CN114325449B - SOH prediction method for lithium ion battery - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 40
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 title claims abstract description 24
- 229910001416 lithium ion Inorganic materials 0.000 title claims abstract description 24
- 230000036541 health Effects 0.000 claims abstract description 97
- 238000007600 charging Methods 0.000 claims abstract description 39
- 238000007599 discharging Methods 0.000 claims abstract description 26
- 239000013598 vector Substances 0.000 claims abstract description 22
- 230000008569 process Effects 0.000 claims abstract description 16
- 230000004927 fusion Effects 0.000 claims abstract description 12
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 claims description 4
- 230000008859 change Effects 0.000 claims description 4
- 229910052744 lithium Inorganic materials 0.000 claims description 4
- 230000007704 transition Effects 0.000 claims description 3
- 238000005457 optimization Methods 0.000 abstract description 2
- 238000012795 verification Methods 0.000 abstract description 2
- 208000028659 discharge Diseases 0.000 description 33
- 238000005259 measurement Methods 0.000 description 5
- 238000011161 development Methods 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 229910052799 carbon Inorganic materials 0.000 description 1
- 238000010277 constant-current charging Methods 0.000 description 1
- 125000004122 cyclic group Chemical group 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- GELKBWJHTRAYNV-UHFFFAOYSA-K lithium iron phosphate Chemical compound [Li+].[Fe+2].[O-]P([O-])([O-])=O GELKBWJHTRAYNV-UHFFFAOYSA-K 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000000691 measurement method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
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Abstract
The application relates to the technical field of batteries, in particular to a lithium ion battery SOH prediction method. The method of the application comprises the following steps: collecting charging and discharging parameters in the charging and discharging processes of the lithium ion battery; calculating a plurality of health factors according to the charge-discharge parameters; and calculating the current SOH value of the battery according to the feature vector, the feature value and the contribution value by adopting a PCA algorithm from the feature vector, the feature value and the contribution value corresponding to each health factor in the plurality of health factors. According to the application, the contribution value of each health factor is considered when the SOH value of the battery is calculated, the health factor is optimized according to the contribution value of each health factor, and finally the SOH of the battery is calculated through the fusion health factor obtained after optimization, so that the SOH calculated through experimental verification is more accurate, and meanwhile, the robustness is better.
Description
Technical Field
The application relates to the technical field of batteries, in particular to a lithium ion battery SOH prediction method.
Background
Under the background of various advocates of energy conservation and low-carbon emission reduction of China, the electric automobile is in a good development situation. While batteries are key components of electric automobiles, research and investigation show that ternary lithium batteries and lithium iron phosphate batteries occupy most of the markets of power batteries. To ensure reliable operation of batteries, state of health (SOH) predictions and residual life predictions have been a current research hotspot. At present, many SOH methods are studied, and are mainly divided into a direct measurement method, a battery model-based filter estimation method and a data-driven prediction-based method. With the gradual development of the internet of vehicles and big data technology, a method based on data-driven SOH prediction should be the mainstream in the future. However, the current SOH prediction method has low prediction accuracy, and cannot meet the requirement of internet online prediction.
Disclosure of Invention
The application mainly solves the technical problem that the existing SOH prediction method has lower prediction precision.
A lithium ion battery SOH prediction method, comprising:
collecting charging and discharging parameters in the charging and discharging processes of the lithium ion battery;
calculating a plurality of health factors according to the charge-discharge parameters;
adopting a PCA algorithm to obtain a feature vector, a feature value and a contribution value corresponding to each health factor in the plurality of health factors;
and calculating the current SOH value of the battery according to the feature vector, the feature value and the contribution value.
In one embodiment, the collecting the charge and discharge parameters during the charge and discharge of the lithium ion battery includes:
and collecting charging voltage, charging current, discharging voltage, discharging current and battery temperature in real time in the charging and discharging processes of the lithium ion battery.
In one embodiment, the calculating a plurality of health factors from the charge-discharge parameters includes:
according to the charging voltage, the charging current, the discharging voltage, the discharging current and the battery temperature, respectively calculating the following health factors: a first health factor HI1, a second health factor HI2, a third health factor HI3, a fourth health factor HI4, a fifth health factor HI5, a sixth health factor HI6, a seventh health factor HI7, an eighth health factor HI8;
wherein HI1 represents an initial voltage dip value, HI2 represents a discharge platform duration, HI3 represents a discharge platform voltage change rate, HI4 represents an average temperature of a battery during discharge, HI5 represents an estimated value of internal resistance of the battery during the discharge platform, HI6 represents an accumulated charge capacity during constant current charge, HI7 represents a time taken to reach a charge cutoff voltage, and HI8 represents a time taken for the charge voltage to reach a second voltage from a first voltage.
