CN115524629B - Method for evaluating health state of vehicle power battery system - Google Patents
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Abstract
The invention relates to an evaluation method of a vehicle power battery system health state, which comprises the steps of screening vehicle historical driving data to obtain a first data segment, obtaining a second data segment, and integrating the first data segment to obtain a third data segment; correcting the battery state evaluation parameters in each charging segment in the third data, and simultaneously calculating corresponding voltage variances of the battery state evaluation parameters to form a relation sequence of the corrected battery state evaluation parameters and the corresponding voltage variances; and calculating the internal resistance value of each battery in each charging stage by the corrected relation sequence and combining each charging data and the battery equivalent model, calculating the ratio gain of the voltage variance sum corresponding to the overlapping region of the battery charge states in the adjacent charging segments and the internal resistance deviation amount of each battery in the adjacent charging segments, and completing the multi-dimensional evaluation of the safety of the power battery system by a battery internal resistance deviation abnormal value screening method according to the set voltage variance ratio gain risk threshold value and the battery internal resistance deviation amount accumulated risk threshold value.
Description
Technical Field
The invention relates to the technical field of safety monitoring of vehicle power battery systems, in particular to a method for evaluating the health state of a vehicle power battery system.
Background
The electric automobile has the advantages of energy conservation, environmental protection, low noise, no pollution, high energy conversion rate and the like, so that the future development prospect of the electric automobile is widely seen. The power battery pack system is one of three electric systems of an electric automobile, and the performance of the power battery pack directly influences the performance of the whole automobile. The SOC is an important index reflecting the residual capacity and the work-doing capability of the power battery, the inconsistency of the power battery pack is a continuously accumulated process, and the difference between single batteries is larger when the time is longer; moreover, the battery pack is influenced by the application environment, particularly the power battery pack on a new energy vehicle is influenced by complex operation conditions, external environment and temperature, the inconsistency of the single batteries is gradually intensified, and the battery parameters of the whole power battery pack are finally unbalanced as the attenuation of some single batteries is accelerated; at present, the estimation of the SOC state of a new energy vehicle battery is mainly carried out in a data-driven mode, and the principle of the method is that an algorithm model is built through machine learning and deep learning, the model precision is improved through continuous training of vehicle off-line data in a vehicle network data platform, then the model is predicted through collected real vehicle data, the model is continuously trained through newly obtained data, and the model estimation precision is further improved through parameter adjustment.
Disclosure of Invention
The invention provides a method for evaluating the health state of a vehicle power battery system, which is characterized in that the health state of a battery is evaluated by acquiring historical operating parameter data of the vehicle power battery system and applying a battery voltage parameter and an internal resistance parameter gain in combination with the physical and chemical characteristics of the battery, a model is simple and reliable, the health states of power battery packs with different capacities of different new energy vehicle types and single batteries in the power battery packs can be evaluated, and the health state of the battery in a circulating charge-discharge test can also be evaluated.
In order to solve the problems in the background art, the invention is realized by the following technical scheme: a method for evaluating the health state of a vehicle power battery system comprises the following steps:
s100, acquiring historical operating parameter data of a vehicle power battery system: extracting a first data segment and a second data segment from historical operating parameter data; the first data segments are all parking charging data segments meeting the requirement of a certain charging span, and the second data segments are all data segments meeting the requirement that the vehicle stands for a certain time and is restarted;
s101, determining battery state evaluation parameters, extracting the battery state evaluation parameters and parameter data with high correlation from the first data segment by using a random forest model or a Pearson correlation coefficient method, and performing disassembly and combination to obtain a third data segment meeting the calculation requirements; filtering and de-duplicating the second data fragment to obtain a first relation matrix;
s102, performing precision correction on the battery state evaluation parameters in the third data segment, and calculating corresponding voltage variances of the battery state evaluation parameters; performing interpolation processing on the first relation matrix to obtain a second relation matrix; a battery equivalent circuit model is constructed, and the third data segment after the battery state evaluation parameter precision correction and the second relation matrix are combined to complete the estimation of the internal resistance of each battery;
s103, calculating voltage variance gains and monomer internal resistance gains in all the front and rear adjacent segments in the third data segment after the accuracy of the battery state evaluation parameter is corrected by using a discrete variable incremental method, and finishing the evaluation of the health state of the power battery system by combining the result of the calculation with the established voltage abnormality identification method, internal resistance abnormality identification method and internal resistance accumulation risk identification method.
Preferably, in S100, the historical operating parameter data includes each cell voltage, each cell temperature, total current, battery state of charge SOC, and mileage;
in S101, the battery state evaluation parameter is a battery state of charge (SOC); the parameter data with higher correlation are the voltage and the total current of each single battery; the third data segment is a charging segment set which meets the requirement of battery SOC span limitation and mileage interval; and the first relation matrix is the state of charge (SOC) of the battery and the corresponding open-circuit voltage.
