CN108037463A - A kind of lithium ion battery life-span prediction method - Google Patents
A kind of lithium ion battery life-span prediction method Download PDFInfo
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Abstract
The present invention relates to lithium ion battery, further to a kind of lithium ion battery life-span prediction method.The described method includes following process：In-service to same model or retired battery operation data are collected, and establish the database for including cell operating temperature, battery discharge multiplying power, the internal resistance of cell and terminal life parameter；Establishing battery life predicting linear regression function model is：H (x)=hθ(x)=θ0+θ1x1+θ2x2+θ3x3Specific model cell operating temperature, battery discharge multiplying power, the internal resistance of cell are substituted into above-mentioned regression model, obtained the terminal life of battery.Cell operating temperature, discharge-rate and internal resistance are to influence the key factor of battery life, and it is effective to introduce cell operating temperature, discharge-rate and internal resistance and be modeled prediction as the influence parameter of battery life.
Description
Technical field：
The present invention relates to lithium ion battery, further to a kind of lithium ion battery life-span prediction method.
Background technology：
More and more extensive with field of lithium battery application, its design capacity is gradually increased, and battery cell is inconsistent
And the difference of operating condition so that the service life of battery differs greatly, and the factor of cell performance decay is more, inside battery
Chemical reaction mechanism is complex so that battery life predicting is relatively difficult to achieve.
Existing battery life predicting model is typically based on two kinds of modeling methods, first, empirical model.Empirical model is usual
Need largely to be tested, obtain test data, by the value that gets parms, obtain the empirical data of capacity attenuation, it is needed
Time it is longer, to put into substantial amounts of resource and be tested to obtain data.Second, physics or based on data-driven model.Due to
The failure mechanism of lithium battery is complex, physical model is difficult to set up, therefore existing most of research concentrates on and establishes data
The method of driving model, such as autoregression (AR) model, Kalman filtering, neutral net, but due to lacking experimental data, cause
Period error is larger after battery operation.
With the development and application of big data, the upload of battery operation historical data and storage gradual perfection, a large amount of batteries
Operation and fault data are preserved, and are realized to consistent in the health degree in cell operation, battery cell life cycle
The analysis of the data such as property changing rule, battery life influence factor, provides for predicting residual useful life of the battery under specific operation
Powerful support.
The content of the invention：
It is lithium battery the problem of life prediction difficulty is big under specific run operating mode for this reason, to be solved by this invention, base
Many data are provided in big data memory technology to be analyzed, and then a kind of battery life predicting method is provided.To solve
Above-mentioned technical problem, the technical solution adopted by the present invention are as follows：
In the present invention, battery life refers to total relative cycle number under battery 100%DOD.
Scheme one：A kind of lithium ion battery life-span prediction method, the described method includes following process：
In-service to same model or retired battery operation data are collected, and foundation includes cell operating temperature, battery is put
The database of electric multiplying power, the internal resistance of cell and terminal life parameter；
Establish battery life predicting linear regression function model：
H (x)=hθ(x)=θ0+θ1x1+θ2x2+θ3x3
Wherein, x={ x1, x2, x3It is to influence battery life parameter, x1For cell operating temperature, x2For battery discharge times
Rate, x3For the internal resistance of cell, h (x) is battery terminal life, θ={ θ1, θ2, θ3It is the influence system that each parameter fails for the service life
Number, θ0For noise, it is 0 to obey average, and variance is the normal distribution of σ；
Specific model cell operating temperature, battery discharge multiplying power, the internal resistance of cell are substituted into above-mentioned regression model, obtained electricity
The terminal life in pond.
The preferred solution of such scheme one, can introduce false assessment function, evaluate the error of battery service life model；Specially
Using the quadratic sum of the estimate of x (i) and actual value y (i) differences as erroneous estimation function：
Scheme two：A kind of lithium ion battery life-span prediction method, the described method includes following process：
In-service to same model or retired battery operation data are collected, and establish temperature, the battery for including battery operation
Discharge-rate, the internal resistance of cell, real-time discharge capacity, active time i and the database of terminal life parameter；It has been on active service
Time i refers to the relative cycle number under battery 100%DOD；
Establish battery life predicting linear regression function model：
H (x)=hθ(x)=θ0+θ1x1+θ2x2+θ3x3
Wherein, x={ x1, x2, x3It is to influence battery life parameter, x1For cell operating temperature, x2For battery discharge times
Rate, x3For the internal resistance of cell, h (x) is battery terminal life, θ={ θ1, θ2, θ3It is the influence system that each parameter fails for the service life
Number, θ0For noise, it is 0 to obey average, and variance is the normal distribution of σ；
Life time decay factor delta is introduced to be modified：
Y=δ h (x)=δ (θ0+θ1x1+θ2x2+θ3x3)
δ is specific model battery life decay factor, is obtained by the database；The battery life decay factor δ
Acquisition methods be：
The active service of same model or retired battery data are screened in the database, are substituted into present battery operating parameter, are obtained
The battery inscribes the average value of discharge capacity of the cell in iCapacity of the size battery under identical parameter service condition
Attenuation, can be expressed by battery life decay factor, and battery life decay factor δ is obtained by equation below：
The QNFor battery present discharge capacity to be predicted.
