CN110187281A - The method of lithium battery health status estimation based on charging stage health characteristics - Google Patents
The method of lithium battery health status estimation based on charging stage health characteristics Download PDFInfo
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- G—PHYSICS
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- 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]
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- G—PHYSICS
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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- 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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Abstract
The method for the lithium battery health status estimation based on charging stage health characteristics that the present invention relates to a kind of, be divided to modeling and health status to estimate two stages: (one) modelling phase includes: step 1.1: from different charging stages, i.e. the data and curves acquisition charging health characteristics of constant-current charge and constant-voltage charge；Step 1.2: calculating the degree of association for extracting charging health characteristics and health status, extract the high feature of the degree of association and constitute health characteristics matrix；Step 1.3: dimensionality reduction being carried out to matrix, obtains health characteristics sequence；Step 1.4: using health characteristics sequence as input and health status sequence as training regression model is exported, establishing SOH appraising model.(2) SOH estimating stage includes: step 2.1: the charging health characteristics extracted in acquisition step 1.2, constructs health characteristics matrix；Step 2.2: dimensionality reduction matrix obtains health characteristics sequence；Step 2.3: with the regression model established in the health characteristics input 1.4 in 2.2, obtaining SOH estimated result.Its application is easy, effect highly significant.
Description
Technical field
It is the present invention relates to a kind of method of lithium battery health status estimation, in particular to a kind of special based on charging stage health
The method of the lithium battery health status estimation of sign.
Background technique
With the aggravation of environment and energy crisis in world wide, lithium battery with its energy density height, long service life, from
The advantages that discharge rate is low, memory-less effect becomes energy storage device most potential at present.The use environment of lithium battery is more multiple
It is miscellaneous, it is easy to appear violent curent change and environmental change, leads to battery performance decline, reduced service life, or even cause
The safety accidents such as fire, explosion, jeopardize the safe and stable operation of whole system.
Health status (state of health, the SOH) estimation of lithium battery be the premise that is effectively managed battery with
It is crucial.SOH is the estimation to the battery in using relative to the variation of new battery storage and release electric energy ability, and essence reflects
The aging and decline situation of battery.SOH parameter is often defined by the volume change ratio of battery, as shown in formula (1), CnowIt indicates
The present capacity of battery, CratedIndicate the rated capacity of battery, i.e., the capacity of one piece brand new cells.But lithium battery bringing onto load type
More, electric discharge operating condition is complicated and changeable, and the real-time monitoring of Yao Shixian discharge capacity proposes very high requirement to sensor, difficulty compared with
Greatly.
SOH=Cnow/Crated (1)
Currently, common lithium battery health status evaluation method can be divided into method and data-driven class side based on model
Method.Data-driven class method has neural network, support vector machines, Bayesian network, particle filter scheduling algorithm.Such methods need not
The internal aging mechanism and identification of Model Parameters for studying battery complexity can set up outside batteries health characteristics and internal aging
The relational model of interprocedual realizes capacity monitoring.Health characteristics are generally from the charge/discharge in the charge and discharge cycles data of lithium battery
It is extracted in electric current and voltage curve, common are pressure drop etc. of discharging such as open-circuit voltage.The selection of health characteristics is to estimation result
Precision and the scope of application of method have a significant impact.
In order to realize the estimation to lithium battery health status under complex working condition, only discharge regime health characteristics are studied
It is also far from enough with the selection method of the experience of dependence, it must also further be studied.
In recent years, in the research to lithium battery health status evaluation method, the extraction of external intact feature is mostly from electric discharge
The problem of voltage and current curve in stage extracts, often big in the presence of measurement difficulty, narrow application range.As open-circuit voltage needs electricity
Pond stands could obtain for a long time, and electric discharge pressure drop is limited to constant-current discharge operating condition, is not suitable for pulsed discharge or interruption electric discharge etc.
