Power battery internal resistance evaluation method and system

Abstract

The invention relates to the technical field of batteries, and discloses a power battery internal resistance evaluation method and system, and the method comprises the steps: firstly, obtaining the operation data of a battery pack in a certain period, and obtaining a time sequence matrix A; then, dividing the data fragments, and selecting a first data fragment from the divided data fragments based on a fragment selection rule; then, on the basis of the monomer voltage values in the first data segment, calculating a deviation average value and a standard deviation of each monomer voltage; and finally, scoring the selected first data fragment based on the calculated data so as to evaluate the internal resistance consistency of the power battery. According to the method, the characteristic frame is captured from the angle of the frame with the maximum change of the front and back frames of the data fragment, the data fragment is scored through the single voltage data to evaluate the internal resistance condition of the battery, and the accuracy and reliability of the internal resistance evaluation of the power battery are improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of batteries, and particularly relates to a power battery internal resistance evaluation method and system. BACKGROUND

[0002] The existing lithium ion battery has the advantages of high energy density, low self-discharge rate, no memory effect and long cycle life, and is widely used in electric two-wheeled vehicles, electric vehicles and energy storage fields. However, with the increase of use time, the capacity output and power output capability of the lithium ion battery will inevitably degrade, affecting the user experience and even causing safety hazards. Therefore, it has great practical significance to evaluate the health state of the lithium ion battery, and is widely concerned by more and more scientific researchers. In the performance evaluation of the lithium ion battery, the battery internal resistance is an important parameter affecting the performance. The so-called lithium battery internal resistance is the resistance suffered by the current flowing through the internal battery, which can be generally divided into ohmic resistance and polarization resistance. The ohmic resistance is composed of electrode materials, electrolyte, diaphragm resistance and contact resistance of each part, and the corresponding time is instantaneous; and the polarization resistance is the resistance caused by polarization during electrochemical reaction, including electrochemical polarization resistance and concentration polarization resistance. Among them, the electrochemical polarization represents the polarization caused by the accumulation of electrons during the electrochemical reaction process on the electrode surface, because the electron transmission speed is faster than the electrochemical reaction speed, and is usually in the order of microseconds. The concentration polarization is that the ion transmission speed is lower than the speed of the reaction on the electrode surface and the solid phase, so that the ion concentration difference appears on the reaction surface, which is the result of mass transfer, and is usually in the order of seconds.

[0003] The existing performance evaluation of the ion battery, such as the battery system SOC consistency evaluation method and internal resistance consistency evaluation method disclosed in Chinese patent CN113253128A, acquires the battery system with a data sampling interval of no more than 30 seconds and a discharge stage at SOC≥20%, which has high limitations. In addition, the concept of absolute deviation degree and relative deviation degree is used to represent the consistency between single bodies, which ignores the upper limit fluctuation of the single bodies around the mean value, cannot reflect the scene of abnormal distribution of each single body around the mean value, and ignores the particularity of voltage change under different SOC ranges and different current excitations. Finally, according to the curve shape, when x increases to a certain extent, it presents a convergence state, that is, when the relative deviation degree increases to a certain extent, the change of the voltage difference has a weak influence on the consistency abnormality rate , which is inconsistent with the consistency detection target. Finally, the higher the internal resistance consistency difference rate is, the more serious the consistency abnormality is, and the abnormal single body cannot be marked and identified.

[0004] Chinese patent CN118465588A discloses a method and system for evaluating the internal resistance consistency of a dynamically reconfigurable battery module. The method includes calculating the current change rate by introducing the nominal capacity of the battery cell, wherein the current change rate calculation method is: , this calculation method uniformly uses the nominal capacity as the denominator and ignores the actual capacity change caused by the polarization effect at different currents. Then the method also includes calculating the internal resistance of each battery cell at the target sampling time according to the target sampling time and constructing an internal resistance matrix; wherein, the method of calculating the internal resistance index ignores the existence of more uncertainty noise in the actual operating data, the existence of problems such as the asynchronous acquisition of voltage and current data, resulting in the stability and accuracy of the calculation results cannot be guaranteed. In addition, the evaluation algorithm used is based on The curve shape shows a convergence state when x increases to a certain extent, that is, when the consistency difference deteriorates to a certain extent, Finally, the patent does not specify the degree of consistency failure, and it is impossible to mark and identify abnormal cells. At the same time, the patent does not clearly define the consistency evaluation data segment. The charge and discharge type and SOC range of the data segment taken will affect the rationality of the evaluation results.

