CN116466250A - Dynamic working condition model error characteristic-based power battery health state estimation method - Google Patents
Dynamic working condition model error characteristic-based power battery health state estimation method Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 59
- 230000036541 health Effects 0.000 title claims abstract description 23
- 230000032683 aging Effects 0.000 claims abstract description 69
- 230000004927 fusion Effects 0.000 claims abstract description 27
- 238000012360 testing method Methods 0.000 claims description 27
- 238000004364 calculation method Methods 0.000 claims description 11
- 230000010287 polarization Effects 0.000 claims description 9
- 230000008569 process Effects 0.000 claims description 8
- 238000004422 calculation algorithm Methods 0.000 claims description 7
- 230000002068 genetic effect Effects 0.000 claims description 7
- 238000005070 sampling Methods 0.000 claims description 7
- 238000004458 analytical method Methods 0.000 claims description 5
- 239000003990 capacitor Substances 0.000 claims description 5
- 230000010354 integration Effects 0.000 claims description 4
- 238000002474 experimental method Methods 0.000 claims description 3
- 101100379081 Emericella variicolor andC gene Proteins 0.000 claims description 2
- 208000028659 discharge Diseases 0.000 description 16
- 238000004088 simulation Methods 0.000 description 10
- 230000006870 function Effects 0.000 description 9
- 238000007599 discharging Methods 0.000 description 4
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 3
- 230000008859 change Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000000605 extraction Methods 0.000 description 3
- 229910052744 lithium Inorganic materials 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 238000013459 approach Methods 0.000 description 2
- 230000001351 cycling effect Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- GELKBWJHTRAYNV-UHFFFAOYSA-K lithium iron phosphate Chemical compound [Li+].[Fe+2].[O-]P([O-])([O-])=O GELKBWJHTRAYNV-UHFFFAOYSA-K 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 238000007619 statistical method Methods 0.000 description 2
- 230000035882 stress Effects 0.000 description 2
- 208000032953 Device battery issue Diseases 0.000 description 1
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
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- 230000007547 defect Effects 0.000 description 1
- 239000003792 electrolyte Substances 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 239000000295 fuel oil Substances 0.000 description 1
- 230000003862 health status Effects 0.000 description 1
- 229910001416 lithium ion Inorganic materials 0.000 description 1
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- 238000012423 maintenance Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
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- 239000000126 substance Substances 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
- G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage measurements
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/389—Measuring internal impedance, internal conductance or related variables
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
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Abstract
The invention provides a power battery health state estimation method based on dynamic working condition model error characteristics, which relates to the technical field of power battery safety and is characterized in that: the method comprises the following steps: s1, constructing a battery model by adopting a Thevenin model; s2, identifying parameters of the battery model; s3, adopting the error area as an aging characteristic of the estimated battery; s4, adopting a current average value of a dynamic working condition as a working condition characteristic; s5, describing the difference of the battery before and after aging by adopting fusion characteristics; s6, establishing an empirical model between the fusion characteristic and the battery SOH. Based on the correlation between the battery aging state and the model voltage error, the SOH estimation model under a plurality of dynamic working conditions is extracted from the model voltage errors of four dynamic working conditions, and the model has good precision and generalization performance.
Description
Technical Field
The invention relates to the technical field of power battery safety, in particular to the field of estimation of the health state of a power battery system of a new energy automobile, and particularly relates to a method for estimating the health state of a power battery discharged under a plurality of dynamic working conditions.
Background
As automobiles are popularized and used on a large scale, the amount of automobile maintenance is increasing. At the same time, the exhaust emission of the fuel oil automobile has more and more serious influence on the atmosphere. Along with the increasing importance of environmental protection problems in various countries in the world, the development of new energy automobiles has become an important strategic means for national energy conservation and emission reduction strategies and low-carbon economy development. Lithium ion batteries are also one of the most promising candidate batteries for electric vehicles as a clean energy storage technology. Although lithium batteries have advantages of high energy density and wide operating temperature range, performance in terms of capacity and power gradually deteriorates with the increase of the time of use. More seriously, battery aging may also cause electrolyte leakage and micro-shorting, which may cause battery failure and cause thermal runaway, resulting in catastrophic failure. Therefore, in order to ensure reliable operation of the electric vehicle and to avoid occurrence of such accidents, the function of the battery management system having the function of monitoring the SOH of the battery is important.