Situation.In addition, environment temperature has larger impact to inside battery physical-chemical reaction, the decline trend of battery can be seriously affected,
But it is less to the research of temperature-dependent characteristics that current health characteristics extract work.
Therefore it provides a kind of design is rationally, using simplicity, estimate that accuracy of judgement is reliable, at low cost, the base that work efficiency is high
In charging stage health characteristics lithium battery health status estimate method, be field technical staff's urgent problem to be solved it
One.
Summary of the invention
It is big for existing health characteristics measurement difficulty it is an object of the invention to overcome the shortcomings of in the prior art, it fits
With narrow range, there are also be not involved in the problems, such as this key factor of temperature, the voltage of the present invention quasi- charging stage from lithium battery, electricity
Stream, temperature curve extract health characteristics.The situation complicated and changeable relative to electric discharge operating condition, the charging process of lithium battery are more solid
It is fixed, and can be generally divided into constant current (CC) and constant pressure (CV) two stages of charging.The health characteristics of charging stage extract, can be effective
Avoid the influence of electric discharge operating condition variation.
On the other hand, health characteristics generally empirically directly give, or carry out and hold just for single health characteristics
Analysis between amount decline lacks the method for the extraction health characteristics of more clear system.For this problem, the present invention is from lithium
In the charging current of battery, voltage and temperature curve, the health characteristics for being easy to measure are chosen, are proposed a set of based on grey correlation
Degree is analyzed and is locally linear embedding into complete, easily to follow health characteristics extracting method, and realizes SOH on this basis and estimate
It calculates；It provides a kind of design rationally, using simplicity, estimates that accuracy of judgement is reliable, at low cost, work efficiency is high based on charging rank
The method of the lithium battery health status estimation of section health characteristics.
The present invention is using technical solution to achieve the goals above: a kind of lithium battery based on charging stage health characteristics
The method of health status estimation, it is characterised in that this method implementation steps are as follows: modeling and health status estimation two are first split into
A stage:
(1) specific steps of modelling phase include:
Step 1.1: on the basis of lithium battery cycle charging data, from the different charging stages, that is, include constant-current charge and
The data and curves in two stages of constant-voltage charge acquire following 4 classes totally 14 charging health characteristics:
(1) time and its ratio used in different phase: L1For the time of constant voltage charging phase, L2For constant-current charging phase
Time, ratio L1/L2；
(2) electric current and the curve of temperature different phase and x-axis constitute the area in region: A1For the electric current of constant-current charging phase
Curve, A2For the current curve of constant voltage charging phase, A is the current curve of entire charging stage；T1For the temperature of constant-current charging phase
It writes music line, T2For the temperature curve of constant voltage charging phase, T is the temperature curve of entire charging stage；
(3) the greatest gradient K of voltage curve1With the greatest gradient K of current curve2；
The ratio of relevant temperature area under the curve and current curve area in (4) (2) classes: T1/A1, T2/A2And T/A；
Step 1.2: calculating the grey relational grade of 14 health characteristics and SOH, and extract the higher n charging of the degree of association
Feature, n construct the health characteristics matrix H F=[HF of m × n according to the actual conditions value of battery data1,HF2,…,
HFi,…,HFn]T, wherein HFiFor the health characteristics data sequence of m dimension, m is the sample number that battery data is concentrated here；
Step 1.3: dimension is more, and data volume is big because being influenced by n for the health characteristics matrix constructed in the step 1.2, adds
Re-computation burden；So will with a kind of dimension-reduction algorithm Principal Component Analysis (principal component analysis, PCA)
Eigenmatrix is reduced to the health characteristics sequence HI of 1 × m by m * n matrix in the case where retaining most of raw information, is reduced
Calculation amount；
Step 1.4: regression model algorithm is used as using Method Using Relevance Vector Machine (relevance vector machine, RVM),
Using health characteristics sequence HI obtained in the step 1.3 as RVM mode input, SOH is exported as RVM model, trained
To regression model；
(2) specific steps of SOH estimating stage include:
Step 2.1: acquiring the lithium battery charging health characteristics extracted in the step 1.2, construct health characteristics matrix H F
=[HF1,HF2,…,HFi,…,HFn]T；Wherein HFiFor the health characteristics data sequence of m dimension, m is what battery data was concentrated here
Sample number；
Step 2.2: with PCA by the eigenmatrix HF dimensionality reduction in step 2.1, obtaining the health characteristics sequence HI of 1 × m；
Step 2.3: inputting the RVM model established in the step 1.4 with the health characteristics HI in the step 2.2, obtain
To SOH estimation result.
