CN108717166A - A kind of method and device of the lithium battery capacity estimation of view-based access control model cognition - Google Patents
A kind of method and device of the lithium battery capacity estimation of view-based access control model cognition Download PDFInfo
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Abstract
The invention discloses a kind of method and devices of the lithium battery capacity estimation of view-based access control model cognition, are related to battery capacity estimation technical field, method includes：Based on non-down sampling contourlet transform and Laplacian Eigenmap method, to by acquired each cycle during battery cycle life electric current and after the visual cognition image that converts of voltage data handles, estimate and obtain battery capacity.Present invention firstly provides visual cognition is applied to battery capacity estimation field, battery capacity estimation can be carried out only with battery charge or discharge voltage value, compares existing method precision higher, more effectively.
Description
Technical field
The present invention relates to battery capacity estimation technical fields, and in particular to a kind of lithium battery capacity of view-based access control model cognition is estimated
The method and device of meter.
Background technology
Battery capacity is considered as an important indicator of battery performance, and by environment temperature, aging and occupation mode etc. are more
The inside and outside mechanism of kind is affected, these factors cause battery performance gradually to fail with the time.Therefore, available battery capacity
It needs accurately to estimate, to ensure that reliability and rational battery use management.
The method for having researched and proposed a variety of estimation battery capacities in recent years.Existing most methods are all based on model
Method, including electrochemistry, equivalent circuit and analytic modell analytical model.These models are based primarily upon the complicated object for considering battery dynamic characteristic
Reason and chemical process, assessment performance are highly dependent on model accuracy.Complete battery electrochemical parameter especially can not be being obtained,
When degradation mechanism and attribute, this class model is difficult often to set up.Moreover, these models are only applicable to production process, electrolysis
The identical concrete type battery of matter, anode and cathode material.Based on state-of-charge (SOC) and open-circuit voltage (OCV) to electricity
The method of pond cycle period capacity estimation is widely used in many practical applications.However, the method based on SOC-OCV relies on
Accurate SOC value and OCV values, it is accurate to obtain what SOC value and OCV values typically took very much.Either with which kind of modeling method
Carry out simulated battery state, it is battery performance information that the battery under the conditions of the different operation that laboratory determines, which is charged and discharged characteristic,
Source.In some applications, these are used to create the charged state about main battery with the initial data that centrifugal pump is sought out
Lookup table database.However, carrying out capacity of lithium ion battery estimation in this way, need in their entire life various
It is tested under operating condition to obtain the battery capacity under different use conditions and different service life states.Otherwise, in database
In the case of coarse, the method precision based on database is relatively low.It is based on Database Similarity recognition methods in the prior art, although
Very high accuracy is reached, but to have taken a significant amount of time the most like curve found and include in database and limit its reality
Using.
Invention content
The technical issues of scheme provided according to embodiments of the present invention solves is to solve in the prior art to battery capacity
Evaluation method is cumbersome and of high cost.
A kind of method of the lithium battery capacity estimation of view-based access control model cognition provided according to embodiments of the present invention, including：
By being adopted to the charging current in each charging process of lithium battery or the discharge voltage in each discharge process
Collection, obtains the charging current data set in each charging process or the discharge voltage data set in each discharge process；
By carrying out data arrangement processing to the charging current data set or the discharge voltage data set, formed
Charging current two dimensional image or discharge voltage two dimensional image；
Utilize NSCT (the Non-subsampled contourlet transform, under non-of view-based access control model multichannel characteristic
Sampled contour wave conversion) multi-resolution decomposition is carried out to the charging current two dimensional image or discharge voltage two dimensional image, obtain height
Tie up charging current feature vector or higher-dimension discharge voltage feature vector；
It is right using the LE (Laplacian eigenmaps, laplacian eigenmaps) of view-based access control model manifold perception characteristics
The higher-dimension charging current feature vector or higher-dimension discharge voltage feature vector carry out dimension-reduction treatment, obtain lying in low-dimensional charging
Low-dimensional charging current degenerative character in the intrinsic manifold of electric current lies in low-dimensional electric discharge in the intrinsic manifold of low-dimensional discharge voltage
Voltage degradation feature；
The lithium battery is held using the low-dimensional charging current degenerative character or the low-dimensional discharge voltage degenerative character
Amount is estimated.
Preferably, described by the charging current in each charging process of lithium battery or the electric discharge in each discharge process
Voltage is acquired, and obtains the charging current data set in each charging process or the discharge voltage number in each discharge process
Include according to set：
When being acquired to the charging current in each charging process of lithium battery, rejecting abnormalities charging current data and perseverance
The charging current data in constant-current charge stage；Or
When being acquired to the discharge voltage in each discharge process of lithium battery, electric discharge early stage sensitive electric discharge electricity is rejected
Press the discharge voltage data of data and latter stage voltage recovery of discharging.
