CN115267546A - Battery life model parameter fitting method - Google Patents

Battery life model parameter fitting method Download PDF

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CN115267546A
CN115267546A CN202210722880.8A CN202210722880A CN115267546A CN 115267546 A CN115267546 A CN 115267546A CN 202210722880 A CN202210722880 A CN 202210722880A CN 115267546 A CN115267546 A CN 115267546A
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邓星
王涛
余小东
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Chongqing Changan Automobile Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
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Abstract

The invention relates to the technical field of power batteries, in particular to a battery life model parameter fitting method, which converts a nonlinear parameter fitting equation of a calendar capacity attenuation rate and a cycle capacity attenuation rate into a linear parameter fitting equation; dividing the data into two groups of data according to the battery capacity attenuation test data of standing and charging and discharging of the single battery; taking logarithms of two values of the two groups of data as an array for parameter solution; removing unreasonable values in the solved parameter array; and performing parameter solution by using a combined equation based on the minimum weighted sum of the absolute values of the errors. The invention can be used for Kcal、Eacal、Zcal、Kcyc、EacycAnd ZcycFast solution, rootAnd the bias of fitting data is ensured according to different requirements, so that the fitting parameters of the battery life model are closer to the test data.

Description

Battery life model parameter fitting method
Technical Field
The invention belongs to the technical field of power batteries, and particularly relates to a battery life model parameter fitting method.
Background
With the increasing market of electric automobiles, the reserve capacity is increased explosively, and the indexes directly related to three packs of power batteries, warranty and quality assurance are involved, so that the state of health (SOH) of the batteries is more and more important, whether the state of health of the batteries of the power batteries can be quickly and accurately predicted is more and more critical, and a battery life prediction model provides a solution for the problem.
At present, a battery life prediction model is divided into a battery capacity decay rate prediction model and a battery internal resistance increase rate prediction model, and the rule is that the worse the battery health state is, the more the battery capacity is reduced relatively to the initial state or the more the battery internal resistance is increased relatively to the initial state, and the battery capacity decay rate is the current mainstream battery health state judgment evidence, so the battery capacity decay rate prediction model is the mainstream prediction model.
The equation of the battery capacity decay rate comprises two aspects:
(1) calendar capacity decay Rate
Figure BDA0003712272530000011
Capacity attenuation rate equation of battery in standing process
Figure BDA0003712272530000012
(2) And rate of decay of cyclic capacity
Figure BDA0003712272530000013
Capacity attenuation rate equation of battery in charging and discharging processes
Figure BDA0003712272530000014
Wherein:
Figure BDA0003712272530000015
representing the calendar battery capacity decay rate;
t represents a battery temperature;
t represents the number of days the battery is left standing;
Figure BDA0003712272530000016
represents the attenuation rate of the charge and discharge capacity of the battery;
n represents the number of charge and discharge times of the battery;
Kcal、Kcycis a correction factor;
Eacal、Eacycis a temperature dependent coefficient;
Zcal、Zcycis a dimensionless constant;
to obtain a coefficient Kcal、Eacal、Zcal、Kcyc、EacycAnd ZcycAt present, the battery capacity fading test of battery standing and battery charging and discharging at different temperatures is generally adopted, wherein the test data of battery standing is data of the number of days of battery standing and the battery capacity, and the battery charging and discharging is data of the number of battery charging and discharging times and the battery capacity. However, the current method for testing the battery capacity attenuation by adopting battery standing and battery charging and discharging at different temperatures cannot ensure the bias of fitting data according to different requirements, so that the parameters of the battery life model fitting deviate from the test data.
Disclosure of Invention
In view of the above, the present invention provides a method for fitting parameters of a battery life model, which can match a coefficient K in a calendar capacity decay rate and a cyclic capacity decay ratecal、Eacal、Zcal、Kcyc、EacycAnd ZcycAnd fast solving, and ensuring the bias of fitting data according to different requirements, so that the parameters fitted by the battery life model are closer to the test data.
The invention solves the technical problems by the following technical means:
a battery life model parameter fitting method is based on test data of battery cell standing and charging and discharging, and data are converted and screened out; and performing parameter solution by adopting a combined equation based on the minimum weighted sum of the absolute values of the errors to obtain corresponding parameters of the battery life model.
Further, the battery life model parameter fitting method specifically comprises the following steps,