The beneficial effects of the present invention are:
(1) health characteristics for using the charging stage are applicable to the battery of all electric discharge operating conditions；
(2) health characteristics proposed are easy to acquire, and test without additional acquisition；
(3) the important environmental factor of this influence cell degradation of temperature is introduced；
(4) the health characteristics extracting method that complete set is clear, is easy to follow is proposed.
In short, the method for the present invention design is rationally, using simplicity, estimate that accuracy of judgement is reliable；This method is greatly improved work
Make efficiency, application effect highly significant, and application prospect is boundless.
Detailed description of the invention
Fig. 1 is the flow diagram of modelling phase of the invention；
Fig. 2 is the flow diagram that the present invention estimates for realization lithium battery health status；
Fig. 3 is the lithium battery health status estimation result curve synoptic diagram the present invention is based on charging stage health characteristics.
Specific embodiment
Below in conjunction with attached drawing and preferred embodiment, the specific embodiment, structure, feature provided according to the present invention is described in detail
It is as follows:
As shown in Figure 1-Figure 3, a method of the lithium battery health status estimation based on charging stage health characteristics；The party
The lithium battery health status estimation object of method in the examples below is the open source data of US National Aeronautics and Space Administration (NASA)
#30~#32 lithium ion battery in library；Wherein, #30, #31 battery establish model as training set, and #32 is carried out as test set
SOH estimation.
This method specific implementation step is first split into modeling and health status estimates two stages:
(1) specific steps of modelling phase include:
Referring to Fig. 1, step 1.1: on the basis of lithium battery cycle charging data, from (including the constant current of different charging stages
Charging and two stages of constant-voltage charge) data and curves acquire following 4 classes totally 14 charging health characteristics:
(1) time and its ratio used in different phase: L1For the time of constant voltage charging phase, L2For constant-current charging phase
Time, ratio L1/L2；
(2) electric current and the curve of temperature different phase and x-axis constitute the area in region: A1For the electric current of constant-current charging phase
Curve, A2For the current curve of constant voltage charging phase, A is the current curve of entire charging stage；T1For the temperature of constant-current charging phase
It writes music line, T2For the temperature curve of constant voltage charging phase, T is the temperature curve of entire charging stage；
(3) the greatest gradient K of voltage curve1With the greatest gradient K of current curve2；
The ratio of relevant temperature area under the curve and current curve area in (4) (2) classes: T1/A1, T2/A2And T/A.
Step 1.2: the basic thought of grey relational grade analysis is the similarity degree for studying the sequence curve of different factors, i.e.,
The geometry of curve is more similar, and the correlation degree between factor is bigger；Otherwise the correlation degree between factor is smaller；When two
When curve is identical, grey relational grade is equal to 1.Reference sequences X0={ x0(k) }, k=1,2 ..., m, and compare sequence Xi=
{xi(k) }, k=1,2 ..., m, i=1,2 ..., n can calculate reference sequences compared between sequence according to formula (2)
Incidence coefficient, wherein ρ is resolution ratio, takes 0.5 here.