[0005] Chinese patent CN118259179A discloses a method for identifying abnormal internal resistance of power battery pack cells based on internal resistance characteristics. The method uses the data volume ratio as the weight calculation method when calculating the internal resistance comprehensive characteristic parameters of each cell, which easily leads to a decrease in the identification sensitivity of abnormal internal resistance consistency fragments when the data volume is small. The universality of the distribution of all monomer consistency characteristics is used as the standard to identify discrete monomers. Only the horizontal consistency between monomers is compared, and the abnormal growth identification scenario in the vertical time series development of the monomer is not considered. In addition, the identification method of abnormal monomers is based on the internal resistance comprehensive characteristic parameter R of the nth cell. zn Satisfy R zn >μ+3 or R zn <μ-3 , then the internal resistance of the nth battery cell is judged to be abnormal; otherwise, the internal resistance of the nth battery cell is judged to be normal; where μ is the average value of the comprehensive characteristic parameters of the internal resistance of all the battery cells in the battery pack; is the standard deviation of the comprehensive characteristic parameters of the internal resistance of all cells in the battery pack. This method uses the universality of the distribution of consistent characteristics of all cells as a standard to identify discrete cells. It only compares the horizontal consistency between cells and does not consider the identification of abnormal growth in the vertical time series of cells. Summary of the Invention

[0006] In view of the above problems, the present invention aims to provide a method for evaluating the internal resistance of a power battery, comprising: Obtaining battery pack operation data within a certain period, and sorting the matrix according to the operation data collection time, the time sequence matrix A; Referring to one or more of the vehicle state, the battery pack charging state, the SOC growth trend and the voltage growth trend, and dividing the data segment based on the sampling time interval, wherein the sampling time interval is the frame time difference before and after the operation data; Based on the segment selection rule, the first data segment is selected from the divided data segment, wherein the first data segment is the data segment in the time sequence matrix A; Based on the single cell voltage value in the first data segment, the average value of each single cell voltage in the i-th frame is calculated And the standard deviation of the voltage deviation value of each single cell in the i-th frame ; Based on the average value of each single cell voltage deviation in the i-th frame And the standard deviation of the voltage deviation value of each single cell in the i-th frame , the first data segment is scored to evaluate the internal resistance consistency of the power battery.

[0007] Further, the battery pack operation data includes one or more of data collection time, battery pack total current, SOC, vehicle speed, cumulative mileage, ambient temperature and single cell voltage time sequence data; The time sequence matrix A is a f-row×k-column time sequence matrix based on data collection time, battery pack total current, SOC, vehicle speed, cumulative mileage, ambient temperature and single cell voltage, wherein f is the total data frame number of the obtained data segment, k is an integer, and k>0.

[0008] Further, it also includes data cleaning, specifically including, The vehicle state and the battery pack charging state are checked through one or more of the vehicle speed, current, SOC, voltage fields, and the inconsistent data frame vehicle state and battery pack charging state are re-matched and valued; Referring to the measurement specifications of the current, voltage and temperature collection sensors, the abnormal values exceeding the measurement range are cleaned; and / or, Through logical judgment between the frame fields before and after the operation data, the stagnant data is cleaned.

[0009] Further, the segment selection rule includes selecting the first data segment as a parking charging segment, wherein the parking charging segment satisfies: the data segment with the state of charge within the first preset range and the ambient temperature within the preset temperature range.

[0010] Further, the segment selection rule further includes, Referring to the battery system charging SOC-current matrix, the charging segment within the SOC range during constant current charging is selected; and / or The average charging temperature of each charging segment is calculated, and historical charging segments are clustered according to the average charging temperature, and charging segments in the same temperature range are selected for comparison.

[0011] Further, based on the single cell voltage values in the first data segment, the average value of the single cell voltage deviation in the i-th frame is calculated and the standard deviation of the single cell voltage deviation in the i-th frame including, The single cell voltage values in the first data segment form a sub-matrix B:

[0012] In the formula, n is the total number of frames of the current first data segment, and n≤f, m is the total number of probes of each frame of data, represents the single cell voltage value of the mth probe in the nth frame; Subtracting the front and back frames of the sub-matrix B, a new matrix C is obtained:

[0013] wherein, represents the single cell voltage change value of the mth probe in the nth frame; Data extraction is performed on the matrix C to obtain the data frame i with the largest voltage change in the matrix C, which is recorded as the i-th frame; Based on the single cell voltage change values in the i-th frame, the single cell voltage deviation in the i-th frame is calculated to form a single cell voltage deviation matrix D i in the i-th frame. Based on the matrix D i the average value of the single cell voltage deviation in the i-th frame is calculated and the standard deviation of the single cell voltage deviation in the i-th frame .

