CN110045288A - A kind of capacity of lithium ion battery On-line Estimation method based on support vector regression - Google Patents
A kind of capacity of lithium ion battery On-line Estimation method based on support vector regression Download PDFInfo
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- CN110045288A CN110045288A CN201910435035.0A CN201910435035A CN110045288A CN 110045288 A CN110045288 A CN 110045288A CN 201910435035 A CN201910435035 A CN 201910435035A CN 110045288 A CN110045288 A CN 110045288A
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- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 title claims abstract description 42
- 229910001416 lithium ion Inorganic materials 0.000 title claims abstract description 42
- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000007600 charging Methods 0.000 claims abstract description 44
- 238000012360 testing method Methods 0.000 claims abstract description 29
- 238000001228 spectrum Methods 0.000 claims abstract description 17
- 238000010219 correlation analysis Methods 0.000 claims abstract description 12
- 125000004122 cyclic group Chemical group 0.000 claims abstract description 6
- 230000007850 degeneration Effects 0.000 claims abstract description 5
- 230000006866 deterioration Effects 0.000 claims description 19
- 238000011156 evaluation Methods 0.000 claims description 8
- 230000015556 catabolic process Effects 0.000 claims description 6
- 238000006731 degradation reaction Methods 0.000 claims description 6
- 238000005259 measurement Methods 0.000 claims description 5
- 238000007599 discharging Methods 0.000 claims description 4
- 238000002474 experimental method Methods 0.000 claims description 4
- 239000000284 extract Substances 0.000 claims description 4
- 230000005611 electricity Effects 0.000 claims description 2
- 239000000203 mixture Substances 0.000 claims description 2
- 229910017435 S2 In Inorganic materials 0.000 claims 1
- 238000000605 extraction Methods 0.000 claims 1
- 238000010183 spectrum analysis Methods 0.000 abstract 1
- 230000000875 corresponding effect Effects 0.000 description 4
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 2
- 238000012417 linear regression Methods 0.000 description 2
- 229910052744 lithium Inorganic materials 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 230000032683 aging Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 150000002500 ions Chemical class 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000002028 premature Effects 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
Classifications
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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
-
- 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
Abstract
The capacity of lithium ion battery On-line Estimation method based on support vector regression that the present invention relates to a kind of.Method includes the following steps: S1. carries out cycle life test to lithium ion battery, corresponding direct current equivalent internal resistance spectrum is obtained；S2. the potential health factor for being able to reflect performance of lithium ion battery degeneration is extracted based on direct current equivalent internal resistance spectrum, and correlation analysis is carried out to it；S3. capacity estimation model is constructed based on support vector regression algorithm；S4. the current charge data of battery to be estimated and charging equivalent internal resistance curve are obtained；S5. according to the capacity estimation model established, the current capacities of battery are determined by the health factor parameter value extracted.Present method solves the capacity On-line Estimation problem of lithium ion battery under different state of cyclic operation, estimated accuracy is high and adaptable.
Description
Technical field
This application involves battery management and battery status analysis field, it is especially a kind of based on the lithium of support vector regression from
Sub- battery capacity On-line Estimation method.
Background technique
Lithium ion battery due to its energy density is high, have extended cycle life, be highly-safe the features such as, be widely used in electronic
Automotive field.With the increase of lithium ion battery cycle-index in use, various aspects external behavior will appear deterioration,
It is embodied in available capacity is reduced, charge and discharge internal resistance increases etc..
The capacity estimation of lithium ion battery is one of key problem of battery management, judge the current deterioration state of battery,
Preestimating battery remaining life avoids battery premature failure etc. and is all of great significance.However, in practical applications,
The capacity of battery is generally full full of the off-line test method put acquisition by carrying out to battery, can not be directed to the real time capacity of battery
Variation is updated.
Summary of the invention
In view of the above-mentioned problems, the present invention provides a kind of capacity of lithium ion battery On-line Estimation side based on support vector regression
Method, by obtaining battery charging equivalent internal resistance value online, as the input of capacity estimation model, so that real-time estimation battery has
Imitate capacity.
Technical solution of the present invention the following steps are included:
S1. cycle life test is carried out to lithium ion battery, obtains corresponding direct current equivalent internal resistance spectrum.