s1, converting a nonlinear parameter fitting equation of a calendar capacity attenuation rate and a cycle capacity attenuation rate into a linear parameter fitting equation to obtain
Figure BDA0003712272530000021
And
Figure BDA0003712272530000022
wherein:
Figure BDA0003712272530000023
the rate of decay of the calendar battery capacity is indicated,
Figure BDA0003712272530000024
represents the attenuation rate of the charge and discharge capacity of the battery;
s2, dividing the data into two groups of data according to the battery capacity attenuation test data of standing and charging and discharging of the single battery
Figure BDA0003712272530000025
And
Figure BDA0003712272530000026
wherein: t represents the temperature of the battery, T represents the number of days for which the battery is left standing, and N represents the number of charge and discharge times of the battery;
s3, taking logarithms of two values of the two groups of data to serve as an array for parameter solution;
s4, removing unreasonable values in the solved parameter array;
and S5, solving parameters by adopting a combined equation with the minimum weighted sum based on the absolute values of the errors.
Further, in the step S1, logarithms are taken on both sides of the calendar capacity decay rate equation and the cycle capacity decay rate equation, and equations of the calendar capacity decay rate and the cycle capacity decay rate are converted.
Further, in the step S1, the equation conversion of the calendar capacity fading specifically includes:
Figure BDA0003712272530000027
wherein: t represents the battery temperature, and T represents the number of days the battery was left to stand.
Further, in step S1, the equation conversion of the cyclic capacity fading specifically includes:
Figure BDA0003712272530000028
wherein: t represents the battery temperature, and N represents the number of charge and discharge of the battery.
Further, in step S3, the logarithms of the two values of the two sets of data are respectively
Figure BDA0003712272530000029
And
Figure BDA00037122725300000210
further, in step S4, when solving the parameters, a weighting coefficient k is introduced based on the absolute value sum of the errors and the minimum, so as to distinguish important fitting points.
Further, the specific equation in step S4 is:
Figure BDA0003712272530000031
solving a, b and c to minimize the value of f (a, b and c), and then inversely substituting the solved a, b and c into the equation to obtain the corresponding Kcal、Eacal、Zcal、Kcyc、EacycAnd ZcycThe value of (c).
The battery life model parameter fitting method has the following advantages:
the battery life model parameter fitting method is based on a combined equation with the minimum weighted sum of absolute error values to solve, and based on test data of battery cell standing and charging and discharging, data are transformed to remove unreasonable data, and then corresponding coefficients are solved according to the parameter solving method with the introduction of weighting coefficients; the method can distinguish the key fitting points, can ensure the bias of fitting data according to different requirements, but not treat all data equally, so that the fitting parameters of the battery life model are closer to the test data.
Drawings
FIG. 1 is a graph of decay rate of battery resting capacity according to the present invention;
FIG. 2 is a graph of the decay rate of the charge-discharge cycle capacity of a battery according to the present invention;
FIG. 3 is a comparison graph of a fitted surface and a transformed array for the battery life model parameter fitting method of the present invention;
FIG. 4 is a comparison graph of a fitted curve and measured data in the parameter fitting method of the present invention.
Detailed Description
The invention will be described in detail below with reference to the following figures and specific examples:
the following description of the embodiments of the present invention is provided by way of specific examples, and those skilled in the art will appreciate the advantages and utilities of the present invention from the disclosure herein. It should be noted that the drawings provided in the following embodiments are only for illustrative purposes, are schematic drawings rather than actual drawings, and are not to be construed as limiting the invention, and in order to better illustrate the embodiments of the invention, some components in the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and their descriptions may be omitted.
In the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "front", "back", etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the drawings, it is only for convenience of describing the present invention and simplifying the description, but not for indicating or implying that the indicated device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore the terms describing the positional relationships in the drawings are only used for illustrative purposes and are not to be construed as limiting the present invention, and those skilled in the art can understand the specific meanings of the terms according to specific situations.
In the embodiment, the battery capacity attenuation test data of battery standing and battery charging and discharging of the battery core of the power battery of the actual mass-produced vehicle of Chongqing Changan new energy automobile technology limited company is adopted, and the result is solved by combining the method provided by the invention.