[0014] Further, data extraction is performed on the matrix C to obtain the data frame i with the largest voltage change in the matrix C, which is recorded as the i-th frame including, The average value of the single cell voltage change value of each frame of the matrix C is calculated:

[0015] wherein, m represents the total number of probes of each frame of data, and j represents the jth probe; The data frame with the largest P value is obtained, that is, the data frame i with the largest voltage change, which is recorded as the i-th frame.

[0016] Further, based on the single cell voltage change values in the i-th frame, the single cell voltage deviation in the i-th frame is calculated to form a single cell voltage deviation matrix D i including, After removing the extreme values ​​of the voltage change values ​​of each monomer in the i-th frame, the average value of the remaining voltage change values ​​of each monomer in the i-th frame is calculated. :

[0017] Among them, m is the total number of probes with single voltage change value, is the monomer voltage change value of the kth probe in the i-th frame, is the maximum cell voltage change value in the i-th frame, is the minimum cell voltage change value in the i-th frame; Based on the average value of the voltage change of each monomer in the i-th frame and the voltage change values ​​of the remaining monomers in the i-th frame , forming the voltage deviation matrix D of each monomer in the i-th frame i :

[0018] Where, represents the monomer voltage deviation of the mth probe in the i-th frame; Based on the matrix D i Calculate the deviation of each monomer voltage from the average value And the standard deviation of each cell voltage deviation include, Remove the voltage deviation matrix D of each monomer in the i-th frame i After the voltage deviation value of each monomer is the extreme value, the matrix D is obtained. i The average value of the remaining monomer voltage deviations is the average monomer voltage deviation value. ; Based on the deviation of the cell voltage from the average value , calculate the matrix D i The standard deviation of the voltage deviation of each cell :

[0019] Among them, m is the total number of probes in each frame of data, The single-cell voltage deviation of the j-th probe in the i-th frame.

[0020] Furthermore, based on the deviation of the voltage of each cell from the average value in the i-th frame and the standard deviation of the voltage deviation of each monomer in the i-th frame Scoring the selected first data segment to evaluate the internal resistance consistency of the power battery includes: by Set up different levels of screening ranges, where h is a constant; Assign values ​​to different levels of screening ranges; The single cell voltage deviation value in the i-th frame single cell voltage deviation matrix D i The single cell voltage deviation value in the i-th frame single cell voltage deviation matrix D i The lowest score in the single cell voltage deviation value in the i-th frame single cell voltage deviation matrix D

[0021] Further, the different level screening ranges include: 6 levels are established. The 6 level screening ranges are respectively assigned values of 100, 90, 80, 70, 60, and <60.

[0022] Further, the battery system is scored, including, If the first data segment appears continuously for a preset number of times with the same level score, the system state score is updated.

[0023] The purpose of the present application is also to provide a power battery internal resistance evaluation system, including, The acquisition module is configured to acquire battery pack operation data within a certain period and sort a time sequence matrix A according to operation data collection time. The segment division module is configured to refer to one or more of vehicle state, battery pack charging state, SOC growth trend, and voltage growth trend, and divide data segments based on sampling time interval, wherein the sampling time interval is the time difference between the front and rear frames of operation data. The segment selection module is configured to select a first data segment from the divided data segments based on segment selection rules, wherein the first data segment is a data segment in the time sequence matrix A. The data processing module is configured to calculate the single cell voltage deviation average value and the standard deviation of the single cell voltage deviation value in the i-th frame based on the single cell voltage value in the first data segment.​​​​​​​​​​​​​​​​​​​​​​​ ; an evaluation module, configured to evaluate the consistency of the internal resistance of the power battery based on the standard deviation of the voltage of each single battery in the i-th frame deviating from the average value and the standard deviation of the voltage of each single battery in the i-th frame deviating from the value , and score the selected first data segment, so as to evaluate the consistency of the internal resistance of the power battery.

[0024] Further, the method further comprises a data cleaning module, configured to perform the following cleaning steps: verify the vehicle state and the battery pack charging state by one or more of the vehicle speed, the current, the SOC and the voltage field, re-match and assign the vehicle state and the battery pack charging state of the inconsistent data frame; clean the abnormal value exceeding the measurement range by referring to the current, voltage and temperature collection sensor measurement specifications; and / or, clean the stagnant data by logical judgment between the front and rear frame fields of the running data.