(1) cyclic charging and discharging test is carried out to lithium ion battery.The experiment condition for setting cycle life test, utilizes fixation
Environment temperature and fixed charge-discharge magnification, carry out uninterrupted cycle charge discharge to battery.
(2) lithium ion battery is evaluated and tested.After every circulation a period of time, pause circulation, and capacity is carried out to battery and is commented
It surveys and charging equivalent internal resistance evaluation and test, acquisition battery available capacity value, charging equivalent internal resistance curve.Wherein, charge equivalent internal resistance
It is calculated by following formula:
(1)
Wherein,For end voltage of the battery when charging carry load work, referred to as charging work voltage,It is battery abundant
End voltage after shelving, referred to as charging open-circuit voltage,For the size of current real-time monitored in charging process.
(3) judge the termination condition of cycle life test.NoteFor the nominal capacity of battery.If battery available capacity value
With the ratio of nominal capacityLess than 0.7, then stop battery cycle life test, otherwise, return step (1) continues to test.
By the cyclic charging and discharging test of step (1), make lithium ion battery accelerated deterioration, is surveyed by the evaluation and test of step (2)
Examination obtains available capacity value and charging equivalent internal resistance spectrum under corresponding degradation, wherein charging equivalent internal resistance spectrum is abscissa
For battery charge state, ordinate is the curve of charging equivalent internal resistance.By completing cycle life test, obtain by a plurality of charging
The lithium ion battery Life cycle internal resistance pedigree column of equivalent internal resistance curve composition.
S2. the potential health factor for being able to reflect performance of lithium ion battery degeneration is extracted based on direct current equivalent internal resistance spectrum, it is right
It carries out correlation analysis.
(1) method for utilizing linear interpolation extracts battery under different deterioration states every 10% since state-of-charge is 0
The charging equivalent internal resistance of sample obtains 9 potential health factors.
(2) calculus of differences is done to charging equivalent internal resistance and state-of-charge, obtains charging equivalent internal resistance incremental rate curve, extracts not
With the internal resistance increment peak value and its peak point of battery sample under deterioration state, 2 potential health factors are obtained.
(3) capacity attenuation amount is selectedAs the measurement index of deterioration of battery degree, capacity attenuation is defined as follows:
(2)
(3)
Wherein,For the nominal capacity of battery,The available capacity value for being battery under certain circulation,To characterize battery
The measurement index of deterioration state.
(4) 11 potential health factors using Spearman related coefficient to above-mentioned steps (1), (2) acquisition and deterioration
IndexDo correlation analysis.
S3. capacity estimation model is constructed based on support vector regression algorithm.
According to the correlation analysis in step S2 as a result, choosing health factor of the related coefficient greater than 0.8 as input quantity,
The available capacity value under corresponding degradation is chosen as output quantity, using the non-of support vector regression algorithm building capacity estimation
Linear regression model (LRM).
S4. the current charge data of battery to be estimated and charging equivalent internal resistance curve, used charging method are obtained
It is identical as in step S1.
S5. the capacity estimation model established according to step S3, determines the current of battery by the health factor parameter value extracted
Capacity.
According to the nonlinear regression model (NLRM) that step S3 is established, using the health factor parameter value that step S4 is extracted as input
Amount calculates the available capacity value for obtaining output, the as current capacities of battery by model.
Beneficial effects of the present invention: it establishes compared with prior art, in the method for the present invention a kind of based on supporting vector time
Reduction method carries out capacity modeling to the lithium ion battery under different state of cyclic operation, realizes health state of lithium ion battery
On-line Estimation；Support vector regression is the special machine learning for solving finite sample situation compared with other algorithms, while can be with
It solves the problems, such as higher-dimension, has the advantages that low computation complexity, low extensive error, is easy to explain.The present invention can be not required to it is to be understood that electricity
In the case where the bulk properties of pond, the monitoring of ageing state, operability can be only carried out by the external behavior that battery is showed
By force.
Detailed description of the invention
Present invention will be further explained below with reference to the attached drawings and examples:
Fig. 1 is overall flow figure of the invention；
Fig. 2 is the experiment condition of cycle life test；
Fig. 3 is the direct current equivalent internal resistance spectrum of lithium ion battery；
Fig. 4 is the direct current equivalent internal resistance incremental rate curve of lithium ion battery；
Fig. 5 is the estimated result that capacity estimation model is established using support vector regression algorithm；
Fig. 6 is using Spearman related coefficient to 11 potential health factors and deterioration indexCarry out correlation analysis table
Lattice；
Fig. 7 is capacity of lithium ion battery estimation model performance evaluation index table.