Preferably, described by carrying out data row to the charging current data set or the discharge voltage data set
Column processing, forms charging current two dimensional image or discharge voltage two dimensional image includes：
Select maximum charging current data and minimum charge current data from the charging current data set, or from
Maximum discharge voltage data and minimum discharge voltage data are selected in the discharge voltage data set；
Using the maximum charging current data and the minimum charge current data, the charging current data is calculated
The normallized current value of each charging current data in set, or utilize the maximum discharge voltage data and the minimum electric discharge
Voltage data calculates the normalized voltage value of each discharge voltage data in the discharge voltage data set；
Data arrangement processing is carried out by regarding each normallized current value as pixel value, forms charging current two dimension
Image, or data arrangement processing is carried out by regarding each normalized voltage value as pixel value, form discharge voltage two dimension
Image.
Preferably, the NSCT using view-based access control model multichannel characteristic is to the charging current two dimensional image or electric discharge electricity
It presses two dimensional image to carry out multi-resolution decomposition, obtains higher-dimension charging current feature vector or higher-dimension discharge voltage feature vector includes：
Hierarchical level using the NSCT of view-based access control model multichannel characteristic is 2 grades, the Directional Decomposition series on two-stage scale
It is chosen for { 1,2 } respectively, multi-resolution decomposition is carried out to the charging current two dimensional image, obtains 1 low frequency sub-band and 6 high frequencies
Subband, and 1 low frequency sub-band Coefficient Mean and variance yields and 6 high-frequency sub-bands coefficient energy value are chosen, it obtains
The charging current feature vector of 8 dimensions；Or
Hierarchical level using the NSCT of view-based access control model multichannel characteristic is 2 grades, the Directional Decomposition series on two-stage scale
It is chosen for { 1,2 } respectively, multi-resolution decomposition is carried out to the discharge voltage two dimensional image, obtains 1 low frequency sub-band and 6 high frequencies
Subband, and 1 low frequency sub-band Coefficient Mean and variance yields and 6 high-frequency sub-bands coefficient energy value are chosen, it obtains
The discharge voltage feature vector of 8 dimensions.
Preferably, described to utilize the low-dimensional charging current degenerative character or the low-dimensional discharge voltage degenerative character to institute
State lithium battery capacity carry out estimation include：
Determine respectively in the intrinsic manifold constructed by the low-dimensional charging current degenerative character initial point and last point it
Between geodesic distance geoEOLAnd the geodesic distance geo between initial point and other pointss；Or really described respectively determine low-dimensional and put
Geodesic distance geo in intrinsic manifold constructed by piezoelectric voltage degenerative character between initial point and last pointEOLAnd initial point
Geodesic distance geo between other pointss；
Utilize the geoEOLWith the geosThe lithium battery capacity is estimated.
A kind of device of the lithium battery capacity estimation of view-based access control model cognition provided according to embodiments of the present invention, including：
Acquisition module, for by each charging process of lithium battery charging current or each discharge process in put
Piezoelectric voltage is acquired, and obtains the charging current data set in each charging process or the discharge voltage in each discharge process
Data acquisition system；
Data sorting processing module, for by the charging current data set or the discharge voltage data set
Data arrangement processing is carried out, charging current two dimensional image or discharge voltage two dimensional image are formed；
Multi-resolution decomposition module, for the non-down sampling contourlet transform NSCT using view-based access control model multichannel characteristic to institute
State charging current two dimensional image or discharge voltage two dimensional image and carry out multi-resolution decomposition, obtain higher-dimension charging current feature vector or
Higher-dimension discharge voltage feature vector；
Dimension-reduction treatment module, for the laplacian eigenmaps LE using view-based access control model manifold perception characteristics to the height
It ties up charging current feature vector or higher-dimension discharge voltage feature vector carries out dimension-reduction treatment, obtain lying in low-dimensional charging current sheet
The low-dimensional discharge voltage levied the low-dimensional charging current degenerative character in manifold or lain in the intrinsic manifold of low-dimensional discharge voltage moves back
Change feature；
Capacity estimation module, it is special for being degenerated using the low-dimensional charging current degenerative character or the low-dimensional discharge voltage
Sign estimates the lithium battery capacity.
Preferably, the acquisition module is specifically used for being acquired to the charging current in each charging process of lithium battery
When, the charging current data of rejecting abnormalities charging current data and constant current charging phase；Or lithium battery is being put every time
When discharge voltage in electric process is acquired, rejects electric discharge early stage sensitive discharge voltage data and electric discharge latter stage voltage restores
Discharge voltage data.
Preferably, the data sorting processing module includes：
Selection unit, for selecting maximum charging current data and minimum charging from the charging current data set
Current data, or maximum discharge voltage data and minimum discharge voltage data are selected from the discharge voltage data set；
Computing unit calculates institute for utilizing the maximum charging current data and the minimum charge current data
The normallized current value of each charging current data in charging current data set is stated, or utilizes the maximum discharge voltage data
With the minimum discharge voltage data, the normalization electricity of each discharge voltage data in the discharge voltage data set is calculated
Pressure value；
Data sorting processing unit, for carrying out data arrangement by regarding each normallized current value as pixel value
Processing forms charging current two dimensional image, or carries out data arrangement by regarding each normalized voltage value as pixel value
Processing forms discharge voltage two dimensional image.