In order to better explain the technical content of the present invention, the theory applied by the present invention will be explained first. The method comprises the steps of firstly carrying out logarithmic transformation on a life attenuation rate equation and test data of battery standing capacity attenuation (shown in figure 1) at different temperatures and battery charging and discharging capacity attenuation (shown in figure 2) at different temperatures, then carrying out corresponding logarithmic transformation on the test data, removing unreasonable values, further solving corresponding parameter values through weighting sum of absolute values of errors, and finally comparing a fitted curve with actually measured data.
The lifetime decay rate equation adopted in this embodiment is as follows:
(1) calendar capacity decay Rate
Figure BDA0003712272530000041
(2) Cyclic capacity attenuation rate
Figure BDA0003712272530000042
Wherein:
Figure BDA0003712272530000043
representing the calendar electric oil capacity decay rate;
t represents a battery temperature;
t represents the number of days the battery is left standing;
Figure BDA0003712272530000044
represents the attenuation rate of the charge and discharge capacity of the battery;
n represents the number of charge and discharge times of the battery;
Kcal、Kcycis a correction factor;
Eacal、Eacycis a temperature dependent coefficient;
Zcal、Zcycis a dimensionless constant;
first, taking the cyclic capacity decay equation as an example, K is obtainedcyc、EacycAnd ZcycThe steps of the values are as follows:
a1, equation conversion of capacity attenuation rate:
taking logarithm on two sides of the cyclic capacity attenuation rate equation, converting the nonlinear parameter fitting equation into a linear parameter fitting equation,
cyclic capacity fading:
Figure BDA0003712272530000045
final equation form:
Figure BDA0003712272530000046
wherein: x represents T +273.15;
y represents ln (N);
a represents ln (K)cyc)；
b represents-Eacyc；
c represents Zcyc；
A2, data processing:
according to the battery capacity attenuation test data of the single battery charge and discharge, firstly combining the data into a whole
Figure BDA0003712272530000047
N is arranged from small to large, T is arranged from small to large,
Figure BDA0003712272530000051
arranging from small to large; then N is added
Figure BDA0003712272530000052
Taking logarithm, and converting the array into
Figure BDA0003712272530000053
And finally, removing unreasonable values in the array.
A3, parameter solving:
the parameter solving process adopts a combined equation based on the weighted sum of the absolute values of the errors to carry out parameter solving, the mathematical principle is to introduce a weighting coefficient k on the basis of the absolute values of the errors and the minimum values, and important fitting areas are distinguished, and the equation is as follows:
Figure BDA0003712272530000054
the meters a, b, c are solved so that the value of f (a, b, c) is minimized. Comparison graph with fitting surface and transformation group (as shown in FIG. 3)
Then the solved a, b and c are inversely substituted into the equation to obtain the corresponding Kcyc、EacycAnd ZcycThe value of (c):
Kcyc＝exp(a)
Zacyc＝-b
a4, comparing and verifying;
k to be solvedcyc、EacycAnd ZcycThe method can distinguish key fitting points, can ensure the bias of fitting data according to different requirements, but does not treat all data equally, so that fitting parameters are closer to the test data.
Then, taking calendar capacity decay equation as an example, to obtain Kcal、Eacal、ZcalThe value of (c).
The parameter solution of the calendar capacity decay equation is consistent with the steps of the cyclic capacity decay equation, and the method specifically comprises the following steps:
a1, equation conversion of capacity attenuation rate:
taking logarithm on two sides of calendar capacity attenuation rate equation, converting nonlinear parameter fitting equation into linear parameter fitting equation,
cyclic capacity fading:
Figure BDA0003712272530000055
final equation form:
Figure BDA0003712272530000056
wherein: x represents T +273.15;
y represents ln (t);
a represents ln (K)cal)；
b represents-Eacal；
c represents Zcal；
A2, data processing:
according to the battery capacity attenuation test data of the standing battery single battery, firstly combining the data into a whole
Figure BDA0003712272530000061
T is arranged from small to large,
Figure BDA0003712272530000062
from small to aligned; then sum t with
Figure BDA0003712272530000063
Taking logarithm and converting the array into
Figure BDA0003712272530000064
Finally, unreasonable values in the array are removed.
A3, parameter solving:
the parameter solving process adopts a combined equation based on the weighted sum of the absolute values of the errors to carry out parameter solving, the mathematical principle is to introduce a weighting coefficient k on the basis of the absolute values of the errors and the minimum values, and important fitting areas are distinguished, and the equation is as follows:
Figure BDA0003712272530000065
solving for a, b, c so that the value of f (a, b, c) is minimum.
Then the solved a, b and c are inversely substituted into the equation to obtain the corresponding Kcal、Eacal、ZcalThe value of (c):
Kcal＝exp(a)
Zacal＝-b
a4, comparing and verifying;
k to be solvedcal、EacalAnd ZcalSubstituting into corresponding equation, and collecting corresponding curve to compare with test data.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the true spirit and scope of the present invention, which is defined by the claims appended hereto. The techniques, shapes, and configurations not described in detail in the present invention are all known techniques.