Preferably, the third data segment obtaining method in S101 is: setting a SOC limit value of a charging start-stop battery and a mileage span interval; filtering the first data segment according to the SOC limit value of the charging start-stop battery and the mileage span interval; and meanwhile, in order to ensure that one charging segment meeting the SOC limit value of the charging start-stop battery is provided in each mileage span interval, combining the data with a few charging segments in the range of the mileage interval in the first data segment to obtain the third data segment.
Preferably, the first relation matrix in S101 is constructed by: in the second data segment, extracting the battery state of charge (SOC) and a corresponding fixed cell voltage value in first frame data in all starting data segments of the vehicle, wherein the corresponding fixed cell voltage is an open-circuit voltage, performing deduplication processing on the extracted battery state of charge (SOC), only retaining the battery state of charge (SOC) and the open-circuit voltage extracted for the first time, and finally forming a group of battery state of charge (SOC) and voltage corresponding sequences according to the ascending order of the battery state of charge (SOC).
Preferably, the precision correction in S102 is to correct the SOC values of the same battery in each charging segment of the third data segment, and only correct the 2 nd value to the last 1 value of the SOC values of the same battery; the precision correction formula is as follows:
SOC i (correction value) For the state of charge SOC values to be corrected,
for the sequence of the current SOC values needing to be corrected in the same SOC,
for minimum accuracy of sampling of the state of charge SOC of the battery,
is the total number of SOC values, SOC of the current continuous same battery Current display value The current uncorrected battery SOC value is not less than 2
≤
。
Preferably, the step of constructing the second relation matrix in S102 is: firstly, setting a minimum increment interval b% of a battery state of charge (SOC), carrying out equidistant interpolation filling on a battery SOC sequence in a first relation matrix according to the set minimum increment interval of the battery SOC, and filling an open-circuit voltage corresponding to the newly inserted battery SOC by adopting any interpolation method of a Lagrange interpolation method, a successive linear interpolation method or a spline interpolation method to obtain a second relation matrix.
Preferably, the method for estimating internal resistance of battery in S102 includes: extracting each independent charging segment in the third data segment after the battery state evaluation parameter precision is corrected, circularly traversing each battery state of charge (SOC) in each independent charging segment, and finding out an open-circuit voltage corresponding to the value closest to the SOC value of the battery from the second relation matrix, namely the open-circuit voltage corresponding to the SOC value of each battery in each charging segment; and establishing a battery equivalent model, combining time sequence data of each monomer voltage and total current in each independent charging segment to obtain each parameter of the battery equivalent model, and further obtaining the internal resistance value of each monomer in each independent charging segment state according to each parameter.
Preferably, the operation step of S103 is:
the discrete variable increment method comprises the following specific calculation steps: acquiring a charging span overlapping region in all charging segments, namely a battery state of charge (SOC) overlapping region, from the third data segment after the accuracy correction of the battery state evaluation parameters is carried out, further extracting monomer voltage variance values corresponding to the same battery SOC in all front and back adjacent charging segments in the charging span overlapping region, then respectively summing voltage variance values corresponding to the same battery SOC in the front and back adjacent charging segments, and then carrying out division calculation on the sum of the voltage variance values of all the front and back adjacent charging segments to form a group of voltage variance sum ratio sequences;
extracting the internal resistance values of the monomers in each charging segment from the third data segment after the battery state evaluation parameter precision correction, completing the calculation of the internal resistance deviation absolute values of the monomers in all adjacent charging segments, and accumulating the internal resistance deviation absolute values of the monomers to form a monomer internal resistance deviation absolute value matrix and a monomer internal resistance deviation accumulated value matrix;
identifying abnormal values of the ratio sequence value of the voltage variance sum by using a voltage abnormality identification method, if the abnormal values exist, judging that the voltage state of the single power battery system is abnormal, otherwise, judging that the voltage state of the single power battery system is normal;
abnormal value identification is carried out on the monomer internal resistance deviation absolute value matrixes by an internal resistance abnormality identification method, and abnormal value screening is further carried out on the monomer internal resistance deviation absolute value matrixes from top to bottom line by line or from left to right line by an internal resistance abnormality identification method; if the abnormal value exists, judging that the internal resistance state of the single body of the power battery system is abnormal, otherwise, judging that the internal resistance state of the single body of the power battery system is normal;
identifying abnormal values of the deviation accumulated value matrix of each monomer internal resistance by using an internal resistance accumulated risk identification method; and identifying the monomer internal resistance deviation accumulated value matrix line by line from top to bottom by using an internal resistance accumulated risk identification method, judging that the monomer internal resistance state of the power battery system is abnormal if an abnormal value exists, and otherwise, judging that the monomer internal resistance state of the power battery system is normal.
Preferably, the voltage abnormality identification method, the internal resistance abnormality identification method, and the internal resistance accumulation risk identification method are any one of a threshold value method, a box diagram, a grassblos criterion, and a density-based clustering method.