Preferably, the multi-resolution decomposition module includes：
First multi-resolution decomposition unit, for using view-based access control model multichannel characteristic NSCT hierarchical level be 2 grades, two
Directional Decomposition series on grade scale is chosen for { 1,2 } and carries out multi-resolution decomposition to the charging current two dimensional image respectively, obtains
To 1 low frequency sub-band and 6 high-frequency sub-bands, and choose 1 low frequency sub-band Coefficient Mean and variance yields and 6 described
High-frequency sub-band coefficient energy value obtains the charging current feature vector of 8 dimensions；Or
Second multi-resolution decomposition unit, for using view-based access control model multichannel characteristic NSCT hierarchical level be 2 grades, two
Directional Decomposition series on grade scale is chosen for { 1,2 } and carries out multi-resolution decomposition to the discharge voltage two dimensional image respectively, it obtains
To 1 low frequency sub-band and 6 high-frequency sub-bands, and choose 1 low frequency sub-band Coefficient Mean and variance yields and 6 described
High-frequency sub-band coefficient energy value obtains the discharge voltage feature vector of 8 dimensions.
Preferably, the capacity estimation module includes：
Determination unit, for determining initial point in the intrinsic manifold constructed by the low-dimensional charging current degenerative character respectively
Geodesic distance geo between last pointEOLAnd the geodesic distance geo between initial point and other pointss；Or it is true respectively
Geodesic distance in intrinsic manifold constructed by the fixed low-dimensional discharge voltage degenerative character between initial point and last point
geoEOLAnd the geodesic distance geo between initial point and other pointss；
Capacity estimation unit, for utilizing the geoEOLWith the geosThe lithium battery capacity is estimated.
The scheme provided according to embodiments of the present invention, the advantage of the invention is that：
(1) the required test data of the present invention is simple, will only with lithium battery charging current or discharge voltage value
The method that Li-Battery monitor signal is converted to two dimensional image carries out capacity estimation using visual cognition theory to lithium battery, and precision is more
Height, more effectively；
(2) present invention is a kind of method of data-driven, avoids complicated lithium battery chemism research and degenerates
Modeling process；
(3) applicability of the present invention is good, is still applicable in when lithium battery formula or experimental condition change.
Description of the drawings
Fig. 1 is a kind of method flow diagram of the lithium battery capacity estimation of view-based access control model cognition provided in an embodiment of the present invention；
Fig. 2 is a kind of schematic device of the lithium battery capacity estimation of view-based access control model cognition provided in an embodiment of the present invention；
Fig. 3 is the image transformation schematic diagram provided in an embodiment of the present invention based on CC/DV values；
Fig. 4 is original CC/DV data provided in an embodiment of the present invention and treated CC/DV schematic diagram datas；
Fig. 5 is single charge/discharge cycle CC/DV Data to Graphic Converting schematic diagrames provided in an embodiment of the present invention；
Fig. 6 is the intrinsic manifold schematic diagram of battery #5 performance degradations provided in an embodiment of the present invention；
Fig. 7 is the battery capacity estimation result signal based on CC/DV under the conditions of different tests provided in an embodiment of the present invention
Figure.
Specific implementation mode
Below in conjunction with attached drawing to a preferred embodiment of the present invention will be described in detail, it should be understood that described below is excellent
Select embodiment only for the purpose of illustrating and explaining the present invention and is not intended to limit the present invention.
Fig. 1 is a kind of method flow diagram of the lithium battery capacity estimation of view-based access control model cognition provided in an embodiment of the present invention,
As shown in Figure 1, including：
Step S101：By to the charging current in each charging process of lithium battery or the electric discharge electricity in each discharge process
Pressure is acquired, and obtains the charging current data set in each charging process or the discharge voltage data in each discharge process
Set；
Step S102：By carrying out data arrangement to the charging current data set or the discharge voltage data set
Processing forms charging current two dimensional image or discharge voltage two dimensional image；
Step S103：Using the non-down sampling contourlet transform NSCT of view-based access control model multichannel characteristic to the charging current
Two dimensional image or discharge voltage two dimensional image carry out multi-resolution decomposition, obtain higher-dimension charging current feature vector or higher-dimension electric discharge electricity
Press feature vector；
Step S104：It is charged to the higher-dimension using the laplacian eigenmaps LE of view-based access control model manifold perception characteristics electric
It flows feature vector or higher-dimension discharge voltage feature vector carries out dimension-reduction treatment, obtain lying in the intrinsic manifold of low-dimensional charging current
Low-dimensional charging current degenerative character or lie in low-dimensional discharge voltage degenerative character in the intrinsic manifold of low-dimensional discharge voltage；
Step S105：Using the low-dimensional charging current degenerative character or the low-dimensional discharge voltage degenerative character to described
Lithium battery capacity is estimated.
Wherein, described by the charging current in each charging process of lithium battery or the electric discharge electricity in each discharge process
Pressure is acquired, and obtains the charging current data set in each charging process or the discharge voltage data in each discharge process
Set includes：When being acquired to the charging current in each charging process of lithium battery, rejecting abnormalities charging current data and
The charging current data of constant current charging phase；Or it is acquired to the discharge voltage in each discharge process of lithium battery
When, reject the discharge voltage data that electric discharge early stage sensitive discharge voltage data and electric discharge latter stage voltage restore.