[0025] The method of the application performs feature frame capture from the perspective of the maximum frame change between the front and rear frames of the data segment, evaluates the consistency of the internal resistance of the power battery based on the standard deviation of the voltage of each single battery in the obtained maximum frame deviating from the average value and the standard deviation of the voltage of each single battery , scores the data segment, so as to reflect the internal resistance of the power battery by the intuitive score, and combine the internal resistance test with the consistency difference, so that the abnormal single battery internal resistance consistency difference will also increase when the single battery fails, and the accurate and timely characterization of the internal resistance aging of the system is realized.

[0026] In addition, the feature frame capture is performed from the perspective of the maximum frame change between the front and rear frames of the voltage segment, which avoids the influence of the non-uniform reporting time on different fields on the result, and improves the accuracy and reliability of the evaluation of the internal resistance of the power battery.

[0027] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application can be realized and achieved by the structures indicated in the specification, claims and drawings. BRIEF DESCRIPTION OF DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without any creative labor.

[0029] Figure 1A flow chart of a method for evaluating internal resistance of a power battery in an embodiment of the present application is shown. Figure 2 A structural diagram of a system for evaluating internal resistance of a power battery in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0030] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described below in detail with reference to the accompanying drawings. Obviously, the described embodiments are only some, but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application.

[0031] As shown in the drawings, Figure 1 a method for evaluating internal resistance of a power battery is introduced in an embodiment of the present application. The method comprises the following steps. First, battery pack operation data in a certain period is acquired, and a matrix is sorted according to operation data collection time, i.e., time sequence matrix A. Second, one or more of vehicle state, battery pack charging state, SOC (State of Charge) growth trend and voltage growth trend are referred to, and data segments are divided based on sampling time interval, wherein the sampling time interval is the time difference between the front and rear frames of operation data. Third, a first data segment is selected from the divided data segments based on segment selection rules, wherein the first data segment is a data segment in the time sequence matrix A. Fourth, based on the single cell voltage values in the first data segment, the average value of each single cell voltage deviation in the i-th frame and the standard deviation of each single cell voltage deviation in the i-th frame are calculated. Finally, the selected first data segment is scored based on the average value of each single cell voltage deviation in the i-th frame and the standard deviation of each single cell voltage deviation in the i-th frame , so as to evaluate the internal resistance consistency of the power battery. The feature frame is captured from the perspective of the maximum frame change between the front and rear frames of the data segment, and the data segment is scored based on the average value of each single cell voltage deviation in the obtained maximum frame and the standard deviation of each single cell voltage deviation , so as to reflect the internal resistance condition of the power battery by the intuitive score. The internal resistance test and consistency difference are combined, so that the abnormal single cell internal resistance consistency difference will also increase when the single cell fails, and the accurate and timely representation of the internal resistance aging condition of the system is realized. In addition, the feature frame is captured from the perspective of the maximum frame change between the front and rear frames of the voltage segment, which avoids the influence of the non-uniform reporting time of different fields on the result, and improves the accuracy and reliability of the evaluation of the internal resistance of the power battery.

[0032] Specifically, the method comprises the following specific steps: S1: data acquisition, acquiring battery pack operation data within a certain period, wherein the operation data comprises data acquisition time, battery pack total current, SOC, vehicle speed, cumulative mileage, ambient temperature and single cell voltage time series data, further, the acquired operation data is sorted according to the data acquisition time to matrix, and a f-row×k-column time series matrix A based on the data acquisition time, the battery pack total current, the SOC, the vehicle speed, the cumulative mileage, the ambient temperature and the single cell voltage battery pack charging state, the SOC, the sampling time interval, the current and the voltage is established, wherein f is the acquired data, k is an integer, and k>0, in the embodiment of the application, k is 7, but is not limited thereto, other values are also applicable to the application.

[0033] S2: data cleaning, including checking the vehicle state and the battery pack charging state through the vehicle speed, the current, the SOC and the voltage fields, and re-matching and assigning the vehicle state and the battery pack charging state of inconsistent data frames. Exemplarily, the vehicle state is divided into vehicle start / vehicle off / other / abnormal / invalid, all of which are represented by corresponding codes, wherein the corresponding codes are 0×01 / 0×02 / 0×03 / 0×0FE / 0×0FF respectively; further, the charging state is divided into parking charging / driving charging / uncharged / charging completed / abnormal / invalid, and the corresponding codes are 0×01 / 0×02 / 0×03 / 0×04 / 0×0FE / 0×0FF respectively. Thus, if the vehicle state is off but the vehicle speed is not 0 or the charging state is parking charging but the vehicle speed is not 0, etc., it is determined to be inconsistent, at this time, the vehicle state and the charging state may be wrong and need to be corrected. Further, the data cleaning also includes cleaning abnormal values outside the measurement range by referring to the current, voltage and temperature acquisition sensor measurement specifications, and / or cleaning stagnant data by logical judgment between the fields of the front and rear frames of the operation data.