Compared with the prior art, the invention has the following beneficial technical effects:
the method has the advantages that the battery health state is evaluated by acquiring the historical operating parameter data of the vehicle power battery system, combining the physical and chemical characteristics of the battery and applying the gain of the voltage parameter and the internal resistance parameter of the battery, the model is simple and reliable, the health states of the power battery packs with different capacities of different new energy vehicle types and the single batteries in the power battery packs can be evaluated, and the health state of the battery in the cycle charge and discharge test can also be evaluated.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a voltage variance diagram obtained by calculating the SOC of each battery of four depth slices before a certain vehicle is thermally out of control according to embodiment 2 of the present invention;
FIG. 3 is a graph showing the variance and ratio gain of adjacent charging segments before thermal runaway occurs in a vehicle according to embodiment 3 of the present invention;
FIG. 4 is a schematic diagram of the 6 σ criterion in embodiment 1 of the present invention;
FIG. 5 is a diagram of a relationship matrix in embodiment 1 of the present invention;
fig. 6 is a schematic diagram of a relationship matrix two in embodiment 1 of the present invention.
Detailed Description
Example 1
As shown in fig. 1, a method for assessing the state of health of a vehicle power battery system includes the steps of:
s100, acquiring historical operating parameter data of a vehicle power battery system: extracting a first data segment and a second data segment from historical operating parameter data; the first data segments are all parking charging data segments meeting the requirement of a certain charging span, and the second data segments are all data segments meeting the requirement that the vehicle stands for a certain time and is restarted;
first data fragment screening principle: and screening all parking charging data segments from the vehicle historical operation data, further screening all parking charging segments according to a set minimum limit value of the SOC span of the parking charging battery, and arranging according to a time sequence. The change of the SOC of the battery in each charging segment that satisfies the minimum limit of the SOC span of the battery is continuous, and in this embodiment, the minimum SOC span of the battery is 30%. The method selects the charging stage data to evaluate the health state of the power battery system, has the main reason that all signals of the battery are less interfered by the outside world and have low noise in the charging stage, and can diagnose the state of the battery through fine data analysis.
Second data fragment screening principle: and screening all data segments after the vehicle is restarted after the parking time exceeds the minimum limit from the historical vehicle running data, and sequentially arranging the screened segments according to the time axis sequence. In the embodiment, the minimum limit time of parking discharge is set to be 5 hours, the data segment after the vehicle is started after the parking discharge exceeds 5 hours is reserved, and in order to avoid screening errors caused by data loss, the mileage of the last frame data of the previous segment and the mileage of the first frame data of the screened data segment before and after the time interval exceeds five hours are compared, and if the data segments are the same, the parking discharge state is judged, and data loss is avoided.
S101, determining battery state evaluation parameters, extracting the battery state evaluation parameters and parameter data with high correlation from the first data segment by using a random forest model or a Pearson correlation coefficient method, and performing disassembly and combination to obtain a third data segment meeting the calculation requirements; filtering and de-duplicating the second data fragment to obtain a first relation matrix;
in the embodiment, the selected SOC is a battery state evaluation parameter, the battery SOC is a state of charge, and is used for reflecting the remaining capacity of the battery, and the state of charge SOC and the characteristic parameters with strong correlation can also evaluate the battery state. The specific method for selecting the characteristic parameters with strong corresponding correlation of the SOC is as follows: extracting data of a certain charging segment in the first data segment, determining two parameters of total current and single battery voltage with high SOC correlation by using a random forest model, extracting three parameter data of the total current, the single battery voltage and the SOC from the first data segment, filtering and integrating the extracted data again, and obtaining a third data segment meeting the calculation requirement. The third data segment is a charging segment set meeting the SOC limit requirements at the beginning and the end of charging, and certain mileage intervals are met among the charging segments. Due to the transportation working conditions in some cities, the new energy is light in weight and is in practical use, drivers rarely have the habit of emptying the vehicle battery to refilling the vehicle battery, so that the charging start state and the charging stop state of charge (SOC) are inconsistent in each parking charging segment, and the charging span interval is long or short. In order to meet the requirements of the charging span charging start and charging stop states of charge SOC and the mileage interval requirement in the third data segment, several short charging segment data within a certain mileage range need to be integrated by a certain strategy. The integration of short data over a range of mileage is mainly aimed at that the state of health of the battery is not generally changed significantly over a range of mileage. In this embodiment, the mileage interval of each adjacent charging segment in the third data segment is set to be less than 500 km, and if several shorter charging segment data need to be integrated, it is only necessary to determine that the mileage interval of the first charging segment and the last charging segment to be integrated is within 500 km. It should be noted that when data of several charging segments are merged, a charging span overlapping region may occur, and a data merging strategy for the overlapping region is as follows: firstly, data sampling intervals in several charging segments to be combined are the same, the minimum change precision of the SOC is a fixed value, the data in the overlapping region of the charging spans are combined, and only all the data corresponding to the mode of the data strip number when the SOC corresponds to each SOC are reserved. If the mode is not unique, the current, the cell voltage and the mileage corresponding to the SOC are averaged, for example, in this embodiment, three pieces of shorter charging segment data within a certain mileage need to be merged, the SOC of the common charging span interval is 40% to 60% and different charging segments appear in the actual charging process, and several pieces of collected charging data correspond to each other under the same SOC, for example, under the SOC =40% condition, the number of sampling data corresponding to the first charging segment is 5; the second charging segment corresponds to 6 sampling data; the third charging segment corresponds to 5 pieces of sample data. According to the mode principle. Only 5 pieces of data with the state of charge SOC =40% are retained, wherein the 5 pieces of data with the state of charge SOC =40% are a first charging segment and a third charging segment, the current corresponding to the state of charge SOC =40%, the cell voltage of each cell is the average value of the current and the voltage corresponding to the first charging segment and the third charging segment, and it is required to say that the mileage values are the average values of the corresponding mileage of the three charging segments, and so on, the data of the adjacent shorter charging segments meeting the condition can be integrated by using the above method.