Wherein, described by carrying out data arrangement to the charging current data set or the discharge voltage data set
Processing, forms charging current two dimensional image or discharge voltage two dimensional image includes：It is chosen from the charging current data set
Go out maximum charging current data and minimum charge current data, or maximum electric discharge is selected from the discharge voltage data set
Voltage data and minimum discharge voltage data；Utilize the maximum charging current data and the minimum charge current data, meter
The normallized current value of each charging current data in the charging current data set is calculated, or utilizes the maximum electric discharge electricity
Data and the minimum discharge voltage data are pressed, returning for each discharge voltage data in the discharge voltage data set is calculated
One changes voltage value；Data arrangement processing is carried out by regarding each normallized current value as pixel value, forms charging current
Two dimensional image, or data arrangement processing is carried out by regarding each normalized voltage value as pixel value, form discharge voltage
Two dimensional image.
Wherein, the NSCT using view-based access control model multichannel characteristic is to the charging current two dimensional image or discharge voltage
Two dimensional image carries out multi-resolution decomposition, obtains higher-dimension charging current feature vector or higher-dimension discharge voltage feature vector includes：Profit
Hierarchical level with the NSCT of view-based access control model multichannel characteristic is 2 grades, and the Directional Decomposition series on two-stage scale is chosen for respectively
{ 1,2 } carries out multi-resolution decomposition to the charging current two dimensional image, obtains 1 low frequency sub-band and 6 high-frequency sub-bands, and choose
1 low frequency sub-band Coefficient Mean and variance yields and 6 high-frequency sub-bands coefficient energy value obtain the charging electricity of 8 dimensions
Flow feature vector；Or it is 2 grades to utilize the hierarchical level of the NSCT of view-based access control model multichannel characteristic, the direction point on two-stage scale
Solution series is chosen for { 1,2 } and carries out multi-resolution decomposition to the discharge voltage two dimensional image respectively, obtains 1 low frequency sub-band and 6
A high-frequency sub-band, and choose 1 low frequency sub-band Coefficient Mean and variance yields and 6 high-frequency sub-bands coefficient energy
Value, obtains the discharge voltage feature vector of 8 dimensions.
Wherein, described to utilize the low-dimensional charging current degenerative character or the low-dimensional discharge voltage degenerative character to described
Lithium battery capacity carries out estimation：It determines respectively initial in the intrinsic manifold constructed by the low-dimensional charging current degenerative character
Geodesic distance geo between point and last pointEOLAnd the geodesic distance geo between initial point and other pointss；Or respectively
Geodesic distance in the true intrinsic manifold determined constructed by low-dimensional discharge voltage degenerative character between initial point and last point
geoEOLAnd the geodesic distance geo between initial point and other pointss；Utilize the geoEOLWith the geosTo the lithium battery
Capacity is estimated.
Fig. 2 is a kind of schematic device of the lithium battery capacity estimation of view-based access control model cognition provided in an embodiment of the present invention,
As shown in Fig. 2, including：Acquisition module 201, for by each charging process of lithium battery charging current or every time electric discharge
Discharge voltage in the process is acquired, and is obtained in charging current data set or each discharge process in each charging process
Discharge voltage data set；Data sorting processing module 202, for by the charging current data set or described putting
Piezoelectric voltage data acquisition system carries out data arrangement processing, forms charging current two dimensional image or discharge voltage two dimensional image；It is multiple dimensioned
Decomposing module 203, for the non-down sampling contourlet transform NSCT using view-based access control model multichannel characteristic to the charging current
Two dimensional image or discharge voltage two dimensional image carry out multi-resolution decomposition, obtain higher-dimension charging current feature vector or higher-dimension electric discharge electricity
Press feature vector；Dimension-reduction treatment module 204, for LE pairs of the laplacian eigenmaps using view-based access control model manifold perception characteristics
The higher-dimension charging current feature vector or higher-dimension discharge voltage feature vector carry out dimension-reduction treatment, obtain lying in low-dimensional charging
Low-dimensional charging current degenerative character in the intrinsic manifold of electric current lies in low-dimensional electric discharge in the intrinsic manifold of low-dimensional discharge voltage
Voltage degradation feature；Capacity estimation module 205, it is electric for being discharged using the low-dimensional charging current degenerative character or the low-dimensional
Pressure degenerative character estimates the lithium battery capacity.
Wherein, the acquisition module 201 is specifically used for adopting to the charging current in each charging process of lithium battery
When collection, the charging current data of rejecting abnormalities charging current data and constant current charging phase；Or each to lithium battery
When discharge voltage in discharge process is acquired, rejects electric discharge early stage sensitive discharge voltage data and electric discharge latter stage voltage is extensive
Multiple discharge voltage data.
Wherein, the data sorting processing module 202 includes：Selection unit is used for from the charging current data set
In select maximum charging current data and minimum charge current data, or selected most from the discharge voltage data set
Big discharge voltage data and minimum discharge voltage data；Computing unit, for utilizing maximum charging current data and described
Minimum charge current data calculate the normallized current value of each charging current data in the charging current data set,
Or it using the maximum discharge voltage data and the minimum discharge voltage data, calculates in the discharge voltage data set
The normalized voltage value of each discharge voltage data；Data sorting processing unit, will each normallized current for passing through
Value carries out data arrangement processing as pixel value, forms charging current two dimensional image, or by will each normalized voltage
Value carries out data arrangement processing as pixel value, forms discharge voltage two dimensional image.