The lithium battery SOH evaluation method can be classified into a plurality of categories according to different classification standards. For example, model-based prediction methods utilize physical, chemical, and electrical principles to build a mathematical model of a battery, and predict the SOH of the battery by estimating model parameters. These methods can be used to study the performance of a battery under different operating conditions and provide a quantitative assessment of the SOH of the battery. Data-driven methods, random techniques, statistical methods, adaptive methods, etc., all belong to data-based methods, these methods are categorized into data-driven categories, the data-driven methods infer the health status of the battery by mainly processing a large amount of field test data, and features are extracted from the data and SOH prediction models are constructed using techniques such as statistical analysis, machine learning, artificial intelligence, etc. The mixing method combines the same or different types of methods together. The hybrid approach combines the advantages of both model-based and data-driven approaches, both to accurately describe the physical behavior of the battery and to make corrections and improvements with large amounts of measured data. These methods may use complementarity between the model and the data to improve prediction accuracy.
In electric vehicle applications, it is an important task to develop a simple and effective battery state of health monitoring system. The existing battery SOH estimation method generally selects data of a battery in a constant-current or constant-voltage charge-discharge stage to conduct feature extraction and modeling, and the requirement on data quality is high. However, due to the randomness of the charging behavior of the battery by the user and the different strategies of the quick charging of the battery, in addition, in the actual running process of the electric automobile, the electric automobile generally involves discharging under various working conditions, so that the charging and discharging characteristics of the battery are changeable, and strong working condition uncertainty exists. Therefore, how to extract reasonable and effective health indexes from the data of the dynamic discharge working condition is still a key for solving the SOH evaluation of the lithium battery under the dynamic working condition.
Disclosure of Invention
In order to solve the defects of the prior art, the invention provides the power battery health state estimation method based on the dynamic working condition model error characteristics, the health characteristics are extracted from the model voltage errors of four dynamic working conditions based on the correlation between the battery aging state and the model voltage errors, and SOH estimation models under a plurality of dynamic working conditions are constructed, so that the models have good precision and generalization performance.
The invention adopts the following technical scheme to realize the aim:
the power battery health state estimation method based on the dynamic working condition model error characteristics is characterized by comprising the following steps of: the method comprises the following steps:
s1, constructing a battery model by adopting a Thevenin model;
s2, identifying parameters of the battery model;
s3, adopting the error area as an aging characteristic of the estimated battery;
s4, adopting a current average value of a dynamic working condition as a working condition characteristic;
s5, describing the difference of the battery before and after aging by adopting fusion characteristics;
s6, establishing an empirical model between the fusion characteristic and the battery SOH.
As a further limitation of the present technical solution, the battery model of the initial cycle of the battery in S1 is as shown in formulas (1), (2) and (3):
（1）
（2）
（3）
wherein: subscript ofkIs the firstkSampling time;U t is the battery terminal voltage;U oc is an open circuit voltage;zis the state of charge of the battery;U p is polarization voltage; coefficients ofa 0 ~a 6 Determined by an off-line OCV experiment; deltatIs the sampling interval;i L the battery working current is positive by taking the discharge.
As a further limitation of the technical scheme, in S2, a genetic algorithm is adopted to identify parameters of the internal parameters of the initial aging point of the battery, and when a battery analysis model is established by using the Thevenin equivalent circuit model, the parameters to be identified include open circuit voltage OCV and ohmic internal resistanceR o Internal resistance of polarizationR p And polarization capacitorC p Wherein OCV is equal toSOCThe relationship between them is obtained by an incremental OCV test,R o 、R p andC p and (3) withSOCThe relationship between the two parameters is obtained by identifying the parameters of the HPPC test data.
As a further limitation of the present technical solution, in S3, the dynamic working condition test data under each aging point is input into the battery model, so as to obtain a voltage error of the model, and error areas of different working conditions under each aging point are extracted as aging characteristics, where a calculation formula of the error areas is as follows:
（4）
in the method, in the process of the invention,S V for the integration of the discharge voltage,U error in order to account for the voltage-at-the-end error,t end is thatSOCTime to reach 0.5.
As a further limitation of the technical scheme, in S4, a current average value of the dynamic working condition discharging test is extracted as a working condition characteristic reflecting a dynamic working condition difference, and a calculation formula of the current average value is as follows:
（5）
in the method, in the process of the invention,as the mean value of the discharge current,Ti (t) represents a function of the current as a function of time, which is the time of one discharge cycle,t end and the discharge time is the discharge time of the dynamic working condition.