S102: performing precision correction on the battery state evaluation parameters in the third data segment, and calculating the corresponding voltage variance of the battery state evaluation parameters; performing interpolation processing on the first relation matrix to obtain a second relation matrix; a battery equivalent circuit model is constructed, and the third data segment after the battery state evaluation parameter precision correction and the second relation matrix are combined to complete the estimation of the internal resistance of each battery;
s103: and calculating voltage variance gains and monomer internal resistance gains in all front and back adjacent segments in the third data segment after the precision of the battery state evaluation parameter is corrected by using a discrete variable increment method, and finishing the evaluation of the health state of the power battery system by combining the result with the established voltage abnormality identification method, internal resistance abnormality identification method and internal resistance accumulation risk identification method.
The discrete variable increment method comprises the following calculation steps: and acquiring charging span overlapping areas in all charging segments in a third data segment after the SOC accuracy of the battery is corrected, calculating the monomer voltage variance corresponding to the same SOC in the charging span overlapping areas in all the front and rear charging segments, summing the voltage variance values calculated in each charging segment, and sequentially dividing the sum of the voltage variance values of all the front and rear adjacent charging segments. And calculating the monomer internal resistance deviation in all adjacent front and back charging sections according to the estimated value of the monomer internal resistance of each battery in each charging section to form a group of voltage variance sum ratio sequence and monomer internal resistance deviation matrix. According to the method, under a stable working condition, the gain change of key parameters in adjacent charging segments is calculated, and the health state of the battery is further analyzed and evaluated through a model, so that the method has obvious theoretical and physical significance.
In S100, historical operation parameter data comprise voltage of each single battery, temperature of each single battery, total current, SOC (state of charge) of the battery and mileage;
in S101, a battery state evaluation parameter is a battery state of charge (SOC); the parameter data with higher correlation are the voltage and the total current of each single battery; the third data segment is a charging segment set which meets the requirement of battery SOC span limitation and mileage interval; the first relation matrix is the state of charge SOC of the battery and the corresponding open-circuit voltage.
The third data segment obtaining method in S101 is: setting SOC limit values of the charging start-stop battery and the mileage span interval; filtering the first data segment according to the SOC limit value of the charging start-stop battery and the mileage span interval; and meanwhile, in order to ensure that one charging segment meeting the SOC limit value of the charging start-stop battery is provided in each mileage span interval, combining the shorter data of a plurality of charging segments in the range of the mileage interval in the first data segment to obtain a third data segment.
The construction method of the first relation matrix in the S101 comprises the following steps: in the second data segment, extracting the battery state of charge (SOC) and the corresponding fixed monomer voltage value in the first frame data in all the starting data segments of the vehicle, wherein the corresponding fixed monomer voltage is the open-circuit voltage, performing deduplication processing on the extracted battery state of charge (SOC), only keeping the battery state of charge (SOC) and the open-circuit voltage extracted for the first time, and finally forming a group of corresponding sequences of the battery state of charge (SOC) and the voltage according to the ascending order of the battery state of charge (SOC).
In the step S102, precision correction is performed, namely the SOC values of the continuous same batteries in each charging section in the third data section are corrected, and only the 2 nd value to the last 1 value of the SOC values of the same batteries are corrected; the precision correction formula is as follows:
SOC
(correction value) To the state-of-charge SOC value that needs correction,
for the sequence of the current SOC values of the battery needing to be corrected in the same SOC,
for minimum accuracy of sampling of the state of charge SOC of the battery,
is the total number of SOC values, SOC of the current continuous same battery Current display value The current uncorrected battery SOC value is not less than 2
≤
。
In this embodiment, in a certain charging segment, the data sampling interval is 10s, and the accuracy of the battery state of charge SOC is 1%. If a total of 5 pieces of sampled data of the battery state of charge SOC =30% is charged to the battery state of charge SOC =31%, the sampled data is changed and increased all the time according to the actual battery state of charge SOC value, mainly due to display accuracy problems, the same battery state of charge SOC value is corrected, further correction of the battery state of charge SOC also brings improvement to the estimation accuracy of the subsequent internal resistance of the battery, the minimum interval value of change of the battery state of charge SOC correction is set to be 1%/5=0.02, the first battery state of charge SOC =30% is not required to be corrected, the second raw value battery state of charge SOC =30% is corrected to 30.2%, the third raw value battery state of charge SOC =30% is corrected to 30.4%, the fourth raw value battery state of charge SOC =30% is corrected to 30.6%, and the fifth raw value battery state of charge =30% is corrected to 30.8%.
