CN108037463B - Lithium ion battery life prediction method - Google Patents
Lithium ion battery life prediction method Download PDFInfo
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- CN108037463B CN108037463B CN201711345909.0A CN201711345909A CN108037463B CN 108037463 B CN108037463 B CN 108037463B CN 201711345909 A CN201711345909 A CN 201711345909A CN 108037463 B CN108037463 B CN 108037463B
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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]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
Abstract
The invention relates to a lithium ion battery, and further relates to a lithium ion battery service life prediction method. The method comprises the following steps: collecting the operation data of the batteries with the same type in service or out of service, and establishing a database comprising the operation temperature of the batteries, the discharge multiplying power of the batteries, the internal resistance of the batteries and the total service life parameters; establishing a linear regression function model for battery life prediction as follows: h (x) ═ hθ(x)＝θ0+θ1x1+θ2x2+θ3x3And substituting the running temperature of the battery with a specific model, the discharge rate of the battery and the internal resistance of the battery into the regression model to obtain the total service life of the battery. The battery operation temperature, the discharge rate and the internal resistance are key factors influencing the service life of the battery, and the modeling prediction by introducing the battery operation temperature, the discharge rate and the internal resistance as influence parameters of the service life of the battery is effective.
Description
The technical field is as follows:
the invention relates to a lithium ion battery, and further relates to a lithium ion battery service life prediction method.
Background art:
with the wider and wider application field of the lithium battery, the design capacity of the lithium battery is gradually increased, and due to the fact that the battery monomers are inconsistent and the operation conditions are different, the service life difference of the battery is larger, the factors of battery performance attenuation are more, the chemical reaction mechanism in the battery is more complex, and the prediction of the service life of the battery is difficult to achieve.
Existing battery life prediction models are typically based on two modeling methods, one being an empirical model. The empirical model usually requires a lot of tests to obtain test data, and by obtaining parameter values, the empirical data of capacity fading is obtained, which requires a long time and requires a lot of resources to be invested in the tests to obtain data. The second is a physical or data-based driven model. Because the failure mechanism of the lithium battery is complex and the physical model is difficult to establish, most of the existing researches are focused on methods for establishing a data driving model, such as an Autoregressive (AR) model, Kalman filtering, a neural network and the like, but the errors in the later operation stage of the battery are large due to the lack of experimental data.
Along with the development and application of big data, the uploading and storage of battery operation historical data are gradually improved, the operation and fault data of a large number of batteries are stored, the analysis of data such as the health degree, the consistency change rule in the life cycle of a battery monomer, the battery service life influence factors and the like in the battery operation process is realized, and powerful support is provided for the prediction of the residual service life of the battery under a specific working condition.
The invention content is as follows:
therefore, the invention aims to solve the problem that the service life prediction difficulty of the lithium battery under a specific operation condition is high, provide multi-aspect data for analysis based on a big data storage technology, and further provide a battery service life prediction method. In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
in the present invention, battery life refers to the total relative number of cycles at 100% DOD of the battery.
The first scheme is as follows: a lithium ion battery life prediction method, the method comprising the process of:
collecting the operation data of the batteries with the same type in service or out of service, and establishing a database comprising the operation temperature of the batteries, the discharge multiplying power of the batteries, the internal resistance of the batteries and the total service life parameters;
establishing a battery life prediction linear regression function model:
h(x)＝hθ(x)＝θ0+θ1x1+θ2x2+θ3x3
wherein x ═ { x ═ x1，x2，x3Is a parameter that affects battery life, x1Is the battery operating temperature, x2Is the discharge rate of the battery, x3For the internal resistance of the cell, h (x) is the total service life of the cell, θ ═ θ1，θ2，θ3Is the influence coefficient of each parameter on the life decay, theta0As noise, a normal distribution with a mean of 0 and a variance of σ is obeyed;
and substituting the running temperature of the battery with a specific model, the discharge rate of the battery and the internal resistance of the battery into the regression model to obtain the total service life of the battery.