Specific embodiment
A kind of capacity of lithium ion battery On-line Estimation method based on support vector regression provided by the invention, overall flow
Figure is as shown in Figure 1.According to an embodiment of the invention, specifically includes the following steps:
S1. cycle life test is carried out to lithium ion battery, obtains corresponding direct current equivalent internal resistance spectrum.
When carrying out cycle life test, charge-discharge magnification, discharge range, depth of discharge and temperature condition are controlled, it is right
Battery carries out uninterrupted cycle charge discharge.Sample under same type lithium ion battery difference operating condition, charge-discharge magnification be provided with 0.5C,
1C；Depth of discharge is provided with 0.30,0.60；Discharge range is set as upper section (average SOC be 85%), (average SOC is in middle section
And lower section (average SOC be 30%) 50%)；Temperature condition is set as 40 DEG C.Specific experiment condition setting is as shown in Figure 2.
After every circulation a period of time, pause circulation, and capacity evaluation and test and charging equivalent internal resistance evaluation and test are carried out to battery, it obtains
Battery available capacity value, charging equivalent internal resistance curve.Due to lithium ion batteryWhen less than 0.7, performance has been unsatisfactory for
The use demand of electric car.Therefore, whenIt, can end loop life test when less than 0.7, whereinFor battery
Nominal capacity.
It is evaluated and tested by internal charging resistance, available equivalent internal resistance spectrum, internal resistance spectrum is reflected with cell degradation, and battery is equivalent
Internal resistance can be also gradually increased, as shown in Figure 3.
The health factor that battery can be extracted based on equivalent internal resistance spectrum, including the internal resistance under different SOC, internal resistance increment peak value
And peak point.Fig. 4 is the embodiment of internal resistance increment peak value and peak point in battery difference deterioration state.
S2. the potential health factor for being able to reflect performance of lithium ion battery degeneration is extracted based on direct current equivalent internal resistance spectrum, it is right
It carries out correlation analysis.
In this step, first with the method for linear interpolation, different deterioration state battery samples are extracted with the interval 10%SOC
This Equivalent DC internal resistance obtains 9 potential health factors altogether；Calculus of differences is done to internal resistance and SOC and obtains internal resistance incremental rate curve,
The internal resistance increment peak value and its peak point of different deterioration state battery samples are extracted, which obtains 2 potential health factors；Choosing
Select measurement index of the capacity attenuation as deterioration of battery degree；It is potential to 11 of above-mentioned acquisition using Spearman related coefficient
Health factor and capacity attenuationCorrelation analysis is done, as shown in Figure 6.
S3. according in step S2 correlation analysis as a result, relevant to capacity health factor is chosen, based on supporting vector
Regression algorithm constructs capacity estimation model using battery training sample.
In this example, selection related coefficient is greater than 0.8 feature: R (SOC=0.7, R (SOC=0.8), internal resistance increment peak
Value and input of the peak point as model.Support vector regression is the special machine for solving finite sample situation compared with other algorithms
Device study, while can solve higher-dimension problem, has the advantages that low computation complexity, low extensive error, is easy to explain, meanwhile, lithium
The degradation of ion battery is different from the internal resistance under working environment and changes non-linear relationship, is calculated using support vector regression
Method can be being not required to it is to be understood that inside battery characteristic variations in the case wheres obtains the deterioration condition of lithium ion battery.
S4. the current charge data of battery to be estimated and charging equivalent internal resistance curve, used charging method are obtained
It is identical as in step S1.
S5. the capacity estimation model established according to step S3, determines the current of battery by the health factor parameter value extracted
Capacity.Fig. 5 is support vector regression result of the sample as test set for choosing 70% sample as training set 30%.Fig. 7 is
Capacity of lithium ion battery estimates model performance evaluation index, and correlated error is less, and estimated accuracy is high, this estimation method is practical can
It leans on.