The step of constructing the second relation matrix in the S102 is as follows: firstly, setting a minimum increment interval b% of a battery state of charge (SOC), carrying out equal interval interpolation filling on a battery state of charge (SOC) sequence in a first relation matrix according to the set minimum increment interval of the battery state of charge (SOC), and filling an open-circuit voltage corresponding to the newly inserted battery state of charge (SOC) by adopting any interpolation method of a Lagrange interpolation method, a successive linear interpolation method or a spline interpolation method to obtain a second relation matrix.
The method for estimating the internal resistance of the battery in the S102 comprises the following steps: extracting each independent charging segment in the third data segment after the accuracy of the battery state evaluation parameter is corrected, circularly traversing each battery SOC in each independent charging segment, and finding out the open-circuit voltage corresponding to the value closest to the battery SOC value from the second relation matrix, namely the open-circuit voltage corresponding to each battery SOC in each charging segment; and establishing a battery equivalent model, combining time sequence data of each monomer voltage and total current in each independent charging segment to obtain each parameter of the battery equivalent model, and further obtaining the internal resistance value of each monomer in each independent charging segment state according to each parameter.
The parameters of each charging segment and the relation matrix I are needed to be used for subsequently estimating the internal resistance value of each monomer in each charging segment, and the battery SOC value of each charging segment is further corrected, so that the battery SOC precision is improved, the relation matrix I needs to be further corrected to obtain a relation matrix II, and the subsequent battery internal resistance estimation precision can be further improved. The first further correction method for the relation matrix is that, in combination with the present embodiment, the minimum sampling precision of the current battery state of charge SOC is 1%, the minimum increment interval of the battery state of charge SOC is set to be 1%/10=0.1%, the battery state of charge SOC sequence value in the first relation matrix is supplemented at equal intervals according to the set minimum increment interval to obtain an updated battery state of charge SOC sequence, the method for solving the open-circuit voltage corresponding to the inserted battery state of charge SOC is adopted in the present embodiment, a cubic spline interpolation method is adopted to obtain the open-circuit voltage value corresponding to the updated battery state of charge SOC sequence, and the relationship matrix second is formed by the battery state of charge SOC sequence after equal interval interpolation and the open-circuit voltage value sequence after interpolation. The purpose of further correction is that the problem of the acquisition and display precision of the actual battery SOC is solved, and if the extracted relation matrix I is not further corrected, the provided relation matrix I has a great influence on the precision of the subsequent battery internal resistance estimation.
And according to the second relation matrix, combining the battery parameter data in each charging segment in the corrected third data segment to complete the internal resistance estimation of each single battery under each charging segment. In this embodiment, the method for calculating the internal resistance of each battery in a certain charging segment includes: and extracting the voltage time sequence value, the total current time sequence value and the SOC time sequence value of the corresponding battery in the charging segment. And circularly traversing each battery SOC value in the battery SOC sequence in the charging segment, and matching the open-circuit voltage corresponding to the battery SOC value closest to the battery SOC value in a relation matrix battery SOC row. And analogizing in sequence to obtain the corresponding relation between the SOC and the open-circuit voltage of each battery under the charging segment. Wherein, each monomer voltage time sequence value is the terminal voltage of each monomer, and the total current time sequence value is the terminal current. Establishing a second-order RC equivalent circuit model, carrying out discretization processing on an expression of the equivalent circuit model to obtain an equation after Rayleigh change, obtaining a difference equation between system input and system output of the equivalent circuit model by adopting bilinear transformation, establishing a recursive least square method, actually obtaining each parameter of the battery in a charging stage, identifying the parameters of the model, obtaining each parameter value in the model by combining the identified parameters with the equivalent circuit model, and further obtaining the internal resistance value of each monomer. And by analogy, the internal resistance values of the monomers in the charging segments in the corrected third data segment are obtained.