As a further limitation of the present technical solution, in S5, the calculation manner of the fusion feature is:
and performing quadratic polynomial fitting on the error areas and the current average values of the four dynamic working conditions of the initial aging point by adopting quadratic polynomial fitting, wherein the fitting relation is as follows:
（6）
wherein:xas the initial aging point current average value,yfor initial ageing pointThe area of the error is defined by the area of the error,k 0 -k 2 is the coefficient of the polynomial,iis a dynamic working condition;
the fusion characteristics are adopted to describe the difference before and after the battery aging, and the calculation formula is as follows:
（7）
wherein:Fin order to fuse the features of the features,jare different aging points.
As a further limitation of the present technical solution, in S6, an empirical model between the fusion feature and the battery SOH is established, wherein parameters of the model are identified by using a genetic algorithm, and a correlation between the SOH and the fusion feature is described by using an empirical model based on a double exponential function, as shown in formula (8):
（8）
wherein: the SOH is in a state of health,Fin order to fuse the features of the features,b 1 ~b 4 and e is an index, which is a parameter to be identified of the model.
Compared with the prior art, the invention has the beneficial effects that:
1. the SOH estimation model established by the method can accurately estimate the SOH of the battery under the condition of uncertainty of working conditions;
2. the power battery SOH estimation model established by the method can estimate the battery SOH by utilizing the characteristics of the aging characteristics and the working condition characteristics after fusion, and has good precision;
3. the power battery SOH estimation model established by the method does not need a large amount of data for training, and can obtain higher estimation precision by only using the data of a plurality of aging points, so that the calculated amount is small.
Drawings
FIG. 1 is a flow chart of the present invention;
fig. 2 is a diagram of a Thevenin equivalent circuit model;
FIG. 3 is an initial cycle OCV-SOCA variation graph;
FIG. 4 is a graph of initial cycle HPPC test terminal voltage versus;
FIG. 5 is a graph of initial cycle HPPC test terminal voltage error;
FIG. 6 is a graph showing voltage contrast at the test terminal of the initial cycle BJDST;
FIG. 7 is a graph of voltage and error at the test terminal of an initial cycle BJDST;
FIG. 8 is a graph of voltage error for four dynamic conditions at different aging points;
FIG. 9 is a schematic diagram of four dynamic operating mode error area characteristics;
FIG. 10 is a graph showing the error area characteristics of four dynamic conditions at each aging point;
FIG. 11 is a graph showing comparison of error area characteristics for four dynamic conditions;
FIG. 12 is a graph showing the correlation between voltage error and current;
FIG. 13 is a graph of current mean for different aging point dynamic conditions;
FIG. 14 is a graph of the fusion characteristics of four dynamic conditions with aging points;
fig. 15 is a schematic diagram of SOH estimation results and errors.
Detailed Description
The patent of the invention is further described below with reference to fig. 1-15, and as shown in fig. 1, a power battery health state estimation method based on error characteristics of a dynamic working condition model is provided. It is to be understood that these examples are illustrative of the present invention and are not intended to limit the scope of the present invention. Further, it will be understood that various changes or modifications may be made by those skilled in the art after reading the teachings of the invention, and such equivalents are intended to fall within the scope of the invention as defined herein.
The present invention will be further described by taking test data of 1100mAh lithium iron phosphate battery cells manufactured by A123 company as a sample.
As shown in fig. 1, a power battery health state estimation method based on dynamic working condition model error features includes the following steps:
s1, as shown in FIG. 2, constructing a battery model by adopting a Thevenin model, wherein the open circuit of the battery is powered onVoltage OCV, ohmic internal resistanceR o Internal resistance of polarizationR p And polarization capacitorC p Parameters for the constructed battery model;
on the premise of neglecting the influence of temperature on battery model parameters, the ohmic internal resistance of the battery is assumedR o Internal resistance of polarizationR p And polarization capacitorC p And the state of charge of the batterySOC) Is irrelevant;
as shown in fig. 3, OCV and OCV were obtained by OCV testSOCA curvilinear relationship between;
based on the data of the initial aging point (characteristic test of 0 th aging cycle) of the battery (this data is obtained in a school laboratory), a model of the initial cycle of the battery is built as shown in the formulas (1), (2), (3):
（1）
（2）
（3）
wherein: subscript ofkIs the firstkSampling time;U t is the battery terminal voltage;U p is polarization voltage;U oc is an open circuit voltage;zis the state of charge of the battery; coefficients ofa 0 ~a 6 Determined by an off-line OCV experiment; deltatIs the sampling interval;i L the battery working current is positive by taking the discharge.