In one embodiment, the feature vector, the feature value, and the contribution value corresponding to each health factor from the plurality of health factors using the PCA algorithm includes:
forming a characteristic variable from the set of health factors;
inputting the characteristic variables into a PCA algorithm model to obtain characteristic vectors corresponding to each health factorCoffeeCharacteristic valuePrincipalContribution valueExplained。
In one embodiment, the calculating the current SOH value of the battery according to the feature vector, the feature value and the contribution value includes: according to the contribution values of the health factors, fusing adjacent health factors by adopting the following formula to obtain a plurality of fused health factors after fusion;
HLi=(1-Explained(i+1))*Principali+Explained(i+1)*Principal(i+1)
wherein, HLirepresents the ith fused health factor after fusion,Principali represents the eigenvalue of the i-th health factor,Principal(i+1) represents the characteristic value of the (i+1) th health factor,Explained(i+1) represents a contribution value of the (i+1) th health factor.
In one embodiment, the calculating the current SOH value of the battery according to the feature vector, the feature value and the contribution value further includes: calculating a capacity attenuation value of the lithium battery after the current cycle and a cycle fusion health factor variation after the current cycle according to the following state equation and observation equation;
in the above, deltaC k Is the firstkA secondary cycle capacity fade value; deltaHL k Is the firstkFusing the health factor variable quantity in a sub-cycle mode;A k-1 is a state transition coefficient;H k the system observation coefficient is used;is system white noise;v k to observe white noise.
In one embodiment, the calculating the current SOH value of the battery according to the feature vector, the feature value and the contribution value further includes: battery SOH was calculated by the following formula:
wherein, representing battery numberKThe health value of the secondary cycle,c 1 representing the capacity value, delta, at the initial time of the batteryC k Is the firstkSub-cycle capacity fade values.
The lithium ion battery SOH prediction method according to the above embodiment includes: collecting charging and discharging parameters in the charging and discharging processes of the lithium ion battery; calculating a plurality of health factors according to the charge-discharge parameters; and calculating the current SOH value of the battery according to the feature vector, the feature value and the contribution value by adopting a PCA algorithm from the feature vector, the feature value and the contribution value corresponding to each health factor in the plurality of health factors. According to the application, the contribution value of each health factor is considered when the SOH value of the battery is calculated, so that the calculated SOH value is more accurate.
Drawings
FIG. 1 is a flowchart of a method for predicting SOH of a battery according to an embodiment of the present application;
fig. 2 is a cycle chart of the lithium ion battery according to the embodiment of the application under a discharging condition;
fig. 3 is a cycle chart of the lithium ion battery according to the embodiment of the application under a charging condition;
fig. 4 is a cycle chart of the lithium ion battery according to the embodiment of the application under the charging condition;
FIG. 5 is a graph showing the variation (delta) of the fusion characteristic factors of different single batteries according to an embodiment of the applicationHI k ) And capacity fade value (delta)C k ) A linear fitting schematic;
FIG. 6 is a current viewCapacity fade value (delta)C k ) From the previous capacity fade value (deltaC k-1 ) Schematic of linear fitting.
Detailed Description
The application will be described in further detail below with reference to the drawings by means of specific embodiments. Wherein like elements in different embodiments are numbered alike in association. In the following embodiments, numerous specific details are set forth in order to provide a better understanding of the present application. However, one skilled in the art will readily recognize that some of the features may be omitted, or replaced by other elements, materials, or methods in different situations. In some instances, related operations of the present application have not been shown or described in the specification in order to avoid obscuring the core portions of the present application, and may be unnecessary to persons skilled in the art from a detailed description of the related operations, which may be presented in the description and general knowledge of one skilled in the art.
Furthermore, the described features, operations, or characteristics of the description may be combined in any suitable manner in various embodiments. Also, various steps or acts in the method descriptions may be interchanged or modified in a manner apparent to those of ordinary skill in the art. Thus, the various orders in the description and drawings are for clarity of description of only certain embodiments, and are not meant to be required orders unless otherwise indicated.
The numbering of the components itself, e.g. "first", "second", etc., is used herein merely to distinguish between the described objects and does not have any sequential or technical meaning.