Wherein, the multi-resolution decomposition module 203 includes：First multi-resolution decomposition unit, for more using view-based access control model
The hierarchical level of the NSCT of channel characteristic is 2 grades, and the Directional Decomposition series on two-stage scale is chosen for { 1,2 } and is filled to described respectively
Electric current two dimensional image carries out multi-resolution decomposition, obtains 1 low frequency sub-band and 6 high-frequency sub-bands, and choose 1 low frequency
Band Coefficient Mean and variance yields and 6 high-frequency sub-bands coefficient energy value, obtain the charging current feature vector of 8 dimensions；Or
Person's the second multi-resolution decomposition unit, the hierarchical level for the NSCT using view-based access control model multichannel characteristic are 2 grades, two-stage scale
On Directional Decomposition series be chosen for respectively { 1,2 } to the discharge voltage two dimensional image carry out multi-resolution decomposition, obtain 1
Low frequency sub-band and 6 high-frequency sub-bands, and choose 1 low frequency sub-band Coefficient Mean and variance yields and 6 high frequency
Band coefficient energy value, obtains the discharge voltage feature vector of 8 dimensions.
Wherein, the capacity estimation module 205 includes：Determination unit, for determining that the low-dimensional charging current is moved back respectively
Change the geodesic distance geo between initial point and last point in the intrinsic manifold constructed by featureEOLAnd initial point and other points
Between geodesic distance geos；Or it is initial in the intrinsic manifold constructed by the determining low-dimensional discharge voltage degenerative character respectively
Geodesic distance geo between point and last pointEOLAnd the geodesic distance geo between initial point and other pointss；Capacity estimation
Unit, for utilizing the geoEOLWith the geosThe lithium battery capacity is estimated.
It is illustrated by taking the capacity estimation of lithium ion battery as an example with reference to Fig. 3 to Fig. 7
NASA lithium ion battery test datas describe：The data used in this research are from the pre- measured center of NASA Ames
Customize battery plan.It is tested by three kinds of different Operation Profiles (charging, electric discharge and impedance) under environment temperature (AT).
It is charged until cell voltage reaches 4.2V with 1.5A under constant-current mode, and is discharged with constant-voltage mode until putting
Electric current drops to 20mA.Discharge process stops at different electric discharge latter stages (EODs).Experiment can proceed to until capacity reduces
To the end of life criteria (EOLC) of formulation.
In order to verify the efficiency of proposed method, typical data (No. 5, No. 7, No. 29, No. 54) has been selected and in 1 difference of table
It is described in the tables of data of operating condition.From table 1 it follows that these data charging current 1.5A having the same, but
Typically exhibit different AT (24 DEG C, 43 DEG C or 4 DEG C), discharge current (DC；2A or 4A), EOD (from 2V to 2.7V), initially
Capacity (IC；From 1.1665Ah to 1.8911Ah) and EOLC (30% or 12.61%).
The tables of data of 1 different operating condition of table
Wherein：AT indicates that environment temperature, CC indicate that charging current, DC indicate that discharge current, EOD indicate electric discharge end, IC
Indicate that initial capacity and EOLC indicate end of life criteria (capacity accounts for initial capacity ratio after degeneration).
1, the structure of visual cognition image
1.1, image conversion method
The virtual condition of arbitrary battery can be identified by charge or discharge.Therefore, it is obtained from charging and discharging process
The charging current (hereinafter referred to as CC) and discharge voltage (hereinafter referred to as DV) curve obtained can directly reflect the practical shape of battery
State.In order to excavate the performance degradation rule for including in these curves, CC the and DV curves of each cycle are transformed into for following
The image of visual cognition.It is consistent to CC with the DV values progress during service life for entirely recycling first according to linear normalization equation
Normalization.Linear normalization equation：Y=(x-MinValue)/(MaxValue-MinValue), wherein x are original CC or DV
Value, y are normalized values, MinValue and Maxvalue be the service life entirely recycled respectively during minimum and maximum CC/DV values.
Normalized data point is aligned to the matrix of M × N, if using the normalization amplitude of each sample as the pixel of a pictures
Value, then M × N matrix will become M × N image, as shown in Figure 3.Following principle is used to ensure the quality of changing image：
(1) changing image should retain the most useful information of each charge/discharge cycle；(2) it should eliminate and differ markedly from other periods
CC and DV data；(3) image of the CC and DV data structure based on each period should have identical size.In order to follow these
Principle needs CC the and DV data of each charge/discharge cycle of selection and processing.
1.2, data selection and processing
To a certain extent, image transformation quality directly influences the result of visual cognition.Therefore, to CC and DV data into
Row selection and processing appropriate are most important to ensuring high quality graphic transformation.