According to the preferable scheme of the first scheme, an error evaluation function can be introduced to evaluate the error of the battery life model; taking the square sum of the difference between the estimated value x (i) and the true value y (i) as an error estimation function:
scheme II: a lithium ion battery life prediction method, the method comprising the process of:
collecting the operation data of the batteries with the same type in service or retired service, and establishing a database comprising the operation temperature of the batteries, the discharge rate of the batteries, the internal resistance of the batteries, the real-time discharge capacity, the service time i and the total service life parameters; the service time i refers to the relative cycle number of the battery under 100% DOD;
establishing a battery life prediction linear regression function model:
h(x)＝hθ(x)＝θ0+θ1x1+θ2x2+θ3x3
wherein x ═ { x ═ x1，x2，x3Is a parameter that affects battery life, x1Is the battery operating temperature, x2Is the discharge rate of the battery, x3For the internal resistance of the cell, h (x) is the total service life of the cell, θ ═ θ1，θ2，θ3Is the influence coefficient of each parameter on the life decay, theta0As noise, a normal distribution with a mean of 0 and a variance of σ is obeyed;
and introducing a life attenuation factor delta for correction:
y＝δh(x)＝δ(θ0+θ1x1+θ2x2+θ3x3)
delta is a specific model battery life attenuation factor and is obtained through the database; the method for acquiring the battery life attenuation factor delta comprises the following steps:
screening the active or retired battery data of the same model in a database, substituting the data into the current battery operation parameters to obtain the average value of the battery discharge capacity of the battery at the moment i
The capacity attenuation condition of the battery of the type under the same parameter operation condition can be expressed by a battery life attenuation factor, and the battery life attenuation factor delta is obtained by the following formula:
said QNThe current discharge capacity of the battery to be predicted.
In a preferred embodiment of the second aspect, the battery life decay factor δ is updated and optimized with the operation of the battery.
Introducing an error evaluation function to evaluate the error of the battery life model; specifically, the square sum of the difference between the estimated value of x (i) and the true value y (i) is used as an error estimation function. Comprises the following steps:
compared with the prior art, the invention has the advantages that:
the method comprises the following steps that (I) the influence of capacity loss of a battery in the charge-discharge cycle process is complex, and the influence factors comprise operating temperature, discharge multiplying power, internal resistance change and the like; when the battery operation temperature exceeds the adaptive temperature range, the battery is accelerated to decay; the higher the discharge rate of the battery is, the greater the conductivity loss between adjacent particles of the active material will be, resulting in uneven current distribution and further affecting the life of the battery; during the operation of the battery pack, the internal resistance of the battery changes within a certain range, but the internal resistance of the battery is obviously increased along with the aging of the battery, and the increase of the internal resistance becomes a main reason for the attenuation of the available capacity of the battery at the end of the operation of the battery; therefore, the battery operating temperature, the discharge rate and the internal resistance are key factors influencing the service life of the battery, and the modeling prediction by introducing the battery operating temperature, the discharge rate and the internal resistance as influencing parameters of the service life of the battery is effective.
And secondly, obtaining a capacity parameter of the battery before a predicted point through the existing battery operation data, introducing a linear regression function, establishing a battery life prediction model, taking the environment temperature, the discharge rate and the internal resistance of the battery operation as battery life model parameters, and analyzing and calculating the operation data of the battery in service or out of service with the same model by using a big data monitoring platform so as to predict the residual cycle life of the battery.
In the second technical scheme, the attenuation factor is introduced as a correction coefficient of the battery life decline model, so that the accuracy of the method can be improved.
And (IV) in the embodiment, in order to train errors of the life prediction model, introducing an error evaluation function to further correct the linear regression function of the battery life.
Description of the drawings:
FIG. 1 is a schematic flow chart of a life prediction method in an embodiment.