One of preferred solution of such scheme two, the battery life decay factor δ are carried out more with the operation of battery
New optimization.
The two of the preferred solution of such scheme two, introduce false assessment function, evaluate the error of battery service life model；Specifically
For using the quadratic sum of the estimate of x (i) and actual value y (i) differences as erroneous estimation function.For：
The present invention is relative to the advantages of prior art：
(1) capacitance loss due to battery during charge and discharge cycles influences complex, comprising running temperature, puts
The influence factor such as electric multiplying power and internal resistance change；When cell operating temperature exceedes the temperature range adapted to, battery will accelerate to decay；
The discharge-rate of battery is higher, and the electrical conductivity loss between active material adjacent particle will increase, and causes electric current distribution uneven, into
And influence the service life of battery；During battery pack is run, the internal resistance of cell will change within the specific limits, but old with battery
Change, the internal resistance of cell will dramatically increase, and in battery operation latter stage, internal resistance increase becomes the main reason for battery active volume decays；
Therefore, cell operating temperature, discharge-rate and internal resistance are to influence the key factor of battery life, introducing cell operating temperature,
It is effective that discharge-rate and internal resistance are modeled prediction as the influence parameter of battery life.
(2) by existing battery operation data, capacity parameter of the battery before future position is obtained, introduces linear return
Return function, establish battery life predicting model, using the environment temperature of battery operation, discharge-rate and internal resistance as battery life
Model parameter, using big data monitor supervision platform, in-service to same model or retired battery operation data carry out analysis calculating, and then
Realize the cycles left life prediction of battery.
In (three) second technical solutions, correction factor of the decay factor as battery life degenerated mode is introduced, can be improved
The accuracy of method.
(4) in embodiment, in order to train the error of Life Prediction Model, false assessment function pair battery life is introduced
Linear regression function is further corrected.
Brief description of the drawings：
Fig. 1 is life-span prediction method flow diagram in embodiment.
Embodiment：
Embodiment：
A kind of lithium ion battery life-span prediction method, the described method includes following process：
In-service to same model or retired battery operation data are collected, and establish temperature, the battery for including battery operation
Discharge-rate, the internal resistance of cell, real-time discharge capacity, active time i and the database of terminal life parameter；
Establish battery life predicting linear regression function model：
H (x)=hθ(x)=θ0+θ1x1+θ2x2+θ3x3
Wherein, x={ x1, x2, x3It is to influence battery life parameter, x1For cell operating temperature, x2For battery discharge times
Rate, x3For the internal resistance of cell, h (x) is battery terminal life, θ={ θ1, θ2, θ3It is the influence system that each parameter fails for the service life
Number, θ0For noise, it is 0 to obey average, and variance is the normal distribution of σ；
Life time decay factor delta is introduced to be modified：
Y=δ h (x)=δ (θ0+θ1x1+θ2x2+θ3x3)
δ is specific model battery life decay factor, is obtained by the database；The battery life decay factor δ
Computational methods be：
The active service of same model or retired battery data are screened in the database, are substituted into present battery operating parameter, are obtained
The battery inscribes the average value of discharge capacity of the cell in iCapacity of the size battery under identical parameter service condition
Attenuation, can be expressed by battery life decay factor, and battery life decay factor δ is obtained by equation below：
The QNFor battery present discharge capacity to be predicted；
Above-mentioned battery life decay factor δ is updated optimization with the operation of battery；
False assessment function is introduced, evaluates the error of battery service life model；Specially by the estimate of x (i) and actual value y
(i) poor quadratic sum is as erroneous estimation function.For：
J (θ) minimum value is solved using least square method, training characteristics are expressed as X matrix, is as a result expressed as y vectors, that
Parameter θ can be calculated by formula below.
θ=(XTX)-1XTy 。
Claims (5)
1. a kind of lithium ion battery life-span prediction method, it is characterised in that the described method includes following process：
In-service to same model or retired battery operation data are collected, and foundation includes cell operating temperature, battery discharge times
The database of rate, the internal resistance of cell and terminal life parameter；
Establish battery life predicting linear regression function model：
H (x)=hθ(x)=θ0+θ1x1+θ2x2+θ3x3
Wherein, x={ x1, x2, x3It is to influence battery life parameter, x1For cell operating temperature, x2For battery discharge multiplying power, x3For
The internal resistance of cell, h (x) are battery terminal life, θ={ θ1, θ2, θ3The influence coefficient that fails for the service life for each parameter, θ0For
Noise, it is 0 to obey average, and variance is the normal distribution of σ；
Specific model cell operating temperature, battery discharge multiplying power, the internal resistance of cell are substituted into above-mentioned regression model, obtained battery
Terminal life.