Present invention experiment has collected two kinds of CC/DV data for including most useful information：(1) the CC numbers of constant voltage charging phase
According to as shown in the b in Fig. 4；(2) the DV data in discharge process are as shown in the d in Fig. 4.Following data is deleted：(A) abnormal
Data, the CC data of (B) constant current charging phase, the sense voltage data and (D) voltage of (C) electric discharge early stage restore data,
As shown in a, c in Fig. 4.
The difference of sample rate (or sample start times) causes CC the or DV data bulks in each period different.This is being formed
Cause problem when the image of identical size.Ensure that the data points of each cycle are identical using interpolation method.If image is too big,
The very big of quantitative change is then calculated, and if image is too small, it cannot reflect specific charge/discharge cycle characteristic.In order to balance this
A little problems select M=N=64.Therefore, each period need in total 4096 data points build image.If from charging/putting
I (i are obtained in the electric period<4096) a data point, then other data points pass through " spline " interpolation algorithm and obtain.In Fig. 5
B, d shows an example of No. 5 Battery disposal CC/DV data and curves.
2, the feature extraction based on non-down sampling contourlet transform NSCT and laplacian eigenmaps LE
2.1, the multi-channel feature extraction based on NSCT
By using NSCT, the changing image from CC/DV values is broken down into multiple spaces with one group of directional subbands
Frequency channel.Subband can indicate as follows：i≤i0；J=2,4,8 ..., m；N ∈ N, m ∈ 2N, wherein i
It is decomposition scale, j is to decompose direction,Indicate low frequency coefficient, CI, jIndicate j-th of directional subband the i-th scale high frequency system
Number.In the present invention, i0=2, j={ 2,4 }.
Low frequency sub-band coefficient reflects the profile information of image, and the coefficient of high-frequency sub-band reflects more detailed information.Cause
This, three time domain indexes are extracted as characteristic value, are the average value (μ of low frequency sub-band coefficient respectively）, variance yields (σ) and high frequency are sub
Energy value (E) with coefficient.The calculation formula of these three indexs is as follows：
Wherein, P (x, y) indicates that each element of coefficient, M × N indicate the size of coefficient matrix.Therefore, from single charging/
The octuple feature vector of each image of discharge cycle conversion, which can be obtained, is：
F=[μ, σ, E1,1, E1,2, E2,1, E2,2, E2,3, E2,4] (13)
2.2, the intrinsic manifold based on LE is established
An intrinsic manifold is established using LE methods.MdSpatial embedding higher-dimensionData in the intrinsic manifold in space disclose
The deterioration law of battery performance.Space is the octuple feature by being extracted by NSCT from the image that CC/DV data convert
What vector was constituted.FromTo MdMapping g=f-1In MdSpace gives 2 dimensions that can describe lithium ion battery degeneration very well
Eigenmatrix.Map g=f-1By four typical datas concentrate the one group of similar service life in complete period (ASL) of each it is original
Test data is established.It is givenIn any point, can pass through map g=f-1It obtains indicating MdMiddle capacity of lithium ion battery
Respective counts strong point.
3, the capacity estimation based on geodesic distance
In this research, electricity is carried out along the geodesic distance of intrinsic manifold in calculating degenerative process between initial point and closest approach
The estimation of tankage.It willAs initial capacity, not usually rated capacity, by CEOLFinal as ASL experimental datas fills
The capacity of electricity/discharge cycle.By intrinsic manifold MdGeodesic distance between upper initial point and other points is expressed as geos, by ASL sheets
The geodesic distance between initial point and last point in sign manifold is expressed as geoEOL。The capacity that each of space is put can
To be estimated as：
The present invention proves the validity of proposed method using No. 5 batteries, wherein Fig. 4 is shown in charging process
Original CC data and curves (Fig. 4 (a)) and discharge process in DV data and curves (Fig. 4 (c)).Fig. 4 (b) and (d) are shown from 5
The respective handling curve that number battery stabilization sub stage obtains.Each of charge/discharge process is followed using conversion scheme shown in Fig. 4
The standardized data of ring is converted to image.Fig. 5 shows the example of the single loop changing image of charging and discharging process.
After image transformation, feature is extracted from changing image using NSCT methods, is consequently formed by calculating low frequency
The mean value and variance of sub-band coefficients and the energy of high-frequency sub-band coefficient and the octuple feature vector constituted.By LE establish fromTo MdMapping g=f-1, construct in two-dimensional space MdOn intrinsic manifold.This describes the deterioration law of battery capacity,
Fig. 6 shows the octuple for being embedded in the feature construction extracted by DV dataThe intrinsic manifold of No. 5 batteries in space.
Fig. 7 be method pair 5 using the present invention, No. 7, No. 29, No. 54 batteries carry out capacity estimation as a result, table 2 arranges
Each battery capacity estimation accuracy table is gone out, wherein AE is estimation absolute error, and RE is estimation relative error, when ET is that algorithm is run
Between.
Estimated result shown in Fig. 7 and table 2 shows the appearance method of estimation of proposed view-based access control model cognition very short
It is highly effective to CC and DV data in time.That is CC DV curves can be selected to carry out battery capacity high-precision in real time
The estimation of degree.