[0034] S3: segment division, referring to one or more of the vehicle state, the battery pack charging state, the SOC and the voltage growth trend, dividing the data segment (i.e. the time series matrix A) based on the sampling time interval, wherein exemplarily, if the vehicle state is 0×01 (off), the charging state is 0×01 (parking charging), the total current is negative (charging) and the SOC continues to grow, then it is divided into an off-parking charging segment. Further, the sampling time interval is the time difference between the front and rear frames of the operation data.

[0035] S4: segment selection, based on the segment selection rule, a first data segment is selected from the divided data segments, specifically, the segment selection rule includes selecting the first data segment as a parking charging segment. Further, considering that there are multiple scenarios such as discharging, power recovery charging and static placement of the battery system when the vehicle is running, there are multiple coupling relationships such as discharging-charging, discharging-static, charging-discharging, charging-static, static-charging and static-discharging, and when the internal resistance consistency is evaluated, the voltage polarization characteristics are difficult to compare, so the data segments are limited, that is, the parking charging segment is used as the model input. In addition, considering the voltage sensitivity to capacity change in different SOC intervals, the data segment that meets the voltage platform in the preset range and the environmental temperature in the preset temperature range is selected from the selected first data segment and brought into the model. Only the state of charge (SOC) near the data segment (that is, the state of charge is in the first preset range) is brought into the model to avoid false positives caused by the large voltage change slope at the beginning and end of the SOC. Preferably, for the data segment in the first preset range of the state of charge, 30-70% of the data segment is used for the lithium iron phosphate system, and 20-80% of the data segment is used for the ternary system. Finally, for the data segment with the environmental temperature in the preset temperature range, the data segment with the environmental temperature in the range of 25±5°C (degrees Celsius) is selected to avoid the influence of inconsistent temperature on the detection of abnormal single cells. However, it is not limited to this, other segment selection rules are also applicable to the present application, and the segment selection rule can also be: the SOC data segment can be a reference battery system charging SOC-current matrix, the data segment in the SOC range during constant current charging, that is, the data segment in the interval range of the state of charge (SOC, State of Charge) of the battery during constant current charging, to avoid the influence of different excitation currents on the voltage deviation of the single cell; and / or calculate the average charging temperature of each charging segment, and layer clustering is performed on the historical charging segments according to the average charging temperature, and the charging segments in the same temperature range are selected to compare the same temperature range segments, to avoid the influence of temperature on the polarization degree of the battery system, causing different single cell voltage deviations.

[0036] S5: data processing, the single cell voltage values in the first data segment form a sub-matrix B, that is, the sub-matrix B is a sub-matrix about the single cell voltage values in a charging segment r in the time sequence matrix A in step S1, and the front and back frames of the sub-matrix B are subtracted to obtain a new matrix C, wherein,

[0037] Further,

[0038] wherein n is the total number of frames of the current charging data segment, and n≤f, m is the total number of probes of each frame of data, represents the monomer voltage value of the mth probe of the nth frame, represents the monomer voltage change value of the mth probe of the nth frame; S6: data extraction, average value calculation is performed on each row element of matrix C (i.e. monomer voltage change value of each frame), that is, , and the data frame i with the maximum P value is found, which is recorded as the ith frame. Wherein, the P value (average value) is maximum, that is, the voltage change is maximum.

[0039] Referring to the direct current internal resistance test method, the impedance of the battery system is characterized by the voltage change divided by the current change at the moment when the current change is maximum, but due to the large sampling interval of the two-wheeled vehicle battery system, the current and voltage transmission time is inconsistent, etc. Therefore, it is difficult to capture data at the moment when the current change is maximum. Considering this problem, in the embodiment of the present application, the identification of the maximum current is abandoned, and the feature frame capture is performed from the perspective of the maximum frame change before and after the voltage, so as to avoid the influence of the inconsistent reporting time of different fields on the result.