The average value of calculate correlation coefficient can obtain the grey relational grade of reference sequences sequence compared with:
For example, using SOH value as reference sequences, health characteristics value calculates the 14 of battery #30 and #31 as sequence is compared
A health characteristics are respectively with the grey relational grade of SOH, and the results are shown in Table 1, and select 8 high feature A of association angle value1,
A,L1,L1/L2,T,T1,T2/A2,K2Construct health characteristics matrix H Fx=[A1,A,L1,L1/L2,T,T1,T2/A2,K2]T。
The preferred range that the higher n charging health characteristics value of the degree of association is extracted in the step 1.2 is 4~10
It is a, can be estimated according to lithium battery health status in 4 class 14 charging health characteristics association and comprehensive, estimation numerical operation
It is simple and direct and its accuracy select.
The grey relational grade of 1 health characteristics of table
Step 1.3: using a kind of dimension-reduction algorithm: Principal Component Analysis (principal component analysis,
PCA) by eigenmatrix HFxDimensionality reduction obtains health characteristics sequence HIx.First by health characteristics matrix H FxIt is standardized as X*, calculate it
Covariance matrix S:
X can be calculated to obtain by formula (5)*Feature vector uiAnd X*Eigenvalue λi, thus i=1 ..., x use formula (6)
Matrix Z after dimensionality reduction is calculated:
Sui=λiui (5)
Z=X*×S (6)
In order to determine the principal component number chosen in Z, the contribution of each principal component after dimensionality reduction can be calculated according to formula (7)
Rate:
The contribution rate calculated result of each principal component is as shown in table 2；There it can be seen that tribute of the principal component 1 to initial data
The rate of offering has had reached 99% or more, remains most information of original matrix；Principal component 1 can be regard as dimensionality reduction knot
Fruit, i.e. health characteristics sequence HIx。
2 principal component contributor rate of table
| Principal component 1 | Principal component 2 | Principal component 3 | |
| #30 | 99.94873% | 0.044322% | 0.005443% |
| #31 | 99.88438% | 0.109917% | 0.005049% |
Step 1.4: regression model algorithm is used as using Method Using Relevance Vector Machine (relevance vector machine, RVM),
By health characteristics sequence HI obtained in the step 1.3xAs RVM mode input, SOH is exported as RVM model, trained
To regression model.
(2) specific steps of SOH estimating stage include:
Referring to fig. 2, step 2.1: acquiring and for example extract 8 charging health characteristics A in the step 1.21,A,L1,L1/L2,
T,T1,T2/A2,K2, construct health characteristics matrix H Ft=[A1,A,L1,L1/L2,T,T1,T2/A2,K2]T。
Step 2.2: with PCA by eigenmatrix HF obtained in step 2.1tDimensionality reduction, the principal component 1 that PCA dimensionality reduction is obtained
As health characteristics sequence HIt。
The dimension-reduction algorithm can also be used be locally linear embedding into or multidimensional scaling dimension-reduction algorithm.
Step 2.3: with the health characteristics HI in the step 2.2tIt inputs the RVM established in the step 1.4 and returns mould
Type obtains SOH estimation result, as shown in Figure 3.
Support vector machines or neural network can also be used in the regression model algorithm established in the step 1.4 or step 2.3
Algorithm.
The main feature and principle of the method for the present invention:
The method of the present invention has determined the health characteristics of 4 class charging stages first, is carrying out ash to these health characteristics and SOH
On the basis of color correlation analysis, chooses the high feature of the degree of association and constitute matrix.Health characteristics matrix is dropped with PCA algorithm
Dimension processing verifies dimensionality reduction effect according to principal component contributor rate, obtains final health characteristics sequence.With the health characteristics sequence of extraction
Column establish RVM regression model as input, realize the estimation to lithium battery SOH.Pass through the processes such as above-mentioned analytic operation, the present invention
Implementation method effectively prevents battery discharge procedure and changes the influence chosen to health characteristics, and proposes one kind and be easy to follow
Health characteristics choose and the process of optimization, choose that provide a kind of application effect non-for the health characteristics of lithium battery SOH estimation
Normal significant new approaches.
It is above-mentioned to be carried out referring to method of the embodiment to the lithium battery health status estimation based on charging stage health characteristics
Detailed description, be illustrative without being restrictive；Therefore the change and modification in the case where not departing from present general inventive concept,
It should belong within protection scope of the present invention.