Claims (8)

1. A battery life model parameter fitting method is characterized in that: based on the test data of battery cell standing and charging and discharging, converting and screening the data; and performing parameter solution by adopting a combined equation based on the minimum weighted sum of the absolute values of the errors to obtain corresponding battery life model parameters.
2. The method of claim 1, wherein the battery life model parameters are fitted to the model parameters: the method specifically comprises the following steps of,
s1, converting a nonlinear parameter fitting equation of a calendar capacity attenuation rate and a cycle capacity attenuation rate into a linear parameter fitting equation to obtain
Figure FDA0003712272520000011
And
Figure FDA0003712272520000012
wherein:
Figure FDA0003712272520000013
the calendar battery capacity decay rate is represented,
Figure FDA0003712272520000014
represents the attenuation rate of the charge and discharge capacity of the battery;
s2, dividing the data into two groups of data according to the battery capacity attenuation test data of standing and charging and discharging of the single battery
Figure FDA0003712272520000015
And
Figure FDA0003712272520000016
wherein: t represents the temperature of the battery, T represents the number of days for which the battery is left standing, and N represents the number of charge and discharge times of the battery;
s3, taking logarithms of two values of the two groups of data to serve as an array for parameter solution;
s4, removing unreasonable values in the solved parameter array;
and S5, solving parameters by adopting a combined equation with the minimum weighted sum based on the absolute values of the errors.
3. The battery life model parameter fitting method of claim 2, wherein: in the step S1, logarithms are taken on both sides of the calendar capacity decay rate equation and the cycle capacity decay rate equation, and equations of the calendar capacity decay rate and the cycle capacity decay rate are converted.
4. A method of fitting parameters of a battery life model according to claim 3, characterized in that: in the step S1, equation conversion of calendar capacity fading specifically includes:
Figure FDA0003712272520000017
wherein: t represents the battery temperature, and T represents the number of days the battery was left to stand.
5. The battery life model parameter fitting method of claim 3, wherein: in step S1, the equation conversion of the cyclic capacity fading specifically includes:
Figure FDA0003712272520000018
wherein: t represents the battery temperature, and N represents the number of charge and discharge of the battery.
6. The method of claim 2, wherein the battery life model parameters are fitted to the model parameters: in step S3, the logarithms of the two values of the two groups of data are respectively
Figure FDA0003712272520000019
And
Figure FDA00037122725200000110
7. the battery life model parameter fitting method of claim 2, wherein: in the step S4, when the parameter is solved, a weighting coefficient k is introduced based on the absolute value and the minimum of the error, so as to distinguish important fitting points.
8. The method of claim 7, wherein the battery life model parameter fitting method comprises: the specific equation in step S4 is:
Figure FDA0003712272520000021
solve a, b, c so that f (a,b, c) is minimum, then the solved a, b, c is inversely substituted into the equation to obtain the corresponding Kcal、Eacal、Zcal、Kcyc、EacycAnd ZcycThe value of (c).
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Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103760494A (en) * 2014-01-21 2014-04-30 清华大学 Method and system for estimating battery capacity online
CN107202960A (en) * 2017-05-25 2017-09-26 安徽江淮汽车集团股份有限公司 Electrokinetic cell life-span prediction method
CN110901470A (en) * 2019-11-29 2020-03-24 安徽江淮汽车集团股份有限公司 Method, device and equipment for predicting service life of battery of electric vehicle and storage medium
CN112198434A (en) * 2020-09-29 2021-01-08 蜂巢能源科技有限公司 Method, system, device, apparatus and medium for identifying battery capacity fading model parameters
CN112816880A (en) * 2021-02-23 2021-05-18 欣旺达电动汽车电池有限公司 Method, device and storage medium for predicting battery life
CN113011012A (en) * 2021-03-02 2021-06-22 傲普(上海)新能源有限公司 Box-Cox change-based energy storage battery residual life prediction method
CN113687235A (en) * 2021-08-03 2021-11-23 天津市捷威动力工业有限公司 Power battery semi-empirical calendar life prediction and evaluation method
CN114325444A (en) * 2021-11-12 2022-04-12 浙江零跑科技股份有限公司 Battery life model parameter fitting method

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103760494A (en) * 2014-01-21 2014-04-30 清华大学 Method and system for estimating battery capacity online
CN107202960A (en) * 2017-05-25 2017-09-26 安徽江淮汽车集团股份有限公司 Electrokinetic cell life-span prediction method
CN110901470A (en) * 2019-11-29 2020-03-24 安徽江淮汽车集团股份有限公司 Method, device and equipment for predicting service life of battery of electric vehicle and storage medium
CN112198434A (en) * 2020-09-29 2021-01-08 蜂巢能源科技有限公司 Method, system, device, apparatus and medium for identifying battery capacity fading model parameters
CN112816880A (en) * 2021-02-23 2021-05-18 欣旺达电动汽车电池有限公司 Method, device and storage medium for predicting battery life
CN113011012A (en) * 2021-03-02 2021-06-22 傲普(上海)新能源有限公司 Box-Cox change-based energy storage battery residual life prediction method
CN113687235A (en) * 2021-08-03 2021-11-23 天津市捷威动力工业有限公司 Power battery semi-empirical calendar life prediction and evaluation method
CN114325444A (en) * 2021-11-12 2022-04-12 浙江零跑科技股份有限公司 Battery life model parameter fitting method

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
黎火林 等: "锂离子电池循环寿命预计模型的研究", 电源技术, vol. 32, no. 4, 30 April 2008 (2008-04-30), pages 242 - 246 *

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