[0040] S7: calculating the voltage change average value of the ith frame, first, removing the extreme value in each monomer voltage change value data of the ith frame of the segment extracted in step S6, and calculating the average value of the remaining data ,

[0041] Wherein, m is the total number of probes of each frame data, is the monomer voltage change value of the kth probe of the ith frame, k∈m, is the maximum monomer voltage change value in the ith frame, is the minimum monomer voltage change value in the ith frame; S7: voltage deviation value calculation, based on each monomer voltage change value of the ith frame in matrix C and each monomer voltage change average value of the ith frame, matrix D is formed i :

[0042] Wherein, the element in matrix D i is the monomer voltage deviation value of the ith frame, m is the total number of probes of each frame data, represents the monomer voltage deviation of the mth probe of the ith frame.

[0043] S8: calculating the standard deviation of each monomer voltage deviation value in the ith frame ;

[0044] Wherein, j represents the jth probe, m is the total number of probes of each frame data, is the monomer voltage deviation value of the jth probe of the ith frame, The monomer voltage deviation is the average value of the matrix D i The monomer voltage deviation is the average value of the matrix D

[0045] S9: Scoring of the charging segment, to Different levels of screening ranges are set, where h is a constant, and the different levels of screening ranges are valued, and the voltage deviation value of each monomer of the i-th frame is scored, and the lowest score of the frame is taken as the segment score. Optionally, to , ]、 , ) , ]、 , ) , ]、 , ) , ]、 , ) , ]、( , ) , )establish 6 levels, respectively valued 100, 90, 80, 70, 60, <60; but not limited to this, other ranges and values for the division and assignment of levels are also applicable to the present application. Thus, by scoring based on the voltage deviation value of each monomer, the score of the charging segment is obtained, which can intuitively show the condition of the internal resistance of the power battery. Further, the lower the score, the worse the internal resistance consistency, and the more serious the fault, so that by scoring, the score can identify that when a monomer fails, the abnormal monomer internal resistance consistency difference increases, accurately and timely representing the internal resistance aging condition of the system, improving the safety and reliability of the battery application.

[0046] S10: System state scoring, the same level score of the charging segment appears continuously for 3 times to update the system state score, to avoid the influence of data fluctuation on the system state score; exemplary, the system score is directly valued 100, when the same scoring result appears continuously for 3 times in the subsequent charging segment, the system scoring result is updated, preferably, the system score is updated to the charging segment score, avoiding the repeated change of the system state score caused by data fluctuation, similarly, the lower the score, the worse the internal resistance consistency, which can make the user have a more intuitive understanding of the battery internal resistance condition, improving the reliability of the electric bicycle battery system.

[0047] In the embodiment of the present application, for the problems of large sampling interval of two-wheeled vehicle battery system (such as some vehicle models, 5 minutes / frame without failure in movement, 10 minutes / frame without failure in static), inconsistent uploading time of various data, etc., a voltage consistency algorithm is adopted, the non-uniformity of the system is described by using the difference value characteristics and the average deviation value characteristics of itself, and the current state of the battery system is evaluated through longitudinal comparison of multiple continuous segments, thereby improving the accuracy and reliability of the evaluation of the internal resistance of the battery.

[0048] As shown in Figure 2 , the embodiment of the present application also introduces a power battery internal resistance evaluation system capable of executing the above method, which comprises an acquisition module, a segment division module, a segment selection module, a data processing module and an evaluation module. The acquisition module is used to acquire the battery pack running data within a certain period, and sort the matrix according to the running data acquisition time, i.e. time sequence matrix A. The segment division module is used to refer to one or more of the vehicle state, the battery pack charging state, the SOC growth trend and the voltage growth trend, and divide the data segments based on the sampling time interval, wherein the sampling time interval is the frame time difference before and after the running data. The segment selection module is used to select the first data segment from the divided data segments based on the segment selection rule, wherein the first data segment is the data segment in the time sequence matrix A. The data processing module is used to calculate the average value of each monomer voltage in the i-th frame and the standard deviation of the voltage deviation value of each monomer in the i-th frame based on the monomer voltage value in the first data segment. The evaluation module is used to score the selected first data segment based on the average value of each monomer voltage in the i-th frame and the standard deviation of the voltage deviation value of each monomer in the i-th frame , so as to evaluate the internal resistance consistency of the power battery. The above system performs feature frame grabbing from the perspective of the maximum frame change before and after the data segment, scores the data segment based on the average value of each monomer voltage in the maximum frame obtained and the standard deviation of the voltage deviation value of each monomer , so as to realize the internal resistance of the power battery by a direct score, and combine the internal resistance test with the consistency difference, so that the abnormal monomer internal resistance consistency difference will also exist the increasing behavior when the monomer fails, and the accurate and timely representation of the internal resistance aging of the system is realized. In addition, the feature frame is grabbed from the perspective of the maximum frame change before and after the voltage segment, which avoids the influence of the non-uniform reporting time of different fields on the result, and improves the accuracy and reliability of the evaluation of the internal resistance of the power battery.