The specific implementation mode is as follows:
example (b):
a lithium ion battery life prediction method, the method comprising the process of:
collecting the operation data of the batteries with the same type in service or retired service, and establishing a database comprising the operation temperature of the batteries, the discharge rate of the batteries, the internal resistance of the batteries, the real-time discharge capacity, the service time i and the total service life parameters;
establishing a battery life prediction linear regression function model:
h(x)＝hθ(x)＝θ0+θ1x1+θ2x2+θ3x3
wherein x ═ { x ═ x1，x2，x3Is a parameter that affects battery life, x1Is the battery operating temperature, x2Is the discharge rate of the battery, x3For the internal resistance of the cell, h (x) is the total service life of the cell, θ ═ θ1，θ2，θ3Is the influence coefficient of each parameter on the life decay, theta0As noise, a normal distribution with a mean of 0 and a variance of σ is obeyed;
and introducing a life attenuation factor delta for correction:
y＝δh(x)＝δ(θ0+θ1x1+θ2x2+θ3x3)
delta is a specific model battery life attenuation factor and is obtained through the database; the method for calculating the battery life attenuation factor delta comprises the following steps:
screening the active or retired battery data of the same model in a database, substituting the data into the current battery operation parameters to obtain the average value of the battery discharge capacity of the battery at the moment i
The capacity attenuation condition of the battery of the type under the same parameter operation condition can be expressed by a battery life attenuation factor, and the battery life attenuation factor delta is obtained by the following formula:
said QNThe current discharge capacity of the battery to be predicted;
updating and optimizing the battery life attenuation factor delta along with the operation of the battery;
introducing an error evaluation function, and evaluating the error of the battery life model; specifically, the square sum of the difference between the estimated value of x (i) and the true value y (i) is used as an error estimation function. Comprises the following steps:
and solving the minimum value of J (theta) by using a least square method, expressing the training characteristics as an X matrix, and expressing the result as a y vector, wherein the parameter theta can be calculated by the following formula.
θ＝(XTX)-1XTy
Claims (5)
1. A lithium ion battery life prediction method is characterized by comprising the following processes:
collecting the operation data of the batteries with the same type in service or out of service, and establishing a database comprising the operation temperature of the batteries, the discharge multiplying power of the batteries, the internal resistance of the batteries and the total service life parameters;
establishing a battery life prediction linear regression function model:
h(x)＝hθ(x)＝θ0+θ1x1+θ2x2+θ3x3
wherein x ═ { x ═ x1，x2，x3Is a parameter that affects battery life, x1Is the battery operating temperature, x2Is the discharge rate of the battery, x3For the internal resistance of the cell, h (x) is the total service life of the cell, θ ═ θ1，θ2，θ3Is the influence coefficient of each parameter on the life decay, theta0As noise, a normal distribution with a mean of 0 and a variance of σ is obeyed;
and substituting the running temperature of the battery with a specific model, the discharge rate of the battery and the internal resistance of the battery into the regression model to obtain the total service life of the battery.
2. The lithium ion battery life prediction method according to claim 1, characterized by introducing an error evaluation function to evaluate an error of the battery life model; specifically, the sum of squares of the differences between the estimated value x (i) and the true value y (i) is used as an error estimation function, and the error estimation function is as follows:
3. a lithium ion battery life prediction method is characterized by comprising the following processes:
collecting the operation data of the batteries with the same type in service or retired service, and establishing a database comprising the operation temperature of the batteries, the discharge rate of the batteries, the internal resistance of the batteries, the real-time discharge capacity, the service time i and the total service life parameters;
establishing a battery life prediction linear regression function model:
h(x)＝hθ(x)＝θ0+θ1x1+θ2x2+θ3x3
wherein x ═ { x ═ x1，x2，x3Is a parameter that affects battery life, x1Is the battery operating temperature, x2Is the discharge rate of the battery, x3For the internal resistance of the cell, h (x) is the total service life of the cell, θ ═ θ1，θ2，θ3Is the influence coefficient of each parameter on the life decay, theta0As noise, a normal distribution with a mean of 0 and a variance of σ is obeyed;
and introducing a life attenuation factor delta for correction:
y＝δh(x)＝δ(θ0+θ1x1+θ2x2+θ3x3)
delta is a specific model battery life attenuation factor and is obtained through the database; the method for acquiring the battery life attenuation factor delta comprises the following steps:
screening the active or retired battery data of the same model in a database, substituting the data into the current battery operation parameters to obtain the average value of the battery discharge capacity of the battery at the moment i
The capacity attenuation condition of the battery of the type under the same parameter operation condition can be expressed by a battery life attenuation factor, and the battery life attenuation factor delta is obtained by the following formula:
said QNThe current discharge capacity of the battery to be predicted.
4. The method of claim 3, wherein the battery life decay factor δ is updated and optimized as the battery operates.
5. The lithium ion battery life prediction method of claim 3, characterized by introducing an error evaluation function to evaluate the error of the battery life model; specifically, the sum of squares of the differences between the estimated value x (i) and the true value y (i) is used as an error estimation function, and the error estimation function is as follows:
。
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| CN112255559B (en) * | 2020-10-12 | 2023-05-09 | 启东沃太新能源有限公司 | Lithium battery energy storage power station residual life prediction method based on multiple linear regression |
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