Claims (5)
1. a kind of method of the lithium battery health status estimation based on charging stage health characteristics, it is characterised in that this method is implemented
Steps are as follows: it is first split into modeling and health status estimates two stages:
(1) specific steps of modelling phase include:
Step 1.1: from the different charging stages, that is, including constant-current charge and constant pressure on the basis of lithium battery cycle charging data
The data and curves in two stages of charging acquire following 4 classes totally 14 charging health characteristics:
(1) time and its ratio used in different phase: L1For the time of constant voltage charging phase, L2For constant-current charging phase when
Between, ratio L1/L2；
(2) electric current and the curve of temperature different phase and x-axis constitute the area in region: A1For the current curve of constant-current charging phase,
A2For the current curve of constant voltage charging phase, A is the current curve of entire charging stage；T1It is bent for the temperature of constant-current charging phase
Line, T2For the temperature curve of constant voltage charging phase, T is the temperature curve of entire charging stage；
(3) the greatest gradient K of voltage curve1With the greatest gradient K of current curve2；
The ratio of relevant temperature area under the curve and current curve area in (4) (2) classes: T1/A1, T2/A2And T/A；
Step 1.2: calculating the grey relational grade of 14 health characteristics and SOH, and it is special to extract the higher n charging of the degree of association
Sign, n construct the health characteristics matrix H F=[HF of m × n according to the actual conditions value of battery data1,HF2,…,HFi,…,
HFn]T, wherein HFiFor the health characteristics data sequence of m dimension, m is the sample number that battery data is concentrated here；
Step 1.3: dimension is more, and data volume is big because being influenced by n for the health characteristics matrix constructed in the step 1.2, aggravates meter
Calculate burden；So with a kind of dimension-reduction algorithm Principal Component Analysis (principal component analysis, PCA) by feature
Matrix is reduced to the health characteristics sequence HI of 1 × m by m * n matrix in the case where retaining most of raw information, is reduced and is calculated
Amount；
Step 1.4: regression model algorithm being used as using Method Using Relevance Vector Machine (relevance vector machine, RVM), by institute
Health characteristics sequence HI obtained in step 1.3 is stated as RVM mode input, SOH is exported as RVM model, trained back
Return model；
(2) specific steps of SOH estimating stage include:
Step 2.1: acquiring the lithium battery charging health characteristics extracted in the step 1.2, construct health characteristics matrix H F=
[HF1,HF2,…,HFi,…,HFn]T；Wherein HFiFor the health characteristics data sequence of m dimension, m is the sample that battery data is concentrated here
This number；
Step 2.2: with PCA by the eigenmatrix HF dimensionality reduction in step 2.1, obtaining the health characteristics sequence HI of 1 × m；
Step 2.3: inputting the RVM model established in the step 1.4 with the health characteristics HI in the step 2.2, obtain SOH
Estimation result.
2. the method for the lithium battery health status estimation according to claim 1 based on charging stage health characteristics, special
Sign is that it is 4~10 that the preferred range of the higher n charging health characteristics value of the degree of association is extracted in the step 1.2.
3. the method for the lithium battery health status estimation according to claim 1 based on charging stage health characteristics, special
Levy the eigenmatrix HF dimension-reduction algorithm being in the step 1.3 or step 2.2: using Principal Component Analysis (principal
Component analysis, PCA) eigenmatrix is down to 1 dimension, obtain health characteristics sequence HIx。
4. the method for the lithium battery health status estimation according to claim 3 based on charging stage health characteristics, special
Sign is that the dimension-reduction algorithm use is locally linear embedding into or multidimensional scales dimension-reduction algorithm.
5. the method for the lithium battery health status estimation according to claim 1 based on charging stage health characteristics, special
Sign is the regression model algorithm established in the step 1.4 or step 2.3 using support vector machines or neural network algorithm.
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