[0049] In the embodiment of the present application, the system further comprises a data cleaning module for performing the following cleaning steps: The vehicle state and the battery pack charging state are checked through one or more of the vehicle speed, the current, the SOC and the voltage fields, and the vehicle state and the battery pack charging state of the inconsistent data frame are re-matched and assigned; The abnormal values exceeding the measurement range are cleaned up with reference to the current, the voltage and the temperature collection sensor measurement specifications; and / or, The stagnant data is cleaned up through the logical judgment between the running data front and rear frame fields.

[0050] Although the present application is described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for evaluating the internal resistance of a power battery, characterized in that: include, Obtain the battery pack operation data within a certain period and sort the matrix according to the operation data collection time, the time series matrix A; Referring to one or more of the vehicle state, battery pack state of charge, SOC growth trend, and voltage growth trend, the data segments are divided based on a sampling time interval, wherein the sampling time interval is a time difference between a previous frame and a next frame of the running data; Based on a segment selection rule, a first data segment is selected from the divided data segments, wherein the first data segment is a data segment in the time series matrix A; Based on the cell voltage values ​​in the first data segment, calculate the deviation of each cell voltage from the average value in the i-th frame and the standard deviation of the voltage deviation of each monomer in the i-th frame ; Based on the deviation of the average value of each monomer voltage in the i-th frame and the standard deviation of the voltage deviation of each monomer in the i-th frame , score the selected first data segment to evaluate the internal resistance consistency of the power battery.

2. The method for evaluating the internal resistance of a power battery according to claim 1, wherein: The battery pack operation data includes one or more of data collection time, total battery pack current, SOC, vehicle speed, accumulated mileage, ambient temperature and single cell voltage time series data; The timing matrix A is an f-row×k-column timing matrix based on data acquisition time, total battery current, SOC, vehicle speed, cumulative mileage, ambient temperature, and cell voltage, where f is the total number of data frames of the acquired data segments, k is an integer, and k>0.

3. The method for evaluating the internal resistance of a power battery according to claim 1 or 2, characterized in that: It also includes data cleaning, specifically including: Verify the vehicle status and battery pack charging status through one or more of the vehicle speed, current, SOC, and voltage fields, and re-match and assign the vehicle status and battery pack charging status of inconsistent data frames; Refer to the measurement specifications of current, voltage and temperature acquisition sensors to clean abnormal values ​​that exceed the measurement range; and / or, By running logical judgments between the fields of the frames before and after the data, stagnant data can be cleaned.

4. The method for evaluating the internal resistance of a power battery according to claim 3, characterized in that: The segment selection rule includes selecting a first data segment as a parking and charging segment, wherein the parking and charging segment satisfies: a state of charge within a first preset range and an ambient temperature within a preset temperature range.

5. The method for evaluating the internal resistance of a power battery according to claim 4, characterized in that: The fragment selection rules also include: Refer to the battery system charging SOC-current matrix and select the charging segment within the SOC range during constant current charging; and / or The average charging temperature of each charging segment is calculated, and the historical charging segments are hierarchically clustered according to the average charging temperature. Charging segments in the same temperature range are selected for comparison.

6. The method for evaluating the internal resistance of a power battery according to claim 5, characterized in that: Based on the cell voltage values ​​in the first data segment, calculate the deviation of each cell voltage from the average value in the i-th frame and the standard deviation of the voltage deviation of each monomer in the i-th frame include, The cell voltage values ​​in the first data segment form a submatrix B: Where n is the total number of frames in the current first data segment, and n≤f, m is the total number of probes in each frame of data, Indicates the single-unit voltage value of the m-th probe in the n-th frame; Subtract the previous and next frames of submatrix B to get the new matrix C: in, Indicates the single-unit voltage change value of the m-th probe in the n-th frame; Extract data from the matrix C and obtain the data frame i with the largest voltage change in the matrix C, which is recorded as the i-th frame; Based on the voltage change value of each monomer in the i-th frame, the voltage deviation of each monomer in the i-th frame is calculated to form the voltage deviation matrix D of each monomer in the i-th frame. i ; Based on the matrix D i Calculate the average deviation of each monomer voltage in the i-th frame and the standard deviation of the voltage deviation of each monomer in the i-th frame .