The operation steps of S103 are:
the discrete variable increment method comprises the following specific calculation steps: acquiring a charging span overlapping region in all charging segments, namely a battery state of charge (SOC) overlapping region, from a third data segment after the accuracy correction of the battery state evaluation parameters, further extracting monomer voltage variance values corresponding to the same battery SOC in all front and back adjacent charging segments in the charging span overlapping region, then respectively summing voltage variance values corresponding to the same battery SOC in the front and back adjacent charging segments, and then performing division calculation on the sum of the voltage variance values of all the front and back adjacent charging segments to form a group of voltage variance sum ratio sequences;
extracting the internal resistance values of the monomers in each charging segment from the third data segment after the precision correction of the battery state evaluation parameters, completing the calculation of the internal resistance deviation absolute values of the monomers in all adjacent charging segments, and accumulating the internal resistance deviation absolute values of the monomers to form an internal resistance deviation absolute value matrix of each monomer and an internal resistance deviation accumulated value matrix of each monomer;
identifying abnormal values of the ratio sequence value of the voltage variance sum by using a voltage abnormality identification method, if the abnormal values exist, judging that the voltage state of the single power battery system is abnormal, otherwise, judging that the voltage state of the single power battery system is normal;
carrying out abnormal value identification on each monomer internal resistance deviation absolute value matrix by using an internal resistance abnormality identification method, and further carrying out abnormal value screening on each monomer internal resistance deviation absolute value matrix from top to bottom line by line or from left to right line by using the internal resistance abnormality identification method; if the abnormal value exists, judging that the internal resistance state of the single body of the power battery system is abnormal, otherwise, judging that the internal resistance state of the single body of the power battery system is normal;
identifying abnormal values of the deviation accumulated value matrix of each monomer internal resistance by using an internal resistance accumulated risk identification method; and identifying the deviation accumulated value matrix of each monomer internal resistance line by line from top to bottom by using an internal resistance accumulated risk identification method, if an abnormal value exists, judging that the monomer internal resistance state of the power battery system is abnormal, otherwise, judging that the monomer internal resistance state of the power battery system is normal.
Further, carrying out abnormal value identification on the ratio sequence value of the voltage variance sum by using a voltage abnormality identification method, if the abnormal value exists, judging that the single voltage state of the power battery system is abnormal in the charging segment which is correspondingly close to the last segment of the two charging segments before and after, otherwise, judging that the single voltage state of the power battery system is normal; in this embodiment, the voltage threshold is set to determine whether the voltage variance and the ratio gain of the charging section before and after approach are abnormal values. In the embodiment, firstly, historical charging segments of 100 normal vehicles in the same vehicle type and the same operation condition are selected, voltage variances and ratio gains of all front and rear charging segments are obtained through calculation, then, voltage variance and ratio gain threshold ranges are determined according to a 6 sigma criterion, and the 6 sigma range is shown in fig. 4; the voltage variance sum threshold obtained according to the 6 sigma criterion is 2 x (mean value (mu) +6 x standard deviation (sigma)), the threshold range of the ratio gain of the voltage variance sum obtained finally is 0.95-1.05, and if the ratio gain exceeds the interval range, the battery voltage abnormity can be judged. According to the method, the threshold value of other parameter related characteristic parameters for judging the state of the battery system can be determined.
Further, abnormal value identification is carried out on the single internal resistance deviation absolute value matrixes by using an internal resistance abnormal identification method, and abnormal value screening is carried out on the single internal resistance deviation absolute value matrixes from top to bottom row by row or from left to right row by row. And if the abnormal value exists, judging that the internal resistance state of the single power battery system is abnormal, otherwise, judging that the internal resistance state of the single power battery system is normal. In the embodiment, the absolute value of the same monomer internal resistance deviation of the adjacent charging sections before and after the third data section is calculated and formed
，
Wherein
The serial number of the battery cell is shown;
representing the serial number of the corresponding charging segment;
the relationship matrix one shown in fig. 5:
And further, identifying abnormal values of the deviation accumulated value matrixes of the internal resistances of the monomers by using an internal resistance accumulation risk identification method. The specific method comprises the following steps: and identifying the monomer internal resistance deviation accumulated value matrix line by line from top to bottom by using an internal resistance accumulated risk identification method, judging that the monomer internal resistance state of the power battery system is abnormal if an abnormal value exists, and otherwise, judging that the monomer internal resistance state of the power battery system is normal. In this embodiment, the calculation results
The internal resistance deviation amount matrix accumulates the absolute value of each internal resistance deviation row, and when the internal resistance change increment sum of a certain single battery exceeds the preset valueAnd when a threshold value is set, judging that the single battery has the capacity attenuation abnormal risk. In this embodiment, a slope method is used to evaluate the cumulative increment of the internal resistance change of each single battery, that is, the above-mentioned
The internal resistance deviation amount matrix is adjusted as follows, the internal resistance deviation amount of a certain row and a certain column is added with all deviation amounts before the current column to form a new accumulated internal resistance deviation amount of the current column of the current row, and the like, and a relation matrix II shown in figure 6 is formed; and simultaneously drawing a line graph of the value of each column, and if the slope of a certain column value is greater than a preset warning value, judging that the internal resistance of the serial number monomer corresponding to the current column is abnormal.
The voltage abnormality identification method, the internal resistance abnormality identification method and the internal resistance accumulation risk identification method are any one of a threshold value method, a box diagram, a Grabbs criterion and a density-based clustering method.