S2, identifying parameters of the battery model, and identifying 10 parameters by using initial aging point HPPC test data by adopting a genetic algorithmSOCThe identification method of each parameter value under the point is as follows:
initial battery charging and discharging (HPPC) testing methodCharacteristic test of aging points, 10 different samples were obtainedSOCData such as battery voltage, current, temperature and the like under the point;
taking the battery model parameters as optimization variables, taking the mean square error between the voltage output by the battery model and the actually measured voltage as an fitness function, and carrying out optimization solution on the battery model parameters by adopting a genetic algorithm;
output 10SOCCell model parameter values under the points;
the identification results are shown in Table 1
TABLE 1 initial aging Point parameter identification results
| SOC | Ohmic internal resistance Ro/mΩ | Polarization internal resistance Rp/mΩ | Polarization capacitor Cp/F |
| 1 | 30.8 | 77.2 | 129.5 |
| 0.9 | 32.3 | 49.0 | 138.9 |
| 0.8 | 33.4 | 51.1 | 135.4 |
| 0.7 | 34.8 | 53.7 | 136.1 |
| 0.6 | 34.5 | 52.9 | 123.5 |
| 0.5 | 36.2 | 55.9 | 125.1 |
| 0.4 | 34.5 | 59.0 | 117.1 |
| 0.3 | 38.9 | 61.5 | 123.5 |
| 0.2 | 40.5 | 65.6 | 122.6 |
| 0.1 | 42.9 | 71.3 | 127.6 |
The method for verifying the accuracy of the established battery model comprises the following steps:
the HPPC test data of the initial aging point is used as an input signal and is input into the established battery model to perform voltage simulation, so that errors of simulation voltage and measured voltage (a comparison curve and an error curve between simulation voltage and measured voltage) are obtained, and the results are shown in fig. 4 and 5;
in addition, the Beijing city driving condition (BJDST) test data is used as an input signal and is input into the established battery model to perform voltage simulation, so as to obtain errors of simulation voltage and actual measurement voltage (a comparison curve and an error curve between the simulation voltage and the actual measurement voltage), and the results are shown in fig. 6 and 7;
the comparison curve and the error curve show that the variation trend of the simulation voltage and the actual voltage is basically consistent, the distribution of the voltage errors is uniform, and the established battery model is proved to have good precision.
S3, adopting the error area as an aging characteristic of the estimated battery; inputting dynamic working condition test data under each aging point into a battery model to obtain voltage errors of the model, extracting error areas of different working conditions under each aging point as aging characteristics, wherein a calculation formula of the error areas is as follows:
（4）
in the method, in the process of the invention,S V for the integration of the discharge voltage,U error in order to account for the voltage-at-the-end error,t end is thatSOCTime to reach 0.5.
Since the parameters of the battery model remain unchanged in the modeling process, the performance of the battery can change along with the aging of the battery, and therefore, the accuracy of the battery model can be reduced along with the aging of the battery, and the battery model is verified by the following method:
aging data (including an initial aging point and a plurality of subsequent aging points) of the lithium iron phosphate battery subjected to characteristic test at 25 ℃;
the test data of four dynamic working conditions (BJDST, DST, FUDS, UDDS) at each aging point are used as input signals and are input into the established battery model, voltage simulation is carried out, voltage errors of the model, namely, differences between simulation voltage and actual measurement voltage are obtained, an error curve is drawn, and the voltage errors of the BJDST, DST, FUDS, UDDS four dynamic working conditions at different aging points are shown in fig. 8;
DST is dynamic stress regime (Dynamic Stress Test, DST); FUDS is federal city and highway cycling conditions (Federal Urban Driving Schedule, FUDS); the UDDS is city dynamic driving circulation working condition (Urban Dynamometer Driving Schedule, UDDS);
as can be seen from the error map, the voltage error obtained by simulation of the battery model under the four dynamic working conditions gradually increases along with the aging of the battery, which indicates that a negative correlation exists between the voltage error and the battery SOH, that is, along with the gradual aging of the battery, when the current voltage data of each aging point is input into the battery model, the voltage error of the model gradually increases.