Table 2：Battery capacity estimation accuracy table
As it can be seen that the present invention proposes a kind of battery capacity estimation method and device of view-based access control model cognition, will be filled from each
CC the or DV data that electricity/discharge cycle is collected are transformed into image, then extract feature from the image of transformation with NSCT.Later,
HVS manifold inductance characteristics are used for reference, the intrinsic manifold in embedded higher-dimension NSCT coefficients are established using LE methods, to disclose battery
The deterioration law of performance estimates battery capacity using the geodesic distance in intrinsic manifold.
The scheme provided according to embodiments of the present invention is tested using CC the or DV data collected from NASA battery data collection
Confirmatory test can be used for carrying out high-precision capacity estimation using CC or DV data using under aging condition different.In addition,
Complicated electrochemical mechanism need not be studied, models or carry out interminable test to become a kind of promising battery appearance
Measure the practical approach of estimation.
Although describing the invention in detail above, but the invention is not restricted to this, those skilled in the art of the present technique
It can be carry out various modifications with principle according to the present invention.Therefore, all to be changed according to made by the principle of the invention, all it should be understood as
Fall into protection scope of the present invention.
Claims (10)
1. a kind of method of the lithium battery capacity estimation of view-based access control model cognition, which is characterized in that including：
By being acquired to the charging current in each charging process of lithium battery or the discharge voltage in each discharge process, obtain
To the charging current data set in each charging process or the discharge voltage data set in each discharge process；
By carrying out data arrangement processing to the charging current data set or the discharge voltage data set, charging is formed
Electric current two dimensional image or discharge voltage two dimensional image；
It to the charging current two dimensional image or is put using the non-down sampling contourlet transform NSCT of view-based access control model multichannel characteristic
Piezoelectric voltage two dimensional image carries out multi-resolution decomposition, obtains higher-dimension charging current feature vector or higher-dimension discharge voltage feature vector；
Using the laplacian eigenmaps LE of view-based access control model manifold perception characteristics to the higher-dimension charging current feature vector or
Higher-dimension discharge voltage feature vector carries out dimension-reduction treatment, obtains lying in the low-dimensional charging electricity in the intrinsic manifold of low-dimensional charging current
It flows degenerative character or lies in the low-dimensional discharge voltage degenerative character in the intrinsic manifold of low-dimensional discharge voltage；
Using the low-dimensional charging current degenerative character or the low-dimensional discharge voltage degenerative character to the lithium battery capacity into
Row estimation.
2. according to the method described in claim 1, it is characterized in that, described by the charging in each charging process of lithium battery
Discharge voltage in electric current or each discharge process is acquired, obtain charging current data set in each charging process or
The discharge voltage data set in discharge process includes every time：
When being acquired to the charging current in each charging process of lithium battery, rejecting abnormalities charging current data and constant electricity
The charging current data in current charge stage；Or
When being acquired to the discharge voltage in each discharge process of lithium battery, electric discharge early stage sensitive discharge voltage number is rejected
According to the discharge voltage data restored with electric discharge latter stage voltage.
3. method according to claim 1 or 2, which is characterized in that it is described by the charging current data set or
The discharge voltage data set carries out data arrangement processing, forms charging current two dimensional image or discharge voltage two dimensional image packet
It includes：
Select maximum charging current data and minimum charge current data from the charging current data set, or from described
Maximum discharge voltage data and minimum discharge voltage data are selected in discharge voltage data set；
Using the maximum charging current data and the minimum charge current data, the charging current data set is calculated
In each charging current data normallized current value, or utilize the maximum discharge voltage data and the minimum discharge voltage
Data calculate the normalized voltage value of each discharge voltage data in the discharge voltage data set；
Data arrangement processing is carried out by regarding each normallized current value as pixel value, forms charging current X-Y scheme
Picture, or data arrangement processing is carried out by regarding each normalized voltage value as pixel value, form discharge voltage X-Y scheme
Picture.
4. according to the method described in claim 3, it is characterized in that, the NSCT using view-based access control model multichannel characteristic is to institute
State charging current two dimensional image or discharge voltage two dimensional image and carry out multi-resolution decomposition, obtain higher-dimension charging current feature vector or
Higher-dimension discharge voltage feature vector includes：
Hierarchical level using the NSCT of view-based access control model multichannel characteristic is 2 grades, the Directional Decomposition series difference on two-stage scale
It is chosen for { 1,2 }, multi-resolution decomposition is carried out to the charging current two dimensional image, obtains 1 low frequency sub-band and 6 high frequency
Band, and 1 low frequency sub-band Coefficient Mean and variance yields and 6 high-frequency sub-bands coefficient energy value are chosen, obtain 8
The charging current feature vector of dimension；Or
Hierarchical level using the NSCT of view-based access control model multichannel characteristic is 2 grades, the Directional Decomposition series difference on two-stage scale
It is chosen for { 1,2 }, multi-resolution decomposition is carried out to the discharge voltage two dimensional image, obtains 1 low frequency sub-band and 6 high frequency
Band, and 1 low frequency sub-band Coefficient Mean and variance yields and 6 high-frequency sub-bands coefficient energy value are chosen, obtain 8
The discharge voltage feature vector of dimension.