Example 2
As shown in fig. 2, a corresponding voltage variance graph is obtained by calculating the states of charge SOC of each battery in four depth segments before thermal runaway occurs in a certain vehicle, where fig. 2, fig. 3, and fig. 4 are voltage variance graphs corresponding to the states of charge SOC of each battery in the last, and fourth depth charging segments before thermal runaway occurs, and fig. 1 is a voltage variance graph corresponding to the states of charge SOC of each battery in the last depth charging segment before thermal runaway occurs, and it is obvious that, by using the above method, the voltage variance variation ranges of the depth charging segments of fig. 2, fig. 3, and fig. 4 are substantially consistent in the middle stage of charging, and the voltage variances corresponding to the states of charge SOC of each battery are not significantly different, and in the middle stage of charging, the voltage variance of the depth charging segments of fig. 1 has significantly deviated from the voltage variances corresponding to the states of charge SOC of each battery in the other three lines, in order to quantify the variation, in this embodiment, a charging region common to four charging segments is selected as a set interval for weighing, that the voltages in four charging segments in the states of charge SOC1 to the battery SOC2 are calculated, and the states of the four charge =40%, and the states of the battery are compared, where the states of charge =60%, and the states of the battery are calculated, and the states of the battery is calculated, and the battery is calculated.
Example 3
As shown in fig. 3, it is a graph of variance and ratio gain of adjacent charging segments before thermal runaway occurs in a certain vehicle, in this example, a voltage variance and ratio dot diagram of adjacent charging voltages of eight charging segments before thermal runaway and a ratio dot diagram of a charging segment before thermal runaway, with abscissa 8/7.. And 2/1, respectively representing a voltage variance and a ratio corresponding to a charging segment before thermal runaway and a charging segment after thermal runaway and a voltage variance and a ratio corresponding to a charging segment before thermal runaway and a charging segment after last 1 are selected, wherein each charging segment meets requirements for each charging segment in a third charging segment, in this example, a range of normal gain intervals is preset as [ a1, a2], a1=0.95, and a2=1.05, it can be seen that a voltage and a ratio corresponding to a charging segment before thermal runaway 8/7,7/6, 6/5.,. 3/2 battery state overlap regions are within a normal range, while a voltage variance and a ratio corresponding to a SOC gain corresponding to a voltage and a ratio of a charging segment before thermal runaway and a last 1 are lower than a threshold of a voltage and a voltage fluctuation is obviously found in a final charging segment, and a charging process is judged that a voltage fluctuation is lower than a threshold of a voltage and a voltage is obviously occurred in a final charging process, and a final charging process is obviously.
Claims (9)
1. A method for evaluating the health state of a vehicle power battery system is characterized in that: the method comprises the following steps:
s100, obtaining historical operating parameter data of the vehicle power battery system, and extracting a first data segment and a second data segment from the historical operating parameter data; the first data segments are all parking charging data segments meeting the requirement of a certain charging span, and the second data segments are all data segments meeting the requirement that the vehicle stands for a certain time and is restarted;
s101, determining battery state evaluation parameters, extracting the battery state evaluation parameters and parameter data with high correlation from the first data segment by using a random forest model or a Pearson correlation coefficient method, and performing disassembly and combination to obtain a third data segment meeting the calculation requirement; filtering and de-duplicating the second data fragment to obtain a first relation matrix;
s102, performing precision correction on the battery state evaluation parameters in the third data segment, and calculating corresponding voltage variances of the battery state evaluation parameters; performing interpolation processing on the first relation matrix to obtain a second relation matrix; a battery equivalent circuit model is constructed, and the third data segment after the battery state evaluation parameter precision correction and the second relation matrix are combined to complete the estimation of the internal resistance of each battery;
s103, calculating voltage variance gains and monomer internal resistance gains in all the front and rear adjacent segments in the third data segment after the accuracy of the battery state evaluation parameter is corrected by using a discrete variable incremental method, and finishing the evaluation of the health state of the power battery system by combining the result of the calculation with the established voltage abnormality identification method, internal resistance abnormality identification method and internal resistance accumulation risk identification method.
2. The vehicle power battery system state of health assessment method according to claim 1, wherein in S100, said historical operating parameter data comprises individual cell voltages, individual cell temperatures, total current, battery state of charge SOC and mileage; in S101, the battery state evaluation parameter is a battery state of charge (SOC); the parameter data with higher correlation are the voltage and the total current of each single battery; the third data segment is a charging segment set which meets the requirement of battery SOC span limitation and mileage interval; and the first relation matrix is the state of charge (SOC) of the battery and the corresponding open-circuit voltage.
3. The vehicle power battery system state of health assessment method according to claim 2, wherein said third data segment obtaining method in S101 is: setting SOC limit values of the charging start-stop battery and the mileage span interval; filtering the first data segment according to the SOC limit value of the charging start-stop battery and the mileage span interval; and meanwhile, in order to ensure that one charging segment meeting the SOC limit value of the charging start-stop battery is provided in each mileage span interval, combining the data with a few charging segments in the range of the mileage interval in the first data segment to obtain the third data segment.