Embodiment one:
the present embodiment provides a method for predicting SOH of a lithium ion battery, as shown in fig. 1, which includes:
step 101: and collecting charge and discharge parameters in the charge and discharge processes of the lithium ion battery.
In step 101, the charging conditions of the lithium ion battery in this embodiment are as follows: and charging the battery by adopting a constant current and constant voltage of 1.5A until the battery voltage reaches 4.2V, and continuously charging until the charging current is less than 20mA. The corresponding discharge working conditions are as follows: the charging is ended by discharging (1A or 2A, etc.) with a fixed current and voltage until the battery voltage drops to a fixed value (2.4V). And collecting the charging voltage, the charging current, the discharging voltage, the discharging current and the battery temperature of the battery in real time in the charging process. In this embodiment, the charging curve of the lithium ion battery is shown in fig. 3 and fig. 4, and the discharging curve is shown in fig. 2 after multiple cycles according to the above charging and discharging conditions.
Step 102: and calculating a plurality of health factors according to the charge-discharge parameters.
In this embodiment, based on the collected charging voltage, charging current, discharging voltage, discharging current, and battery temperature, the following health factors are calculated respectively: a first health factor HI1, a second health factor HI2, a third health factor HI3, a fourth health factor HI4, a fifth health factor HI5, a sixth health factor HI6, a seventh health factor HI7, and an eighth health factor HI8.
Wherein HI1 represents the initial slump valueV start The initial voltage dip value of the embodiment is defined as the initial voltage measurement value and the first voltage measurement valuenThe difference between the secondary voltage measurements, e.g., the initial voltage dip, is defined as the difference between the primary voltage measurement and the tenth voltage measurement. The present embodiment is defined as:V start (i)= V 1 (i)-V n (i) WhereinV 1 (i) Is the firstiThe voltage is measured for the first time during the secondary cycle,V n (i) Is the firstiThe second cycle is timenThe voltage is measured a second time.
HI2 represents the discharge plateau durationt plat The example defines a discharge platform as 3.4V, and the duration of the discharge platform is the corresponding duration of the voltage drop of the lithium ion battery from the initial voltage to 3.4V during discharge;
it is defined as:t plat (i)=t end (i)-t start (i)
wherein the method comprises the steps oft end (i) Is the firstiThe voltage reaches 3.4V for a corresponding time at the time of the secondary discharge,t start (i) Is the firstiThe initial voltage at the time of the secondary discharge was 3.4V for a corresponding time. The discharge plateau duration is graphically represented in the discharge curve as shown in fig. 3 and 4.
HI3 represents the discharge plateau voltage change rate of the present embodimentLvThe discharge plateau voltage change rate is defined as: the ratio of the current cyclic voltage drop value to the discharge plateau time.
It is defined as:Lv(i)=V diff (i)/t plat (i)
wherein the method comprises the steps ofV diff (i) Is the firstiThe difference between the initial value of the voltage and the final value of the voltage at the time of sub-discharge,t plat (i) Is the firstiThe secondary discharge process corresponds to time. For example the initial voltage value at discharge is 4.2V,t plat (i) For the time from 4.2V discharge to 3.4V arrival. HI4 represents the average temperature of the battery during dischargeT avr 。
HI5 represents the discharge plateau in-cell resistance estimate Re,
in the middle ofV k (i) Is the firstiThe sub-discharge open circuit voltage is set,V d (i) Is the firstiThe terminal voltage of the secondary discharge battery is equal to the terminal voltage of the secondary discharge battery,Iis the discharge current.
HI6 represents the accumulated charge capacity during constant current chargingjIt is expressed as:
in the middle oft v3.8 Indicating the time when the charging voltage was 3.8V，t v4.2 Represents the time when the charging voltage was 4.2V,i(t) Representing the charging current.
HI7 represents the time taken to reach the charge cutoff voltaget f It is expressed as:
t f (i)=t v4.2 (i)-t v3.8 (i)
in the method, in the process of the application,t f (i) Represent the firstiTime to charge up to voltage;t v4.2 (i) Represent the firstiThe time when the secondary charging voltage reaches 4.2V;t v3.8 (i) First, theiThe time when the secondary charging voltage reached 3.8V.
HI8 represents the time taken for the charging voltage to reach the second voltage from the first voltage, which is expressed as:
t fh (i)=t v4.0 (i)-t v3.8 (i)
in this embodiment, the first voltage is 3.8V, the second voltage is 4.0V,t v4.0 (i) Indicating the time when the charge voltage of the battery reached 4.0V,t v3.8 (i) The time when the charging voltage of the battery reached 3.8V is indicated.