Claims (6)
1. a kind of capacity of lithium ion battery On-line Estimation method based on support vector regression, it is characterised in that including following step
It is rapid:
S1. cycle life test is carried out to lithium ion battery, obtains corresponding direct current equivalent internal resistance spectrum；
S2. based on direct current equivalent internal resistance spectrum extract be able to reflect performance of lithium ion battery degeneration potential health factor, to its into
Row correlation analysis；
S3. capacity estimation model is constructed based on support vector regression algorithm；
S4. the current charge data of battery to be estimated and charging equivalent internal resistance curve, used charging method and step are obtained
It is identical in rapid S1；
S5. the capacity estimation model established according to step S3, the current appearance of battery is determined by the health factor parameter value extracted
Amount.
2. a kind of capacity of lithium ion battery On-line Estimation method based on support vector regression according to claim 1,
It is characterized in that: cycle life test being carried out to lithium ion battery in the step S1, obtain corresponding direct current equivalent internal resistance spectrum
Specific steps include:
(1) cyclic charging and discharging test is carried out to lithium ion battery, the experiment condition of setting cycle life test utilizes fixed environment
Temperature and fixed charge-discharge magnification, carry out uninterrupted cycle charge discharge to battery；
(2) lithium ion battery is evaluated and tested, after every circulation a period of time, pause circulation, and to battery carry out capacity evaluation and test with
The equivalent internal resistance that charges is evaluated and tested, and battery available capacity value is obtained, charging equivalent internal resistance curve, wherein charging equivalent internal resistance by such as
Lower formula calculates:
(1)
Wherein,For end voltage of the battery when charging carry load work, referred to as charging work voltage,It is being filled for battery
Divide the end voltage after shelving, referred to as charging open-circuit voltage,For the size of current real-time monitored in charging process；
(3) judge the termination condition of cycle life test, noteFor the nominal capacity of battery, if battery available capacity value and nominal
The ratio of capacityLess than 0.7, then stop battery cycle life test, otherwise, return step (1) continues to test；
By the cyclic charging and discharging test of step (1), make lithium ion battery accelerated deterioration, is tested, obtained by the evaluation and test of step (2)
Available capacity value and charging equivalent internal resistance spectrum under corresponding degradation, wherein charging equivalent internal resistance spectrum is abscissa for electricity
Pond state-of-charge, ordinate are the curve of charging equivalent internal resistance；By completing cycle life test, obtain equivalent by a plurality of charging
The lithium ion battery Life cycle internal resistance pedigree column of internal drag curve composition.
3. a kind of capacity of lithium ion battery On-line Estimation method based on support vector regression according to claim 1,
It is characterized in that: being extracted in the step S2 based on direct current equivalent internal resistance spectrum and be able to reflect the potential strong of performance of lithium ion battery degeneration
Kang Yinzi includes: to its specific steps for carrying out correlation analysis
(1) method for utilizing linear interpolation extracts battery sample under different deterioration states every 10% since state-of-charge is 0
Charging equivalent internal resistance, obtain 9 potential health factors；
(2) calculus of differences is done to charging equivalent internal resistance and state-of-charge, obtains charging equivalent internal resistance incremental rate curve, extracted different bad
The internal resistance increment peak value and its peak point of battery sample under change state obtain 2 potential health factors；
(3) capacity attenuation amount is selectedAs the measurement index of deterioration of battery degree, capacity attenuation is defined as follows:
(2)
(3)
Wherein,For the nominal capacity of battery,The available capacity value for being battery under certain circulation,To characterize battery
The measurement index of deterioration state；
(4) using Spearman related coefficient to the 11 potential health factors and deterioration index of above-mentioned steps (1), (2) acquisitionDo correlation analysis.
4. a kind of capacity of lithium ion battery On-line Estimation method based on support vector regression according to claim 1,
It is characterized in that: the step of capacity estimation model is constructed based on support vector regression algorithm in the step S3 are as follows: according to step S2
In correlation analysis as a result, choose related coefficient greater than 0.8 health factor as input quantity, choose under corresponding degradation
Available capacity value as output quantity, using the nonlinear regression model (NLRM) of support vector regression algorithm building capacity estimation.
5. a kind of capacity of lithium ion battery On-line Estimation method based on support vector regression according to claim 1,
It is characterized in that: obtaining the current charge data of battery to be estimated and charging equivalent internal resistance curve in the step S4, used
Charging method and charging equivalent internal resistance formula it is identical as in step S1.