4. The vehicle power battery system state of health assessment method of claim 2, wherein the first relation matrix in S101 is constructed by: in the second data segment, extracting the battery state of charge (SOC) and the corresponding fixed cell voltage value in the first frame data in all the starting data segments of the vehicle, wherein the corresponding fixed cell voltage is the open-circuit voltage, performing deduplication processing on the extracted battery state of charge (SOC), only keeping the battery state of charge (SOC) and the open-circuit voltage extracted for the first time, and finally forming a group of corresponding sequences of the battery state of charge (SOC) and the voltage according to the ascending order of the battery state of charge (SOC).
5. The vehicle power battery system state of health assessment method according to claim 4, wherein said precision correction in S102 is to correct consecutive identical battery SOC values in each charging segment of said third data segment, and only the 2 nd to the last 1 value of identical battery SOC values are corrected; the precision correction formula is as follows:
therein, SOC i (correction value) For the state of charge SOC values to be corrected,
for the sequence of the current SOC values of the battery needing to be corrected in the same SOC,
for minimum accuracy of sampling of the state of charge SOC of the battery,
for the present continuous same electricityTotal number of state of charge SOC values of the cell, SOC Current display value The current uncorrected battery SOC value is not less than 2
≤
。
6. The vehicle power battery system state of health assessment method of claim 1, wherein the step of constructing the second relationship matrix in S102 is: firstly, setting a minimum increment interval b% of a battery state of charge (SOC), carrying out equidistant interpolation filling on a battery state of charge (SOC) sequence in a first relation matrix according to the set minimum increment interval of the battery state of charge (SOC), and filling an open-circuit voltage corresponding to the newly inserted battery state of charge (SOC) by adopting any one interpolation method of a Lagrange interpolation method, a successive linear interpolation method or a spline interpolation method to obtain a second relation matrix.
7. The vehicle power battery system state of health evaluation method of claim 1, wherein the battery internal resistance estimation method in S102 is: extracting each independent charging segment in the third data segment after the battery state evaluation parameter precision is corrected, circularly traversing each battery state of charge (SOC) in each independent charging segment, and finding out an open-circuit voltage corresponding to the value closest to the SOC value of the battery from the second relation matrix, namely the open-circuit voltage corresponding to the SOC value of each battery in each charging segment; and establishing a battery equivalent model, combining time sequence data of each monomer voltage and total current in each independent charging segment to obtain each parameter of the battery equivalent model, and further obtaining the internal resistance value of each monomer in each independent charging segment state according to each parameter.
8. The method for assessing the state of health of a vehicle power battery system according to claim 1, characterized in that said operating step S103 is:
the discrete variable increment method comprises the following specific calculation steps: acquiring a charging span overlapping region in all charging segments, namely a battery state of charge (SOC) overlapping region, from the third data segment after the accuracy correction of the battery state evaluation parameters is carried out, further extracting monomer voltage variance values corresponding to the same battery SOC in all front and back adjacent charging segments in the charging span overlapping region, then respectively summing voltage variance values corresponding to the same battery SOC in the front and back adjacent charging segments, and then carrying out division calculation on the sum of the voltage variance values of all the front and back adjacent charging segments to form a group of voltage variance sum ratio sequences;
extracting the internal resistance values of the monomers in each charging section from the third data section after the battery state evaluation parameter precision is corrected, completing the calculation of the internal resistance deviation absolute values of the monomers in all adjacent charging sections, and accumulating the internal resistance deviation absolute values of the monomers to form a monomer internal resistance deviation absolute value matrix and a monomer internal resistance deviation accumulated value matrix;
identifying abnormal values of the ratio sequence value of the voltage variance sum by using a voltage abnormality identification method, if the abnormal values exist, judging that the voltage state of the single power battery system is abnormal, otherwise, judging that the voltage state of the single power battery system is normal;
carrying out abnormal value identification on the absolute value matrix of the deviation of the internal resistance of each monomer by using an internal resistance abnormal identification method, and further carrying out abnormal value screening on the absolute value matrix of the deviation of the internal resistance of each monomer from top to bottom line by line or from left to right line by using the internal resistance abnormal identification method; if the abnormal value exists, judging that the internal resistance state of the single power battery system is abnormal, otherwise, judging that the internal resistance state of the single power battery system is normal;
identifying abnormal values of the deviation accumulated value matrix of each monomer internal resistance by using an internal resistance accumulated risk identification method; and identifying the monomer internal resistance deviation accumulated value matrix line by line from top to bottom by using an internal resistance accumulated risk identification method, judging that the monomer internal resistance state of the power battery system is abnormal if an abnormal value exists, and otherwise, judging that the monomer internal resistance state of the power battery system is normal.
9. The vehicle power battery system state of health assessment method according to claim 8, wherein the voltage anomaly identification method, the internal resistance anomaly identification method and the internal resistance cumulative risk identification method are any one of a threshold method, a box diagram, a Grabbs criterion, a density-based clustering method.
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