Step 103: and adopting a PCA algorithm to obtain a feature vector, a feature value and a contribution value corresponding to each health factor in the plurality of health factors.
In the embodiment, a characteristic variable is formed by a set of eight health factors; feature variables x= { HI1, HI2, HI3, HI4, HI5, HI6, HI7, HI8}, and feature vectors corresponding to each health factor are obtained by inputting the feature variables into the PCA algorithm modelCoffeeCharacteristic valuePrincipalContribution valueExplained。
The following matlab algorithm pseudocode is specifically used as follows:
input: x= { HI1, HI2, HI3, HI4, HI5, HI6, HI7, HI8}
The process comprises the following steps: function PCAProcess (X)
1: each column HI in for X k do
2：HI k Standardization sHI k =（HI k -mean(HI k )）/std(HI k )
3：end for
4: calculate covariance matrix cm=corrcoef (sHI)
5: calling PCA algorithm [ coffee, principle, explained ] =pcacov (CM)
And (3) outputting: feature vector Coffe, feature value Principal, contribution degree expanded.
Step 104: and calculating the current SOH value of the battery according to the feature vector, the feature value and the contribution value.
Specifically, according to the contribution values of the health factors, the adjacent health factors are fused by adopting the following formula to obtain a plurality of fused health factors after fusion;
HLi=(1-Explained(i+1))*Principali+Explained(i+1)*Principal(i+1)
wherein, HLirepresenting the post-fusion firstiThe health factors are fused together to form a single health factor,Principali represents the firstiThe characteristic value of the individual health factor is,Principal(i+1) represents the characteristic value of the (i+1) th health factor, Explained(i+1) represents a contribution value of the (i+1) th health factor. The eight health factors can be optimized according to the contribution values through the formula, and eight fusion health factors are obtained.
Further, in this embodiment, a Δhi and Δc (decay capacity) observation equation and a state equation of the current decay capacity and the decay capacity at the previous moment are established, and the HIF filtering algorithm is used to predict the SOH of the battery. Calculating a capacity attenuation value of the lithium battery after the current cycle and a cycle fusion health factor variation after the current cycle according to the following state equation and observation equation;
in the above, deltaC k Is the firstkA secondary cycle capacity fade value;is the firstkFusing the health factor variable quantity in a sub-cycle mode;A k-1 is a state transition coefficient;H k the system observation coefficient is used; />Is system white noise;v k for observing white noise, the noise may be pre-calculated from actual experimental data, for example, by repeating the above-described process in an experimental environment.A k-1 AndH k the obtained experimental data can be obtained by performing the linear fitting after the processing of the obtained experimental data, and the fitting curve can be seen in fig. 5 and 6. Will deltaC k And deltaC k-1 The slope of the fitting curve is used as a state coefficient, and delta is calculatedHI k And deltaC k Fitting curve slope as observation coefficient, +.>Andv k is the error in the fitting process.
Delta is calculated by the above methodC k Then, the following formula is obtained through the HIF filtering algorithm to calculate the SOH of the battery.
Wherein, representing battery numberKThe health value of the secondary cycle,c 1 representing the capacity value, delta, at the initial time of the batteryC k Is the firstkSub-cycle capacity fade values.
In the embodiment, the SOH of the battery is calculated through extracting a plurality of health factors and optimizing the health factors according to the contribution value of each health factor, and finally, the SOH calculated through experimental verification is more accurate and the robustness is higher through fusion health factors obtained after optimization.
The foregoing description of the application has been presented for purposes of illustration and description, and is not intended to be limiting. Several simple deductions, modifications or substitutions may also be made by a person skilled in the art to which the application pertains, based on the idea of the application.
