time and a second aging time, and calculate a first mean value for each input current or each input voltage supplied to the BMS system collected during the first aging time, and calculate a second mean value for each input current or each input voltage supplied to the BMS system collected during the second aging time; A2, searching for a first value closest to the first mean value among the input currents or input voltages collected during the first aging time, and searching for a second value closest to the second mean value among the input currents or input voltages collected during the second aging time; A3, using the first value and the second value as independent variables and the corresponding first true value and the second true value as dependent variables, solve the term coefficients and the constant term of the fitting sub-function.

6. The BMS system lifecycle accuracy correction method according to claim 3 or 5, characterized in that: The fitting function or the fitting sub-function is a binary linear equation expressed by the following formula (1): Y=k×X+b (1) In formula (1), k and b represent the coefficient of the term to be solved and the constant term respectively; X represents the first value or the second value; Y represents the first real value corresponding to the first value, or the second real value corresponding to the second value.

7. The BMS system lifecycle accuracy correction method according to any one of claims 1 to 5, characterized in that: The aging characteristic linear proportional equation calculated in step S4 is expressed as follows: In formula (2), X represents the experience t1 ′ The input current or input voltage value actually measured of the BMS system to be evaluated having a long service life and operating at the ambient temperature T1; Y represents the correction value of X; k T1 , b T1 They respectively represent the term coefficients and the constant term of the aging characteristic linear proportional equation expressed by formula (2); k T1 It is calculated by the following formula (3): b T1 It is calculated by the following formula (4): In formulas (3)-(4), T r Indicates normal temperature value; T h Indicates high temperature threshold; k h1 , b h1 They are respectively working at the first temperature T r The test BMS system under t1 ′ The length of service is expressed as Y = k r1 ×X+b r1 The term coefficients and constant term of the first fitting function; k r1 , b r1 They are respectively operated at the second temperature T h The test BMS system under t1 ′ The length of service is expressed as Y = k h1 ×X+b h1 The coefficients of the second fitting function and the constant term; T r <T1≤T h 。

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