2. a kind of lithium ion battery life-span prediction method according to claim 1, it is characterised in that introduce false assessment letter
Number, evaluates the error of battery service life model；Specially using the quadratic sum of the estimate of x (i) and actual value y (i) differences as mistake
Estimation function.For：
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3. a kind of lithium ion battery life-span prediction method, it is characterised in that the described method includes following process：
In-service to same model or retired battery operation data are collected, and establish temperature, the battery discharge for including battery operation
Multiplying power, the internal resistance of cell, real-time discharge capacity, active time i and the database of terminal life parameter；
Establish battery life predicting linear regression function model：
H (x)=hθ(x)=θ0+θ1x1+θ2x2+θ3x3
Wherein, x={ x1, x2, x3It is to influence battery life parameter, x1For cell operating temperature, x2For battery discharge multiplying power, x3For
The internal resistance of cell, h (x) are battery terminal life, θ={ θ1, θ2, θ3The influence coefficient that fails for the service life for each parameter, θ0For
Noise, it is 0 to obey average, and variance is the normal distribution of σ；
Life time decay factor delta is introduced to be modified：
Y=δ h (x)=δ (θ0+θ1x1+θ2x2+θ3x3)
δ is specific model battery life decay factor, is obtained by the database；The battery life decay factor δ's obtains
The method is taken to be：
The active service of same model or retired battery data are screened in the database, substitutes into present battery operating parameter, are obtained described
Battery inscribes the average value of discharge capacity of the cell in iCapacity attenuation of the size battery under identical parameter service condition
Situation, can be expressed by battery life decay factor, and battery life decay factor δ is obtained by equation below：
<mrow>
<mi>&delta;</mi>
<mo>=</mo>
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<mi>Q</mi>
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</mrow>
The QNFor battery present discharge capacity to be predicted.
4. a kind of lithium ion battery life-span prediction method according to claim 3, it is characterised in that the battery life declines
Subtracting coefficient δ is updated optimization with the operation of battery.
5. a kind of lithium ion battery life-span prediction method according to claim 3, it is characterised in that introduce false assessment letter
Number, evaluates the error of battery service life model；Specially using the quadratic sum of the estimate of x (i) and actual value y (i) differences as mistake
Estimation function.For：
<mfenced open = "" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mi>J</mi>
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| CN110568817A (en) * | 2019-09-13 | 2019-12-13 | 深圳市烨嘉为技术有限公司 | machine tool motion temperature difference compensation method based on big data analysis and prejudgment |
| CN110703118A (en) * | 2018-06-21 | 2020-01-17 | 中信国安盟固利动力科技有限公司 | Method for extracting universal battery operation condition in region for predicting service life of vehicle-mounted battery |
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| CN110750874A (en) * | 2019-09-26 | 2020-02-04 | 长沙理工大学 | Method for predicting service life of retired power battery |
| CN111060835A (en) * | 2019-12-31 | 2020-04-24 | 湖州师范学院 | Method and system for predicting remaining life of lithium battery and readable storage medium |
| CN111707957A (en) * | 2020-04-23 | 2020-09-25 | 北京邮电大学 | Method and device for estimating residual value of battery of electric vehicle |
| CN111610448A (en) * | 2020-06-01 | 2020-09-01 | 北京理工大学 | Lithium ion battery life prediction method applying digital twinning technology |
| CN111610448B (en) * | 2020-06-01 | 2021-05-04 | 北京理工大学 | Lithium ion battery life prediction method applying digital twinning technology |
| CN112255559A (en) * | 2020-10-12 | 2021-01-22 | 江苏慧智能源工程技术创新研究院有限公司 | Method for predicting residual life of lithium battery energy storage power station based on multiple linear regression |
| CN112485688A (en) * | 2020-10-12 | 2021-03-12 | 江苏慧智能源工程技术创新研究院有限公司 | Method for predicting service life of retired battery energy storage power station based on multivariate nonlinear regression |
| CN112485688B (en) * | 2020-10-12 | 2024-06-04 | 启东沃太新能源有限公司 | Retired battery energy storage power station service life prediction method based on multiple nonlinear regression |
| CN112363077A (en) * | 2020-11-11 | 2021-02-12 | 上海电享信息科技有限公司 | Battery health degree capacity estimation method |
| CN114295995A (en) * | 2021-12-28 | 2022-04-08 | 深圳大学 | Aluminum-air battery cathode service life evaluation system and method |
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