5. according to the method described in claim 1, it is characterized in that, described utilize the low-dimensional charging current degenerative character or institute
State low-dimensional discharge voltage degenerative character to the lithium battery capacity carry out estimation include：
It determines respectively in the intrinsic manifold constructed by the low-dimensional charging current degenerative character between initial point and last point
Geodesic distance geoEOLAnd the geodesic distance geo between initial point and other pointss；Or really described respectively determine low-dimensional electric discharge electric
Press the geodesic distance geo between initial point and last point in the intrinsic manifold constructed by degenerative characterEOLAnd initial point and its
He put between geodesic distance geos；
Utilize the geoEOLWith the geosThe lithium battery capacity is estimated.
6. a kind of device of the lithium battery capacity estimation of view-based access control model cognition, which is characterized in that including：
Acquisition module, for by the charging current in each charging process of lithium battery or the electric discharge electricity in each discharge process
Pressure is acquired, and obtains the charging current data set in each charging process or the discharge voltage data in each discharge process
Set；
Data sorting processing module, for by the charging current data set or discharge voltage data set progress
Data arrangement processing, forms charging current two dimensional image or discharge voltage two dimensional image；
Multi-resolution decomposition module, for being filled to described using the non-down sampling contourlet transform NSCT of view-based access control model multichannel characteristic
Electric current two dimensional image or discharge voltage two dimensional image carry out multi-resolution decomposition, obtain higher-dimension charging current feature vector or higher-dimension
Discharge voltage feature vector；
Dimension-reduction treatment module, for being filled to the higher-dimension using the laplacian eigenmaps LE of view-based access control model manifold perception characteristics
Electric current characteristic vector or higher-dimension discharge voltage feature vector carry out dimension-reduction treatment, obtain lying in the intrinsic stream of low-dimensional charging current
Low-dimensional charging current degenerative character in shape or the low-dimensional discharge voltage lain in the intrinsic manifold of low-dimensional discharge voltage are degenerated special
Sign；
Capacity estimation module, for utilizing the low-dimensional charging current degenerative character or the low-dimensional discharge voltage degenerative character pair
The lithium battery capacity is estimated.
7. device according to claim 6, which is characterized in that the acquisition module is specifically used for filling lithium battery every time
When charging current in electric process is acquired, the charging current of rejecting abnormalities charging current data and constant current charging phase
Data；Or when being acquired to the discharge voltage in each discharge process of lithium battery, the sensitive electric discharge of electric discharge early stage is rejected
The discharge voltage data that voltage data and electric discharge latter stage voltage restore.
8. the device described according to claim 6 or 7, which is characterized in that the data sorting processing module includes：
Selection unit, for selecting maximum charging current data and minimum charge current from the charging current data set
Data, or maximum discharge voltage data and minimum discharge voltage data are selected from the discharge voltage data set；
Computing unit calculates described fill for utilizing the maximum charging current data and the minimum charge current data
The normallized current value of each charging current data in electric current data set, or utilize the maximum discharge voltage data and institute
Minimum discharge voltage data is stated, the normalized voltage of each discharge voltage data in the discharge voltage data set is calculated
Value；
Data sorting processing unit, for being carried out at data arrangement by regarding each normallized current value as pixel value
Reason forms charging current two dimensional image, or is carried out at data arrangement by regarding each normalized voltage value as pixel value
Reason forms discharge voltage two dimensional image.
9. device according to claim 8, which is characterized in that the multi-resolution decomposition module includes：
First multi-resolution decomposition unit, the hierarchical level for the NSCT using view-based access control model multichannel characteristic are 2 grades, two-stage ruler
Directional Decomposition series on degree is chosen for { 1,2 } and carries out multi-resolution decomposition to the charging current two dimensional image respectively, obtains 1
Low frequency sub-band and 6 high-frequency sub-bands, and choose 1 low frequency sub-band Coefficient Mean and variance yields and 6 high frequency
Band coefficient energy value, obtains the charging current feature vector of 8 dimensions；Or
Second multi-resolution decomposition unit, the hierarchical level for the NSCT using view-based access control model multichannel characteristic are 2 grades, two-stage ruler
Directional Decomposition series on degree is chosen for { 1,2 } and carries out multi-resolution decomposition to the discharge voltage two dimensional image respectively, obtain 1
A low frequency sub-band and 6 high-frequency sub-bands, and choose 1 low frequency sub-band Coefficient Mean and variance yields and 6 high frequencies
Sub-band coefficients energy value obtains the discharge voltage feature vector of 8 dimensions.
10. device according to claim 6, which is characterized in that the capacity estimation module includes：
Determination unit, for determining in the intrinsic manifold constructed by the low-dimensional charging current degenerative character initial point and most respectively
Geodesic distance geo between latter pointEOLAnd the geodesic distance geo between initial point and other pointss；Or institute is determined respectively
State the geodesic distance geo between initial point and last point in the intrinsic manifold constructed by low-dimensional discharge voltage degenerative characterEOLWith
And the geodesic distance geo between initial point and other pointss；
Capacity estimation unit, for utilizing the geoEOLWith the geosThe lithium battery capacity is estimated.
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