In the invention, the relation extraction method between the voltage error and the battery aging is as follows:
selecting one of voltage error curves of four dynamic working conditions under each aging pointSOCThe interval is taken as the range of feature extraction, and the invention selects [0.35, 0.5]As the section, within the sectionSOCThe values are obtained from the battery modelSOCThe calculation module is calculated according to an ampere-hour integration method, and is shown as a formula (9):
（9）
wherein: subscript ofkIs the firstkSampling time;ηthe charge and discharge efficiency is 1;Q o for initial cycling battery capacity, due toQ o The battery is kept unchanged, and as the battery ages,SOCthe calculation error of (c) will be gradually increased,SOCin the form of a state of charge,SOC k is thatkThe state of charge at the moment.
At the selected positionSOCIn the interval, taking the error of-0.05V as a reference baseline, calculating the area between a voltage error curve and the reference baseline, and taking the area as a characteristic value of the voltage error, as shown in FIG. 9;
extracting error areas of different working conditions under each aging point as aging characteristics, and drawing a graph, wherein the result is shown in fig. 10;
the analysis graph can find that the characteristic values of the error area under four dynamic working conditions gradually increase along with the aging of the battery, so that a negative correlation relationship exists between the characteristic values of the error area and the state of health (SOH) of the battery, namely, the error area gradually increases along with the aging of the battery.
S4, adopting a current average value of a dynamic working condition as a working condition characteristic, wherein a calculation formula of the current average value is as follows:
（5）
in the method, in the process of the invention,as the mean value of the discharge current,Ti (t) represents a function of the current as a function of time, which is the time of one discharge cycle,t end and the discharge time is the discharge time of the dynamic working condition.
Extracting a current mean value of the dynamic working condition discharge test as a working condition characteristic value reflecting the dynamic working condition difference, and drawing a graph as shown in fig. 13;
comparison with fig. 11 shows that there is an error area and a current average; under the correlation, namely the same aging point, the larger the current average value of the dynamic working condition is, the larger the error area is;
the analysis graph 13 can find that the current average value of the same dynamic working condition under different aging points is basically the same, and the different dynamic working conditions have differences, so that the current average value can be used as the working condition characteristic value of the dynamic working condition;
as shown in fig. 12, the DST working condition test is taken as an example to analyze the correlation between the voltage error and the dynamic working condition current, extract the voltage error corresponding to a part of the current segments, compare and find that the magnitude of the voltage error changes along with the change of the current, and have approximately the same change trend.
S5: describing the difference of the battery before and after aging by adopting fusion characteristics;
in order to more reliably estimate SOH of the battery and fuse the aging characteristic value and the working condition characteristic value, the invention adopts a quadratic polynomial fitting method to fit the error areas and the current average values of four dynamic working conditions of the initial aging point, and a fitting relation formula is shown in a formula (6);
（6）
wherein:xas the initial aging point current average value,yfor the initial burn-in point error area,k 0 -k 2 is the coefficient of the polynomial,ifour dynamic conditions are adopted.
Then carrying out feature fusion according to the formula (7) to obtain a fusion feature value, and drawing a graph as shown in fig. 14;
（7）
wherein:Fin order to fuse the features of the features,jare different aging points.
By analyzing the graph 14, it can be found that the fusion characteristic values under different dynamic conditions are basically the same under the same aging point, which indicates that the fusion characteristic values can eliminate the difference between the aging characteristic values under different dynamic conditions.
S6, establishing an empirical model between the fusion characteristics and the SOH of the battery;
the invention adopts an empirical model based on a double-exponential function to describe the correlation between SOH and fusion characteristics, as shown in a formula (8):
（8）
wherein: the SOH is in a state of health,Fin order to fuse the features of the features,b 1 ~b 4 and e is an index, which is a parameter to be identified of the model.
Parameters of an empirical model are identified by adopting a genetic algorithm and recorded in table 2, an empirical model between fusion characteristics and a battery SOH is established, and the identification result of the model parameters is shown in table 2:
TABLE 2 model parameters to be identified results
| Parameters (parameters) | b1 | b2 | b3 | b4 |
| Results | 0.6902 | -0.0197 | 1.6572 | -1.6784 |
The fusion characteristics of the four dynamic working conditions under each aging point are put into an empirical model for verification, and are drawn into a graph, and the result is shown in fig. 15;
according to the analysis graph, the battery SOH can be estimated better by constructing an empirical model with the battery SOH by utilizing the fusion characteristic, the estimation error is mostly within 1% under four working conditions, the root mean square error of the model is 1.165%, and the average absolute error is only 0.802%.