6. a kind of capacity of lithium ion battery On-line Estimation method based on support vector regression according to claim 1,
It is characterized in that: the specific steps of the step S5 are as follows: according to the nonlinear regression model (NLRM) that step S3 is established, with step S4 extraction
Health factor parameter value as input quantity, pass through model and calculate the available capacity value for obtaining output, the as current appearance of battery
Amount.
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Cited By (9)
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| CN111142036A (en) * | 2019-12-18 | 2020-05-12 | 同济大学 | Lithium ion battery online rapid capacity estimation method based on capacity increment analysis |
| CN111443294A (en) * | 2020-04-10 | 2020-07-24 | 华东理工大学 | Method and device for indirectly predicting remaining life of lithium ion battery |
| CN112269137A (en) * | 2020-10-19 | 2021-01-26 | 中山大学 | Battery health state estimation method based on dynamic parameter identification |
| CN113109715A (en) * | 2021-03-29 | 2021-07-13 | 东华大学 | Battery health condition prediction method based on feature selection and support vector regression |
| CN113158947A (en) * | 2021-04-29 | 2021-07-23 | 重庆长安新能源汽车科技有限公司 | Power battery health scoring method, system and storage medium |
| WO2021226797A1 (en) * | 2020-05-11 | 2021-11-18 | 东莞新能德科技有限公司 | Battery capacity estimation method, electronic device and storage medium |
| CN113743541A (en) * | 2021-11-04 | 2021-12-03 | 华中科技大学 | Method for predicting residual life of marine power system bearing based on degradation mode |
| CN116699412A (en) * | 2023-05-17 | 2023-09-05 | 盐城工学院 | Residual capacity estimation method of energy storage battery module |
| CN117092541A (en) * | 2023-08-23 | 2023-11-21 | 苏州吉智能源科技有限公司 | Analysis method for calculating battery health by direct-current charging big data |
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Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111142036A (en) * | 2019-12-18 | 2020-05-12 | 同济大学 | Lithium ion battery online rapid capacity estimation method based on capacity increment analysis |
| CN111443294A (en) * | 2020-04-10 | 2020-07-24 | 华东理工大学 | Method and device for indirectly predicting remaining life of lithium ion battery |
| CN111443294B (en) * | 2020-04-10 | 2022-09-23 | 华东理工大学 | Method and device for indirectly predicting remaining life of lithium ion battery |
| WO2021226797A1 (en) * | 2020-05-11 | 2021-11-18 | 东莞新能德科技有限公司 | Battery capacity estimation method, electronic device and storage medium |
| CN112269137A (en) * | 2020-10-19 | 2021-01-26 | 中山大学 | Battery health state estimation method based on dynamic parameter identification |
| CN113109715A (en) * | 2021-03-29 | 2021-07-13 | 东华大学 | Battery health condition prediction method based on feature selection and support vector regression |
| CN113109715B (en) * | 2021-03-29 | 2022-07-05 | 东华大学 | Battery health condition prediction method based on feature selection and support vector regression |
| CN113158947A (en) * | 2021-04-29 | 2021-07-23 | 重庆长安新能源汽车科技有限公司 | Power battery health scoring method, system and storage medium |
| CN113158947B (en) * | 2021-04-29 | 2023-04-07 | 重庆长安新能源汽车科技有限公司 | Power battery health scoring method, system and storage medium |
| CN113743541A (en) * | 2021-11-04 | 2021-12-03 | 华中科技大学 | Method for predicting residual life of marine power system bearing based on degradation mode |
| CN113743541B (en) * | 2021-11-04 | 2022-02-08 | 华中科技大学 | Method for predicting residual life of marine power system bearing based on degradation mode |
| CN116699412A (en) * | 2023-05-17 | 2023-09-05 | 盐城工学院 | Residual capacity estimation method of energy storage battery module |
| CN117092541A (en) * | 2023-08-23 | 2023-11-21 | 苏州吉智能源科技有限公司 | Analysis method for calculating battery health by direct-current charging big data |
| CN117092541B (en) * | 2023-08-23 | 2024-04-30 | 苏州吉智能源科技有限公司 | Analysis method for calculating battery health by direct-current charging big data |
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