7. The method for evaluating the internal resistance of a power battery according to claim 6, characterized in that: Extract data from matrix C and obtain the data frame i with the largest voltage change in matrix C, which is recorded as the i-th frame. The average value of the monomer voltage change value of each frame of matrix C is calculated: Where m represents the total number of probes in each frame of data, and j represents the jth probe; The data frame with the largest P value is obtained, that is, the data frame i with the largest voltage change, which is recorded as the i-th frame.

8. The method for evaluating the internal resistance of a power battery according to claim 7, characterized in that: Based on the voltage change value of each monomer in the i-th frame, the voltage deviation of each monomer in the i-th frame is calculated to form the voltage deviation matrix D of each monomer in the i-th frame. i include, After removing the extreme values ​​of the voltage change values ​​of each monomer in the i-th frame, the average value of the remaining voltage change values ​​of each monomer in the i-th frame is calculated. : Among them, m is the total number of probes in each frame of data, is the monomer voltage change value of the kth probe in the i-th frame, is the maximum cell voltage change value in the i-th frame, is the minimum cell voltage change value in the i-th frame; Based on the average value of the voltage change of each monomer in the i-th frame and the voltage change values ​​of the remaining monomers in the i-th frame , forming the voltage deviation matrix D of each monomer in the i-th frame i : Where, represents the monomer voltage deviation of the mth probe in the i-th frame; Based on the matrix D i Calculate the deviation of each monomer voltage from the average value And the standard deviation of each cell voltage deviation include, Remove the voltage deviation matrix D of each monomer in the i-th frame i After the voltage deviation value of each monomer is the extreme value, the matrix D is obtained. i The average value of the remaining monomer voltage deviations is the average monomer voltage deviation value. ; Based on the deviation of the cell voltage from the average value , calculate the matrix D i The standard deviation of the voltage deviation of each cell : Among them, m is the total number of probes in each frame of data, The single-cell voltage deviation of the j-th probe in the i-th frame.

9. The method for evaluating the internal resistance of a power battery according to claim 8, characterized in that: Based on the deviation of each cell voltage from the average value And the standard deviation of each cell voltage deviation Scoring the selected first data segment to evaluate the internal resistance consistency of the power battery includes: by Set up different levels of screening ranges, where h is a constant; Assign values ​​to different levels of screening ranges; For each monomer voltage deviation matrix D in the i-th frame i The voltage deviation values ​​of each monomer in the i-th frame are scored, and the voltage deviation matrix D of each monomer in the i-th frame is used i The lowest score among the voltage deviation values ​​of each cell in the data segment is used as the score of the first data segment.

10. The method for evaluating the internal resistance of a power battery according to claim 9, wherein: by The establishment of different levels of screening scope includes: by , ]、 , ) , ]、 , ) , ]、 , ) , ]、 , ) , ]、( , ) , ) Establish 6 levels; The 6-level screening ranges are assigned values ​​of 100, 90, 80, 70, 60, and <60 respectively.

11. The method for evaluating the internal resistance of a power battery according to claim 9, wherein: It also includes scoring of battery systems, include, If the first data segment appears for a preset number of consecutive times with the same level score, the system status score is updated.

12. A power battery internal resistance evaluation system, characterized in that: include, The acquisition module is used to obtain the battery pack operation data within a certain period and sort the matrix according to the operation data acquisition time, the time series matrix A; a segmentation module for segmenting data based on one or more of the vehicle state, battery pack state of charge, SOC growth trend, and voltage growth trend, and based on a sampling time interval, wherein the sampling time interval is the time difference between the previous and next frames of the running data; a segment selection module, configured to select a first data segment from the divided data segments based on a segment selection rule, wherein the first data segment is a data segment in the time series matrix A; A data processing module is used to calculate the average deviation of each cell voltage in the i-th frame based on the cell voltage value in the first data segment. and the standard deviation of the voltage deviation of each monomer in the i-th frame ; Evaluation module for the deviation of each cell voltage from the average value in the i-th frame and the standard deviation of the voltage deviation of each monomer in the i-th frame , score the selected first data segment to evaluate the internal resistance consistency of the power battery.

13. The power battery internal resistance evaluation system according to claim 12, characterized in that: It also includes a data cleaning module that performs the following cleaning steps: Verify the vehicle status and battery pack charging status through one or more of the vehicle speed, current, SOC, and voltage fields, and re-match and assign the vehicle status and battery pack charging status of inconsistent data frames; Refer to the measurement specifications of current, voltage and temperature acquisition sensors to clean abnormal values ​​that exceed the measurement range; and / or, By running logical judgments between the fields of the frames before and after the data, stagnant data can be cleaned.

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