The result shows that the SOH of the battery can be accurately estimated according to the condition of working condition uncertainty by extracting the fusion characteristic constructed by the aging characteristic and the working condition characteristic.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation. The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The examples are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (7)
1. The power battery health state estimation method based on the dynamic working condition model error characteristics is characterized by comprising the following steps of: the method comprises the following steps:
s1, constructing a battery model by adopting a Thevenin model;
s2, identifying parameters of the battery model;
s3, adopting the error area as an aging characteristic of the estimated battery;
s4, adopting a current average value of a dynamic working condition as a working condition characteristic;
s5, describing the difference of the battery before and after aging by adopting fusion characteristics;
s6, establishing an empirical model between the fusion characteristic and the battery SOH.
2. The power battery health state estimation method based on the dynamic working condition model error characteristics according to claim 1, wherein the method comprises the following steps: the battery model of the initial cycle of the battery in the S1 is shown in the formulas (1), (2) and (3):
（1）
（2）
（3）
wherein: subscript ofkIs the firstkSampling time;U t is the battery terminal voltage;U oc is an open circuit voltage;zis the state of charge of the battery;U p is polarization voltage; coefficients ofa 0 ~a 6 Determined by an off-line OCV experiment; deltatIs the sampling interval;i L the battery working current is positive by taking the discharge.
3. The power battery health state estimation method based on the dynamic working condition model error characteristics according to claim 1, wherein the method comprises the following steps: in the S2, a genetic algorithm is adopted to identify parameters of the internal parameters of the initial aging point of the battery, and when a battery analysis model is established by using the Thevenin equivalent circuit model, the parameters to be identified comprise open circuit voltage OCV and ohmic internal resistanceR o Internal resistance of polarizationR p And polarization capacitorC p Wherein OCV is equal toSOCThe relationship between them is obtained by an incremental OCV test,R o 、R p andC p and (3) withSOCThe relationship between the two parameters is obtained by identifying the parameters of the HPPC test data.
4. The power battery health state estimation method based on the dynamic working condition model error characteristics according to claim 1, wherein the method comprises the following steps: in the step S3, the dynamic working condition test data under each aging point is input into a battery model to obtain a voltage error of the model, error areas of different working conditions under each aging point are extracted to serve as aging characteristics, and a calculation formula of the error areas is as follows:（4）
in the method, in the process of the invention,S V for the integration of the discharge voltage,U error in order to account for the voltage-at-the-end error,t end is thatSOCTime to reach 0.5.
5. The power battery health state estimation method based on the dynamic working condition model error characteristics according to claim 1, wherein the method comprises the following steps: in the step S4, extracting a current average value of the dynamic working condition discharge test as a working condition characteristic reflecting the dynamic working condition difference, wherein a calculation formula of the current average value is as follows:
（5）
in the method, in the process of the invention,as the mean value of the discharge current,Ti (t) represents a function of the current as a function of time, which is the time of one discharge cycle,t end and the discharge time is the discharge time of the dynamic working condition.
6. The power battery health state estimation method based on the dynamic working condition model error characteristics according to claim 1, wherein the method comprises the following steps: in the step S5, the fusion feature is calculated in the following manner:
and performing quadratic polynomial fitting on the error areas and the current average values of the four dynamic working conditions of the initial aging point by adopting quadratic polynomial fitting, wherein the fitting relation is as follows:
（6）
wherein:xas the initial aging point current average value,yfor the initial burn-in point error area,k 0 -k 2 is the coefficient of the polynomial,iis a dynamic working condition;
the fusion characteristics are adopted to describe the difference before and after the battery aging, and the calculation formula is as follows:
（7）
wherein:Fin order to fuse the features of the features,jare different aging points.
7. The power battery health state estimation method based on the dynamic working condition model error characteristics according to claim 1, wherein the method comprises the following steps: in S6, an empirical model between the fusion feature and the battery SOH is established, wherein parameters of the model are identified by adopting a genetic algorithm, and correlation between the SOH and the fusion feature is described by adopting an empirical model based on a double-exponential function, as shown in formula (8):
（8）
wherein: the SOH is in a state of health,Fin order to fuse the features of the features,b 1 ~b 4 and e is an index, which is a parameter to be identified of the model.
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