Claims (3)
1. The SOH prediction method for the lithium ion battery is characterized by comprising the following steps of:
collecting charging and discharging parameters in the charging and discharging processes of the lithium ion battery;
calculating a plurality of health factors according to the charge-discharge parameters;
obtaining a feature vector, a feature value and a contribution value corresponding to each health factor from the plurality of health factors by adopting a PCA algorithm;
calculating the current SOH value of the battery according to the characteristic vector, the characteristic value and the contribution value;
the collecting of the charge and discharge parameters in the charge and discharge process of the lithium ion battery comprises the following steps:
collecting charging voltage, charging current, discharging voltage, discharging current and battery temperature in real time in the charging and discharging processes of the lithium ion battery;
the calculating a plurality of health factors according to the charge-discharge parameters includes:
according to the charging voltage, the charging current, the discharging voltage, the discharging current and the battery temperature, respectively calculating the following health factors: a first health factor HI1, a second health factor HI2, a third health factor HI3, a fourth health factor HI4, a fifth health factor HI5, a sixth health factor HI6, a seventh health factor HI7, an eighth health factor HI8;
wherein HI1 represents an initial voltage dip value, HI2 represents a discharge platform duration, HI3 represents a discharge platform voltage change rate, HI4 represents an average temperature of a battery in a discharge process, HI5 represents an estimated value of internal resistance of the battery in the discharge platform period, HI6 represents an accumulated charge capacity in a constant current charge process, HI7 represents a time for reaching a charge cut-off voltage, and HI8 represents a time for reaching a charge voltage from a first voltage to a second voltage;
the obtaining the feature vector, the feature value and the contribution value corresponding to each health factor from the plurality of health factors by adopting the PCA algorithm comprises the following steps:
forming a characteristic variable from the set of health factors;
inputting the characteristic variables into a PCA algorithm model to obtain characteristic vectors corresponding to each health factorCoffeeCharacteristic valuePrincipalContribution valueExplained；
The calculating the current SOH value of the battery according to the feature vector, the feature value and the contribution value comprises: according to the contribution values of the health factors, fusing adjacent health factors by adopting the following formula to obtain a plurality of fused health factors after fusion;
HLi=(1-Explained(i+1))*Principali+Explained(i+1)*Principal(i+1) ；
wherein, HLirepresents the ith fused health factor after fusion,Principali represents the eigenvalue of the i-th health factor,Principal(i+1) represents the characteristic value of the (i+1) th health factor,Explained(i+1) represents a contribution value of the (i+1) th health factor.
2. The method of claim 1, wherein calculating the current SOH value of the battery based on the feature vector, the feature value, and the contribution value further comprises: calculating a capacity attenuation value of the lithium battery after the current cycle and a cycle fusion health factor variation after the current cycle according to the following state equation and observation equation;
；
in the above, deltaC k Is the firstkA secondary cycle capacity fade value; deltaHL k Is the firstkFusing the health factor variable quantity in a sub-cycle mode;A k-1 is a state transition coefficient;H k the system observation coefficient is used; omega k-1 Is system white noise;v k to observe white noise.
3. The SOH prediction method of a lithium ion battery according to claim 2, wherein calculating the current SOH value of the battery according to the feature vector, the feature value, and the contribution value further comprises: battery SOH was calculated by the following formula:
；
wherein, SOH k representing battery numberkThe health value of the secondary cycle,c 1 representing the capacity value, delta, at the initial time of the batteryC k Is the firstkSub-cycle capacity fade values.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101839141B1 (en) * | 2016-10-31 | 2018-03-15 | 한국기술교육대학교 산학협력단 | Method for predicting battery health in consideration of temperature of battery management system |
| CN108535656A (en) * | 2018-03-22 | 2018-09-14 | 中北大学 | Lithium ion battery remaining life prediction technique and system based on PCA-NARX neural networks |
| CN110187281A (en) * | 2019-05-22 | 2019-08-30 | 天津大学 | The method of lithium battery health status estimation based on charging stage health characteristics |
| CN112684363A (en) * | 2020-12-18 | 2021-04-20 | 北京工业大学 | Lithium ion battery health state estimation method based on discharge process |
| CN113805064A (en) * | 2021-09-18 | 2021-12-17 | 北京航空航天大学 | Lithium ion battery pack health state prediction method based on deep learning |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101839141B1 (en) * | 2016-10-31 | 2018-03-15 | 한국기술교육대학교 산학협력단 | Method for predicting battery health in consideration of temperature of battery management system |
| CN108535656A (en) * | 2018-03-22 | 2018-09-14 | 中北大学 | Lithium ion battery remaining life prediction technique and system based on PCA-NARX neural networks |
| CN110187281A (en) * | 2019-05-22 | 2019-08-30 | 天津大学 | The method of lithium battery health status estimation based on charging stage health characteristics |
| CN112684363A (en) * | 2020-12-18 | 2021-04-20 | 北京工业大学 | Lithium ion battery health state estimation method based on discharge process |
| CN113805064A (en) * | 2021-09-18 | 2021-12-17 | 北京航空航天大学 | Lithium ion battery pack health state prediction method based on deep learning |
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